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Sample records for airborne hyperspectral imager

  1. Airborne Hyperspectral Imaging System

    NASA Technical Reports Server (NTRS)

    Behar, Alberto E.; Cooper, Moogega; Adler, John; Jacobson, Tobias

    2012-01-01

    A document discusses a hyperspectral imaging instrument package designed to be carried aboard a helicopter. It was developed to map the depths of Greenland's supraglacial lakes. The instrument is capable of telescoping to twice its original length, allowing it to be retracted with the door closed during takeoff and landing, and manually extended in mid-flight. While extended, the instrument platform provides the attached hyperspectral imager a nadir-centered and unobstructed view of the ground. Before flight, the instrument mount is retracted and securely strapped down to existing anchor points on the floor of the helicopter. When the helicopter reaches the destination lake, the door is opened and the instrument mount is manually extended. Power to the instrument package is turned on, and the data acquisition computer is commanded via a serial cable from an onboard user-operated laptop to begin data collection. After data collection is complete, the instrument package is powered down and the mount retracted, allowing the door to be closed in preparation for landing. The present design for the instrument mount consists of a three-segment telescoping cantilever to allow for a sufficient extended length to see around the landing struts and provide a nadir-centered and unobstructed field of view for the hyperspectral imager. This instrument works on the premise that water preferentially absorbs light with longer wavelengths on the red side of the visible spectrum. This property can be exploited in order to remotely determine the depths of bodies of pure freshwater. An imager flying over such a lake receives light scattered from the surface, the bulk of the water column, and from the lake bottom. The strength of absorption of longer-wavelength light depends on the depth of the water column. Through calibration with in situ measurements of the water depths, a depth-determining algorithm may be developed to determine lake depth from these spectral properties of the

  2. High spectral resolution airborne short wave infrared hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Wei, Liqing; Yuan, Liyin; Wang, Yueming; Zhuang, Xiaoqiong

    2016-05-01

    Short Wave InfraRed(SWIR) spectral imager is good at detecting difference between materials and penetrating fog and mist. High spectral resolution SWIR hyperspectral imager plays a key role in developing earth observing technology. Hyperspectral data cube can help band selections that is very important for multispectral imager design. Up to now, the spectral resolution of many SWIR hyperspectral imagers is about 10nm. A high sensitivity airborne SWIR hyperspectral imager with narrower spectral band will be presented. The system consists of TMA telescope, slit, spectrometer with planar blazed grating and high sensitivity MCT FPA. The spectral sampling interval is about 3nm. The IFOV is 0.5mrad. To eliminate the influence of the thermal background, a cold shield is designed in the dewar. The pixel number of spatial dimension is 640. Performance measurement in laboratory and image analysis for flight test will also be presented.

  3. Airborne Hyperspectral Imaging of Seagrass and Coral Reef

    NASA Astrophysics Data System (ADS)

    Merrill, J.; Pan, Z.; Mewes, T.; Herwitz, S.

    2013-12-01

    This talk presents the process of project preparation, airborne data collection, data pre-processing and comparative analysis of a series of airborne hyperspectral projects focused on the mapping of seagrass and coral reef communities in the Florida Keys. As part of a series of large collaborative projects funded by the NASA ROSES program and the Florida Fish and Wildlife Conservation Commission and administered by the NASA UAV Collaborative, a series of airborne hyperspectral datasets were collected over six sites in the Florida Keys in May 2012, October 2012 and May 2013 by Galileo Group, Inc. using a manned Cessna 172 and NASA's SIERRA Unmanned Aerial Vehicle. Precise solar and tidal data were used to calculate airborne collection parameters and develop flight plans designed to optimize data quality. Two independent Visible and Near-Infrared (VNIR) hyperspectral imaging systems covering 400-100nm were used to collect imagery over six Areas of Interest (AOIs). Multiple collections were performed over all sites across strict solar windows in the mornings and afternoons. Independently developed pre-processing algorithms were employed to radiometrically correct, synchronize and georectify individual flight lines which were then combined into color balanced mosaics for each Area of Interest. The use of two different hyperspectral sensor as well as environmental variations between each collection allow for the comparative analysis of data quality as well as the iterative refinement of flight planning and collection parameters.

  4. Real-time airborne hyperspectral imaging of land mines

    NASA Astrophysics Data System (ADS)

    Ivanco, Tyler; Achal, Steve; McFee, John E.; Anger, Cliff; Young, Jane

    2007-04-01

    DRDC Suffeld and Itres Research have jointly investigated the use of visible and infrared hyperspectral imaging (HSI) for surface and buried land mine detection since 1989. These studies have demonstrated reliable passive HSI detection of surface-laid mines, based on their reflectance spectra, from airborne and ground-based platforms. Commercial HSI instruments collect and store image data at aircraft speeds, but the data are analysed off- line. This is useful for humanitarian demining, but unacceptable for military countermine operations. We have developed a hardware and software system with algorithms that can process the raw hyperspectral data in real time to detect mines. The custom algorithms perform radiometric correction of the raw data, then classify pixels of the corrected data, referencing a spectral signature library. The classification results are stored and displayed in real time, that is, within a few frame times of the data acquisition. Such real-time mine detection was demonstrated for the first time from a slowly moving land vehicle in March 2000. This paper describes an improved system which can achieve real-time detection of mines from an airborne platform, with its commensurately higher data rates. The system is presently compatible with the Itres family of visible/near infrared, short wave infrared and thermal infrared pushbroom hyperspectral imagers and its broadband thermal infrared pushbroom imager. Experiments to detect mines from an airborne platform in real time were conducted at DRDC Suffield in November 2006. Surface-laid land mines were detected in real time from a slowly moving helicopter with generally good detection rates and low false alarm rates. To the authors' knowledge, this is the first time that land mines have been detected from an airborne platform in real time using hyperspectral imaging.

  5. DETECTION AND IDENTIFICATION OF TOXIC AIR POLLUTANTS USING AIRBORNE LWIR HYPERSPECTRAL IMAGING

    EPA Science Inventory

    Airborne longwave infrared LWIR) hyperspectral imagery was utilized to detect and identify gaseous chemical release plumes at sites in sourthern Texzas. The Airborne Hysperspectral Imager (AHI), developed by the University of Hawaii was flown over a petrochemical facility and a ...

  6. Detection of single graves by airborne hyperspectral imaging.

    PubMed

    Leblanc, G; Kalacska, M; Soffer, R

    2014-12-01

    Airborne hyperspectral imaging (HSI) was assessed as a potential tool to locate single grave sites. While airborne HSI has shown to be useful to locate mass graves, it is expected the location of single graves would be an order of magnitude more difficult due to the smaller size and reduced mass of the targets. Two clearings were evaluated (through a blind test) as potential sites for containing at least one set of buried remains. At no time prior to submitting the locations of the potential burial sites from the HSI were the actual locations of the sites released or shared with anyone from the analysis team. The two HSI sensors onboard the aircraft span the range of 408-2524nm. A range of indicators that exploit the narrow spectral and spatial resolutions of the two complimentary HSI sensors onboard the aircraft were calculated. Based on the co-occurrence of anomalous pixels within the expected range of the indicators three potential areas conforming to our underlying assumptions of the expected spectral responses (and spatial area) were determined. After submission of the predicted burial locations it was revealed that two of the targets were located within GPS error (10m) of the true burial locations. Furthermore, due to the history of the TPOF site for burial work, investigation of the third target is being considered in the near future. The results clearly demonstrate promise for hyperspectral imaging to aid in the detection of buried remains, however further work is required before these results can justifiably be used in routine scenarios. PMID:25447169

  7. Detection of single graves by airborne hyperspectral imaging.

    PubMed

    Leblanc, G; Kalacska, M; Soffer, R

    2014-12-01

    Airborne hyperspectral imaging (HSI) was assessed as a potential tool to locate single grave sites. While airborne HSI has shown to be useful to locate mass graves, it is expected the location of single graves would be an order of magnitude more difficult due to the smaller size and reduced mass of the targets. Two clearings were evaluated (through a blind test) as potential sites for containing at least one set of buried remains. At no time prior to submitting the locations of the potential burial sites from the HSI were the actual locations of the sites released or shared with anyone from the analysis team. The two HSI sensors onboard the aircraft span the range of 408-2524nm. A range of indicators that exploit the narrow spectral and spatial resolutions of the two complimentary HSI sensors onboard the aircraft were calculated. Based on the co-occurrence of anomalous pixels within the expected range of the indicators three potential areas conforming to our underlying assumptions of the expected spectral responses (and spatial area) were determined. After submission of the predicted burial locations it was revealed that two of the targets were located within GPS error (10m) of the true burial locations. Furthermore, due to the history of the TPOF site for burial work, investigation of the third target is being considered in the near future. The results clearly demonstrate promise for hyperspectral imaging to aid in the detection of buried remains, however further work is required before these results can justifiably be used in routine scenarios.

  8. Airborne infrared hyperspectral imager for intelligence, surveillance and reconnaissance applications

    NASA Astrophysics Data System (ADS)

    Lagueux, Philippe; Puckrin, Eldon; Turcotte, Caroline S.; Gagnon, Marc-André; Bastedo, John; Farley, Vincent; Chamberland, Martin

    2012-09-01

    Persistent surveillance and collection of airborne intelligence, surveillance and reconnaissance information is critical in today's warfare against terrorism. High resolution imagery in visible and infrared bands provides valuable detection capabilities based on target shapes and temperatures. However, the spectral resolution provided by a hyperspectral imager adds a spectral dimension to the measurements, leading to additional tools for detection and identification of targets, based on their spectral signature. The Telops Hyper-Cam sensor is an interferometer-based imaging system that enables the spatial and spectral analysis of targets using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320×256 pixels at spectral resolutions as fine as 0.25 cm-1. The LWIR version covers the 8.0 to 11.8 μm spectral range. The Hyper-Cam has been recently used for the first time in two compact airborne platforms: a bellymounted gyro-stabilized platform and a gyro-stabilized gimbal ball. Both platforms are described in this paper, and successful results of high-altitude detection and identification of targets, including industrial plumes, and chemical spills are presented.

  9. Airborne infrared hyperspectral imager for intelligence, surveillance, and reconnaissance applications

    NASA Astrophysics Data System (ADS)

    Puckrin, Eldon; Turcotte, Caroline S.; Gagnon, Marc-André; Bastedo, John; Farley, Vincent; Chamberland, Martin

    2012-06-01

    Persistent surveillance and collection of airborne intelligence, surveillance and reconnaissance information is critical in today's warfare against terrorism. High resolution imagery in visible and infrared bands provides valuable detection capabilities based on target shapes and temperatures. However, the spectral resolution provided by a hyperspectral imager adds a spectral dimension to the measurements, leading to additional tools for detection and identification of targets, based on their spectral signature. The Telops Hyper-Cam sensor is an interferometer-based imaging system that enables the spatial and spectral analysis of targets using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320×256 pixels at spectral resolutions as fine as 0.25 cm-1. The LWIR version covers the 8.0 to 11.8 μm spectral range. The Hyper-Cam has been recently used for the first time in two compact airborne platforms: a belly-mounted gyro-stabilized platform and a gyro-stabilized gimbal ball. Both platforms are described in this paper, and successful results of high-altitude detection and identification of targets, including industrial plumes, and chemical spills are presented.

  10. Determination of pasture quality using airborne hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Pullanagari, R. R.; Kereszturi, G.; Yule, Ian J.; Irwin, M. E.

    2015-10-01

    Pasture quality is a critical determinant which influences animal performance (live weight gain, milk and meat production) and animal health. Assessment of pasture quality is therefore required to assist farmers with grazing planning and management, benchmarking between seasons and years. Traditionally, pasture quality is determined by field sampling which is laborious, expensive and time consuming, and the information is not available in real-time. Hyperspectral remote sensing has potential to accurately quantify biochemical composition of pasture over wide areas in great spatial detail. In this study an airborne imaging spectrometer (AisaFENIX, Specim) was used with a spectral range of 380-2500 nm with 448 spectral bands. A case study of a 600 ha hill country farm in New Zealand is used to illustrate the use of the system. Radiometric and atmospheric corrections, along with automatized georectification of the imagery using Digital Elevation Model (DEM), were applied to the raw images to convert into geocoded reflectance images. Then a multivariate statistical method, partial least squares (PLS), was applied to estimate pasture quality such as crude protein (CP) and metabolisable energy (ME) from canopy reflectance. The results from this study revealed that estimates of CP and ME had a R2 of 0.77 and 0.79, and RMSECV of 2.97 and 0.81 respectively. By utilizing these regression models, spatial maps were created over the imaged area. These pasture quality maps can be used for adopting precision agriculture practices which improves farm profitability and environmental sustainability.

  11. Detection and identification of toxic air pollutants using airborne LWIR hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Williams, David J.; Feldman, Barry L.; Williams, Tim J.; Pilant, Drew; Lucey, Paul G.; Worthy, L. D.

    2005-01-01

    Airborne longwave infrared (LWIR) hyperspectral imagery was utilized to detect and identify gaseous chemical release plumes at sites in southern Texas. The Airborne Hyperspectral Imager (AHI), developed by the University of Hawai"i, was flown over a petrochemical facility and a confined animal feeding operation on a modified DC-3 during April, 2004. Data collected by the AHI system was successfully used to detect and identify numerous plumes at both sites. Preliminary results indicate the presence of benzene and ammonia and several other organic compounds. Emissions were identified using regression analysis on atmospherically compensated data. Data validation was conducted using facility emission inventories. This technology has great promise for monitoring and inventorying facility emissions, and may be used as means to assist ground inspection teams to focus on actual fugitive emission points.

  12. Airborne measurements in the infrared using FTIR-based imaging hyperspectral sensors

    NASA Astrophysics Data System (ADS)

    Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Farley, V.; Lagueux, P.; Marcotte, F.; Chamberland, M.

    2009-05-01

    Hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. Push-broom dispersive sensors have been typically used for airborne hyperspectral mapping. However, extending the spectral range towards the mid-wave and long-wave infrared brings performance limitations due to the self emission of the sensor itself. The Fourier-transform spectrometer technology has been extensively used in the infrared spectral range due to its high transmittance as well as throughput and multiplex advantages, thereby reducing the sensor self-emission problem. Telops has developed the Hyper-Cam, a rugged and compact infrared hyperspectral imager. The Hyper-Cam is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides passive signature measurement capability, with up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The Hyper-Cam has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. A special pointing module was designed to compensate for airplane attitude and forward motion. To our knowledge, the Hyper-Cam is the first commercial airborne hyperspectral imaging sensor based on Fourier-transform infrared technology. The first airborne measurements and some preliminary performance criteria for the Hyper-Cam are presented in

  13. Airborne measurements in the infrared using FTIR-based imaging hyperspectral sensors

    NASA Astrophysics Data System (ADS)

    Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Lagueux, P.; Farley, V.; Marcotte, F.; Chamberland, M.

    2009-09-01

    Hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. Push-broom dispersive sensors have been typically used for airborne hyperspectral mapping. However, extending the spectral range towards the mid-wave and long-wave infrared brings performance limitations due to the self emission of the sensor itself. The Fourier-transform spectrometer technology has been extensively used in the infrared spectral range due to its high transmittance as well as throughput and multiplex advantages, thereby reducing the sensor self-emission problem. Telops has developed the Hyper-Cam, a rugged and compact infrared hyperspectral imager. The Hyper-Cam is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides passive signature measurement capability, with up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The Hyper-Cam has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. A special pointing module was designed to compensate for airplane attitude and forward motion. To our knowledge, the Hyper-Cam is the first commercial airborne hyperspectral imaging sensor based on Fourier-transform infrared technology. The first airborne measurements and some preliminary performance criteria for the Hyper-Cam are presented in

  14. Airborne measurements in the longwave infrared using an imaging hyperspectral sensor

    NASA Astrophysics Data System (ADS)

    Allard, Jean-Pierre; Chamberland, Martin; Farley, Vincent; Marcotte, Frédérick; Rolland, Matthias; Vallières, Alexandre; Villemaire, André

    2008-07-01

    Emerging applications in Defense and Security require sensors with state-of-the-art sensitivity and capabilities. Among these sensors, the imaging spectrometer is an instrument yielding a large amount of rich information about the measured scene. Standoff detection, identification and quantification of chemicals in the gaseous state is one important application. Analysis of the surface emissivity as a means to classify ground properties and usage is another one. Imaging spectrometers have unmatched capabilities to meet the requirements of these applications. Telops has developed the FIRST, a LWIR hyperspectral imager. The FIRST is based on the Fourier Transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. The FIRST, a man portable sensor, provides datacubes of up to 320×256 pixels at 0.35mrad spatial resolution over the 8-12 μm spectral range at spectral resolutions of up to 0.25cm-1. The FIRST has been used in several field campaigns, including the demonstration of standoff chemical agent detection [http://dx.doi.org/10.1117/12.788027.1]. More recently, an airborne system integrating the FIRST has been developed to provide airborne hyperspectral measurement capabilities. The airborne system and its capabilities are presented in this paper. The FIRST sensor modularity enables operation in various configurations such as tripod-mounted and airborne. In the airborne configuration, the FIRST can be operated in push-broom mode, or in staring mode with image motion compensation. This paper focuses on the airborne operation of the FIRST sensor.

  15. Multipurpose hyperspectral imaging system

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging system of high spectral and spatial resolution that incorporates several innovative features has been developed to incorporate a focal plane scanner (U.S. Patent 6,166,373). This feature enables the system to be used for both airborne/spaceborne and laboratory hyperspectral i...

  16. MTU-Kestrel airborne hyperspectral imaging campaigns of the Lake Superior ecosystem

    NASA Astrophysics Data System (ADS)

    Rafert, J. Bruce; Slough, William J.; Rohde, Charles A.; Pilant, Andrew; Otten, Leonard J.; Meigs, Andrew D.; Jones, Al; Butler, Eugene W.

    1999-10-01

    The clear waters of Lake Superior constitute the heart of one of the most significant fresh water ecosystems in the world. Lake Superior is the world's largest lake by surface area (82,100 km2) holding approximately 10% of the earth's freshwater (12,230 km3) that is not locked into glaciers or ice caps. Although Superior is arguably the most significant fresh water ecosystem on earth, questions relating to the lake and its watershed remain unanswered, including the effects of human habitation, exploitation, and economic potential of the region. There is a great diversity of scientific disciplines with a common interest in remote sensing of the Lake Superior ecosystem which have the need for data at all spatial, spectral, and temporal scales-from scales supplied by satellites, ships or aircraft at low spatial, spectral or temporal resolution, to a requirement for synoptic high resolution spatial (approximately 1 meter)/spectral (1 - 10 nm) data. During May and August of 1998, two week-long data collection campaigns were performed using the Kestrel airborne visible hyperspectral imager to acquire hyperspectral data of a broad taxonomy of ecologically significant targets, including forests, urban areas, lakeshore zones and rivers, mining industry tailing basins, and the Lake itself. We will describe the Kestrel airborne hyperspectral sensor, the collection and data reduction methodology, and flight imagery from both campaigns.

  17. Field-Based and Airborne Hyperspectral Imaging for Applied Research in the State of Alaska

    NASA Astrophysics Data System (ADS)

    Prakash, A.; Buchhorn, M.; Cristobal, J.; Kokaly, R. F.; Graham, P. R.; Waigl, C. F.; Hampton, D. L.; Werdon, M.; Guldager, N.; Bertram, M.; Stuefer, M.

    2015-12-01

    Hyperspectral imagery acquired using Hyspex VNIR-1800 and SWIR-384 camera systems have provided unique information on terrestrial and aquatic biogeochemical parameters, and diagnostic mineral properties in exposed outcrops in selected sites in the state of Alaska. The Hyspex system was configured for in-situ and field scanning by attaching it to a gimbal-mounted rotational stage on a robust tripod. Scans of vertical faces of vegetation and rock outcrops were made close to the campus of the University of Alaska Fairbanks, in an abandoned mine near Fairbanks, and on exposures of Orange Hill in Wrangell-St. Elias National Park. Atmospherically corrected integrated VNIR_SWIR spectra were extracted which helped to study varying nitrogen content in the vegetation, and helped to distinguish the various micas. Processed imagery helped to pull out carbonates, clays, sulfates, and alteration-related minerals. The same instrument was also mounted in airborne configuration on two different aircrafts, a DeHavilland Beaver and a Found Bush Hawk. Test flights were flown over urban and wilderness areas that presented a variety of landcover types. Processed imagery shows promise in mapping man-made surfaces, phytoplankton, and dissolved materials in inland water bodies. Sample data and products are available on the University of Alaska Fairbanks Hyperspectral Imaging Laboratory (HyLab) website at http://hyperspectral.alaska.edu.

  18. Airborne Hyperspectral Infrared Imaging Survey of the Southern San Andreas Fault

    NASA Astrophysics Data System (ADS)

    Lynch, D. K.; Tratt, D. M.; Buckland, K. N.; Johnson, P. D.

    2014-12-01

    The San Andreas Fault (SAF) between Desert Hot Springs and Bombay Beach has been surveyed with Mako, an airborne hyperspectral imager operating across the wavelength range 7.6-13.2 μm in the thermal-infrared (TIR) spectral region. The data were acquired with a 4-km swath width centered on the SAF, and many tectonic features are recorded in the imagery. Spectral analysis using diagnostic features of minerals can identify rocks, soils and vegetation. Mako imagery can also locate rupture zones and measure slip distances. Designed and built by The Aerospace Corporation, the innovative and highly capable airborne imaging spectrometer used for this work enables low-noise performance (NEΔT ≲ 0.1 K @ 10 μm) at small pixel IFOV (0.55 mrad) and high frame rates, making possible an area-coverage rate of 20 km2 per minute with 2-m ground resolution from 12,500 ft (3.8 km) above-ground altitude. Since its commissioning in 2010, Mako has been used in numerous studies involving other earthquake fault systems (Hector Mine, S. Bristol Mts.), mapping of surface geology, geothermal sources (fumaroles near the Salton Sea), urban surveys, and the detection, quantification, and tracking of natural and anthropogenic gaseous emission plumes. Mako is available for airborne field studies and new applications are of particular interest. It can be flown at any altitude below 20,000 ft to achieve the desired GSD.

  19. High Resolution Airborne Laser Scanning and Hyperspectral Imaging with a Small Uav Platform

    NASA Astrophysics Data System (ADS)

    Gallay, Michal; Eck, Christoph; Zgraggen, Carlo; Kaňuk, Ján; Dvorný, Eduard

    2016-06-01

    The capabilities of unmanned airborne systems (UAS) have become diverse with the recent development of lightweight remote sensing instruments. In this paper, we demonstrate our custom integration of the state-of-the-art technologies within an unmanned aerial platform capable of high-resolution and high-accuracy laser scanning, hyperspectral imaging, and photographic imaging. The technological solution comprises the latest development of a completely autonomous, unmanned helicopter by Aeroscout, the Scout B1-100 UAV helicopter. The helicopter is powered by a gasoline two-stroke engine and it allows for integrating 18 kg of a customized payload unit. The whole system is modular providing flexibility of payload options, which comprises the main advantage of the UAS. The UAS integrates two kinds of payloads which can be altered. Both payloads integrate a GPS/IMU with a dual GPS antenna configuration provided by OXTS for accurate navigation and position measurements during the data acquisition. The first payload comprises a VUX-1 laser scanner by RIEGL and a Sony A6000 E-Mount photo camera. The second payload for hyperspectral scanning integrates a push-broom imager AISA KESTREL 10 by SPECIM. The UAS was designed for research of various aspects of landscape dynamics (landslides, erosion, flooding, or phenology) in high spectral and spatial resolution.

  20. NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

    NASA Technical Reports Server (NTRS)

    Cook, Bruce D.; Corp, Lawrence A.; Nelson, Ross F.; Middleton, Elizabeth M.; Morton, Douglas C.; McCorkel, Joel T.; Masek, Jeffrey G.; Ranson, Kenneth J.; Ly, Vuong; Montesano, Paul M.

    2013-01-01

    The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (approximately 1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT's data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA's Data and Information policy.

  1. Use of field reflectance data for crop mapping using airborne hyperspectral image

    NASA Astrophysics Data System (ADS)

    Nidamanuri, Rama Rao; Zbell, Bernd

    2011-09-01

    Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question "what is the prospect of using independent reference reflectance spectra for image classification", while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of "non-existence of characteristic reflectance spectral signatures for vegetation", results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.

  2. SYSIPHE system: a state of the art airborne hyperspectral imaging system: initial results from the first airborne campaign

    NASA Astrophysics Data System (ADS)

    Rousset-Rouviere, Laurent; Coudrain, Christophe; Fabre, Sophie; Poutier, Laurent; Løke, Trond; Fridman, Andrei; Blaaberg, Søren; Baarstad, Ivar; Skauli, Torbjorn; Mocoeur, Isabelle

    2014-10-01

    SYSIPHE is an airborne hyperspectral imaging system, result of a cooperation between France (Onera and DGA) and Norway (NEO and FFI). It is a unique system by its spatial sampling -0.5m with a 500m swath at a ground height of 2000m- combined with its wide spectral coverage -from 0.4μm to 11.5μm in the atmospheric transmission bands. Its infrared component, named SIELETERS, consists in two high étendue imaging static Fourier transform spectrometers, one for the midwave infrared and one for the longwave infrared. These two imaging spectrometers are closely similar in design, since both are made of a Michelson interferometer, a refractive imaging system, and a large IRFPA (1016x440 pixels). Moreover, both are cryogenically cooled and mounted on their own stabilization platform which allows the line of sight to be controlled and recorded. These data are useful to reconstruct and to georeference the spectral image from the raw interferometric images. The visible and shortwave infrared component, named Hyspex ODIN-1024, consists of two spectrographs for VNIR and SWIR based on transmissive gratings. These share a common fore-optics and a common slit, to ensure perfect registration between the VNIR and the SWIR images. The spectral resolution varies from 5nm in the visible to 6nm in the shortwave infrared. In addition, the STAD, the post processing and archiving system, is developed to provide spectral reflectance and temperature products (SRT products) from calibrated georeferenced and inter-band registered spectral images at the sensor level acquired and pre-processed by SIELETERS and Hyspex ODIN-1024 systems.

  3. Novel compact airborne platform for remote sensing applications using the Hyper-Cam infrared hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Turcotte, Caroline S.; Puckrin, Eldon; Aube, Françoys; Farley, Vincent; Savary, Simon; Chamberland, Martin

    2013-05-01

    High resolution broad-band imagery in the visible and infrared bands provides valuable detection capabilities based on target shapes and temperatures. However, the spectral resolution provided by a hyperspectral imager adds a spectral dimension to the measurements, which leads to an additional means of detecting and identifying targets based on their spectral signature. The Telops Hyper-Cam sensor is an interferometer-based imaging system that enables the spatial and spectral analysis of targets using a single sensor. It is based on the Fourier-transform technology, which yields high spectral resolution and enables a high accuracy radiometric calibration. It provides datacubes of up to 320×256 pixels at spectral resolutions as fine as 0.25 cm-1. The LWIR version covers the 8.0 to 11.8 μm spectral range. The Hyper-Cam has been recently integrated and flown on a novel airborne gyro-stabilized platform inside a fixed-wing aircraft. The new platform, more compact and more advanced than its predecessor, is described in this paper. The first results of target detection and identification are also presented.

  4. Sunglint effects on the characterization of optically active substances in high spatial resolution airborne hyperspectral images

    NASA Astrophysics Data System (ADS)

    Streher, A. S.; Faria Barbosa, C. Clemente; Soares Galvão, L.; Goodman, J. A.; Silva, T. S.

    2013-05-01

    Sunglint, also known as the specular reflection of light from water surfaces, is a component of sensor-received radiance that represents a confounding factor on the characterization of water bodies by remote sensing. In airborne remote sensing images, the effect of sunglint can be minimized by optimizing the flight paths, directing the sensor towards or away from the Sun, and by keeping solar zenith angles between 30° and 60°. However, these guidelines cannot always be applied, often due to the irregular spatial pattern of lakes, estuaries and coastlines. The present study assessed the impact of sunglint on the relationship between the optically active substances (OAS) concentration, in optically complex waters, and the spectral information provided by an airborne high spatial resolution hyperspectral sensor (SpecTIR). The Ibitinga reservoir, located in southeastern Brazil (state of São Paulo), was selected as the study area because of its meandering shape. As a result, there is demanding constant changes in data acquisition geometry to achieve complete coverage, therefore not allowing sunglint conditions to be minimized during image acquisition. Field data collection was carried out on October 23 and 24, 2011. During these two days, 15 water stations along the reservoir were sampled, concurrently with the SpecTIR image acquisition in 357 bands (398-2455 nm) and at 3 m spatial resolution. Chlorophyll, pheophytin, total suspended solids, organic and inorganic suspended solids and colored dissolved matter were determined in laboratory. The images were corrected for the atmospheric effects using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm and then geometrically corrected. In order to evaluate the sunglint effects on the OAS characterization, the images were corrected for such effects using the deglint algorithm from Goodman et al. (2008). The SpecTIR 662-nm band reflectance was selected to be correlated to the OAS due to

  5. Analysis of Coincident HICO and Airborne Hyperspectral Images Over Lake Erie Western Basin HABs

    NASA Astrophysics Data System (ADS)

    Cline, M., Jr.; Becker, R.; Lekki, J.; Bridgeman, T. B.; Tokars, R. P.; Anderson, R. C.

    2015-12-01

    Harmful algal blooms (HABs) produce waterborne toxins that pose a significant threat to people, livestock, and wildlife. 40 million people in both Canada and the U.S. depend on Great Lakes water. In the summer of 2014, in the Lake Erie Western Basin, an HAB of the cyanobacteria Microsystis was so severe that a water-use ban was in effect for the greater Toledo area, Ohio. This shut off the water supply to over 400,000 people from a single water intake. We investigated bloom intensity, composition, and spatial variability by comparing hyperspectral data from NASA's HICO, multispectral data from MODIS spaceborne imagers and NASA GRC's HSI imagers to on-lake ASD radiometer measurements using in situ water quality testing as ground reference data, all acquired on a single day during the bloom in 2014. HICO imagery acquired on Aug 15, 2014 was spatially georeferenced and atmospherically corrected using empirical line method utilizing on-lake ASD spectra. HSI imagery were processed in a similar way. Cyanobacteria Index (CI) images were created from processed images using the Wynne (2010) algorithm, previously used for MODIS and MERIS imagery. This algorithm-generated CI images provide reliable results for both ground level (R²=0.7784), and satellite imagery (R²=0.7794) for seven sampling points in Lake Erie's western basin. Spatial variability in the bloom was high, and was not completely characterized by the lower spatial resolution MODIS data. The ability to robustly atmospherically correct and generate useful CI maps from airborne and satellite sensors can provide a time- and cost-effective method for HABs analysis. Timely processing of these high spatial and spectral resolution remote sensing data can aid in management of water intake resources.

  6. [Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data].

    PubMed

    Tang, Jian-min; Liao, Qin-hong; Liu, Yi-qing; Yang, Gui-jun; Feng, Hai-kuanr; Wang, Ji-hua

    2015-05-01

    The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R2 and RMSE of estimation model were 0. 52, 0. 45 (p<0. 01) , respectively. For red-edge vegetation index, CIred edge took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI. NDSI(569.00, 654.80) and RSI(597.60, 654.80) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R2 of estimation model used NDSI(569.00, 654.80) was 0. 77(p<0. 000 1), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI(569.00, 654.80) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy. PMID:26415459

  7. Target detection algorithm for airborne thermal hyperspectral data

    NASA Astrophysics Data System (ADS)

    Marwaha, R.; Kumar, A.; Raju, P. L. N.; Krishna Murthy, Y. V. N.

    2014-11-01

    Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm-1 to 1280 cm-1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm-1 (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

  8. Multipurpose Hyperspectral Imaging System

    NASA Technical Reports Server (NTRS)

    Mao, Chengye; Smith, David; Lanoue, Mark A.; Poole, Gavin H.; Heitschmidt, Jerry; Martinez, Luis; Windham, William A.; Lawrence, Kurt C.; Park, Bosoon

    2005-01-01

    A hyperspectral imaging system of high spectral and spatial resolution that incorporates several innovative features has been developed to incorporate a focal plane scanner (U.S. Patent 6,166,373). This feature enables the system to be used for both airborne/spaceborne and laboratory hyperspectral imaging with or without relative movement of the imaging system, and it can be used to scan a target of any size as long as the target can be imaged at the focal plane; for example, automated inspection of food items and identification of single-celled organisms. The spectral resolution of this system is greater than that of prior terrestrial multispectral imaging systems. Moreover, unlike prior high-spectral resolution airborne and spaceborne hyperspectral imaging systems, this system does not rely on relative movement of the target and the imaging system to sweep an imaging line across a scene. This compact system (see figure) consists of a front objective mounted at a translation stage with a motorized actuator, and a line-slit imaging spectrograph mounted within a rotary assembly with a rear adaptor to a charged-coupled-device (CCD) camera. Push-broom scanning is carried out by the motorized actuator which can be controlled either manually by an operator or automatically by a computer to drive the line-slit across an image at a focal plane of the front objective. To reduce the cost, the system has been designed to integrate as many as possible off-the-shelf components including the CCD camera and spectrograph. The system has achieved high spectral and spatial resolutions by using a high-quality CCD camera, spectrograph, and front objective lens. Fixtures for attachment of the system to a microscope (U.S. Patent 6,495,818 B1) make it possible to acquire multispectral images of single cells and other microscopic objects.

  9. Hyperspectral image processing methods

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral image processing refers to the use of computer algorithms to extract, store and manipulate both spatial and spectral information contained in hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. A typical hyperspectral image processing work...

  10. How Cities Breathe: Ground-Referenced, Airborne Hyperspectral Imaging Precursor Measurements To Space-Based Monitoring

    NASA Technical Reports Server (NTRS)

    Leifer, Ira; Tratt, David; Quattrochi, Dale; Bovensmann, Heinrich; Gerilowski, Konstantin; Buchwitz, Michael; Burrows, John

    2013-01-01

    Methane's (CH4) large global warming potential (Shindell et al., 2012) and likely increasing future emissions due to global warming feedbacks emphasize its importance to anthropogenic greenhouse warming (IPCC, 2007). Furthermore, CH4 regulation has far greater near-term climate change mitigation potential versus carbon dioxide CO2, the other major anthropogenic Greenhouse Gas (GHG) (Shindell et al., 2009). Uncertainties in CH4 budgets arise from the poor state of knowledge of CH4 sources - in part from a lack of sufficiently accurate assessments of the temporal and spatial emissions and controlling factors of highly variable anthropogenic and natural CH4 surface fluxes (IPCC, 2007) and the lack of global-scale (satellite) data at sufficiently high spatial resolution to resolve sources. Many important methane (and other trace gases) sources arise from urban and mega-urban landscapes where anthropogenic activities are centered - most of humanity lives in urban areas. Studying these complex landscape tapestries is challenged by a wide and varied range of activities at small spatial scale, and difficulty in obtaining up-to-date landuse data in the developed world - a key desire of policy makers towards development of effective regulations. In the developing world, challenges are multiplied with additional political access challenges. As high spatial resolution satellite and airborne data has become available, activity mapping applications have blossomed - i.e., Google maps; however, tap a minute fraction of remote sensing capabilities due to limited (three band) spectral information. Next generation approaches that incorporate high spatial resolution hyperspectral and ultraspectral data will allow detangling of the highly heterogeneous usage megacity patterns by providing diagnostic identification of chemical composition from solids (refs) to gases (refs). To properly enable these next generation technologies for megacity include atmospheric radiative transfer modeling

  11. High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

    NASA Astrophysics Data System (ADS)

    Hulley, Glynn C.; Duren, Riley M.; Hopkins, Francesca M.; Hook, Simon J.; Vance, Nick; Guillevic, Pierre; Johnson, William R.; Eng, Bjorn T.; Mihaly, Jonathan M.; Jovanovic, Veljko M.; Chazanoff, Seth L.; Staniszewski, Zak K.; Kuai, Le; Worden, John; Frankenberg, Christian; Rivera, Gerardo; Aubrey, Andrew D.; Miller, Charles E.; Malakar, Nabin K.; Sánchez Tomás, Juan M.; Holmes, Kendall T.

    2016-06-01

    Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1-2 km), and high spatial resolution (˜ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

  12. A hyperspectral image projector for hyperspectral imagers

    NASA Astrophysics Data System (ADS)

    Rice, Joseph P.; Brown, Steven W.; Neira, Jorge E.; Bousquet, Robert R.

    2007-04-01

    We have developed and demonstrated a Hyperspectral Image Projector (HIP) intended for system-level validation testing of hyperspectral imagers, including the instrument and any associated spectral unmixing algorithms. HIP, based on the same digital micromirror arrays used in commercial digital light processing (DLP*) displays, is capable of projecting any combination of many different arbitrarily programmable basis spectra into each image pixel at up to video frame rates. We use a scheme whereby one micromirror array is used to produce light having the spectra of endmembers (i.e. vegetation, water, minerals, etc.), and a second micromirror array, optically in series with the first, projects any combination of these arbitrarily-programmable spectra into the pixels of a 1024 x 768 element spatial image, thereby producing temporally-integrated images having spectrally mixed pixels. HIP goes beyond conventional DLP projectors in that each spatial pixel can have an arbitrary spectrum, not just arbitrary color. As such, the resulting spectral and spatial content of the projected image can simulate realistic scenes that a hyperspectral imager will measure during its use. Also, the spectral radiance of the projected scenes can be measured with a calibrated spectroradiometer, such that the spectral radiance projected into each pixel of the hyperspectral imager can be accurately known. Use of such projected scenes in a controlled laboratory setting would alleviate expensive field testing of instruments, allow better separation of environmental effects from instrument effects, and enable system-level performance testing and validation of hyperspectral imagers as used with analysis algorithms. For example, known mixtures of relevant endmember spectra could be projected into arbitrary spatial pixels in a hyperspectral imager, enabling tests of how well a full system, consisting of the instrument + calibration + analysis algorithm, performs in unmixing (i.e. de-convolving) the

  13. An airborne real-time hyperspectral target detection system

    NASA Astrophysics Data System (ADS)

    Skauli, Torbjorn; Haavardsholm, Trym V.; Kåsen, Ingebjørg; Arisholm, Gunnar; Kavara, Amela; Opsahl, Thomas Olsvik; Skaugen, Atle

    2010-04-01

    An airborne system for hyperspectral target detection is described. The main sensor is a HySpex pushbroom hyperspectral imager for the visible and near-infrared spectral range with 1600 pixels across track, supplemented by a panchromatic line imager. An optional third sensor can be added, either a SWIR hyperspectral camera or a thermal camera. In real time, the system performs radiometric calibration and georeferencing of the images, followed by image processing for target detection and visualization. The current version of the system implements only spectral anomaly detection, based on normal mixture models. Image processing runs on a PC with a multicore Intel processor and an Nvidia graphics processing unit (GPU). The processing runs in a software framework optimized for large sustained data rates. The platform is a Cessna 172 aircraft based close to FFI, modified with a camera port in the floor.

  14. G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

    NASA Technical Reports Server (NTRS)

    Cook, Bruce; Corp, Lawrence; Nelson, Ross; Morton, Douglas; Ranson, Kenneth J.; Masek, Jeffrey; Middleton, Elizabeth

    2012-01-01

    Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation

  15. Detection of gaseous plumes in airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Agassi, Eyal; Hirsch, Eitan; Chamberland, Martin; Gagnon, Marc-André; Eichstaedt, Holger

    2016-05-01

    The thermal hyperspectral sensor Hyper-Cam was mounted on a light aircraft and measured continuous releases of several atmospheric tracers from a height of 2 km. A unique detection algorithm that eliminates the need for clear background estimation was operated over the acquired data with excellent detection results. The data-cubes were acquired in a "target mode", which is a unique method of operation of the Hyper-Cam sensor. This method provides multiple views of the plume which can be exploited to enhance the detection performance. These encouraging results demonstrate the utility of airborne LWIR hyperspectral imaging for efficient detection and mapping of effluent gases for environmental monitoring.

  16. 1994-1995 CNR LARA project airborne hyperspectral campaigns

    SciTech Connect

    Bianchi, R.; Cavalli, R.M.; Fiumi, L.

    1996-08-01

    CNR established a new laboratory for airborne hyperspectral imaging devoted to environmental problems and since the end of last June 1994 the project (LARA Project) is fully operative to provide hyperspectral data to the national and international scientific community. The Daedalus AA5000 MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) instrument, acquired by CNR (Italian National Research Council) in the framework of its LARA (Airborne Laboratory for Environmental Studies) Project, has been intensively operative. A number of MIVIS deployments have been carried out in Italy and Europe in cooperation with national and international institutions on a variety of sites, including active volcanoes, coastlines, lagoons and ocean, vegetated and cultivated areas, oil polluted surfaces, waste discharges, and archeological sites. One year of activity has shown the high system efficiency, from the survey to data preprocessing and dissemination.

  17. Miniaturized handheld hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Wu, Huawen; Haibach, Frederick G.; Bergles, Eric; Qian, Jack; Zhang, Charlie; Yang, William

    2014-05-01

    A miniaturized hyperspectral imager is enabled with image sensor integrated with dispersing elements in a very compact form factor, removing the need for expensive, moving, bulky and complex optics that have been used in conventional hyperspectral imagers for decades. The result is a handheld spectral imager that can be installed on miniature UAV drones or conveyor belts in production lines. Eventually, small handhelds can be adapted for use in outpatient medical clinics for point-of-care diagnostics and other in-field applications.

  18. Hyperspectral light field imaging

    NASA Astrophysics Data System (ADS)

    Leitner, Raimund; Kenda, Andreas; Tortschanoff, Andreas

    2015-05-01

    A light field camera acquires the intensity and direction of rays from a scene providing a 4D representation L(x,y,u,v) called the light field. The acquired light field allows to virtually change view point and selectively re-focus regions algorithmically, an important feature for many applications in imaging and microscopy. The combination with hyperspectral imaging provides the additional advantage that small objects (beads, cells, nuclei) can be categorised using their spectroscopic signatures. Using an inverse fluorescence microscope, a LCTF tuneable filter and a light field setup as a test-bed, fluorescence-marked beads have been imaged and reconstructed into a 4D hyper-spectral image cube LHSI(x,y,z,λ). The results demonstrate the advantages of the approach for fluorescence microscopy providing extended depth of focus (DoF) and the fidelity of hyper-spectral imaging.

  19. Hyperspectral imaging system for UAV

    NASA Astrophysics Data System (ADS)

    Zhang, Da; Zheng, Yuquan

    2015-10-01

    Hyperspectral imaging system for Unmanned Aerial Vehicle (UAV) is proposed under airborne remote sensing application background. By the application of Offner convex spherical grating spectral imaging system and using large area array detector push-broom imaging, hyperspectral imaging system with the indicators of 0.4μm to 1.0μm spectral range, 120 spectral bands, 5nm spectral resolution and 1m ground sampling interval (flight altitude 5km) is developed and completed. The Offner convex grating spectral imaging system is selected to achieve non-spectral line bending and colorless distortion design results. The diffraction efficiency is 15%-30% in the range of 0.4μm to 1.0μm wavelength. The system performances are tested by taking spectral and radiometric calibration methods in the laboratory. Based on monochromatic collimated light method for spectral performance parameters calibration of hyperspectral optical remote sensor, the analysis results of spectral calibration data show that the calibration test repeatability is less than 0.2 nm within one hour. The spectral scaling results show that the average spectral resolution of hyperspectral optical remote sensor is 4.94 nm, and the spatial dimension of the high-spectral optical remote sensor spectral resolution is less than 5 nm, the average of the typical spectral bandwidth is about 6 nm, the system average signal-to-noise ratio (SNR) is up to 43dB under typical operating conditions. Finally the system functionalities and performance indicators are verified by the aviation flight tests, which it's equipped on UAV. The actual image quality is good, and the spectral position is stable.

  20. Quality assessment for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Shen, Weimin

    2014-11-01

    Image quality assessment is an essential value judgement approach for many applications. Multi & hyper spectral imaging has more judging essentials than grey scale or RGB imaging and its image quality assessment job has to cover up all-around evaluating factors. This paper presents an integrating spectral imaging quality assessment project, in which spectral-based, radiometric-based and spatial-based statistical behavior for three hyperspectral imagers are jointly executed. Spectral response function is worked out based on discrete illumination images and its spectral performance is deduced according to its FWHM and spectral excursion value. Radiometric response ability of different spectral channel under both on-ground and airborne imaging condition is judged by SNR computing based upon local RMS extraction and statistics method. Spatial response evaluation of the spectral imaging instrument is worked out by MTF computing with slanted edge analysis method. Reported pioneering systemic work in hyperspectral imaging quality assessment is carried out with the help of several domestic dominating work units, which not only has significance in the development of on-ground and in-orbit instrument performance evaluation technique but also takes on reference value for index demonstration and design optimization for instrument development.

  1. Hyperspectral imaging utility for transportation systems

    NASA Astrophysics Data System (ADS)

    Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver

    2015-03-01

    The global transportation system is massive, open, and dynamic. Existing performance and condition assessments of the complex interacting networks of roadways, bridges, railroads, pipelines, waterways, airways, and intermodal ports are expensive. Hyperspectral imaging is an emerging remote sensing technique for the non-destructive evaluation of multimodal transportation infrastructure. Unlike panchromatic, color, and infrared imaging, each layer of a hyperspectral image pixel records reflectance intensity from one of dozens or hundreds of relatively narrow wavelength bands that span a broad range of the electromagnetic spectrum. Hence, every pixel of a hyperspectral scene provides a unique spectral signature that offers new opportunities for informed decision-making in transportation systems development, operations, and maintenance. Spaceborne systems capture images of vast areas in a short period but provide lower spatial resolution than airborne systems. Practitioners use manned aircraft to achieve higher spatial and spectral resolution, but at the price of custom missions and narrow focus. The rapid size and cost reduction of unmanned aircraft systems promise a third alternative that offers hybrid benefits at affordable prices by conducting multiple parallel missions. This research formulates a theoretical framework for a pushbroom type of hyperspectral imaging system on each type of data acquisition platform. The study then applies the framework to assess the relative potential utility of hyperspectral imaging for previously proposed remote sensing applications in transportation. The authors also introduce and suggest new potential applications of hyperspectral imaging in transportation asset management, network performance evaluation, and risk assessments to enable effective and objective decision- and policy-making.

  2. Hyperspectral fundus imager

    NASA Astrophysics Data System (ADS)

    Truitt, Paul W.; Soliz, Peter; Meigs, Andrew D.; Otten, Leonard John, III

    2000-11-01

    A Fourier Transform hyperspectral imager was integrated onto a standard clinical fundus camera, a Zeiss FF3, for the purposes of spectrally characterizing normal anatomical and pathological features in the human ocular fundus. To develop this instrument an existing FDA approved retinal camera was selected to avoid the difficulties of obtaining new FDA approval. Because of this, several unusual design constraints were imposed on the optical configuration. Techniques to calibrate the sensor and to define where the hyperspectral pushbroom stripe was located on the retina were developed, including the manufacturing of an artificial eye with calibration features suitable for a spectral imager. In this implementation the Fourier transform hyperspectral imager can collect over a hundred 86 cm-1 spectrally resolved bands with 12 micro meter/pixel spatial resolution within the 1050 nm to 450 nm band. This equates to 2 nm to 8 nm spectral resolution depending on the wavelength. For retinal observations the band of interest tends to lie between 475 nm and 790 nm. The instrument has been in use over the last year successfully collecting hyperspectral images of the optic disc, retinal vessels, choroidal vessels, retinal backgrounds, and macula diabetic macular edema, and lesions of age-related macular degeneration.

  3. APEX - the Hyperspectral ESA Airborne Prism Experiment

    PubMed Central

    Itten, Klaus I.; Dell'Endice, Francesco; Hueni, Andreas; Kneubühler, Mathias; Schläpfer, Daniel; Odermatt, Daniel; Seidel, Felix; Huber, Silvia; Schopfer, Jürg; Kellenberger, Tobias; Bühler, Yves; D'Odorico, Petra; Nieke, Jens; Alberti, Edoardo; Meuleman, Koen

    2008-01-01

    The airborne ESA-APEX (Airborne Prism Experiment) hyperspectral mission simulator is described with its distinct specifications to provide high quality remote sensing data. The concept of an automatic calibration, performed in the Calibration Home Base (CHB) by using the Control Test Master (CTM), the In-Flight Calibration facility (IFC), quality flagging (QF) and specific processing in a dedicated Processing and Archiving Facility (PAF), and vicarious calibration experiments are presented. A preview on major applications and the corresponding development efforts to provide scientific data products up to level 2/3 to the user is presented for limnology, vegetation, aerosols, general classification routines and rapid mapping tasks. BRDF (Bidirectional Reflectance Distribution Function) issues are discussed and the spectral database SPECCHIO (Spectral Input/Output) introduced. The optical performance as well as the dedicated software utilities make APEX a state-of-the-art hyperspectral sensor, capable of (a) satisfying the needs of several research communities and (b) helping the understanding of the Earth's complex mechanisms.

  4. Data products of NASA Goddard's LiDAR, hyperspectral, and thermal airborne imager (G-LiHT)

    NASA Astrophysics Data System (ADS)

    Corp, Lawrence A.; Cook, Bruce D.; McCorkel, Joel; Middleton, Elizabeth M.

    2015-06-01

    Scientists in the Biospheric Sciences Laboratory at NASA's Goddard Space Flight Center have undertaken a unique instrument fusion effort for an airborne package that integrates commercial off the shelf LiDAR, Hyperspectral, and Thermal components. G-LiHT is a compact, lightweight and portable system that can be used on a wide range of airborne platforms to support a number of NASA Earth Science research projects and space-based missions. G-LiHT permits simultaneous and complementary measurements of surface reflectance, vegetation structure, and temperature, which provide an analytical framework for the development of new algorithms for mapping plant species composition, plant functional types, biodiversity, biomass, carbon stocks, and plant growth. G-LiHT and its supporting database are designed to give scientists open access to the data that are needed to understand the relationship between ecosystem form and function and to stimulate the advancement of synergistic algorithms. This system will enhance our ability to design new missions and produce data products related to biodiversity and climate change. G-LiHT has been operational since 2011 and has been used to collect data for a number of NASA and USFS sponsored studies, including NASA's Carbon Monitoring System (CMS) and the American ICESat/GLAS Assessment of Carbon (AMIGA-Carb). These acquisitions target a broad diversity of forest communities and ecoregions across the United States and Mexico. Here, we will discuss the components of G-LiHT, their calibration and performance characteristics, operational implementation, and data processing workflows. We will also provide examples of higher level data products that are currently available.

  5. Airborne Hyperspectral Imagery for the Detection of Agricultural Crop Stress

    NASA Technical Reports Server (NTRS)

    Cassady, Philip E.; Perry, Eileen M.; Gardner, Margaret E.; Roberts, Dar A.

    2001-01-01

    Multispectral digital imagery from aircraft or satellite is presently being used to derive basic assessments of crop health for growers and others involved in the agricultural industry. Research indicates that narrow band stress indices derived from hyperspectral imagery should have improved sensitivity to provide more specific information on the type and cause of crop stress, Under funding from the NASA Earth Observation Commercial Applications Program (EOCAP) we are identifying and evaluating scientific and commercial applications of hyperspectral imagery for the remote characterization of agricultural crop stress. During the summer of 1999 a field experiment was conducted with varying nitrogen treatments on a production corn-field in eastern Nebraska. The AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) hyperspectral imager was flown at two critical dates during crop development, at two different altitudes, providing images with approximately 18m pixels and 3m pixels. Simultaneous supporting soil and crop characterization included spectral reflectance measurements above the canopy, biomass characterization, soil sampling, and aerial photography. In this paper we describe the experiment and results, and examine the following three issues relative to the utility of hyperspectral imagery for scientific study and commercial crop stress products: (1) Accuracy of reflectance derived stress indices relative to conventional measures of stress. We compare reflectance-derived indices (both field radiometer and AVIRIS) with applied nitrogen and with leaf level measurement of nitrogen availability and chlorophyll concentrations over the experimental plots (4 replications of 5 different nitrogen levels); (2) Ability of the hyperspectral sensors to detect sub-pixel areas under crop stress. We applied the stress indices to both the 3m and 18m AVIRIS imagery for the entire production corn field using several sub-pixel areas within the field to compare the relative

  6. Landmine detection using passive hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    McFee, John E.; Anger, Cliff; Achal, Steve; Ivanco, Tyler

    2007-04-01

    Airborne hyperspectral imaging has been studied since the late 1980s as a tool to detect minefields for military countermine operations and for level I clearance for humanitarian demining. Hyperspectral imaging employed on unmanned ground vehicles may also be used to augment or replace broadband imagers to detect individual mines. This paper will discuss the ability of different optical wavebands - the visible/near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) - to detect surface-laid and buried mines. The phenomenology that determines performance in the different bands is discussed. Hyperspectral imagers have usually been designed and built for general purpose remote sensing applications and often do not meet the requirements of mine detection. The DRDC mine detection research program has sponsored the development by Itres Research of VNIR, SWIR and TIR instruments specifically intended for mine detection. The requirements for such imagers are described, as well as the instruments. Some results of mine detection experiments are presented. To date, reliable day time detection of surface-laid mines in non-real-time, independent of solar angle, time of day and season has been demonstrated in the VNIR and SWIR. Real-time analysis, necessary for military applications, has been demonstrated from low speed ground vehicles and recently from airborne platforms. Reliable, repeatable detection of buried mines has yet to be demonstrated, although a recently completed TIR hyperspectral imager will soon be tested for such a capability.

  7. Quantitative Hyperspectral Reflectance Imaging

    PubMed Central

    Klein, Marvin E.; Aalderink, Bernard J.; Padoan, Roberto; de Bruin, Gerrit; Steemers, Ted A.G.

    2008-01-01

    Hyperspectral imaging is a non-destructive optical analysis technique that can for instance be used to obtain information from cultural heritage objects unavailable with conventional colour or multi-spectral photography. This technique can be used to distinguish and recognize materials, to enhance the visibility of faint or obscured features, to detect signs of degradation and study the effect of environmental conditions on the object. We describe the basic concept, working principles, construction and performance of a laboratory instrument specifically developed for the analysis of historical documents. The instrument measures calibrated spectral reflectance images at 70 wavelengths ranging from 365 to 1100 nm (near-ultraviolet, visible and near-infrared). By using a wavelength tunable narrow-bandwidth light-source, the light energy used to illuminate the measured object is minimal, so that any light-induced degradation can be excluded. Basic analysis of the hyperspectral data includes a qualitative comparison of the spectral images and the extraction of quantitative data such as mean spectral reflectance curves and statistical information from user-defined regions-of-interest. More sophisticated mathematical feature extraction and classification techniques can be used to map areas on the document, where different types of ink had been applied or where one ink shows various degrees of degradation. The developed quantitative hyperspectral imager is currently in use by the Nationaal Archief (National Archives of The Netherlands) to study degradation effects of artificial samples and original documents, exposed in their permanent exhibition area or stored in their deposit rooms.

  8. Automation of hyperspectral airborne remote sensing data processing

    NASA Astrophysics Data System (ADS)

    Kozoderov, V. V.; Egorov, V. D.

    2014-12-01

    An automated system is proposed for discriminating the spectral radiances registered by the hyperspectral airborne instruments based on average spectra and their interclass variability while distinguishing pixels related to the illuminated and shaded elements of the crown trees for various species and ages. Maps of the ground-based inventory for the selected area of airborne remote sensing are used as prior information. The system automatically forms databases of the selected classes of objects using the contours of these objects drawn on the image under processing. An opportunity to distinguish these classes is demonstrated in the red edge region of the spectra transition from the chlorophyll spectral band to the maximum of the spectral vegetation reflectivity.

  9. Hyperspectral image projector applications

    NASA Astrophysics Data System (ADS)

    Rice, Joseph P.; Brown, Steven W.; Allen, David W.; Yoon, Howard W.; Litorja, Maritoni; Hwang, Jeeseong C.

    2012-03-01

    For the past several years NIST has been developing, along with several collaborators, a Hyperspectral Image Projector (HIP). This scene projector produces high-resolution programmable spectra and projects them into dynamic two-dimensional images. The current digital micromirror device (DMD) based HIP prototype has a spatial resolution of 1024 x 768 pixels and a spectral range of 450 nm to 2400 nm, with spectral resolution from 2 nm in the visible to 5 nm in the short-wave infrared. It disperses light from a supercontinuum fiber source across two DMDs to produce the programmable spectra, which then globally-illuminate a third DMD to form the spatial images. The HIP can simulate top-of-the atmosphere spectral radiance over a 10 mm x 14 mm, f/3 image, and this can be collimated to stimulate remote sensing instruments. Also, the spectral radiance of the projected scenes can be measured with a NIST-calibrated spectroradiometer, such that the spectral radiance projected into each pixel can be accurately known. The HIP was originally developed for applications in multi-spectral and hyperspectral imager testing, calibration, and performance validation, and examples of this application will be reviewed. Conceivable applications for the HIP in photovoltaic device characterization and optical medical imaging will also be discussed.

  10. Comparison of different detection methods for citrus greening disease based on airborne multispectral and hyperspectral imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Citrus greening or Huanglongbing (HLB) is a devastating disease spread in many citrus groves since first found in 2005 in Florida. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were taken to detect citrus greening infected trees in 2007 and 2010. Ground truthi...

  11. Hyperspectral Systems Increase Imaging Capabilities

    NASA Technical Reports Server (NTRS)

    2010-01-01

    In 1983, NASA started developing hyperspectral systems to image in the ultraviolet and infrared wavelengths. In 2001, the first on-orbit hyperspectral imager, Hyperion, was launched aboard the Earth Observing-1 spacecraft. Based on the hyperspectral imaging sensors used in Earth observation satellites, Stennis Space Center engineers and Institute for Technology Development researchers collaborated on a new design that was smaller and used an improved scanner. Featured in Spinoff 2007, the technology is now exclusively licensed by Themis Vision Systems LLC, of Richmond, Virginia, and is widely used in medical and life sciences, defense and security, forensics, and microscopy.

  12. Medical hyperspectral imaging: a review

    PubMed Central

    Lu, Guolan; Fei, Baowei

    2014-01-01

    Abstract. Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. PMID:24441941

  13. Remote sensing of soil moisture using airborne hyperspectral data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Institute for Technology Development (ITD) has developed an airborne hyperspectral sensor system that collects electromagnetic reflectance data of the terrain. The system consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near Infrare...

  14. The information of oil and gas micro-seepage in Dongsheng region of inner Mongolia based on the airborne hyperspectral remote sensing image

    NASA Astrophysics Data System (ADS)

    Tian, Shu-Fang; Chen, Jian-Ping; Zhou, Mi

    2008-11-01

    The technology of hyper-spectral remote sensing which has higher spatial resolution characteristic, and optimizes the qualification of identifying and extracting salt mines, not only enhances the capacity of natural scenes detection and recognition, but also advances the level of quantitative remote sensing. It has important meaning for using the technology of hyper-spectral remote sensing to quantitative extraction. The paper investigate gas micro-seepage based on the Airborne Hyper-spectral Remote Sensing in Dongsheng of Inner Mongolia on the basis of gas micro-seepage theory using EO-1 Hyperion data collected by Satellite-Borne Sensor which has highest spatial resolution presently in the world. On the basis of data pretreated this paper adopts band math extracted the distribution of oil and gas micro-seepage using diagnostic assimilating spectrum of alteration minerals by the numbers. With eigenvector length model evaluates the research area comprehensive index, oil and gas micro-seepage information model of the research area is established and key regions of oil and gas micro-seepage are confirmed, which offers academic gist for oil and gas resource exploitation of Dongsheng.

  15. Application research of using CASI/SASI airborne hyperspectral remote sensing on lithology identification

    NASA Astrophysics Data System (ADS)

    Zhou, Jiajing; Qin, Kai

    2016-04-01

    Remote sensing provides an advanced method for lithology identification, which is one of the important research fields in geological prospecting. In theory, each lithology is of individual spectrum characteristics. Based on the spectral differences between them, we can identify different lithologies by remote sensing images. At present, the studies on lithology identification by remote sensing are primarily conducted on the multispectral images, such as Landsat 7 ETM+, SPOT-5, QuickBird and WorldView-2. Hyperspectral remote sensing images provide richer information, making it easier to identify the lithologies, but studied rarely. CASI/SASI is an airborne hyperspectral system covering a wavelength range of 0.38-2.45μm. With hundreds of bands, the hyperspectral images are useful to identify the spectrum characteristics of lithology. In addition, images are of high spatial resolution, with CASI of about 1m and SASI of about 2-2.5m, which make lithology identification more accurate. CASI/SASI hyperspectral data was collected in Beishan metallogenic belt in northwest China, as same as the ground spectral data of the lithologies. After data preprocessing, we divided different lithologies using CASI/SASI hyperspectral images and lithology spectrum, identified some important lithologies related to mineralization, and successfully found a few new ore clues.

  16. Development of hyperspectral image projectors

    NASA Astrophysics Data System (ADS)

    Rice, J. P.; Brown, S. W.; Neira, J. E.

    2006-08-01

    We present design concepts for calibrated hyperspectral image projectors (HIP) and related sources intended for system-level testing of instruments ranging from complex hyperspectral or multispectral imagers to simple filter radiometers. HIP, based on the same digital mirror arrays used in commercial digital light processing (DLP) displays, is capable of projecting any combination of many different arbitrarily programmable basis spectra into each pixel of the unit under test (UUT) at video frame rates. The resulting spectral and spatial content of the image entering the UUT can simulate, at typical video frame rates and integration times, realistic scenes to which the UUT will be exposed during use. Also, its spectral radiance can be measured with a calibrated spectroradiometer, such that the hyperspectral photon field entering the UUT is well known. Use of such generated scenes in a controlled laboratory setting would alleviate expensive field testing, allow better separation of environmental effects from instrument effects, and enable system-level performance testing and validation. Example potential applications include system-level testing of complex hyperspectral imaging instruments as implemented with data reduction algorithms when viewing realistic scenes, testing the performance of simple fighter-fighter infrared cameras under simulated adverse conditions, and hardware-in-the-loop testing of multispectral and hyperspectral systems.

  17. Synergetics Framework for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Müller, R.; Cerra, D.; Reinartz, P.

    2013-05-01

    In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics - originally introduced by the physicist H. Haken - is an interdisciplinary theory to find general rules for pattern formation through selforganization and has been successfully applied in fields ranging from biology to ecology, chemistry, cosmology, and thermodynamics up to sociology. Although this theory describes general rules for pattern formation it was linked also to pattern recognition. Pattern recognition algorithms based on synergetics theory have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analysed independently. The classification scheme based on synergetics introduces also methods for spatial regularization to get rid of "salt and pepper" classification results and for iterative parameter tuning to optimize class weights. The paper reports an experiment on a benchmark data set frequently used for method comparisons. This data set consists of a hyperspectral scene acquired by the Airborne Visible Infrared Imaging Spectrometer AVIRIS sensor of the Jet Propulsion Laboratory acquired over the Salinas Valley in CA, USA, with 15 vegetation classes. The results are compared to state-of-the-art methodologies like Support Vector Machines (SVM), Spectral Information Divergence (SID), Neural Networks, Logistic Regression, Factor Graphs or Spectral Angle Mapper (SAM). The outcomes are promising and often outperform state-of-the-art classification methodologies.

  18. Airborne hyperspectral systems for solving remote sensing problems

    NASA Astrophysics Data System (ADS)

    Rodionov, I. D.; Rodionov, A. I.; Vedeshin, L. A.; Vinogradov, A. N.; Egorov, V. V.; Kalinin, A. P.

    2014-12-01

    A retrospective of airborne hyperspectrometer projects carried out in the ZAO Reagent Scientific Technical Center is presented. Hyperspectral devices developed during the period since the end of 1990s to the present day are described. The line of hyperspectrometers designed in recent times covers the ranges from ultraviolet (0.2 μm) to near infrared (1.0 μm). These devices can be mounted on airborne and automobile carriers, including small-size ones. By now, the developments of hyperspectral devices in ZAO Reagent have reached the finished state and have been prepared for serial production. Their technical characteristics permit one to speak of the creation of wide-range high-aperture ultraspectral high spatial resolution equipment with a possibility of real-time data processing on board.

  19. Subtropical Forest Biomass Estimation Using Airborne LiDAR and Hyperspectral Data

    NASA Astrophysics Data System (ADS)

    Pang, Yong; Li, Zengyuan

    2016-06-01

    Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF's (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

  20. Hyperspectral image compressive projection algorithm

    NASA Astrophysics Data System (ADS)

    Rice, Joseph P.; Allen, David W.

    2009-05-01

    We describe a compressive projection algorithm and experimentally assess its performance when used with a Hyperspectral Image Projector (HIP). The HIP is being developed by NIST for system-level performance testing of hyperspectral and multispectral imagers. It projects a two-dimensional image into the unit under test (UUT), whereby each pixel can have an independently programmable arbitrary spectrum. To efficiently project a single frame of dynamic realistic hyperspectral imagery through the collimator into the UUT, a compression algorithm has been developed whereby the series of abundance images and corresponding endmember spectra that comprise the image cube of that frame are first computed using an automated endmember-finding algorithm such as the Sequential Maximum Angle Convex Cone (SMACC) endmember model. Then these endmember spectra are projected sequentially on the HIP spectral engine in sync with the projection of the abundance images on the HIP spatial engine, during the singleframe exposure time of the UUT. The integrated spatial image captured by the UUT is the endmember-weighted sum of the abundance images, which results in the formation of a datacube for that frame. Compressive projection enables a much smaller set of broadband spectra to be projected than monochromatic projection, and thus utilizes the inherent multiplex advantage of the HIP spectral engine. As a result, radiometric brightness and projection frame rate are enhanced. In this paper, we use a visible breadboard HIP to experimentally assess the compressive projection algorithm performance.

  1. An analysis task comparison of uncorrected vs. geo-registered airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Sun, Yihang; Kerekes, John

    2015-05-01

    Geo-registration is the task of assigning geospatial coordinates to the pixels of an image and placing them in a geographic coordinate system. However, the process of geo-registration can impair the quality of the image. This paper studies this topic by applying a comparison methodology to uncorrected and geo-registered airborne hyperspectral images obtained from the RIT SHARE 2012 data set. The uncorrected image was analyzed directly as collected by the sensor without being treated, while the geo-registered image was corrected using the nearest neighbor resampling approach. A comparison of performance was done for the analysis tasks of spectral unmixing and subpixel target detection, which can represent a measure of utility. The comparison demonstrates that the geo-registration process can affect the utility of hyperspectral imagery to a limited extent.

  2. Bayesian segmentation of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Mohammadpour, Adel; Féron, Olivier; Mohammad-Djafari, Ali

    2004-11-01

    In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.

  3. Skin detection in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Sanchez, Stephanie M.; Velez-Reyes, Miguel

    2015-05-01

    Hyperspectral imagers collect information of the scene being imaged at close contiguous bands in the electromagnetic spectrum at high spectral resolutions. The number of applications for these imagers has grown over the years as they are now used in various fields. Many algorithms are described in the literature for skin detection in color imagery. However increased detection accuracy, in particularly over cluttered backgrounds, and of small targets and in low spatial resolution systems can be achieved by taking advantage of the spectral information that can be collected with multi/hyperspectral imagers. The ultimate goal of our research work is the development of a human presence detection system over different backgrounds using hyperspectral imaging in the 400-1000nm region of the spectrum that can be used in the context of search and rescue operations, and surveillance in defense and security applications. The 400-1000 nm region is chosen because of availability of low cost imagers in this region of the spectrum. This paper presents preliminary results in the use of combinations of normalized difference indices that can be used to detect regions of interest in a scene that can be used as a pre-processor in a human detection system. A new normalized difference ratio, the Skin Normalized Difference Index (SNDI) is proposed. Experimental results show that a combination the NDGRI+NDVI+SNDI results in a probability of detection similar to that of the NDGRI. However, the combination of features results in a much lower probability of false alarm.

  4. Hyperspectral imaging of ischemic wounds

    NASA Astrophysics Data System (ADS)

    Gnyawali, Surya C.; Elgharably, Haytham; Melvin, James; Huang, Kun; Bergdall, Valerie; Allen, David W.; Hwang, Jeeseong; Litorja, Maritoni; Shirley, Eric; Sen, Chandan K.; Xu, Ronald

    2012-03-01

    Optical imaging has the potential to achieve high spatial resolution and high functional sensitivity in wound assessment. However, clinical acceptance of many optical imaging devices is hampered by poor reproducibility, low accuracy, and lack of biological interpretation. We developed an in vivo model of ischemic flap for non-contact assessment of wound tissue functional parameters and spectral characteristics. The model was created by elevating the bipedicle skin flaps of a domestic pig from the underlying vascular bed and inhibiting graft bed reperfusion by a silastic sheet. Hyperspectral imaging was carried out on the ischemic flap model and compared with transcutaneous oxygen tension and perfusion measurements at different positions of the wound. Hyperspectral images have also been captured continuously during a post-occlusive reactive hyperemia (PORH) procedure. Tissue spectral characteristics obtained by hyperspectral imaging correlated well with cutaneous tissue oxygen tension, blood perfusion, and microscopic changes of tissue morphology. Our experiments not only demonstrated the technical feasibility for quantitative assessment of chronic wound but also provided a potential digital phantom platform for quantitative characterization and calibration of medical optical devices.

  5. Water turbidity estimation from airborne hyperspectral imagery and full waveform bathymetric LiDAR

    NASA Astrophysics Data System (ADS)

    Pan, Z.; Glennie, C. L.; Fernandez-Diaz, J. C.

    2015-12-01

    The spatial and temporal variations in water turbidity are of great interest for the study of fluvial and coastal environments; and for predicting the performance of remote sensing systems that are used to map these. Conventional water turbidity estimates from remote sensing observations have normally been derived using near infrared reflectance. We have investigated the potential of determining water turbidity from additional remote sensing sources, namely airborne hyperspectral imagery and single wavelength bathymetric LiDAR (Light Detection and Ranging). The confluence area of the Blue and Colorado River, CO was utilized as a study area to investigate the capabilities of both airborne bathymetric LiDAR and hyperspectral imagery for water turbidity estimation. Discrete and full waveform bathymetric data were collected using Optech's Gemini (1064 nm) and Aquarius (532 nm) LiDAR sensors. Hyperspectral imagery (1.2 m pixel resolution and 72 spectral bands) was acquired using an ITRES CASI-1500 imaging system. As an independent reference, measurements of turbidity were collected concurrent with the airborne remote sensing acquisitions, using a WET Labs EcoTriplet deployed from a kayak and turbidity was then derived from the measured backscatter. The bathymetric full waveform dataset contains a discretized sample of the full backscatter of water column and benthic layer. Therefore, the full waveform records encapsulate the water column characteristics of turbidity. A nonparametric support vector regression method is utilized to estimate water turbidity from both hyperspectral imagery and voxelized full waveform LiDAR returns, both individually and as a fused dataset. Results of all the evaluations will be presented, showing an initial turbidity prediction accuracy of approximately 1.0 NTU. We will also discuss our future strategy for enhanced fusion of the full waveform LiDAR and hyperspectral imagery for improved turbidity estimation.

  6. Remote sensing of soil moisture using airborne hyperspectral data

    USGS Publications Warehouse

    Finn, Michael P.; Lewis, Mark (David); Bosch, David D.; Giraldo, Mario; Yamamoto, Kristina H.; Sullivan, Dana G.; Kincaid, Russell; Luna, Ronaldo; Allam, Gopala Krishna; Kvien, Craig; Williams, Michael S.

    2011-01-01

    Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

  7. Remote sensing of soil moisture using airborne hyperspectral data

    USGS Publications Warehouse

    Finn, M.; Lewis, M.; Bosch, D.; Giraldo, Mario; Yamamoto, K.; Sullivan, D.; Kincaid, R.; Luna, R.; Allam, G.; Kvien, Craig; Williams, M.

    2011-01-01

    Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

  8. Hyperspectral imaging of bruised skin

    NASA Astrophysics Data System (ADS)

    Randeberg, Lise L.; Baarstad, Ivar; Løke, Trond; Kaspersen, Peter; Svaasand, Lars O.

    2006-02-01

    Bruises can be important evidence in legal medicine, for example in cases of child abuse. Optical techniques can be used to discriminate and quantify the chromophores present in bruised skin, and thereby aid dating of an injury. However, spectroscopic techniques provide only average chromophore concentrations for the sampled volume, and contain little information about the spatial chromophore distribution in the bruise. Hyperspectral imaging combines the power of imaging and spectroscopy, and can provide both spectroscopic and spatial information. In this study a hyperspectral imaging system developed by Norsk Elektro Optikk AS was used to measure the temporal development of bruised skin in a human volunteer. The bruises were inflicted by paintball bullets. The wavelength ranges used were 400 - 1000 nm (VNIR) and 900 - 1700 nm (SWIR), and the spectral sampling intervals were 3.7 and 5 nm, respectively. Preliminary results show good spatial discrimination of the bruised areas compared to normal skin. Development of a white spot can be seen in the central zone of the bruises. This central white zone was found to resemble the shape of the object hitting the skin, and is believed to develop in areas where the impact caused vessel damage. These results show that hyperspectral imaging is a promising technique to evaluate the temporal and spatial development of bruises on human skin.

  9. Longwave infrared compressive hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Dupuis, Julia R.; Kirby, Michael; Cosofret, Bogdan R.

    2015-06-01

    Physical Sciences Inc. (PSI) is developing a longwave infrared (LWIR) compressive sensing hyperspectral imager (CS HSI) based on a single pixel architecture for standoff vapor phase plume detection. The sensor employs novel use of a high throughput stationary interferometer and a digital micromirror device (DMD) converted for LWIR operation in place of the traditional cooled LWIR focal plane array. The CS HSI represents a substantial cost reduction over the state of the art in LWIR HSI instruments. Radiometric improvements for using the DMD in the LWIR spectral range have been identified and implemented. In addition, CS measurement and sparsity bases specifically tailored to the CS HSI instrument and chemical plume imaging have been developed and validated using LWIR hyperspectral image streams of chemical plumes. These bases enable comparable statistics to detection based on uncompressed data. In this paper, we present a system model predicting the overall performance of the CS HSI system. Results from a breadboard build and test validating the system model are reported. In addition, the measurement and sparsity basis work demonstrating the plume detection on compressed hyperspectral images is presented.

  10. On-orbit characterization of hyperspectral imagers

    NASA Astrophysics Data System (ADS)

    McCorkel, Joel

    Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne- and satellite-based sensors. Often, ground-truth measurements at these tests sites are not always successful due to weather and funding availability. Therefore, RSG has also employed automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor. This dissertation presents a method for determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on a multispectral sensor, Moderate-resolution Imaging Spectroradiometer (MODIS), as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. A method to predict hyperspectral surface reflectance using a combination of MODIS data and spectral shape information is developed and applied for the characterization of Hyperion. Spectral shape information is based on RSG's historical in situ data for the Railroad Valley test site and spectral library data for the Libyan test site. Average atmospheric parameters, also based on historical measurements, are used in reflectance prediction and transfer to space. Results of several cross-calibration scenarios that differ in image acquisition coincidence, test site, and reference sensor are found for the characterization of Hyperion. These are compared with results from the reflectance-based approach of vicarious calibration, a well-documented method developed by the RSG that serves as a baseline for calibration performance for the cross-calibration method developed here. Cross-calibration provides results that are within 2% of those of reflectance-based results in most spectral regions. Larger disagreements exist

  11. Compact hyperspectral image sensor based on a novel hyperspectral encoder

    NASA Astrophysics Data System (ADS)

    Hegyi, Alex N.; Martini, Joerg

    2015-06-01

    A novel hyperspectral imaging sensor is demonstrated that can enable breakthrough applications of hyperspectral imaging in domains not previously accessible. Our technology consists of a planar hyperspectral encoder combined with a traditional monochrome image sensor. The encoder adds negligibly to the sensor's overall size, weight, power requirement, and cost (SWaP-C); therefore, the new imager can be incorporated wherever image sensors are currently used, such as in cell phones and other consumer electronics. In analogy to Fourier spectroscopy, the technique maintains a high optical throughput because narrow-band spectral filters are unnecessary. Unlike conventional Fourier techniques that rely on Michelson interferometry, our hyperspectral encoder is robust to vibration and amenable to planar integration. The device can be viewed within a computational optics paradigm: the hardware is uncomplicated and serves to increase the information content of the acquired data, and the complexity of the system, that is, the decoding of the spectral information, is shifted to computation. Consequently, system tradeoffs, for example, between spectral resolution and imaging speed or spatial resolution, are selectable in software. Our prototype demonstration of the hyperspectral imager is based on a commercially-available silicon CCD. The prototype encoder was inserted within the camera's ~1 cu. in. housing. The prototype can image about 49 independent spectral bands distributed from 350 nm to 1250 nm, but the technology may be extendable over a wavelength range from ~300 nm to ~10 microns, with suitable choice of detector.

  12. Hyperspectral Imaging of Forest Resources: The Malaysian Experience

    NASA Astrophysics Data System (ADS)

    Mohd Hasmadi, I.; Kamaruzaman, J.

    2008-08-01

    Remote sensing using satellite and aircraft images are well established technology. Remote sensing application of hyperspectral imaging, however, is relatively new to Malaysian forestry. Through a wide range of wavelengths hyperspectral data are precisely capable to capture narrow bands of spectra. Airborne sensors typically offer greatly enhanced spatial and spectral resolution over their satellite counterparts, and able to control experimental design closely during image acquisition. The first study using hyperspectral imaging for forest inventory in Malaysia were conducted by Professor Hj. Kamaruzaman from the Faculty of Forestry, Universiti Putra Malaysia in 2002 using the AISA sensor manufactured by Specim Ltd, Finland. The main objective has been to develop methods that are directly suited for practical tropical forestry application at the high level of accuracy. Forest inventory and tree classification including development of single spectral signatures have been the most important interest at the current practices. Experiences from the studies showed that retrieval of timber volume and tree discrimination using this system is well and some or rather is better than other remote sensing methods. This article reviews the research and application of airborne hyperspectral remote sensing for forest survey and assessment in Malaysia.

  13. Airborne Hyperspectral Remote Sensing for Identification Grassland Vegetation

    NASA Astrophysics Data System (ADS)

    Burai, P.; Tomor, T.; Bekő, L.; Deák, B.

    2015-08-01

    In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400-1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes.

  14. Hyperspectral imaging for nondestructive evaluation of tomatoes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Machine vision methods for quality and defect evaluation of tomatoes have been studied for online sorting and robotic harvesting applications. We investigated the use of a hyperspectral imaging system for quality evaluation and defect detection for tomatoes. Hyperspectral reflectance images were a...

  15. Hyperspectral Imaging of human arm

    NASA Technical Reports Server (NTRS)

    2003-01-01

    ProVision Technologies, a NASA research partnership center at Sternis Space Center in Mississippi, has developed a new hyperspectral imaging (HSI) system that is much smaller than the original large units used aboard remote sensing aircraft and satellites. The new apparatus is about the size of a breadbox. Health-related applications of HSI include non-invasive analysis of human skin to characterize wounds and wound healing rates (especially important for space travelers who heal more slowly), determining if burns are first-, second-, or third degree (rather than painful punch biopsies). The work is sponsored under NASA's Space Product Development (SPD) program.

  16. Hyperspectral imaging camera using wavefront division interference.

    PubMed

    Bahalul, Eran; Bronfeld, Asaf; Epshtein, Shlomi; Saban, Yoram; Karsenty, Avi; Arieli, Yoel

    2016-03-01

    An approach for performing hyperspectral imaging is introduced. The hyperspectral imaging is based on Fourier transform spectroscopy, where the interference is performed by wavefront division interference rather than amplitude division interference. A variable phase delay between two parts of the wavefront emanating from each point of an object is created by a spatial light modulator (SLM) to obtain variable interference patterns. The SLM is placed in the exit pupil of an imaging system, thus enabling conversion of a general imaging optical system into an imaging hyperspectral optical system. The physical basis of the new approach is introduced, and an optical apparatus is built. PMID:26974085

  17. Airborne Hyperspectral Survey of Afghanistan 2007: Flight Line Planning and HyMap Data Collection

    USGS Publications Warehouse

    Kokaly, Raymond F.; King, Trude V.V.; Livo, K. Eric

    2008-01-01

    Hyperspectral remote sensing data were acquired over Afghanistan with the HyMap imaging spectrometer (Cocks and others, 1998) operating on the WB-57 high altitude NASA research aircraft (http://jsc-aircraft-ops.jsc.nasa.gov/wb57/index.html). These data were acquired during the interval of August 22, 2007 to October 2, 2007, as part of the United States Geological Survey (USGS) project 'Oil and Gas Resources Assessment of the Katawaz and Helmand Basins'. A total of 218 flight lines of hyperspectral remote sensing data were collected over the country. This report describes the planning of the airborne survey and the flight lines that were flown. Included with this report are digital files of the nadir tracks of the flight lines, including a map of the labeled flight lines and corresponding vector shape files for geographic information systems (GIS).

  18. Common hyperspectral image database design

    NASA Astrophysics Data System (ADS)

    Tian, Lixun; Liao, Ningfang; Chai, Ali

    2009-11-01

    This paper is to introduce Common hyperspectral image database with a demand-oriented Database design method (CHIDB), which comprehensively set ground-based spectra, standardized hyperspectral cube, spectral analysis together to meet some applications. The paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies, some data mining ideas and functions were associated into CHIDB to make it more suitable to serve in agriculture, geological and environmental areas. A broad range of data from multiple regions of the electromagnetic spectrum is supported, including ultraviolet, visible, near-infrared, thermal infrared, and fluorescence. CHIDB is based on dotnet framework and designed by MVC architecture including five main functional modules: Data importer/exporter, Image/spectrum Viewer, Data Processor, Parameter Extractor, and On-line Analyzer. The original data were all stored in SQL server2008 for efficient search, query and update, and some advance Spectral image data Processing technology are used such as Parallel processing in C#; Finally an application case is presented in agricultural disease detecting area.

  19. a Review of Hyperspectral Imaging in Close Range Applications

    NASA Astrophysics Data System (ADS)

    Kurz, T. H.; Buckley, S. J.

    2016-06-01

    Hyperspectral imaging is an established method for material mapping, which has been conventionally applied from airborne and spaceborne platforms for a range of applications, including mineral and vegetation mapping, change detection and environmental studies. The main advantage of lightweight hyperspectral imagers lies in the flexibility to deploy them from various platforms (terrestrial imaging and from unmanned aerial vehicles; UAVs), as well as the high spectral resolution to cover an expanding wavelength range. In addition, spatial resolution allows object sampling distances from micrometres to tens of centimetres - complementary to conventional nadir-looking systems. When this new type of imaging device was initially released, few instruments were available and the applicability and potential of the method was restricted. Today, a wider range of instruments, with a range of specifications, is available, with significant improvements over the first generation of technology. In this contribution, the state-of-the-art of hyperspectral imaging will be reviewed from a close range measurement perspective, highlighting how the method supplements geometric modelling techniques. An overview of the processing workflow, adjusted to the more complex close range imaging scenario will be given. This includes the integration with 3D laser scanning and photogrammetric models to provide a geometric framework and real world coordinate system for the hyperspectral imagery.

  20. Extraction, modelling, and use of linear features for restitution of airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Lee, Changno; Bethel, James S.

    This paper presents an approach for the restitution of airborne hyperspectral imagery with linear features. The approach consisted of semi-automatic line extraction and mathematical modelling of the linear features. First, the line was approximately determined manually and refined using dynamic programming. The extracted lines could then be used as control data with the ground information of the lines, or as constraints with simple assumption for the ground information of the line. The experimental results are presented numerically in tables of RMS residuals of check points as well as visually in ortho-rectified images.

  1. Content-based hyperspectral image retrieval using spectral unmixing

    NASA Astrophysics Data System (ADS)

    Plaza, Antonio J.

    2011-11-01

    The purpose of content-based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. A major challenge for the development of efficient CBIR systems in the context of hyperspectral remote sensing applications is how to deal with the extremely large volumes of data produced by current Earth-observing (EO) imaging spectrometers. The data resulting from EO campaigns often comprises many Gigabytes per flight. When multiple instruments or timelines are combined, this leads to the collection of massive amounts of data coming from heterogeneous sources, and these data sets need to be effectively stored, managed, shared and retrieved. Furthermore, the growth in size and number of hyperspectral data archives demands more sophisticated search capabilities to allow users to locate and reuse data acquired in the past. In this paper we develop a new strategy to effectively retrieve hyperspectral image data sets using spectral unmixing concepts. Spectral unmixing is a very important task for hyperspectral data exploitation since the spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. In this work, we use the information provided by spectral unmixing (i.e. the spectral endmembers and their corresponding abundances in the scene) as effective meta-data to develop a new CBIR system that can assist users in the task of efficiently searching hyperspectral image instances in large data repositories. The proposed approach is validated using a collection of 154 hyperspectral data sets (comprising seven full flightlines) gathered by NASA using the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) area in New York City during the last two weeks of September, 2001, only a few days after the terrorist attacks that

  2. Research on method of geometry and spectral calibration of pushbroom dispersive hyperspectral imager

    NASA Astrophysics Data System (ADS)

    He, Zhiping; Shu, Rong; Wang, Jianyu

    2012-11-01

    Development and application of airborne and aerospace hyperspectral imager press for high precision geometry and spectral calibration of pixels of image cube. The research of geometry and spectral calibration of pushbroom hyperspectral imager, its target is giving the coordinate of angle field of view and center wavelength of each detect unit in focal plane detector of hyperspectral imager, and achieves the high precision, full field of view, full channel geometry and spectral calibration. It is importance for imaging quantitative and deep application of hyperspectal imager. The paper takes the geometry and spectral calibration of pushbroom dispersive hyperspectral imager as case study, and research on the constitution and analysis of imaging mathematical model. Aimed especially at grating-dispersive hyperspectral imaging, the specialty of the imaging mode and dispersive method has been concretely analyzed. Based on the analysis, the theory and feasible method of geometry and spectral calibration of dispersive hyperspectral imager is set up. The key technique has been solved is As follows: 1). the imaging mathematical model and feasible method of geometry and spectral calibration for full pixels of image cube has been set up, the feasibility of the calibration method has been analyzed. 2). the engineering model and method of the geometry and spectral calibration of pushbroom dispersive hyperspectral imager has been set up and the calibration equipment has been constructed, and the calibration precision has been analyzed.

  3. Mapping mine tailings using airborne geophysical and hyperspectral remote sensing data

    NASA Astrophysics Data System (ADS)

    Shang, Jiali

    Mine tailings are the waste products from mining operations. Most mine tailings contain a considerable amount of reactive sulphides which can cause acid mine drainage (AMD) when exposed to air and water. AMD constitutes a threat both to the environment and to public health. Increased awareness of AMD has led to growing activities in mine-tailing monitoring and reclamation worldwide. Mining companies in Canada are required to provide information to provincial governments about their waste disposal and control activities. There is an urgent need to develop new automated ways to provide information on short- to long-term evolution of tailings, thus enabling the mining companies to monitor their tailings more effectively. The overall goal of the thesis is to explore the potential of hyperspectral remote sensing and geophysical techniques for mapping variations within and immediately outside of the tailings. Data used for this study are from three sources: airborne geophysical data, hyperspectral casi and Probe-1 data, and field data. This study has contributed to both the remote sensing data analysis techniques and the understanding of mine-tailing surface and subsurface processes. Specifically, this study has the following important findings: (1) Airborne magnetic and electromagnetic data can provide information regarding the subsurface distribution of mine tailings on the basis of sulphide mineral content. A procedure has been developed in this study to use these data sources for rapidly surveying large tailings areas. This procedure can minimize expenditures for mining companies when designing remedial plans for the closure of the mines. This study has also identified regions of enhanced conductivity that extend beyond the tailing containment area. This information indicates seepage pathways, and is important for monitoring the effectiveness of tailing containment structures. (2) High-spatial-resolution hyperspectral casi (Compact Airborne Spectrographic Imagery

  4. Reflectance and fluorescence hyperspectral elastic image registration

    NASA Astrophysics Data System (ADS)

    Lange, Holger; Baker, Ross; Hakansson, Johan; Gustafsson, Ulf P.

    2004-05-01

    Science and Technology International (STI) presents a novel multi-modal elastic image registration approach for a new hyperspectral medical imaging modality. STI's HyperSpectral Diagnostic Imaging (HSDI) cervical instrument is used for the early detection of uterine cervical cancer. A Computer-Aided-Diagnostic (CAD) system is being developed to aid the physician with the diagnosis of pre-cancerous and cancerous tissue regions. The CAD system uses the fusion of multiple data sources to optimize its performance. The key enabling technology for the data fusion is image registration. The difficulty lies in the image registration of fluorescence and reflectance hyperspectral data due to the occurrence of soft tissue movement and the limited resemblance of these types of imagery. The presented approach is based on embedding a reflectance image in the fluorescence hyperspectral imagery. Having a reflectance image in both data sets resolves the resemblance problem and thereby enables the use of elastic image registration algorithms required to compensate for soft tissue movements. Several methods of embedding the reflectance image in the fluorescence hyperspectral imagery are described. Initial experiments with human subject data are presented where a reflectance image is embedded in the fluorescence hyperspectral imagery.

  5. Evaluating airborne hyperspectral imagery for mapping saltcedar infestations in west Texas

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Rio Grande of west Texas contains by far the largest infestation of saltcedar (Tamarix spp.) in Texas. The objective of this study was to evaluate airborne hyperspectral imagery and different classification techniques for mapping saltcedar infestations. Hyperspectral imagery with 102 usable band...

  6. Using airborne hyperspectral imagery for mapping saltcedar infestations in west Texas

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Rio Grande of west Texas contains, by far, the largest infestation of saltcedar (Tamarix spp.) in Texas. The objective of this study was to evaluate airborne hyperspectral imagery and different classification techniques for mapping saltcedar infestations. Hyperspectral imagery with 102 usable ba...

  7. Hyperspectral range imaging for transportation systems evaluation

    NASA Astrophysics Data System (ADS)

    Bridgelall, Raj; Rafert, J. B.; Atwood, Don; Tolliver, Denver D.

    2016-04-01

    Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However, cost and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the network. Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing techniques offer the potential for more rapid and extensive non-destructive evaluations of the multimodal transportation infrastructure. However, optical occlusions and limitations in the spatial resolution of typical airborne and space-borne platforms limit their applicability. This research proposes hyperspectral image classification to isolate transportation infrastructure targets for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned aircraft systems will capture images with centimeter-scale spatial resolution, large swaths, and polarization diversity. The light field solution will incorporate structure-from-motion techniques to reconstruct three-dimensional details of the isolated targets from sequences of two-dimensional images. A comparative analysis of existing low-power wireless communications standards suggests an application dependent tradeoff in selecting the best-suited link to coordinate swarming operations. This study further produced a taxonomy of specific roadway and railway defects, distress symptoms, and other anomalies that the proposed plenoptic swarm sensing system would identify and characterize to estimate risk levels.

  8. Great Lakes Hyperspectral Water Quality Instrument Suite for Airborne Monitoring of Algal Blooms

    NASA Technical Reports Server (NTRS)

    Lekki, John; Leshkevich, George; Nguyen, Quang-Viet; Flatico, Joseph; Prokop, Norman; Kojima, Jun; Anderson, Robert; Demers, James; Krasowski, Michael

    2007-01-01

    NASA Glenn Research Center and NOAA Great Lakes Environmental Research Lab are collaborating to utilize an airborne hyperspectral imaging sensor suite to monitor Harmful Algal Blooms (HABs) in the western basin of Lake Erie. The HABs are very dynamic events as they form, spread and then disappear within a 4 to 8 week time period in late summer. They are a concern for human health, fish and wildlife because they can contain blue green toxic algae. Because of this toxicity there is a need for the blooms to be continually monitored. This situation is well suited for aircraft based monitoring because the blooms are a very dynamic event and they can spread over a large area. High resolution satellite data is not suitable by itself because it will not give the temporal resolution due to the infrequent overpasses of the quickly changing blooms. A custom designed hyperspectral imager and a point spectrometer mounted on aT 34 aircraft have been used to obtain data on an algal bloom that formed in the western basin of Lake Erie during September 2006. The sensor suite and operations will be described and preliminary hyperspectral data of this event will be presented

  9. Retrieval of Topsoil Properties of Vegetation-Covered Terrain Using Airborne Hyperspectral Data

    NASA Astrophysics Data System (ADS)

    Liu, Lanfa; Buchroithner, Manfred

    2016-04-01

    Soil spectroscopy is a promising technique for topsoil analysis, and has been successfully utilized in the laboratory. When it is applied from airborne platforms, the presence of vegetation significantly affects imaging spectroscopy or hyperspectral imaging when retrieving topsoil properties. A Forced Invariance Approach has been proved to be able to effectively suppress the vegetation signal in mixed pixels. However, the approach is still mainly limited to lithological mapping. In this paper, we attempted to apply it to the retrieval of topsoil properties (soil moisture and soil salinity at depths 4 cm and 10 cm) using airborne hyperspectral data. The corresponding ground truth data was obtained from an eco-hydrological wireless sensing network in the Zhangye Oasis in the middle stream of the Heihe River Basin, China. The General Linear Model with Logit Link Function was adopted to model the relationships between measured soil properties and the spectra. The vegetation suppression result demonstrates that the spectral response curves of hyperspectral image pixels are flattened and the shapes are rather similar to the soil endmenber spectrum. From the modelling results it can be seen that the Forced Invariance Approach is more effective for soil moisture than for soil salinity at depth 10 cm, as the salt content is comparatively lower than the water content in soil, and the corresponding spectral response is weaker. This approach did not work for soil at a depth of 4 cm. The reason for this is that surface soil is significantly influenced by exterior factors like irrigation and wind, and landscape fragmentation and cultivation activities also contribute to the high spatial heterogeneity of the surface soil properties.

  10. Hyperspectral imaging of atherosclerotic plaques in vitro

    NASA Astrophysics Data System (ADS)

    Larsen, Eivind L. P.; Randeberg, Lise L.; Olstad, Elisabeth; Haugen, Olav A.; Aksnes, Astrid; Svaasand, Lars O.

    2011-02-01

    Vulnerable plaques constitute a risk for serious heart problems, and are difficult to identify using existing methods. Hyperspectral imaging combines spectral- and spatial information, providing new possibilities for precise optical characterization of atherosclerotic lesions. Hyperspectral data were collected from excised aorta samples (n = 11) using both white-light and ultraviolet illumination. Single lesions (n = 42) were chosen for further investigation, and classified according to histological findings. The corresponding hyperspectral images were characterized using statistical image analysis tools (minimum noise fraction, K-means clustering, principal component analysis) and evaluation of reflectance/fluorescence spectra. Image analysis combined with histology revealed the complexity and heterogeneity of aortic plaques. Plaque features such as lipids and calcifications could be identified from the hyperspectral images. Most of the advanced lesions had a central region surrounded by an outer rim or shoulder-region of the plaque, which is considered a weak spot in vulnerable lesions. These features could be identified in both the white-light and fluorescence data. Hyperspectral imaging was shown to be a promising tool for detection and characterization of advanced atherosclerotic plaques in vitro. Hyperspectral imaging provides more diagnostic information about the heterogeneity of the lesions than conventional single point spectroscopic measurements.

  11. Potential of Airborne Imaging Spectroscopy at Czechglobe

    NASA Astrophysics Data System (ADS)

    Hanuš, J.; Fabiánek, T.; Fajmon, L.

    2016-06-01

    Ecosystems, their services, structures and functions are affected by complex environmental processes, which are both natural and human-induced and globally changing. In order to understand how ecosystems behave in globally changing environment, it is important to monitor the current status of ecosystems and their structural and functional changes in time and space. An essential tool allowing monitoring of ecosystems is remote sensing (RS). Many ecosystems variables are being translated into a spectral response recorded by RS instruments. It is however important to understand the complexity and synergies of the key ecosystem variables influencing the reflected signal. This can be achieved by analysing high resolution RS data from multiple sources acquired simultaneously from the same platform. Such a system has been recently built at CzechGlobe - Global Change Research Institute (The Czech Academy of Sciences). CzechGlobe has been significantly extending its research infrastructure in the last years, which allows advanced monitoring of ecosystem changes at hierarchical levels spanning from molecules to entire ecosystems. One of the CzechGlobe components is a laboratory of imaging spectroscopy. The laboratory is now operating a new platform for advanced remote sensing observations called FLIS (Flying Laboratory of Imaging Spectroscopy). FLIS consists of an airborne carrier equipped with passive RS systems. The core instrument of FLIS is a hyperspectral imaging system provided by Itres Ltd. The hyperspectral system consists of three spectroradiometers (CASI 1500, SASI 600 and TASI 600) that cover the reflective spectral range from 380 to 2450 nm, as well as the thermal range from 8 to 11.5 μm. The airborne platform is prepared for mounting of full-waveform laser scanner Riegl-Q780 as well, however a laser scanner is not a permanent part of FLIS. In 2014 the installation of the hyperspectral scanners was completed and the first flights were carried out with all

  12. Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas

    NASA Astrophysics Data System (ADS)

    Kozoderov, Vladimir; Kondranin, Timofei; Dmitriev, Egor; Kamentsev, Vladimir

    2015-06-01

    Pattern recognition problem is outlined in the context of textural and spectral analysis of remote sensing imagery processing. Main attention is paid to Bayesian classifier that can be used to realize the processing procedures based on parallel machine-learning algorithms and high-productive computers. We consider the maximum of the posterior probability principle and the formalism of Markov random fields for the neighborhood description of the pixels for the related classes of objects with the emphasis on forests of different species and ages. The energy category of the selected classes serves to account for the likelihood measure between the registered radiances and the theoretical distribution functions approximating remotely sensed data. Optimization procedures are undertaken to solve the pattern recognition problem of the texture description for the forest classes together with finding thin nuances of their spectral distribution in the feature space. As a result, possible redundancy of the channels for imaging spectrometer due to their correlations is removed. Difficulties are revealed due to different sampling data while separating pixels, which characterize the sunlit tops, shaded space and intermediate cases of the Sun illumination conditions on the hyperspectral images. Such separation of pixels for the forest classes is maintained to enhance the recognition accuracy, but learning ensembles of data need to be agreed for these categories of pixels. We present some results of the Bayesian classifier applicability for recognizing airborne hyperspectral images using the relevant improvements in separating such pixels for the forest classes on a test area of the 4 × 10 km size encompassed by 13 airborne tracks, each forming the images by 500 pixels across the track and from 10,000 to 14,000 pixels along the track. The spatial resolution of each image is near to 1 m from the altitude near to 2 km above the ground level. The results of the hyperspectral imagery

  13. Novel hyperspectral imager for lightweight UAVs

    NASA Astrophysics Data System (ADS)

    Saari, Heikki; Aallos, Ville-Veikko; Holmlund, Christer; Mäkynen, Jussi; Delauré, Bavo; Nackaerts, Kris; Michiels, Bart

    2010-04-01

    VTT Technical Research Centre of Finland has developed a new miniaturized staring hyperspectral imager with a weight of 350 g making the system compatible with lightweight UAS platforms. The instrument is able to record 2D spatial images at the selected wavelength bands simultaneously. The concept of the hyperspectral imager has been published in the SPIE Proc. 74741. The operational wavelength range of the imager can be tuned in the range 400 - 1100 nm and spectral resolution is in the range 5 - 10 nm @ FWHM. Presently the spatial resolution is 480 × 750 pixels but it can be increased simply by changing the image sensor. The field of view of the system is 20 × 30 degrees and ground pixel size at 100 m flying altitude is around 7.5 cm. The system contains batteries, image acquisition control system and memory for the image data. It can operate autonomously recording hyperspectral data cubes continuously or controlled by the autopilot system of the UAS. The new hyperspectral imager prototype was first tried in co-operation with the Flemish Institute for Technological Research (VITO) on their UAS helicopter. The instrument was configured for the spectral range 500 - 900 nm selected for the vegetation and natural water monitoring applications. The design of the UAS hyperspectral imager and its characterization results together with the analysis of the spectral data from first test flights will be presented.

  14. Airborne infrared-hyperspectral mapping for detection of gaseous and solid targets

    NASA Astrophysics Data System (ADS)

    Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Farley, V.; Lagueux, P.; Marcotte, F.; Chamberland, M.

    2010-04-01

    Airborne hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. The Telops Hyper-Cam is a rugged and compact infrared hyperspectral imager based on the Fourier-transform technology. It has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. The technology offers fine spectral resolution (up to 0.25 cm-1) and high accuracy radiometric calibration (better than 1 degree Celsius). Furthermore, the spectral resolution, spatial resolution, swath width, integration time and sensitivity are all flexible parameters that can be selected and optimized to best address the specific objectives of each mission. The system performance and a few measurements have been presented in previous publications. This paper focuses on analyzing additional measurements in which detection of fertilizer and Freon gas has been demonstrated.

  15. Portable Hyperspectral Imaging Broadens Sensing Horizons

    NASA Technical Reports Server (NTRS)

    2007-01-01

    Broadband multispectral imaging can be very helpful in showing differences in energy being radiated and is often employed by NASA satellites to monitor temperature and climate changes. In addition, hyperspectral imaging is ideal for advanced laboratory uses, biomedical imaging, forensics, counter-terrorism, skin health, food safety, and Earth imaging. Lextel Intelligence Systems, LLC, of Jackson, Mississippi purchased Photon Industries Inc., a spinoff company of NASA's Stennis Space Center and the Institute for Technology Development dedicated to developing new hyperspectral imaging technologies. Lextel has added new features to and expanded the applicability of the hyperspectral imaging systems. It has made advances in the size, usability, and cost of the instruments. The company now offers a suite of turnkey hyperspectral imaging systems based on the original NASA groundwork. It currently has four lines of hyperspectral imaging products: the EagleEye VNIR 100E, the EagleEye SWIR 100E, the EagleEye SWIR 200E, and the EagleEye UV 100E. These Lextel instruments are used worldwide for a wide variety of applications including medical, military, forensics, and food safety.

  16. Alteration mineral mapping and metallogenic prediction using CASI/SASI airborne hyperspectral data in Mingshujing area of Gansu Province, NW China

    NASA Astrophysics Data System (ADS)

    Sun, Yu; Zhao, Yingjun; Qin, Kai; Tian, Feng

    2016-04-01

    Hyperspectral remote sensing is a frontier of remote sensing. Due to its advantage of integrated image with spectrum, it can realize objects identification, superior to objects classification of multispectral remote sensing. Taken the Mingshujing area in Gansu Province of China as an example, this study extracted the alteration minerals and thus to do metallogenic prediction using CASI/SASI airborne hyperspectral data. The Mingshujing area, located in Liuyuan region of Gansu Province, is dominated by middle Variscan granites and Indosinian granites, with well developed EW- and NE-trending faults. In July 2012, our project team obtained the CASI/SASI hyperspectral data of Liuyuan region by aerial flight. The CASI hyperspectral data have 32 bands and the SASI hyperspectral data have 88 bands, with spectral resolution of 15nm for both. The hyperspectral raw data were first preprocessed, including radiometric correction and geometric correction. We then conducted atmospheric correction using empirical line method based on synchronously measured ground spectra to obtain hyperspectral reflectance data. Spectral dimension of hyperspectral data was reduced by the minimum noise fraction transformation method, and then purity pixels were selected. After these steps, image endmember spectra were obtained. We used the endmember spectrum election method based on expert knowledge to analyze the image endmember spectra. Then, the mixture tuned matched filter (MTMF) mapping method was used to extract mineral information, including limonite, Al-rich sericite, Al-poor sericite and chlorite. Finally, the distribution of minerals in the Mingshujing area was mapped. According to the distribution of limonite and Al-rich sericite mapped by CASI/SASI hyperspectral data, we delineated five gold prospecting areas, and further conducted field verification in these areas. It is shown that there are significant gold mineralized anomalies in surface in the Baixianishan and Xitan prospecting

  17. Uncooled long-wave infrared hyperspectral imaging

    NASA Technical Reports Server (NTRS)

    Lucey, Paul G. (Inventor)

    2006-01-01

    A long-wave infrared hyperspectral sensor device employs a combination of an interferometer with an uncooled microbolometer array camera to produce hyperspectral images without the use of bulky, power-hungry motorized components, making it suitable for UAV vehicles, small mobile platforms, or in extraterrestrial environments. The sensor device can provide signal-to-noise ratios near 200 for ambient temperature scenes with 33 wavenumber resolution at a frame rate of 50 Hz, with higher results indicated by ongoing component improvements.

  18. [Remote sensing of chlorophyll fluorescence at airborne level based on unmanned airship platform and hyperspectral sensor].

    PubMed

    Yang, Pei-Qi; Liu, Zhi-Gang; Ni, Zhuo-Ya; Wang, Ran; Wang, Qing-Shan

    2013-11-01

    The solar-induced chlorophyll fluorescence (ChlF) has a close relationship with photosynthetic and is considered as a probe of plant photosynthetic activity. In this study, an airborne fluorescence detecting system was constructed by using a hyperspectral imager on board an unmanned airship. Both Fraunhofer Line Discriminator (FLD) and 3FLD used to extract ChlF require the incident solar irradiance, which is always difficult to receive at airborne level. Alternative FLD (aFLD) can overcome the problem by selecting non-fluorescent emitter in the image. However, aFLD is based on the assumption that reflectance is identical around the Fraunhofer line, which is not realistic. A new method, a3FLD, is proposed, which assumes that reflectance varies linearly with the wavelength around Fraunhofer line. The result of simulated data shows that ChlF retrieval error of a3FLD is significantly lower than that of aFLD when vegetation reflectance varies near the Fraunhofer line. The results of hyperspectral remote sensing data with the airborne fluorescence detecting system show that the relative values of retrieved ChlF of 5 kinds of plants extracted by both aFLD and a3FLD are consistent with vegetation growth stage and the ground-level ChlF. The ChlF values of aFLD are about 15% greater than a3FLD. In addition, using aFLD, some non-fluorescent objects have considerable ChlF value, while a3FLD can effectively overcome the problem.

  19. [Remote sensing of chlorophyll fluorescence at airborne level based on unmanned airship platform and hyperspectral sensor].

    PubMed

    Yang, Pei-Qi; Liu, Zhi-Gang; Ni, Zhuo-Ya; Wang, Ran; Wang, Qing-Shan

    2013-11-01

    The solar-induced chlorophyll fluorescence (ChlF) has a close relationship with photosynthetic and is considered as a probe of plant photosynthetic activity. In this study, an airborne fluorescence detecting system was constructed by using a hyperspectral imager on board an unmanned airship. Both Fraunhofer Line Discriminator (FLD) and 3FLD used to extract ChlF require the incident solar irradiance, which is always difficult to receive at airborne level. Alternative FLD (aFLD) can overcome the problem by selecting non-fluorescent emitter in the image. However, aFLD is based on the assumption that reflectance is identical around the Fraunhofer line, which is not realistic. A new method, a3FLD, is proposed, which assumes that reflectance varies linearly with the wavelength around Fraunhofer line. The result of simulated data shows that ChlF retrieval error of a3FLD is significantly lower than that of aFLD when vegetation reflectance varies near the Fraunhofer line. The results of hyperspectral remote sensing data with the airborne fluorescence detecting system show that the relative values of retrieved ChlF of 5 kinds of plants extracted by both aFLD and a3FLD are consistent with vegetation growth stage and the ground-level ChlF. The ChlF values of aFLD are about 15% greater than a3FLD. In addition, using aFLD, some non-fluorescent objects have considerable ChlF value, while a3FLD can effectively overcome the problem. PMID:24555390

  20. Real-time snapshot hyperspectral imaging endoscope.

    PubMed

    Kester, Robert T; Bedard, Noah; Gao, Liang; Tkaczyk, Tomasz S

    2011-05-01

    Hyperspectral imaging has tremendous potential to detect important molecular biomarkers of early cancer based on their unique spectral signatures. Several drawbacks have limited its use for in vivo screening applications: most notably the poor temporal and spatial resolution, high expense, and low optical throughput of existing hyperspectral imagers. We present the development of a new real-time hyperspectral endoscope (called the image mapping spectroscopy endoscope) based on an image mapping technique capable of addressing these challenges. The parallel high throughput nature of this technique enables the device to operate at frame rates of 5.2 frames per second while collecting a (x, y, λ) datacube of 350 × 350 × 48. We have successfully imaged tissue in vivo, resolving a vasculature pattern of the lower lip while simultaneously detecting oxy-hemoglobin. PMID:21639573

  1. Temperature and emissivity separation and mineral mapping based on airborne TASI hyperspectral thermal infrared data

    NASA Astrophysics Data System (ADS)

    Cui, Jing; Yan, Bokun; Dong, Xinfeng; Zhang, Shimin; Zhang, Jingfa; Tian, Feng; Wang, Runsheng

    2015-08-01

    Thermal infrared remote sensing (8-12 μm) (TIR) has great potential for geologic remote sensing studies. TIR has been successfully used for terrestrial and planetary geologic studies to map surface materials. However, the complexity of the physics and the lack of hyperspectral data make the studies under-investigated. A new generation of commercial hyperspectral infrared sensors, known as Thermal Airborne Spectrographic Imager (TASI), was used for image analysis and mineral mapping in this study. In this paper, a combined method integrating normalized emissivity method (NEM), ratio algorithm (RATIO) and maximum-minimum apparent emissivity difference (MMD), being applied in multispectral data, has been modified and used to determine whether this method is suitable for retrieving emissivity from TASI hyperspectral data. MODTRAN 4 has been used for the atmospheric correction. The retrieved emissivity spectra matched well with the field measured spectra except for bands 1, 2, and 32. Quartz, calcite, diopside/hedenbergite, hornblende and microcline have been mapped by the emissivity image. Mineral mapping results agree with the dominant minerals identified by laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Both of the results indicated that the atmospheric correction method and the combined temperature-emissivitiy method are suitable for TASI image. Carbonate skarnization was first found in the study area by the spatial extent of diopside. Chemical analyses of the skarn samples determined that the Au content was 0.32-1.74 g/t, with an average Au content of 0.73 g/t. This information provides an important resource for prospecting for skarn type gold deposits. It is also suggested that TASI is suitable for prospect and deposit scale exploration.

  2. MEMS FPI-based smartphone hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Rissanen, Anna; Saari, Heikki; Rainio, Kari; Stuns, Ingmar; Viherkanto, Kai; Holmlund, Christer; Näkki, Ismo; Ojanen, Harri

    2016-05-01

    This paper demonstrates a mobile phone- compatible hyperspectral imager based on a tunable MEMS Fabry-Perot interferometer. The realized iPhone 5s hyperspectral imager (HSI) demonstrator utilizes MEMS FPI tunable filter for visible-range, which consist of atomic layer deposited (ALD) Al2O3/TiO2-thin film Bragg reflectors. Characterization results for the mobile phone hyperspectral imager utilizing MEMS FPI chip optimized for 500 nm is presented; the operation range is λ = 450 - 550 nm with FWHM between 8 - 15 nm. Also a configuration of two cascaded FPIs (λ = 500 nm and λ = 650 nm) combined with an RGB colour camera is presented. With this tandem configuration, the overall wavelength tuning range of MEMS hyperspectral imagers can be extended to cover a larger range than with a single FPI chip. The potential applications of mobile hyperspectral imagers in the vis-NIR range include authentication, counterfeit detection and potential health/wellness and food sensing applications.

  3. Hyperspectral imager development at Army Research Laboratory

    NASA Astrophysics Data System (ADS)

    Gupta, Neelam

    2008-04-01

    Development of robust compact optical imagers that can acquire both spectral and spatial features from a scene of interest is of utmost importance for standoff detection of chemical and biological agents as well as targets and backgrounds. Spectral features arise due to the material properties of objects as a result of the emission, reflection, and absorption of light. Using hyperspectral imaging one can acquire images with narrow spectral bands and take advantage of the characteristic spectral signatures of different materials making up the scene in detection of objects. Traditional hyperspectral imaging systems use gratings and prisms that acquire one-dimensional spectral images and require relative motion of sensor and scene in addition to data processing to form a two-dimensional image cube. There is much interest in developing hyperspectral imagers using tunable filters that acquire a two-dimensional spectral image and build up an image cube as a function of time. At the Army Research Laboratory (ARL), we are developing hyperspectral imagers using a number of novel tunable filter technologies. These include acousto-optic tunable filters (AOTFs) that can provide adaptive no-moving-parts imagers from the UV to the long wave infrared, diffractive optics technology that can provide image cubes either in a single spectral region or simultaneously in different spectral regions using a single moving lens or by using a lenslet array, and micro-electromechanical systems (MEMS)-based Fabry-Perot (FP) tunable etalons to develop miniature sensors that take advantage of the advances in microfabrication and packaging technologies. New materials are being developed to design AOTFs and a full Stokes polarization imager has been developed, diffractive optics lenslet arrays are being explored, and novel FP tunable filters are under fabrication for the development of novel miniature hyperspectral imagers. Here we will brief on all the technologies being developed and present

  4. Within-field and regional-scale accuracies of topsoil organic carbon content prediction from an airborne visible near-infrared hyperspectral image combined with synchronous field spectra for temperate croplands

    NASA Astrophysics Data System (ADS)

    Vaudour, Emmanuelle; Gilliot, Jean-Marc; Bel, Liliane; Lefevre, Josias; Chehdi, Kacem

    2016-04-01

    This study was carried out in the framework of the TOSCA-PLEIADES-CO of the French Space Agency and benefited data from the earlier PROSTOCK-Gessol3 project supported by the French Environment and Energy Management Agency (ADEME). It aimed at identifying the potential of airborne hyperspectral visible near-infrared AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with intensive annual crop cultivation and both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle images (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks. Tracks were atmospherically corrected then mosaicked at a 2 m-resolution using a set of 24 synchronous field spectra of bare soils, black and white targets and impervious surfaces. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then calculation and thresholding of NDVI from an atmospherically corrected SPOT4 image acquired the same day enabled to map agricultural fields with bare soil. A total of 101 sites, which were sampled either at the regional scale or within one field, were identified as bare by means of this map. Predictions were made from the mosaic AISA spectra which were related to SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples, considering those 75 sites outside cloud shadows only, and different sampling strategies for selecting calibration samples. Validation root-mean-square errors (RMSE) were comprised between 3.73 and 4.49 g. Kg-1 and were ~4 g. Kg-1 in median. The most performing models in terms of coefficient of determination (R²) and Residual Prediction Deviation (RPD) values were the

  5. Airborne hyperspectral and LiDAR data integration for weed detection

    NASA Astrophysics Data System (ADS)

    Tamás, János; Lehoczky, Éva; Fehér, János; Fórián, Tünde; Nagy, Attila; Bozsik, Éva; Gálya, Bernadett; Riczu, Péter

    2014-05-01

    Agriculture uses 70% of global available fresh water. However, ca. 50-70% of water used by cultivated plants, the rest of water transpirated by the weeds. Thus, to define the distribution of weeds is very important in precision agriculture and horticulture as well. To survey weeds on larger fields by traditional methods is often time consuming. Remote sensing instruments are useful to detect weeds in larger area. In our investigation a 3D airborne laser scanner (RIEGL LMS-Q680i) was used in agricultural field near Sopron to scouting weeds. Beside the airborne LiDAR, hyperspectral imaging system (AISA DUAL) and air photos helped to investigate weed coverage. The LiDAR survey was carried out at early April, 2012, before sprouting of cultivated plants. Thus, there could be detected emerging of weeds and direction of cultivation. However airborne LiDAR system was ideal to detect weeds, identification of weeds at species level was infeasible. Higher point density LiDAR - Terrestrial laser scanning - systems are appropriate to distinguish weed species. Based on the results, laser scanner is an effective tool to scouting of weeds. Appropriate weed detection and mapping systems could contribute to elaborate water and herbicide saving management technique. This publication was supported by the OTKA project K 105789.

  6. [Research on airborne hyperspectral identification of red tide organism dominant species based on SVM].

    PubMed

    Ma, Yi; Zhang, Jie; Cui, Ting-wei

    2006-12-01

    Airborne hyperspectral identification of red tide organism dominant species can provide technique for distinguishing red tide and its toxin, and provide support for scaling the disaster. Based on support vector machine(SVM), the present paper provides an identification model of red tide dominant species. Utilizing this model, the authors accomplished three identification experiments with the hyperspectral data obtained on 16th July, and 19th and 25th August, 2001. It is shown from the identification results that the model has a high precision and is not restricted by high dimension of the hyperspectral data.

  7. Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Henrot, Simon; Chanussot, Jocelyn; Jutten, Christian

    2016-07-01

    In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant. The spectral signatures and fractional abundances of the pure materials in the scene are seen as latent variables, and assumed to follow a general dynamical structure. Based on a simplified version of this model, we derive an efficient spectral unmixing algorithm to estimate the latent variables by performing alternating minimizations. The performance of the proposed approach is demonstrated on synthetic and real multitemporal hyperspectral images.

  8. Miniaturization of high spectral spatial resolution hyperspectral imagers on unmanned aerial systems

    NASA Astrophysics Data System (ADS)

    Hill, Samuel L.; Clemens, Peter

    2015-06-01

    Traditional airborne environmental monitoring has frequently deployed hyperspectral imaging as a leading tool for characterizing and analyzing a scene's critical spectrum-based signatures for applications in agriculture genomics and crop health, vegetation and mineral monitoring, and hazardous material detection. As the acceptance of hyperspectral evaluation grows in the airborne community, there has been a dramatic trend in moving the technology from use on midsize aircraft to Unmanned Aerial Systems (UAS). The use of UAS accomplishes a number of goals including the reduction in cost to run multiple seasonal evaluations over smaller but highly valuable land-areas, the ability to use frequent data collections to make rapid decisions on land management, and the improvement of spatial resolution by flying at lower altitudes (<500 ft.). Despite this trend, there are several key parameters affecting the use of traditional hyperspectral instruments in UAS with payloads less than 10 lbs. where size, weight and power (SWAP) are critical to how high and how far a given UAS can fly. Additionally, on many of the light-weight UAS, users are frequently trying to capture data from one or more instruments to augment the hyperspectral data collection, thus reducing the amount of SWAP available to the hyperspectral instrumentation. The following manuscript will provide an analysis on a newly-developed miniaturized hyperspectral imaging platform, the Nano-Hyperspec®, which provides full hyperspectral resolution and traditional hyperspectral capabilities without sacrificing performance to accommodate the decreasing SWAP of smaller and smaller UAS platforms. The analysis will examine the Nano-Hyperspec flown in several UAS airborne environments and the correlation of the systems data with LiDAR and other GIS datasets.

  9. Overview of hyperspectral remote sensing for mapping marine benthic habitats from airborne and underwater sensors

    NASA Astrophysics Data System (ADS)

    Dierssen, Heidi M.

    2013-09-01

    The seafloor, with its diverse and dynamic benthic habitats varying on meter to centimeter scales, is difficult to accurately monitor with traditional techniques. The technology used to build imaging spectrometers has rapidly advanced in recent years with the advent of smaller sensors and better signal-to-noise capabilities that has facilitated their use in mapping fine-scale benthic features. Here, the use of such sensors for hyperspectral remote sensing of the seafloor from both airborne and underwater platforms is discussed. Benthic constituents provide a so-called optical fingerprint with spectral properties that are often too subtle to be discerned with simple color photographs or multichannel spectrometers. Applications include the recent field validation of the airborne Portable Remote Imaging SpectroMeter (PRISM), a new imaging sensor package optimized for coastal ocean processes in Elkorn Slough California. In these turbid sediment-laden waters, only subtle spectral differences differentiate seafloor with sediment from that with eelgrass. The ultimate goal is to provide robust radiometric approaches that accurately consider light attenuation by the water column and are able to be applied to diverse habitats without considerable foreknowledge.

  10. Unsupervised linear unmixing of hyperspectral image for crop yield estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multispectral and hyperspectral imagery are often used for estimating crop yield. This paper describes an unsupervised unmixing scheme of hyperspectral images to estimate crop yield. From the hyperspectral images, the endmembers and their abundance maps are computed by unsupervised unmixing. The abu...

  11. Relating Hyperspectral Airborne Data to Ground Measurements in a Complex and Discontinuous Canopy

    NASA Astrophysics Data System (ADS)

    Calleja, Javier F.; Hellmann, Christine; Mendiguren, Gorka; Punalekar, Suvarna; Peón, Juanjo; MacArthur, Alasdair; Alonso, Luis

    2015-12-01

    The work described in this paper is aimed at validating hyperspectral airborne reflectance data collected during the Regional Experiments For Land-atmosphere EXchanges (REFLEX) campaign. Ground reflectance data measured in a vineyard were compared with airborne reflectance data. A sampling strategy and subsequent ground data processing had to be devised so as to capture a representative spectral sample of this complex crop. A linear model between airborne and ground data was tried and statistically tested. Results reveal a sound correspondence between ground and airborne reflectance data (R2 > 0.97), validating the atmospheric correction of the latter.

  12. Biometric study using hyperspectral imaging during stress

    NASA Astrophysics Data System (ADS)

    Nagaraj, Sheela; Quoraishee, Shafik; Chan, Gabriel; Short, Kenneth R.

    2010-04-01

    To the casual observer, transient stress results in a variety of physiological changes that can be seen in the face. Although the conditions can be seen visibly, the conditions affect the emissivity and absorption properties of the skin, which imaging spectrometers, commonly referred to as Hyperspectral (HS) cameras, can quantify at every image pixel. The study reported on in this paper, using Hyperspectral cameras, provides a basis for continued study of HS imaging to eventually quantify biometric stress. This study was limited to the visible to near infrared (VNIR) spectral range. Signal processing tools and algorithms have been developed and are described for using HS face data from human subjects. The subjects were placed in psychologically stressful situations and the camera data were analyzed to detect stress through changes in dermal reflectance and emissivity. Results indicate that hyperspectral imaging may potentially serve as a non-invasive tool to measure changes in skin emissivity indicative of a stressful incident. Particular narrow spectral bands in the near-infrared region of the electromagnetic spectrum seem especially important. Further studies need to be performed to determine the optimal spectral bands and to generalize the conclusions. The enormous information available in hyperspectral imaging needs further analysis and more spectral regions need to be exploited. Non-invasive stress detection is a prominent area of research with countless applications for both military and commercial use including border patrol, stand-off interrogation, access control, surveillance, and non-invasive and un-attended patient monitoring.

  13. Design and modeling of spectral-thermal unmixing targets for airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Clare, Phil

    2006-05-01

    Techniques to determine the proportions of constituent materials within a single pixel spectrum are well documented in the reflective (0.4-2.5μm) domain. The same capability is also desirable for the thermal (7-14μm) domain, but is complicated by the thermal contributions to the measured spectral radiance. Atmospheric compensation schemes for the thermal domain have been described along with methods for estimating the spectral emissivity from a spectral radiance measurement and hence the next stage to be tackled is the unmixing of thermal spectral signatures. In order to pursue this goal it is necessary to collect data of well-calibrated targets which will expose the limits of the available techniques and enable more robust methods to be designed. This paper describes the design of a set of ground targets for an airborne hyperspectral imager, which will test the effectiveness of available methods. The set of targets include panels to explore a number of difficult scenarios such as isothermal (different materials at identical temperature), isochromal (identical materials, but at differing temperatures), thermal adjacency and thermal point sources. Practical fabrication issues for heated targets and selection of appropriate materials are described. Mathematical modelling of the experiments has enabled prediction of at-sensor measured radiances which are used to assess the design parameters. Finally, a number of useful lessons learned during the fielding of these actual targets are presented to assist those planning future trials of thermal hyperspectral sensors.

  14. LIFTERS-hyperspectral imaging at LLNL

    SciTech Connect

    Fields, D.; Bennett, C.; Carter, M.

    1994-11-15

    LIFTIRS, the Livermore Imaging Fourier Transform InfraRed Spectrometer, recently developed at LLNL, is an instrument which enables extremely efficient collection and analysis of hyperspectral imaging data. LIFTIRS produces a spatial format of 128x128 pixels, with spectral resolution arbitrarily variable up to a maximum of 0.25 inverse centimeters. Time resolution and spectral resolution can be traded off for each other with great flexibility. We will discuss recent measurements made with this instrument, and present typical images and spectra.

  15. Quality evaluation of fruit by hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This chapter presents new applications of hyperspectral imaging for measuring the optical properties of fruits and assessing their quality attributes. A brief overview is given of current techniques for measuring optical properties of turbid and opaque biological materials. Then a detailed descripti...

  16. Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

    NASA Astrophysics Data System (ADS)

    Zhou, Xianfeng; Huang, Wenjiang; Kong, Weiping; Ye, Huichun; Luo, Juhua; Chen, Pengfei

    2016-11-01

    Timely and accurate assessment of canopy nitrogen content (CNC) provides valuable insight into rapid and real-time nitrogen status monitoring in crops. A semi-empirical approach based on spectral index was extensively used for nitrogen content estimation. However, in many cases, due to specific vegetation types or local conditions, the applicability and robustness of established spectral indices for nitrogen retrieval were limited. The objective of this study was to investigate the optimal spectral index for winter wheat (Triticum aestivum L.) CNC estimation using Pushbroom Hyperspectral Imager (PHI) airborne hyperspectral data. Data collected from two different field experiments that were conducted during the major growth stages of winter wheat in 2002 and 2003 were used. Our results showed that a significant linear relationship existed between nitrogen and chlorophyll content at the canopy level, and it was not affected by cultivars, growing conditions and nutritional status of winter wheat. Nevertheless, it varied with growth stages. Periods around heading stage mainly worsened the relationship and CNC estimation, and CNC assessment for growth stages before and after heading could improve CNC retrieval accuracy to some extent. CNC assessment with PHI airborne hyperspectra suggested that spectral indices based on red-edge band including narrowband and broadband CIred-edge, NDVI-like and ND705 showed convincing results in CNC retrieval. NDVI-like and ND705 were sensitive to detect CNC changes less than 5 g/m2, narrowband and broadband CIred-edge were sensitive to a wide range of CNC variations. Further evaluation of CNC retrieval using field measured hyperspectra indicated that NDVI-like was robust and exhibited the highest accuracy in CNC assessment, and spectral indices (CIred-edge and CIgreen) that established on narrow or broad bands showed no obvious difference in CNC assessment. Overall, our study suggested that NDVI-like was the optimal indicator for winter

  17. Metric Learning to Enhance Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

    Thompson, David R.; Castano, Rebecca; Bue, Brian; Gilmore, Martha S.

    2013-01-01

    Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

  18. ICER-3D Hyperspectral Image Compression Software

    NASA Technical Reports Server (NTRS)

    Xie, Hua; Kiely, Aaron; Klimesh, matthew; Aranki, Nazeeh

    2010-01-01

    Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received

  19. Illumination system characterization for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Katrašnik, Jaka; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    Near-infrared hyperspectral imaging is becoming a popular tool in the biomedical field, especially for detection and analysis of different types of cancers, analysis of skin burns and bruises, imaging of blood vessels and for many other applications. As in all imaging systems, proper illumination is crucial to attain optimal image quality that is needed for best performance of image analysis algorithms. In hyperspectral imaging based on filters (AOTF, LCTF and filter wheel) the acquired spectral signature has to be representative in all parts of the imaged object. Therefore, the whole object must be equally well illuminated - without shadows and specular reflections. As there are no restrictions imposed on the material and geometry of the object, the desired object illumination can only be achieved with completely diffuse illumination. In order to minimize shadows and specular reflections in diffuse illumination the light illuminating the object must be spatially, angularly and spectrally uniform. We present and test two diffuse illumination system designs that try to achieve optimal uniformity of the above mentioned properties. The illumination uniformity properties were measured with an AOTF based hyperspectral imaging system utilizing a standard white diffuse reflectance target and a specially designed calibration target for estimating the spatial and angular illumination uniformity.

  20. Mapping Soil Organic Matter with Hyperspectral Imaging

    NASA Astrophysics Data System (ADS)

    Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel

    2014-05-01

    Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our

  1. Detection of soil properties with airborne hyperspectral measurements of bare fields.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Airborne remote sensing data, using a hyperspectral (HSI) camera, were collected for a flight over two fields with a total of 128 ha. of recently seeded and nearly bare soil. The within-field spatial distribution of several soil properties was found by using multiple linear regression to select the ...

  2. Nonnegative Matrix Factorization for Efficient Hyperspectral Image Projection

    NASA Technical Reports Server (NTRS)

    Iacchetta, Alexander S.; Fienup, James R.; Leisawitz, David T.; Bolcar, Matthew R.

    2015-01-01

    Hyperspectral imaging for remote sensing has prompted development of hyperspectral image projectors that can be used to characterize hyperspectral imaging cameras and techniques in the lab. One such emerging astronomical hyperspectral imaging technique is wide-field double-Fourier interferometry. NASA's current, state-of-the-art, Wide-field Imaging Interferometry Testbed (WIIT) uses a Calibrated Hyperspectral Image Projector (CHIP) to generate test scenes and provide a more complete understanding of wide-field double-Fourier interferometry. Given enough time, the CHIP is capable of projecting scenes with astronomically realistic spatial and spectral complexity. However, this would require a very lengthy data collection process. For accurate but time-efficient projection of complicated hyperspectral images with the CHIP, the field must be decomposed both spectrally and spatially in a way that provides a favorable trade-off between accurately projecting the hyperspectral image and the time required for data collection. We apply nonnegative matrix factorization (NMF) to decompose hyperspectral astronomical datacubes into eigenspectra and eigenimages that allow time-efficient projection with the CHIP. Included is a brief analysis of NMF parameters that affect accuracy, including the number of eigenspectra and eigenimages used to approximate the hyperspectral image to be projected. For the chosen field, the normalized mean squared synthesis error is under 0.01 with just 8 eigenspectra. NMF of hyperspectral astronomical fields better utilizes the CHIP's capabilities, providing time-efficient and accurate representations of astronomical scenes to be imaged with the WIIT.

  3. Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data.

    PubMed

    Moses, Wesley J; Gitelson, Anatoly A; Perk, Richard L; Gurlin, Daniela; Rundquist, Donald C; Leavitt, Bryan C; Barrow, Tadd M; Brakhage, Paul

    2012-03-15

    Algorithms based on red and near infra-red (NIR) reflectances measured using field spectrometers have been previously shown to yield accurate estimates of chlorophyll-a concentration in turbid productive waters, irrespective of variations in the bio-optical characteristics of water. The objective of this study was to investigate the performance of NIR-red models when applied to multi-temporal airborne reflectance data acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non-uniform atmospheric effects across the dates of data acquisition. The results demonstrated the capability of the NIR-red models to capture the spatial distribution of chlorophyll-a in surface waters without the need for atmospheric correction. However, the variable atmospheric effects did affect the accuracy of chlorophyll-a retrieval. Two atmospheric correction procedures, namely, Fast Line-of-sight Atmospheric Adjustment of Spectral Hypercubes (FLAASH) and QUick Atmospheric Correction (QUAC), were applied to AISA data and their results were compared. QUAC produced a robust atmospheric correction, which led to NIR-red algorithms that were able to accurately estimate chlorophyll-a concentration, with a root mean square error of 5.54 mg m(-3) for chlorophyll-a concentrations in the range 2.27-81.17 mg m(-3). PMID:22209281

  4. Radiometric Characterization of Hyperspectral Imagers using Multispectral Sensors

    NASA Technical Reports Server (NTRS)

    McCorkel, Joel; Kurt, Thome; Leisso, Nathan; Anderson, Nikolaus; Czapla-Myers, Jeff

    2009-01-01

    The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these test sites are not always successful due to weather and funding availability. Therefore, RSG has also automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor, This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral a imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (M0DIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of M0DlS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most brands as well as similar agreement between results that employ the different MODIS sensors as a reference.

  5. Estimating Leaf Water Potential of Giant Sequoia Trees from Airborne Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Francis, E. J.; Asner, G. P.

    2015-12-01

    Recent drought-induced forest dieback events have motivated research on the mechanisms of tree survival and mortality during drought. Leaf water potential, a measure of the force exerted by the evaporation of water from the leaf surface, is an indicator of plant water stress and can help predict tree mortality in response to drought. Scientists have traditionally measured water potentials on a tree-by-tree basis, but have not been able to produce maps of tree water potential at the scale of a whole forest, leaving forest managers unaware of forest drought stress patterns and their ecosystem-level consequences. Imaging spectroscopy, a technique for remote measurement of chemical properties, has been used to successfully estimate leaf water potentials in wheat and maize crops and pinyon-pine and juniper trees, but these estimates have never been scaled to the canopy level. We used hyperspectral reflectance data collected by the Carnegie Airborne Observatory (CAO) to map leaf water potentials of giant sequoia trees (Sequoiadendron giganteum) in an 800-hectare grove in Sequoia National Park. During the current severe drought in California, we measured predawn and midday leaf water potentials of 48 giant sequoia trees, using the pressure bomb method on treetop foliage samples collected with tree-climbing techniques. The CAO collected hyperspectral reflectance data at 1-meter resolution from the same grove within 1-2 weeks of the tree-level measurements. A partial least squares regression was used to correlate reflectance data extracted from the 48 focal trees with their water potentials, producing a model that predicts water potential of giant sequoia trees. Results show that giant sequoia trees can be mapped in the imagery with a classification accuracy of 0.94, and we predicted the water potential of the mapped trees to assess 1) similarities and differences between a leaf water potential map and a canopy water content map produced from airborne hyperspectral data, 2

  6. Construction of a small and lightweight hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

    Vogel, Britta; Hünniger, Dirk; Bastian, Georg

    2014-05-01

    The analysis of the reflected sunlight offers great opportunity to gain information about the environment, including vegetation and soil. In the case of plants the wavelength ratio of the reflected light usually undergoes a change if the state of growth or state of health changes. So the measurement of the reflected light allows drawing conclusions about the state of, amongst others, vegetation. Using a hyperspectral imaging system for data acquisition leads to a large dataset, which can be evaluated with respect to several different questions to obtain various information by one measurement. Based on commercially available plain optical components we developed a small and lightweight hyperspectral imaging system within the INTERREG IV A-Project SMART INSPECTORS. The project SMART INSPECTORS [Smart Aerial Test Rigs with Infrared Spectrometers and Radar] deals with the fusion of airborne visible and infrared imaging remote sensing instruments and wireless sensor networks for precision agriculture and environmental research. A high performance camera was required in terms of good signal, good wavelength resolution and good spatial resolution, while severe constraints of size, proportions and mass had to be met due to the intended use on small unmanned aerial vehicles. The detector was chosen to operate without additional cooling. The refractive and focusing optical components were identified by supporting works with an optical raytracing software and a self-developed program. We present details of design and construction of our camera system, test results to confirm the optical simulation predictions as well as our first measurements.

  7. Hyperspectral imaging for cancer surgical margin delineation: registration of hyperspectral and histological images

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo G.; Fei, Baowei

    2014-03-01

    The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection.

  8. Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images.

    PubMed

    Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2014-03-12

    The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection. PMID:25328640

  9. Fusion of full waveform Laserscanning and airborne hyperspectral remote sensing data for the characterization of forest stands

    NASA Astrophysics Data System (ADS)

    Buddenbaum, Henning

    2010-05-01

    Hyperspectral data offer the maximum spectral reflectance information available from remote sensing. A continuous spectrum of narrow bands with near-laboratory quality is recorded for each pixel. This data can be used for difficult classification tasks or detailed quantitative analyses, e.g. determination of chlorophyll or water content in leaves. But in forested areas, discerning between different age classes of the same tree species is still error-prone. Airborne Laserscanning measures the three-dimensional position of every reflecting object and can be used to map tree heights and crown volumes. These are highly correlated with tree age and timber volume. In addition, Laserscanner data can be used to differentiate between coniferous and deciduous trees either by analysing crown shapes that lead to different surface roughness or by exploiting the intensity information of laser echoes from the crowns. But a more detailed determination of tree species is not possible using Laserscanning alone. The combination of hyperspectral and Laserscanning data promises the possibility to map both tree species and age classes. We used a HyMap data set with 122 bands recorded in 2003 and a full waveform Laserscanning recorded in 2005 in the same area, Idarwald Forest in South-western Germany. To combine both datasets, we defined voxels above the HyMap pixels, containing the mean laser intensity in slices of 50 cm height. These voxels form a second hyperspectral dataset of 76 bands with the same geometry as the HyMap image, so that they could be fused into a 198 band image. The joined image performed better in a classification of tree species and age classes than each of the single images and also better than a dataset consisting of the hyperspectral image and a tree height map. Apart from classification, it can also be used to derive tree heights and crown base heights and to estimate biomass, leaf area index and timber volume and to characterize the vertical forest structure.

  10. A global shutter CMOS image sensor for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Stefanov, Konstantin D.; Dryer, Ben J.; Hall, David J.; Holland, Andrew D.; Pratlong, Jérôme; Fryer, Martin; Pike, Andrew

    2015-09-01

    Hyperspectral imaging has been providing vital information on the Earth landscape in response to the changing environment, land use and natural phenomena. While conventional hyperspectral imaging instruments have typically used rows of linescan CCDs, CMOS image sensors (CIS) have been slowly penetrating space instrumentation for the past decade, and Earth observation (EO) is no exception. CIS provide distinct advantages over CCDs that are relevant to EO hyperspectral imaging. The lack of charge transfer through the array allows the reduction of cross talk usually present in CCDs due to imperfect charge transfer efficiency, and random pixel addressing makes variable integration time possible, and thus improves the camera sensitivity and dynamic range. We have developed a 10T pixel design that integrates a pinned photodiode with global shutter and in-pixel correlated double sampling (CDS) to increase the signal to noise ratio in less intense spectral regimes, allowing for both high resolution and low noise hyperspectral imaging for EO. This paper details the characterization of a test device, providing baseline performance measurements of the array such as noise, responsivity, dark current and global shutter efficiency, and also discussing benchmark hyperspectral imaging requirements such as dynamic range, pixel crosstalk, and image lag.

  11. Quantification and threshold detection in real-time hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Driver, Richard D.

    2009-05-01

    The technical challenges of applying hyperspectral imaging techniques to on-line real-time food monitoring is discussed. System optimization must be applied to the design of the hyperspectral imaging spectrograph, the choice and operation of the imaging detector, the design of the illumination system and finally the development of software algorithms to correctly quantify the hyperspectral images. The signal to noise limitation of hyperspectral detection is discussed with particular emphasis on the detection of moving objects at high measurement bandwidths. An example is given of the development of a simple but accurate algorithm for the detection and discrimination of rust particles on leaves.

  12. Excitation-scanning hyperspectral imaging microscope.

    PubMed

    Favreau, Peter F; Hernandez, Clarissa; Heaster, Tiffany; Alvarez, Diego F; Rich, Thomas C; Prabhat, Prashant; Leavesley, Silas J

    2014-04-01

    Hyperspectral imaging is a versatile tool that has recently been applied to a variety of biomedical applications, notably live-cell and whole-tissue signaling. Traditional hyperspectral imaging approaches filter the fluorescence emission over a broad wavelength range while exciting at a single band. However, these emission-scanning approaches have shown reduced sensitivity due to light attenuation from spectral filtering. Consequently, emission scanning has limited applicability for time-sensitive studies and photosensitive applications. In this work, we have developed an excitation-scanning hyperspectral imaging microscope that overcomes these limitations by providing high transmission with short acquisition times. This is achieved by filtering the fluorescence excitation rather than the emission. We tested the efficacy of the excitation-scanning microscope in a side-by-side comparison with emission scanning for detection of green fluorescent protein (GFP)-expressing endothelial cells in highly autofluorescent lung tissue. Excitation scanning provided higher signal-to-noise characteristics, as well as shorter acquisition times (300  ms/wavelength band with excitation scanning versus 3  s/wavelength band with emission scanning). Excitation scanning also provided higher delineation of nuclear and cell borders, and increased identification of GFP regions in highly autofluorescent tissue. These results demonstrate excitation scanning has utility in a wide range of time-dependent and photosensitive applications. PMID:24727909

  13. Hyperspectral imaging of skin and lung cancers

    NASA Astrophysics Data System (ADS)

    Zherdeva, Larisa A.; Bratchenko, Ivan A.; Alonova, Marina V.; Myakinin, Oleg O.; Artemyev, Dmitry N.; Moryatov, Alexander A.; Kozlov, Sergey V.; Zakharov, Valery P.

    2016-04-01

    The problem of cancer control requires design of new approaches for instrumental diagnostics, as the accuracy of cancer detection on the first step of diagnostics in clinics is slightly more than 50%. In this study, we present a method of visualization and diagnostics of skin and lung tumours based on registration and processing of tissues hyperspectral images. In a series of experiments registration of hyperspectral images of skin and lung tissue samples is carried out. Melanoma, basal cell carcinoma, nevi and benign tumours are studied in skin ex vivo and in vivo experiments; adenocarcinomas and squamous cell carcinomas are studied in ex vivo lung experiments. In a series of experiments the typical features of diffuse reflection spectra for pathological and normal tissues were found. Changes in tissues morphology during the tumour growth lead to the changes of blood and pigments concentration, such as melanin in skin. That is why tumours and normal tissues maybe differentiated with information about spectral response in 500-600 nm and 600 - 670 nm areas. Thus, hyperspectral imaging in the visible region may be a useful tool for cancer detection as it helps to estimate spectral properties of tissues and determine malignant regions for precise resection of tumours.

  14. Excitation-scanning hyperspectral imaging microscope

    PubMed Central

    Favreau, Peter F.; Hernandez, Clarissa; Heaster, Tiffany; Alvarez, Diego F.; Rich, Thomas C.; Prabhat, Prashant; Leavesley, Silas J.

    2014-01-01

    Abstract. Hyperspectral imaging is a versatile tool that has recently been applied to a variety of biomedical applications, notably live-cell and whole-tissue signaling. Traditional hyperspectral imaging approaches filter the fluorescence emission over a broad wavelength range while exciting at a single band. However, these emission-scanning approaches have shown reduced sensitivity due to light attenuation from spectral filtering. Consequently, emission scanning has limited applicability for time-sensitive studies and photosensitive applications. In this work, we have developed an excitation-scanning hyperspectral imaging microscope that overcomes these limitations by providing high transmission with short acquisition times. This is achieved by filtering the fluorescence excitation rather than the emission. We tested the efficacy of the excitation-scanning microscope in a side-by-side comparison with emission scanning for detection of green fluorescent protein (GFP)-expressing endothelial cells in highly autofluorescent lung tissue. Excitation scanning provided higher signal-to-noise characteristics, as well as shorter acquisition times (300  ms/wavelength band with excitation scanning versus 3  s/wavelength band with emission scanning). Excitation scanning also provided higher delineation of nuclear and cell borders, and increased identification of GFP regions in highly autofluorescent tissue. These results demonstrate excitation scanning has utility in a wide range of time-dependent and photosensitive applications. PMID:24727909

  15. Information efficiency in hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

    Reichenbach, Stephen E.; Cao, Luyin; Narayanan, Ram M.

    2002-07-01

    In this work we develop a method for assessing the information density and efficiency of hyperspectral imaging systems that have spectral bands of nonuniform width. Imaging system designs with spectral bands of nonuniform width can efficiently gather information about a scene by allocating bandwidth among the bands according to their information content. The information efficiency is the ratio of information density to data density and is a function of the scene's spectral radiance, hyperspectral system design, and signal-to-noise ratio. The assessment can be used to produce an efficient system design. For example, one approach to determining the number and width of the spectral bands for an information-efficient design is to begin with a design that has a single band and then to iteratively divide a band into two bands until no further division improves the system's efficiency. Two experiments illustrate this approach, one using a simple mathematical model for the scene spectral-radiance autocorrelation function and the other using the deterministic spectral-radiance autocorrelation function of a hyperspectral image from NASA's Advanced Solid-State Array Spectroradiometer. The approach could be used either to determine a fixed system design or to dynamically control a system with variable-width spectral bands (e.g., using on-board processing in a satellite system).

  16. Morphology-based fusion method of hyperspectral image

    NASA Astrophysics Data System (ADS)

    Yue, Song; Zhang, Zhijie; Ren, Tingting; Wang, Chensheng; Yu, Hui

    2014-11-01

    Hyperspectral image analysis method is widely used in all kinds of application including agriculture identification and forest investigation and atmospheric pollution monitoring. In order to accurately and steadily analyze hyperspectral image, considering the spectrum and spatial information which is provided by hyperspectral data together is necessary. The hyperspectral image has the characteristics of large amount of wave bands and information. Corresponding to the characteristics of hyperspectral image, a fast image fusion method that can fuse the hyperspectral image with high fidelity is studied and proposed in this paper. First of all, hyperspectral image is preprocessed before the morphological close operation. The close operation is used to extract wave band characteristic to reduce dimensionality of hyperspectral image. The spectral data is smoothed at the same time to avoid the discontinuity of the data by combination of spatial information and spectral information. On this basis, Mean-shift method is adopted to register key frames. Finally, the selected key frames by fused into one fusing image by the pyramid fusion method. The experiment results show that this method can fuse hyper spectral image in high quality. The fused image's attributes is better than the original spectral images comparing to the spectral images and reach the objective of fusion.

  17. Hyperspectral Transformation from EO-1 ALI Imagery Using Pseudo-Hyperspectral Image Synthesis Algorithm

    NASA Astrophysics Data System (ADS)

    Tien Hoang, Nguyen; Koike, Katsuaki

    2016-06-01

    Hyperspectral remote sensing is more effective than multispectral remote sensing in many application fields because of having hundreds of observation bands with high spectral resolution. However, hyperspectral remote sensing resources are limited both in temporal and spatial coverage. Therefore, simulation of hyperspectral imagery from multispectral imagery with a small number of bands must be one of innovative topics. Based on this background, we have recently developed a method, Pseudo-Hyperspectral Image Synthesis Algorithm (PHISA), to transform Landsat imagery into hyperspectral imagery using the correlation of reflectance at the corresponding bands between Landsat and EO-1 Hyperion data. This study extends PHISA to simulate pseudo-hyperspectral imagery from EO-1 ALI imagery. The pseudo-hyperspectral imagery has the same number of bands as that of high-quality Hyperion bands and the same swath width as ALI scene. The hyperspectral reflectance data simulated from the ALI data show stronger correlation with the original Hyperion data than the one simulated from Landsat data. This high correlation originates from the concurrent observation by the ALI and Hyperion sensors that are on-board the same satellite. The accuracy of simulation results are verified by a statistical analysis and a surface mineral mapping. With a combination of the advantages of both ALI and Hyperion image types, the pseudo-hyperspectral imagery is proved to be useful for detailed identification of minerals for the areas outside the Hyperion coverage.

  18. Remote Sensing of Vegetation Species Diversity: The Utility of Integrated Airborne Hyperspectral and Lidar Data

    NASA Astrophysics Data System (ADS)

    Krause, Keith Stuart

    The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts are underway to measure, monitor, and protect habitats that contain high species diversity. Remote sensing technology shows extreme value for monitoring species diversity by mapping ecosystems and using those land cover maps or other derived data as proxies to species number and distribution. The National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) consists of remote sensing instruments such as an imaging spectrometer, a full-waveform lidar, and a high-resolution color camera. AOP collected data over the Ordway-Swisher Biological Station (OSBS) in May 2014. A majority of the OSBS site is covered by the Sandhill ecosystem, which contains a very high diversity of vegetation species and is a native habitat for several threatened fauna species. The research presented here investigates ways to analyze the AOP data to map ecosystems at the OSBS site. The research attempts to leverage the high spatial resolution data and study the variability of the data within a ground plot scale along with integrating data from the different sensors. Mathematical features are derived from the data and brought into a decision tree classification algorithm (rpart), in order to create an ecosystem map for the site. The hyperspectral and lidar features serve as proxies for chemical, functional, and structural differences in the vegetation types for each of the ecosystems. K-folds cross validation shows a training accuracy of 91%, a validation accuracy of 78%, and a 66% accuracy using independent ground validation. The results presented here represent an important contribution to utilizing integrated hyperspectral and lidar remote sensing data for ecosystem mapping, by relating the spatial variability of the data within a ground plot scale to a collection of vegetation types that make up a given ecosystem.

  19. Unsupervised data fusion for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Jimenez-Rodriguez, Luis O.; Velez-Reyes, Miguel; Rivera-Medina, Jorge; Velasquez, Hector

    2002-01-01

    Hyperspectral images contain a great amount of information in terms of hundreds of narrowband channels. This should lead to better parameter estimation and to more accurate classifications. However, traditional classification methods based on multispectral analysis fail to work properly on this type of data. High dimensional space poses a difficulty in obtaining accurate parameter estimates and as a consequence this makes unsupervised classification a challenge that requires new techniques. Thus, alternative methods are needed to take advantage of the information provided by the hyperdimensional data. Data fusion is an alternative when dealing with such large data sets in order to improve classification accuracy. Data fusion is an important process in the areas of environmental systems, surveillance, automation, medical imaging, and robotics. The uses of this technique in Remote Sensing have been recently expanding. A relevant application is to adapt the data fusion approaches to be used on hyperspectral imagery taking into consideration the special characteristics of such data. The approach of this paper is to presents a scheme that integrates information from most of the hyperspectral narrow-bands in order to increase the discrimination accuracy in unsupervised classification.

  20. Automatic Extraction of Optimal Endmembers from Airborne Hyperspectral Imagery Using Iterative Error Analysis (IEA) and Spectral Discrimination Measurements

    PubMed Central

    Song, Ahram; Chang, Anjin; Choi, Jaewan; Choi, Seokkeun; Kim, Yongil

    2015-01-01

    Pure surface materials denoted by endmembers play an important role in hyperspectral processing in various fields. Many endmember extraction algorithms (EEAs) have been proposed to find appropriate endmember sets. Most studies involving the automatic extraction of appropriate endmembers without a priori information have focused on N-FINDR. Although there are many different versions of N-FINDR algorithms, computational complexity issues still remain and these algorithms cannot consider the case where spectrally mixed materials are extracted as final endmembers. A sequential endmember extraction-based algorithm may be more effective when the number of endmembers to be extracted is unknown. In this study, we propose a simple but accurate method to automatically determine the optimal endmembers using such a method. The proposed method consists of three steps for determining the proper number of endmembers and for removing endmembers that are repeated or contain mixed signatures using the Root Mean Square Error (RMSE) images obtained from Iterative Error Analysis (IEA) and spectral discrimination measurements. A synthetic hyperpsectral image and two different airborne images such as Airborne Imaging Spectrometer for Application (AISA) and Compact Airborne Spectrographic Imager (CASI) data were tested using the proposed method, and our experimental results indicate that the final endmember set contained all of the distinct signatures without redundant endmembers and errors from mixed materials. PMID:25625907

  1. Hyperspectral imaging of polymer/fullerene blends

    NASA Astrophysics Data System (ADS)

    Torreggiani, Armida; Tinti, Francesca; Savoini, Alberto; Melchiorre, Michele; Po, Riccardo; Camaioni, Nadia

    2014-10-01

    The effectiveness of a hyperspectral imaging system integrated on an enhanced dark-field microscope for probing the microscale morphology of model poly(3- hexylthiopene): [6,6]-phenyl-C61- butyric acid methyl ester (P3HT:PCBM) blends is demonstrated. This non-contact technique provides both spectral and spatial information in one measurement, providing an effective mapping of the presence and location of the component materials in the investigated P3HT:PCBM blends spincoated over different substrates (zinc oxide, poly(3,4- ethylenedioxythiophene):poly(styrenesulfonate). The hyperspectral analysis accounts for the micro-scale morphology of P3HT:PCBM blends, even in case of high film roughness, and the quantitative determination of blend components reveals a preferential accumulation of the lowenergy material (P3HT) at the interface with air, confirming the findings reported with other mapping techniques

  2. Hyperspectral all-sky imaging of auroras.

    PubMed

    Sigernes, Fred; Ivanov, Yuriy; Chernouss, Sergey; Trondsen, Trond; Roldugin, Alexey; Fedorenko, Yury; Kozelov, Boris; Kirillov, Andrey; Kornilov, Ilia; Safargaleev, Vladimir; Holmen, Silje; Dyrland, Margit; Lorentzen, Dag; Baddeley, Lisa

    2012-12-01

    A prototype auroral hyperspectral all-sky camera has been constructed and tested. It uses electro-optical tunable filters to image the night sky as a function of wavelength throughout the visible spectrum with no moving mechanical parts. The core optical system includes a new high power all-sky lens with F-number equal to f/1.1. The camera has been tested at the Kjell Henriksen Observatory (KHO) during the auroral season of 2011/2012. It detects all sub classes of aurora above ~½ of the sub visual 1kR green intensity threshold at an exposure time of only one second. Supervised classification of the hyperspectral data shows promise as a new method to process and identify auroral forms. PMID:23262713

  3. Hyperspectral all-sky imaging of auroras.

    PubMed

    Sigernes, Fred; Ivanov, Yuriy; Chernouss, Sergey; Trondsen, Trond; Roldugin, Alexey; Fedorenko, Yury; Kozelov, Boris; Kirillov, Andrey; Kornilov, Ilia; Safargaleev, Vladimir; Holmen, Silje; Dyrland, Margit; Lorentzen, Dag; Baddeley, Lisa

    2012-12-01

    A prototype auroral hyperspectral all-sky camera has been constructed and tested. It uses electro-optical tunable filters to image the night sky as a function of wavelength throughout the visible spectrum with no moving mechanical parts. The core optical system includes a new high power all-sky lens with F-number equal to f/1.1. The camera has been tested at the Kjell Henriksen Observatory (KHO) during the auroral season of 2011/2012. It detects all sub classes of aurora above ~½ of the sub visual 1kR green intensity threshold at an exposure time of only one second. Supervised classification of the hyperspectral data shows promise as a new method to process and identify auroral forms.

  4. Design of a concise Féry-prism hyperspectral imaging system based on multi-configuration

    NASA Astrophysics Data System (ADS)

    Dong, Wei; Nie, Yun-feng; Zhou, Jin-song

    2013-08-01

    In order to meet the needs of space borne and airborne hyperspectral imaging system for light weight, simplification and high spatial resolution, a novel design of Féry-prism hyperspectral imaging system based on Zemax multi-configuration method is presented. The novel structure is well arranged by analyzing optical monochromatic aberrations theoretically, and the optical structure of this design is concise. The fundamental of this design is Offner relay configuration, whereas the secondary mirror is replaced by Féry-prism with curved surfaces and a reflective front face. By reflection, the light beam passes through the Féry-prism twice, which promotes spectral resolution and enhances image quality at the same time. The result shows that the system can achieve light weight and simplification, compared to other hyperspectral imaging systems. Composed of merely two spherical mirrors and one achromatized Féry-prism to perform both dispersion and imaging functions, this structure is concise and compact. The average spectral resolution is 6.2nm; The MTFs for 0.45~1.00um spectral range are greater than 0.75, RMSs are less than 2.4um; The maximal smile is less than 10% pixel, while the keystones is less than 2.8% pixel; image quality approximates the diffraction limit. The design result shows that hyperspectral imaging system with one modified Féry-prism substituting the secondary mirror of Offner relay configuration is feasible from the perspective of both theory and practice, and possesses the merits of simple structure, convenient optical alignment, and good image quality, high resolution in space and spectra, adjustable dispersive nonlinearity. The system satisfies the requirements of airborne or space borne hyperspectral imaging system.

  5. Onboard Image Processing System for Hyperspectral Sensor.

    PubMed

    Hihara, Hiroki; Moritani, Kotaro; Inoue, Masao; Hoshi, Yoshihiro; Iwasaki, Akira; Takada, Jun; Inada, Hitomi; Suzuki, Makoto; Seki, Taeko; Ichikawa, Satoshi; Tanii, Jun

    2015-09-25

    Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS's performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost.

  6. Onboard Image Processing System for Hyperspectral Sensor

    PubMed Central

    Hihara, Hiroki; Moritani, Kotaro; Inoue, Masao; Hoshi, Yoshihiro; Iwasaki, Akira; Takada, Jun; Inada, Hitomi; Suzuki, Makoto; Seki, Taeko; Ichikawa, Satoshi; Tanii, Jun

    2015-01-01

    Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost. PMID:26404281

  7. Onboard Image Processing System for Hyperspectral Sensor.

    PubMed

    Hihara, Hiroki; Moritani, Kotaro; Inoue, Masao; Hoshi, Yoshihiro; Iwasaki, Akira; Takada, Jun; Inada, Hitomi; Suzuki, Makoto; Seki, Taeko; Ichikawa, Satoshi; Tanii, Jun

    2015-01-01

    Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS's performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost. PMID:26404281

  8. Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

    USGS Publications Warehouse

    Hively, W. Dean; McCarty, Gregory W.; Reeves, James B.; Lang, Megan W.; Oesterling, Robert A.; Delwiche, Stephen R.

    2011-01-01

    Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

  9. Airborne LiDAR and hyperspectral mapping of snow depth and albedo in the Upper Colorado River Basin, Colorado, USA by the NASA JPL Airborne Snow Observatory

    NASA Astrophysics Data System (ADS)

    Deems, J. S.; Painter, T. H.

    2014-12-01

    Operational hydrologic simulation and forecasting in snowmelt-dominated watersheds currently relies on indices of snow accumulation and melt from measurements at a small number of point locations or geographically-limited manual surveys. These data sources cannot adequately characterize the spatial distribution of snow depth/water equivalent, which is the primary determinant of snowpack volume and runoff rates. The NASA JPL Airborne Snow Observatory's airborne laser scanning system maps snow depth at high spatial and temporal resolutions, and is paired with a hyperspectral imager to provide an unprecedented snowpack monitoring capability and enabling a new operational paradigm. We present the initial results from this new application of multi-temporal LiDAR and hyperspectral mapping. During the snowmelt seasons of 2013 and 2014, the ASO mapped snow depth and albedo in the Uncompahgre River Basin in Colorado's Upper Colorado River Basin on a nominally monthly basis. These products enable an assessment and comparison of spatial snow accumulation and melt processes in two years with very different snowmelt hydrographs.

  10. A parallel unmixing algorithm for hyperspectral images

    NASA Astrophysics Data System (ADS)

    Robila, Stefan A.; Maciak, Lukasz G.

    2006-10-01

    We present a new algorithm for feature extraction in hyperspectral images based on source separation and parallel computing. In source separation, given a linear mixture of sources, the goal is to recover the components by producing an unmixing matrix. In hyperspectral imagery, the mixing transform and the separated components can be associated with endmembers and their abundances. Source separation based methods have been employed for target detection and classification of hyperspectral images. However, these methods usually involve restrictive conditions on the nature of the results such as orthogonality (in Principal Component Analysis - PCA and Orthogonal Subspace Projection - OSP) of the endmembers or statistical independence (in Independent Component Analysis - ICA) of the abundances nor do they fully satisfy all the conditions included in the Linear Mixing Model. Compared to this, our approach is based on the Nonnegative Matrix Factorization (NMF), a less constraining unmixing method. NMF has the advantage of producing positively defined data, and, with several modifications that we introduce also ensures addition to one. The endmember vectors and the abundances are obtained through a gradient based optimization approach. The algorithm is further modified to run in a parallel environment. The parallel NMF (P-NMF) significantly reduces the time complexity and is shown to also easily port to a distributed environment. Experiments with in-house and Hydice data suggest that NMF outperforms ICA, PCA and OSP for unsupervised endmember extraction. Coupled with its parallel implementation, the new method provides an efficient way for unsupervised unmixing further supporting our efforts in the development of a real time hyperspectral sensing environment with applications to industry and life sciences.

  11. Hyperspectral confocal fluorescence imaging of cells

    NASA Astrophysics Data System (ADS)

    Haaland, David M.; Jones, Howland D. T.; Sinclair, Michael B.; Carson, Bryan; Branda, Catherine; Poschet, Jens F.; Rebeil, Roberto; Tian, Bing; Liu, Ping; Brasier, Allan R.

    2007-09-01

    Confocal fluorescence imaging of biological systems is an important method by which researchers can investigate molecular processes occurring in live cells. We have developed a new 3D hyperspectral confocal fluorescence microscope that can further enhance the usefulness of fluorescence microscopy in studying biological systems. The new microscope can increase the information content obtained from the image since, at each voxel, the microscope records 512 wavelengths from the emission spectrum (490 to 800 nm) while providing optical sectioning of samples with diffraction-limited spatial resolution. When coupled with multivariate curve resolution (MCR) analyses, the microscope can resolve multiple spatially and spectrally overlapped emission components, thereby greatly increasing the number of fluorescent labels, relative to most commercial microscopes, that can be monitored simultaneously. The MCR algorithm allows the "discovery" of all emitting sources and estimation of their relative concentrations without cross talk, including those emission sources that might not have been expected in the imaged cells. In this work, we have used the new microscope to obtain time-resolved hyperspectral images of cellular processes. We have quantitatively monitored the translocation of the GFP-labeled RelA protein (without interference from autofluorescence) into and out of the nucleus of live HeLa cells in response to continuous stimulation by the cytokine, TNFα. These studies have been extended to imaging live mouse macrophage cells with YFP-labeled RelA and GFP-labeled IRF3 protein. Hyperspectral imaging coupled with MCR analysis makes possible, for the first time, quantitative analysis of GFP, YFP, and autofluorescence without concern for cross-talk between emission sources. The significant power and quantitative capabilities of the new hyperspectral imaging system are further demonstrated with the imaging of a simple fluorescence dye (SYTO 13) traditionally used to stain the

  12. Pork grade evaluation using hyperspectral imaging techniques

    NASA Astrophysics Data System (ADS)

    Zhou, Rui; Cai, Bo; Wang, Shoubing; Ji, Huihua; Chen, Huacai

    2011-11-01

    The method to evaluate the grade of the pork based on hyperspectral imaging techniques was studied. Principal component analysis (PCA) was performed on the hyperspectral image data to extract the principal components which were used as the inputs of the evaluation model. By comparing the different discriminating rates in the calibration set and the validation set under different information, the choice of the components can be optimized. Experimental results showed that the classification evaluation model was the optimal when the principal of component (PC) of spectra was 3, while the corresponding discriminating rate was 89.1% in the calibration set and 84.9% in the validation set. It was also good when the PC of images was 9, while the corresponding discriminating rate was 97.2% in the calibration set and 91.1% in the validation set. The evaluation model based on both information of spectra and images was built, in which the corresponding PCs of spectra and images were used as the inputs. This model performed very well in grade classification evaluation, and the discriminating rates of calibration set and validation set were 99.5% and 92.7%, respectively, which were better than the two evaluation models based on single information of spectra or images.

  13. Hyperspectral Imaging for Defect Detection of Pickling Cucumber

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This book chapter reviews the recent progress on hyperspectral imaging technology for defect inspection of pickling cucumbers. The chapter first describes near-infrared hyperspectral reflectance imaging technique for the detection of bruises on pickling cucumbers. The technique showed good detection...

  14. Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability.

    PubMed

    Thouvenin, Pierre-Antoine; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2016-09-01

    Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may vary spectrally from an image to another due to varying acquisition conditions, thus inducing possibly significant estimation errors. Against this background, the hyperspectral unmixing of several images acquired over the same area is of considerable interest. Indeed, such an analysis enables the endmembers of the scene to be tracked and the corresponding endmember variability to be characterized. Sequential endmember estimation from a set of hyperspectral images is expected to provide improved performance when compared with methods analyzing the images independently. However, the significant size of the hyperspectral data precludes the use of batch procedures to jointly estimate the mixture parameters of a sequence of hyperspectral images. Provided that each elementary component is present in at least one image of the sequence, we propose to perform an online hyperspectral unmixing accounting for temporal endmember variability. The online hyperspectral unmixing is formulated as a two-stage stochastic program, which can be solved using a stochastic approximation. The performance of the proposed method is evaluated on synthetic and real data. Finally, a comparison with independent unmixing algorithms illustrates the interest of the proposed strategy.

  15. Video-rate visible to LWIR hyperspectral image generation exploitation

    NASA Astrophysics Data System (ADS)

    Dombrowski, Mark S.; Willson, Paul

    1999-10-01

    Hyperspectral imaging is the latest advent in imaging technology, providing the potential to extract information about the objects in a scene that is unavailable to panchromatic imagers. This increased utility, however, comes at the cost of tremendously increased data. The ultimate utility of hyperspectral imagery is in the information that can be gleaned from the spectral dimension, rather than in the hyperspectral imagery itself. To have the broadest range of applications, extraction of this information must occur in real-time. Attempting to produce and exploit complete cubes of hyperspectral imagery at video rates, however, presents unique problems for both the imager and the processor, since data rates are scaled by the number of spectral planes in the cube. MIDIS, the Multi-band Identification and Discrimination Imaging Spectroradiometer, allows both real-time collection and processing of hyperspectral imagery over the range of 0.4 micrometer to 12 micrometer. Presented here are the major design challenges and solutions associated with producing high-speed, high-sensitivity hyperspectral imagers operating in the Vis/NIR, SWIR/MWIR and LWIR, and of the electronics capable of handling data rates up to 160 mega-pixels per second, continuously. Beyond design and performance issues associated with producing and processing hyperspectral imagery at such high speeds, this paper also discusses applications of real-time hyperspectral imaging technology. Example imagery includes such problems as buried mine detection, inspecting surfaces, and countering CCD (camouflage, concealment, and deception).

  16. Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability.

    PubMed

    Thouvenin, Pierre-Antoine; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2016-09-01

    Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may vary spectrally from an image to another due to varying acquisition conditions, thus inducing possibly significant estimation errors. Against this background, the hyperspectral unmixing of several images acquired over the same area is of considerable interest. Indeed, such an analysis enables the endmembers of the scene to be tracked and the corresponding endmember variability to be characterized. Sequential endmember estimation from a set of hyperspectral images is expected to provide improved performance when compared with methods analyzing the images independently. However, the significant size of the hyperspectral data precludes the use of batch procedures to jointly estimate the mixture parameters of a sequence of hyperspectral images. Provided that each elementary component is present in at least one image of the sequence, we propose to perform an online hyperspectral unmixing accounting for temporal endmember variability. The online hyperspectral unmixing is formulated as a two-stage stochastic program, which can be solved using a stochastic approximation. The performance of the proposed method is evaluated on synthetic and real data. Finally, a comparison with independent unmixing algorithms illustrates the interest of the proposed strategy. PMID:27305679

  17. Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability

    NASA Astrophysics Data System (ADS)

    Thouvenin, Pierre-Antoine; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2016-09-01

    Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing an hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may vary spectrally from an image to another due to varying acquisition conditions, thus inducing possibly significant estimation errors. Against this background, hyperspectral unmixing of several images acquired over the same area is of considerable interest. Indeed, such an analysis enables the endmembers of the scene to be tracked and the corresponding endmember variability to be characterized. Sequential endmember estimation from a set of hyperspectral images is expected to provide improved performance when compared to methods analyzing the images independently. However, the significant size of hyperspectral data precludes the use of batch procedures to jointly estimate the mixture parameters of a sequence of hyperspectral images. Provided that each elementary component is present in at least one image of the sequence, we propose to perform an online hyperspectral unmixing accounting for temporal endmember variability. The online hyperspectral unmixing is formulated as a two-stage stochastic program, which can be solved using a stochastic approximation. The performance of the proposed method is evaluated on synthetic and real data. A comparison with independent unmixing algorithms finally illustrates the interest of the proposed strategy.

  18. Commodity cluster and hardware-based massively parallel implementations of hyperspectral imaging algorithms

    NASA Astrophysics Data System (ADS)

    Plaza, Antonio; Chang, Chein-I.; Plaza, Javier; Valencia, David

    2006-05-01

    The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several applications exist, however, where having the desired information calculated quickly enough for practical use is highly desirable. High computing performance of algorithm analysis is particularly important in homeland defense and security applications, in which swift decisions often involve detection of (sub-pixel) military targets (including hostile weaponry, camouflage, concealment, and decoys) or chemical/biological agents. In order to speed-up computational performance of hyperspectral imaging algorithms, this paper develops several fast parallel data processing techniques. Techniques include four classes of algorithms: (1) unsupervised classification, (2) spectral unmixing, and (3) automatic target recognition, and (4) onboard data compression. A massively parallel Beowulf cluster (Thunderhead) at NASA's Goddard Space Flight Center in Maryland is used to measure parallel performance of the proposed algorithms. In order to explore the viability of developing onboard, real-time hyperspectral data compression algorithms, a Xilinx Virtex-II field programmable gate array (FPGA) is also used in experiments. Our quantitative and comparative assessment of parallel techniques and strategies may help image analysts in selection of parallel hyperspectral algorithms for specific applications.

  19. MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor.

    PubMed

    Hardie, Russell C; Eismann, Michael T; Wilson, Gregory L

    2004-09-01

    This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchomatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate observation model relating the "true" scene with the low-resolutions observations. Experimental results with hyperspectral data derived from the airborne visible-infrared imaging spectrometer are presented to demonstrate the efficacy of the proposed estimator.

  20. Hyperspectral imaging applied to forensic medicine

    NASA Astrophysics Data System (ADS)

    Malkoff, Donald B.; Oliver, William R.

    2000-03-01

    Remote sensing techniques now include the use of hyperspectral infrared imaging sensors covering the mid-and- long wave regions of the spectrum. They have found use in military surveillance applications due to their capability for detection and classification of a large variety of both naturally occurring and man-made substances. The images they produce reveal the spatial distributions of spectral patterns that reflect differences in material temperature, texture, and composition. A program is proposed for demonstrating proof-of-concept in using a portable sensor of this type for crime scene investigations. It is anticipated to be useful in discovering and documenting the affects of trauma and/or naturally occurring illnesses, as well as detecting blood spills, tire patterns, toxic chemicals, skin injection sites, blunt traumas to the body, fluid accumulations, congenital biochemical defects, and a host of other conditions and diseases. This approach can significantly enhance capabilities for determining the circumstances of death. Potential users include law enforcement organizations (police, FBI, CIA), medical examiners, hospitals/emergency rooms, and medical laboratories. Many of the image analysis algorithms already in place for hyperspectral remote sensing and crime scene investigations can be applied to the interpretation of data obtained in this program.

  1. SWIR hyperspectral imaging detector for surface residues

    NASA Astrophysics Data System (ADS)

    Nelson, Matthew P.; Mangold, Paul; Gomer, Nathaniel; Klueva, Oksana; Treado, Patrick

    2013-05-01

    ChemImage has developed a SWIR Hyperspectral Imaging (HSI) sensor which uses hyperspectral imaging for wide area surveillance and standoff detection of surface residues. Existing detection technologies often require close proximity for sensing or detecting, endangering operators and costly equipment. Furthermore, most of the existing sensors do not support autonomous, real-time, mobile platform based detection of threats. The SWIR HSI sensor provides real-time standoff detection of surface residues. The SWIR HSI sensor provides wide area surveillance and HSI capability enabled by liquid crystal tunable filter technology. Easy-to-use detection software with a simple, intuitive user interface produces automated alarms and real-time display of threat and type. The system has potential to be used for the detection of variety of threats including chemicals and illicit drug substances and allows for easy updates in the field for detection of new hazardous materials. SWIR HSI technology could be used by law enforcement for standoff screening of suspicious locations and vehicles in pursuit of illegal labs or combat engineers to support route-clearance applications- ultimately to save the lives of soldiers and civilians. In this paper, results from a SWIR HSI sensor, which include detection of various materials in bulk form, as well as residue amounts on vehicles, people and other surfaces, will be discussed.

  2. Practical example for use of the supervised vicarious calibration (SVC) method on multisource hyperspectral imagery data - ValCalHyp airborne hyperspectral campaign under the EUFAR framework

    NASA Astrophysics Data System (ADS)

    Brook, A.; Ben Dor, E.

    2014-09-01

    A novel approach for radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data, termed supervised vicarious calibration (SVC) was proposed by Brook and Ben-Dor in 2010. The present study was aimed at validating this SVC approach by simultaneously using several different airborne HSR sensors that acquired HSR data over several selected sites at the same time. The general goal of this study was to apply a cross-calibration approach to examine the capability and stability of the SVC method and to examine its validity. This paper reports the result of the multi sensors campaign took place over Salon de Provenance, France on behalf of the ValCalHyp project took place in 2011. The SVC method enabled the rectification of the radiometric drift of each sensor and improves their performance significantly. The flight direction of the SVC targets was found to be a critical issue for such correction and recommendations have been set for future utilization of this novel method. The results of the SVC method were examined by comparing ground-truth spectra of several selected validation targets with the image spectra as well as by comparing the classified water quality images generated from all sensors over selected water bodies.

  3. Dental caries imaging using hyperspectral stimulated Raman scattering microscopy

    NASA Astrophysics Data System (ADS)

    Wang, Zi; Zheng, Wei; Jian, Lin; Huang, Zhiwei

    2016-03-01

    We report the development of a polarization-resolved hyperspectral stimulated Raman scattering (SRS) imaging technique based on a picosecond (ps) laser-pumped optical parametric oscillator system for label-free imaging of dental caries. In our imaging system, hyperspectral SRS images (512×512 pixels) in both fingerprint region (800-1800 cm-1) and high-wavenumber region (2800-3600 cm-1) are acquired in minutes by scanning the wavelength of OPO output, which is a thousand times faster than conventional confocal micro Raman imaging. SRS spectra variations from normal enamel to caries obtained from the hyperspectral SRS images show the loss of phosphate and carbonate in the carious region. While polarization-resolved SRS images at 959 cm-1 demonstrate that the caries has higher depolarization ratio. Our results demonstrate that the polarization resolved-hyperspectral SRS imaging technique developed allows for rapid identification of the biochemical and structural changes of dental caries.

  4. Identification of unknown waste sites using MIVIS hyperspectral images

    SciTech Connect

    Gomarasca, M.A.; Strobelt, S.

    1996-11-01

    This paper presents the results on the individuation of known and unknown (illegal) waste sites using Landsat TM satellite imagery and airborne MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data for detailed analysis in Italy. Previous results with Landsat TM imagery were partially positive for large waste site identification and negative for small sites. Information acquired by the MIVIS hyperspectral system presents three main characteristics: local scale study, possibility to plan the proper period based on the objectives of the study, high number of spectral bands with high spectral and geometrical resolution. MIVIS airborne shootings were carried out on 7 July 1994 at noon with 4x4 m pixel resolution. The MIVIS 102 bands` sensors can distinguish even objects with similar spectral behavior, thanks to its high spectral resolution. Identification of degraded sites is obtained using traditional spectral and statistical operators (NDVI, Principal Component Analysis, Maximum Likelihood classifier) and innovative combination of filtered band ratios realized to extract specific waste elements (acid slimes or contaminated soils). One of the aims that concerns with this study is the definition of an operative program for the characterization, identification and classification of defined categories of waste disposal sites. The best schedule for the data collection by airborne MIVIS oriented to this target is defined. The planning of the proper flight, based on the waste sites features, is important to optimize this technology. One of the most efficient methods for detecting hidden waste sites is the thermal inertia so two images are necessary: one during low sun load and one with high sun load. The results obtained are operationally useful and winning. This instrument, supported by correct analysis techniques, may offer new interesting prospects in territorial management and environmental monitoring. 5 refs., 5 figs., 1 tab.

  5. Detecting leafy spurge in native grassland using hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Kloppenburg, Catherine

    Leafy spurge (Euphoria esula L.) is a perennial noxious weed that has been encroaches on the native grassland regions of North America resulting in biological and economic impacts. Leafy spurge growth is most prevalent along river banks and in pasture areas. Due to poor accessibility and the cost and labour associated with data collection, estimates of number and size of leafy spurge infestations is poor. Remote sensing has the ability to cover large areas, providing an alternate means to ground surveys and will allow for the capability to create an accurate baseline of infestations. Airborne hyperspectral data were collected over the two test sites selected on the Blood Reserve in Southern Alberta using a combined Airborne Imaging Spectrometer for different Applications (AISA) Eagle and Hawk sensor systems in July, 2010. This study used advanced analysis tools, including spectral mixture analysis, spectral angle mapper and mixture-tuned matched filter techniques to evaluate the ability to detect leafy spurge patches. The results show that patches of leafy spurge with flowering stem density >40 stems m-2 were identified with 85 % accuracy while identification of lower density stems were less accurate (10 - 40 %). The results are promising with respect to quantifying areas of significant leafy spurge infestation and targeting biological control and potential insect release sites.

  6. Recent applications of hyperspectral imaging in microbiology.

    PubMed

    Gowen, Aoife A; Feng, Yaoze; Gaston, Edurne; Valdramidis, Vasilis

    2015-05-01

    Hyperspectral chemical imaging (HSI) is a broad term encompassing spatially resolved spectral data obtained through a variety of modalities (e.g. Raman scattering, Fourier transform infrared microscopy, fluorescence and near-infrared chemical imaging). It goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from objects at spatial resolutions varying from the level of single cells up to macroscopic objects (e.g. foods). In tandem with recent developments in instrumentation and sampling protocols, applications of HSI in microbiology have increased rapidly. This article gives a brief overview of the fundamentals of HSI and a comprehensive review of applications of HSI in microbiology over the past 10 years. Technical challenges and future perspectives for these techniques are also discussed.

  7. Hyperspectral imaging for melanoma screening

    NASA Astrophysics Data System (ADS)

    Martin, Justin; Krueger, James; Gareau, Daniel

    2014-03-01

    The 5-year survival rate for patients diagnosed with Melanoma, a deadly form of skin cancer, in its latest stages is about 15%, compared to over 90% for early detection and treatment. We present an imaging system and algorithm that can be used to automatically generate a melanoma risk score to aid clinicians in the early identification of this form of skin cancer. Our system images the patient's skin at a series of different wavelengths and then analyzes several key dermoscopic features to generate this risk score. We have found that shorter wavelengths of light are sensitive to information in the superficial areas of the skin while longer wavelengths can be used to gather information at greater depths. This accompanying diagnostic computer algorithm has demonstrated much higher sensitivity and specificity than the currently commercialized system in preliminary trials and has the potential to improve the early detection of melanoma.

  8. Dimensionality reduction of hyperspectral images using kernel ICA

    NASA Astrophysics Data System (ADS)

    Khan, Asif; Kim, Intaek; Kong, Seong G.

    2009-05-01

    Computational burden due to high dimensionality of Hyperspectral images is an obstacle in efficient analysis and processing of Hyperspectral images. In this paper, we use Kernel Independent Component Analysis (KICA) for dimensionality reduction of Hyperspectraql images based on band selection. Commonly used ICA and PCA based dimensionality reduction methods do not consider non linear transformations and assumes that data has non-gaussian distribution. When the relation of source signals (pure materials) and observed Hyperspectral images is nonlinear then these methods drop a lot of information during dimensionality reduction process. Recent research shows that kernel-based methods are effective in nonlinear transformations. KICA is robust technique of blind source separation and can even work on near-gaussina data. We use Kernel Independent Component Analysis (KICA) for the selection of minimum number of bands that contain maximum information for detection in Hyperspectral images. The reduction of bands is basd on the evaluation of weight matrix generated by KICA. From the selected lower number of bands, we generate a new spectral image with reduced dimension and use it for hyperspectral image analysis. We use this technique as preprocessing step in detection and classification of poultry skin tumors. The hyperspectral iamge samples of chicken tumors used contain 65 spectral bands of fluorescence in the visible region of the spectrum. Experimental results show that KICA based band selection has high accuracy than that of fastICA based band selection for dimensionality reduction and analysis for Hyperspectral images.

  9. Data correction techniques for the airborne large-aperture static image spectrometer based on image registration

    NASA Astrophysics Data System (ADS)

    Zhang, Geng; Shi, Dalian; Wang, Shuang; Yu, Tao; Hu, Bingliang

    2015-01-01

    We propose an approach to correct the data of the airborne large-aperture static image spectrometer (LASIS). LASIS is a kind of stationary interferometer which compromises flux output and device stability. It acquires a series of interferograms to reconstruct the hyperspectral image cube. Reconstruction precision of the airborne LASIS data suffers from the instability of the plane platform. Usually, changes of plane attitudes, such as yaws, pitches, and rolls, can be precisely measured by the inertial measurement unit. However, the along-track and across-track translation errors are difficult to measure precisely. To solve this problem, we propose a co-optimization approach to compute the translation errors between the interferograms using an image registration technique which helps to correct the interferograms with subpixel precision. To demonstrate the effectiveness of our approach, experiments are run on real airborne LASIS data and our results are compared with those of the state-of-the-art approaches.

  10. Mapping pigment distribution in mud samples through hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Mehrübeoglu, Mehrube; Nicula, Cosmina; Trombley, Christopher; Smith, Shane W.; Smith, Dustin K.; Shanks, Elizabeth S.; Zimba, Paul V.

    2015-09-01

    Mud samples collected from bodies of water reveal information about the distribution of microorganisms in the local sediments. Hyperspectral imaging has been investigated as a technology to identify phototropic organisms living on sediments collected from the Texas Coastal Bend area based on their spectral pigment profiles and spatial arrangement. The top pigment profiles identified through high-performance liquid chromatography (HPLC) have been correlated with spectral signatures extracted from the hyperspectral data of mud using fast Fourier transform (FFT). Spatial distributions have also been investigated using 2D hyperspectral image processing. 2D pigment distribution maps have been created based on the correlation with pigment profiles in the FFT domain. Among the tested pigments, the results show match among four out of five pigment distribution trends between HPLC and hyperspectral data analysis. Differences are attributed mainly to the difference between area and volume of scale between the HPLC analysis and area covered by hyperspectral imaging.

  11. Dried fruits quality assessment by hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Serranti, Silvia; Gargiulo, Aldo; Bonifazi, Giuseppe

    2012-05-01

    Dried fruits products present different market values according to their quality. Such a quality is usually quantified in terms of freshness of the products, as well as presence of contaminants (pieces of shell, husk, and small stones), defects, mould and decays. The combination of these parameters, in terms of relative presence, represent a fundamental set of attributes conditioning dried fruits humans-senses-detectable-attributes (visual appearance, organolectic properties, etc.) and their overall quality in terms of marketable products. Sorting-selection strategies exist but sometimes they fail when a higher degree of detection is required especially if addressed to discriminate between dried fruits of relatively small dimensions and when aiming to perform an "early detection" of pathogen agents responsible of future moulds and decays development. Surface characteristics of dried fruits can be investigated by hyperspectral imaging (HSI). In this paper, specific and "ad hoc" applications addressed to propose quality detection logics, adopting a hyperspectral imaging (HSI) based approach, are described, compared and critically evaluated. Reflectance spectra of selected dried fruits (hazelnuts) of different quality and characterized by the presence of different contaminants and defects have been acquired by a laboratory device equipped with two HSI systems working in two different spectral ranges: visible-near infrared field (400-1000 nm) and near infrared field (1000-1700 nm). The spectra have been processed and results evaluated adopting both a simple and fast wavelength band ratio approach and a more sophisticated classification logic based on principal component (PCA) analysis.

  12. Image visualization of hyperspectral spectrum for LWIR

    NASA Astrophysics Data System (ADS)

    Chong, Eugene; Jeong, Young-Su; Lee, Jai-Hoon; Park, Dong Jo; Kim, Ju Hyun

    2015-07-01

    The image visualization of a real-time hyperspectral spectrum in the long-wave infrared (LWIR) range of 900-1450 cm-1 by a color-matching function is addressed. It is well known that the absorption spectra of main toxic industrial chemical (TIC) and chemical warfare agent (CWA) clouds are detected in this spectral region. Furthermore, a significant spectral peak due to various background species and unknown targets are also present. However, those are dismissed as noise, resulting in utilization limit. Herein, we applied a color-matching function that uses the information from hyperspectral data, which is emitted from the materials and surfaces of artificial or natural backgrounds in the LWIR region. This information was used to classify and differentiate the background signals from the targeted substances, and the results were visualized as image data without additional visual equipment. The tristimulus value based visualization information can quickly identify the background species and target in real-time detection in LWIR.

  13. Detection of explosives by differential hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Dubroca, Thierry; Brown, Gregory; Hummel, Rolf E.

    2014-02-01

    Our team has pioneered an explosives detection technique based on hyperspectral imaging of surfaces. Briefly, differential reflectometry (DR) shines ultraviolet (UV) and blue light on two close-by areas on a surface (for example, a piece of luggage on a moving conveyer belt). Upon reflection, the light is collected with a spectrometer combined with a charge coupled device (CCD) camera. A computer processes the data and produces in turn differential reflection spectra taken from these two adjacent areas on the surface. This differential technique is highly sensitive and provides spectroscopic data of materials, particularly of explosives. As an example, 2,4,6-trinitrotoluene displays strong and distinct features in differential reflectograms near 420 and 250 nm, that is, in the near-UV region. Similar, but distinctly different features are observed for other explosives. Finally, a custom algorithm classifies the collected spectral data and outputs an acoustic signal if a threat is detected. This paper presents the complete DR hyperspectral imager which we have designed and built from the hardware to the software, complete with an analysis of the device specifications.

  14. Miniaturized Airborne Imaging Central Server System

    NASA Technical Reports Server (NTRS)

    Sun, Xiuhong

    2011-01-01

    In recent years, some remote-sensing applications require advanced airborne multi-sensor systems to provide high performance reflective and emissive spectral imaging measurement rapidly over large areas. The key or unique problem of characteristics is associated with a black box back-end system that operates a suite of cutting-edge imaging sensors to collect simultaneously the high throughput reflective and emissive spectral imaging data with precision georeference. This back-end system needs to be portable, easy-to-use, and reliable with advanced onboard processing. The innovation of the black box backend is a miniaturized airborne imaging central server system (MAICSS). MAICSS integrates a complex embedded system of systems with dedicated power and signal electronic circuits inside to serve a suite of configurable cutting-edge electro- optical (EO), long-wave infrared (LWIR), and medium-wave infrared (MWIR) cameras, a hyperspectral imaging scanner, and a GPS and inertial measurement unit (IMU) for atmospheric and surface remote sensing. Its compatible sensor packages include NASA s 1,024 1,024 pixel LWIR quantum well infrared photodetector (QWIP) imager; a 60.5 megapixel BuckEye EO camera; and a fast (e.g. 200+ scanlines/s) and wide swath-width (e.g., 1,920+ pixels) CCD/InGaAs imager-based visible/near infrared reflectance (VNIR) and shortwave infrared (SWIR) imaging spectrometer. MAICSS records continuous precision georeferenced and time-tagged multisensor throughputs to mass storage devices at a high aggregate rate, typically 60 MB/s for its LWIR/EO payload. MAICSS is a complete stand-alone imaging server instrument with an easy-to-use software package for either autonomous data collection or interactive airborne operation. Advanced multisensor data acquisition and onboard processing software features have been implemented for MAICSS. With the onboard processing for real time image development, correction, histogram-equalization, compression, georeference, and

  15. Active Volcano Monitoring using a Space-based Hyperspectral Imager

    NASA Astrophysics Data System (ADS)

    Cipar, J. J.; Dunn, R.; Cooley, T.

    2010-12-01

    Active volcanoes occur on every continent, often in close proximity to heavily populated areas. While ground-based studies are essential for scientific research and disaster mitigation, remote sensing from space can provide rapid and continuous monitoring of active and potentially active volcanoes [Ramsey and Flynn, 2004]. In this paper, we report on hyperspectral measurements of Kilauea volcano, Hawaii. Hyperspectral images obtained by the US Air Force TacSat-3/ARTEMIS sensor [Lockwood et al, 2006] are used to obtain estimates of the surface temperatures for the volcano. ARTEMIS measures surface-reflected light in the visible, near-infrared, and short-wave infrared bands (VNIR-SWIR). The SWIR bands are known to be sensitive to thermal radiation [Green, 1996]. For example, images from the NASA Hyperion hyperspectral sensor have shown the extent of wildfires and active volcanoes [Young, 2009]. We employ the methodology described by Dennison et al, (2006) to obtain an estimate of the temperature of the active region of Kilauea. Both day and night-time images were used in the analysis. To improve the estimate, we aggregated neighboring pixels. The active rim of the lava lake is clearly discernable in the temperature image, with a measured temperature exceeding 1100o C. The temperature decreases markedly on the exterior of the summit crater. While a long-wave infrared (LWIR) sensor would be ideal for volcano monitoring, we have shown that the thermal state of an active volcano can be monitored using the SWIR channels of a reflective hyperspectral imager. References: Dennison, Philip E., Kraivut Charoensiri, Dar A. Roberts, Seth H. Peterson, and Robert O. Green (2006). Wildfire temperature and land cover modeling using hyperspectral data, Remote Sens. Environ., vol. 100, pp. 212-222. Green, R. O. (1996). Estimation of biomass fire temperature and areal extent from calibrated AVIRIS spectra, in Summaries of the 6th Annual JPL Airborne Earth Science Workshop, Pasadena, CA

  16. Performance and application of real-time hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Dombrowski, Mark S.; Willson, Paul D.; LaBaw, Clayton C.

    1998-10-01

    Hyperspectral imaging is the latest advent in imaging technology, providing the potential to extract information about the objects in a scene that is unavailable to panchromatic imagers. This increased utility, however, comes at the cost of tremendously increased data. The ultimate utility of hyperspectral imagery is in the information that can be gleaned from the spectral dimension, rather than in the hyperspectral imagery itself. To have the broadest range of applications, extraction of this information must occur in real-time. Attempting to produce and exploit complete cubes of hyperspectral imagery at video rates, however, present unique problems for both the imager and the processor, since data rates are scaled by the number of spectral planes in the cube. MIDIS, the Multi-band Identification and Discrimination Imaging Spectroradiometer, allows both real-time here are the major design innovations associated with producing high-speed, high-sensitivity hyperspectral imagers operating in the SWIR and LWIR, and of the electronics capable of handling data rates up to 160 megapixels per second, continuously. Discussion of real-time algorithms capable of exploiting the spectral dimension of the imagery is also included. Beyond design and performance issues associated with producing and processing hyperspectral imagery at such high speeds, this paper also discusses applications of real-time hyperspectral imaging technology. Example imagery includes such problems as detecting counterfeit money, inspecting surfaces, and countering CCD.

  17. Hyperspectral Fluorescence and Reflectance Imaging Instrument

    NASA Technical Reports Server (NTRS)

    Ryan, Robert E.; O'Neal, S. Duane; Lanoue, Mark; Russell, Jeffrey

    2008-01-01

    The system is a single hyperspectral imaging instrument that has the unique capability to acquire both fluorescence and reflectance high-spatial-resolution data that is inherently spatially and spectrally registered. Potential uses of this instrument include plant stress monitoring, counterfeit document detection, biomedical imaging, forensic imaging, and general materials identification. Until now, reflectance and fluorescence spectral imaging have been performed by separate instruments. Neither a reflectance spectral image nor a fluorescence spectral image alone yields as much information about a target surface as does a combination of the two modalities. Before this system was developed, to benefit from this combination, analysts needed to perform time-consuming post-processing efforts to co-register the reflective and fluorescence information. With this instrument, the inherent spatial and spectral registration of the reflectance and fluorescence images minimizes the need for this post-processing step. The main challenge for this technology is to detect the fluorescence signal in the presence of a much stronger reflectance signal. To meet this challenge, the instrument modulates artificial light sources from ultraviolet through the visible to the near-infrared part of the spectrum; in this way, both the reflective and fluorescence signals can be measured through differencing processes to optimize fluorescence and reflectance spectra as needed. The main functional components of the instrument are a hyperspectral imager, an illumination system, and an image-plane scanner. The hyperspectral imager is a one-dimensional (line) imaging spectrometer that includes a spectrally dispersive element and a two-dimensional focal plane detector array. The spectral range of the current imaging spectrometer is between 400 to 1,000 nm, and the wavelength resolution is approximately 3 nm. The illumination system consists of narrowband blue, ultraviolet, and other discrete

  18. Regional prediction of soil organic carbon content over croplands using airborne hyperspectral data

    NASA Astrophysics Data System (ADS)

    Vaudour, Emmanuelle; Gilliot, Jean-Marc; Bel, Liliane; Lefebvre, Josias; Chehdi, Kacem

    2015-04-01

    This study was carried out in the framework of the Prostock-Gessol3 and the BASC-SOCSENSIT projects, dedicated to the spatial monitoring of the effects of exogenous organic matter land application on soil organic carbon storage. It aims at identifying the potential of airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks which were georeferenced. Tracks were atmospherically corrected using a set of 22 synchronous field spectra of both bare soils, black and white targets and impervious surfaces. Atmospherically corrected track tiles were mosaicked at a 2 m-resolution resulting in a 66 Gb image. A SPOT4 satellite image was acquired the same day in the framework of the SPOT4-Take Five program of the French Space Agency (CNES) which provided it with atmospheric correction. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then NDVI calculation and thresholding enabled to map agricultural fields with bare soil. All 18 sampled sites known to be bare at this very date were correctly included in this map. A total of 85 sites sampled in 2013 or in the 3 previous years were identified as bare by means of this map. Predictions were made from the mosaic spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples. The use of the total sample including 27 sites under cloud shadows led to non-significant results. Considering 43 sites outside cloud shadows only, median

  19. Detection in urban scenario using combined airborne imaging sensors

    NASA Astrophysics Data System (ADS)

    Renhorn, Ingmar; Axelsson, Maria; Benoist, Koen; Bourghys, Dirk; Boucher, Yannick; Briottet, Xavier; De Ceglie, Sergio; Dekker, Rob; Dimmeler, Alwin; Dost, Remco; Friman, Ola; Kåsen, Ingebjørg; Maerker, Jochen; van Persie, Mark; Resta, Salvatore; Schwering, Piet; Shimoni, Michal; Haavardsholm, Trym Vegard

    2012-06-01

    The EDA project "Detection in Urban scenario using Combined Airborne imaging Sensors" (DUCAS) is in progress. The aim of the project is to investigate the potential benefit of combined high spatial and spectral resolution airborne imagery for several defense applications in the urban area. The project is taking advantage of the combined resources from 7 contributing nations within the EDA framework. An extensive field trial has been carried out in the city of Zeebrugge at the Belgian coast in June 2011. The Belgian armed forces contributed with platforms, weapons, personnel (soldiers) and logistics for the trial. Ground truth measurements with respect to geometrical characteristics, optical material properties and weather conditions were obtained in addition to hyperspectral, multispectral and high resolution spatial imagery. High spectral/spatial resolution sensor data are used for detection, classification, identification and tracking.

  20. Compressive hyperspectral and multispectral imaging fusion

    NASA Astrophysics Data System (ADS)

    Espitia, Óscar; Castillo, Sergio; Arguello, Henry

    2016-05-01

    Image fusion is a valuable framework which combines two or more images of the same scene from one or multiple sensors, allowing to improve the resolution of the images and increase the interpretable content. In remote sensing a common fusion problem consists of merging hyperspectral (HS) and multispectral (MS) images that involve large amount of redundant data, which ignores the highly correlated structure of the datacube along the spatial and spectral dimensions. Compressive HS and MS systems compress the spectral data in the acquisition step allowing to reduce the data redundancy by using different sampling patterns. This work presents a compressed HS and MS image fusion approach, which uses a high dimensional joint sparse model. The joint sparse model is formulated by combining HS and MS compressive acquisition models. The high spectral and spatial resolution image is reconstructed by using sparse optimization algorithms. Different fusion spectral image scenarios are used to explore the performance of the proposed scheme. Several simulations with synthetic and real datacubes show promising results as the reliable reconstruction of a high spectral and spatial resolution image can be achieved by using as few as just the 50% of the datacube.

  1. System and method for progressive band selection for hyperspectral images

    NASA Technical Reports Server (NTRS)

    Fisher, Kevin (Inventor)

    2013-01-01

    Disclosed herein are systems, methods, and non-transitory computer-readable storage media for progressive band selection for hyperspectral images. A system having module configured to control a processor to practice the method calculates a virtual dimensionality of a hyperspectral image having multiple bands to determine a quantity Q of how many bands are needed for a threshold level of information, ranks each band based on a statistical measure, selects Q bands from the multiple bands to generate a subset of bands based on the virtual dimensionality, and generates a reduced image based on the subset of bands. This approach can create reduced datasets of full hyperspectral images tailored for individual applications. The system uses a metric specific to a target application to rank the image bands, and then selects the most useful bands. The number of bands selected can be specified manually or calculated from the hyperspectral image's virtual dimensionality.

  2. Hyperspectral image reconstruction for diffuse optical tomography

    PubMed Central

    Larusson, Fridrik; Fantini, Sergio; Miller, Eric L.

    2011-01-01

    We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths. PMID:21483616

  3. Metric Learning for Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

    Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

    2011-01-01

    We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

  4. Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Pullanagari, R. R.; Kereszturi, Gábor; Yule, I. J.

    2016-07-01

    On-farm assessment of mixed pasture nutrient concentrations is important for animal production and pasture management. Hyperspectral imaging is recognized as a potential tool to quantify the nutrient content of vegetation. However, it is a great challenge to estimate macro and micro nutrients in heterogeneous mixed pastures. In this study, canopy reflectance data was measured by using a high resolution airborne visible-to-shortwave infrared (Vis-SWIR) imaging spectrometer measuring in the wavelength region 380-2500 nm to predict nutrient concentrations, nitrogen (N) phosphorus (P), potassium (K), sulfur (S), zinc (Zn), sodium (Na), manganese (Mn) copper (Cu) and magnesium (Mg) in heterogeneous mixed pastures across a sheep and beef farm in hill country, within New Zealand. Prediction models were developed using four different methods which are included partial least squares regression (PLSR), kernel PLSR, support vector regression (SVR), random forest regression (RFR) algorithms and their performance compared using the test data. The results from the study revealed that RFR produced highest accuracy (0.55 ⩽ R2CV ⩽ 0.78; 6.68% ⩽ nRMSECV ⩽ 26.47%) compared to all other algorithms for the majority of nutrients (N, P, K, Zn, Na, Cu and Mg) described, and the remaining nutrients (S and Mn) were predicted with high accuracy (0.68 ⩽ R2CV ⩽ 0.86; 13.00% ⩽ nRMSECV ⩽ 14.64%) using SVR. The best training models were used to extrapolate over the whole farm with the purpose of predicting those pasture nutrients and expressed through pixel based spatial maps. These spatially registered nutrient maps demonstrate the range and geographical location of often large differences in pasture nutrient values which are normally not measured and therefore not included in decision making when considering more effective ways to utilized pasture.

  5. A grid service-based tool for hyperspectral imaging analysis

    NASA Astrophysics Data System (ADS)

    Carvajal, Carmen L.; Lugo, Wilfredo; Rivera, Wilson; Sanabria, John

    2005-06-01

    This paper outlines the design and implementation of Grid-HSI, a Service Oriented Architecture-based Grid application to enable hyperspectral imaging analysis. Grid-HSI provides users with a transparent interface to access computational resources and perform remotely hyperspectral imaging analysis through a set of Grid services. Grid-HSI is composed by a Portal Grid Interface, a Data Broker and a set of specialized Grid services. Grid based applications, contrary to other clientserver approaches, provide the capabilities of persistence and potential transient process on the web. Our experimental results on Grid-HSI show the suitability of the prototype system to perform efficiently hyperspectral imaging analysis.

  6. Development of image mappers for hyperspectral biomedical imaging applications

    PubMed Central

    Kester, Robert T.; Gao, Liang; Tkaczyk, Tomasz S.

    2010-01-01

    A new design and fabrication method is presented for creating large-format (>100 mirror facets) image mappers for a snapshot hyperspectral biomedical imaging system called an image mapping spectrometer (IMS). To verify this approach a 250 facet image mapper with 25 multiple-tilt angles is designed for a compact IMS that groups the 25 subpupils in a 5 × 5 matrix residing within a single collecting objective's pupil. The image mapper is fabricated by precision diamond raster fly cutting using surface-shaped tools. The individual mirror facets have minimal edge eating, tilt errors of <1 mrad, and an average roughness of 5.4 nm. PMID:20357875

  7. Predicting the detectability of thin gaseous plumes in hyperspectral images using basis vectors

    SciTech Connect

    Anderson, Kevin K.; Tardiff, Mark F.; Chilton, Lawrence

    2010-09-01

    This paper describes a new method for predicting the detectability of thin gaseous plumes in hyperspectral images. The novelty of this method is the use of basis vectors for each of the spectral channels of a collection instrument to calculate noise-equivalent concentration-pathlengths instead of matching scene pixels to absorbance spectra of gases in a library. This method provides insight into regions of the spectrum where gas detection will be relatively easier or harder, as influenced by ground emissivity, temperature contrast, and the atmosphere. We relate a three-layer physics-based radiance model to basis vector noise-equivalent concentration-pathlengths, to signal-to-noise ratios, and finally to minimum detectable concentration-pathlengths. We illustrate the method using an Airborne Hyperspectral Imager image. Our results show that data collection planning could be in°uenced by information about when potential plumes are likely to be over background segments that are most conducive to detection.

  8. Geometric and Reflectance Signature Characterization of Complex Canopies Using Hyperspectral Stereoscopic Images from Uav and Terrestrial Platforms

    NASA Astrophysics Data System (ADS)

    Honkavaara, E.; Hakala, T.; Nevalainen, O.; Viljanen, N.; Rosnell, T.; Khoramshahi, E.; Näsi, R.; Oliveira, R.; Tommaselli, A.

    2016-06-01

    Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

  9. Food quality assessment by NIR hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Whitworth, Martin B.; Millar, Samuel J.; Chau, Astor

    2010-04-01

    Near infrared reflectance (NIR) spectroscopy is well established in the food industry for rapid compositional analysis of bulk samples. NIR hyperspectral imaging provides new opportunities to measure the spatial distribution of components such as moisture and fat, and to identify and measure specific regions of composite samples. An NIR hyperspectral imaging system has been constructed for food research applications, incorporating a SWIR camera with a cooled 14 bit HgCdTe detector and N25E spectrograph (Specim Ltd, Finland). Samples are scanned in a pushbroom mode using a motorised stage. The system has a spectral resolution of 256 pixels covering a range of 970-2500 nm and a spatial resolution of 320 pixels covering a swathe adjustable from 8 to 300 mm. Images are acquired at a rate of up to 100 lines s-1, enabling samples to be scanned within a few seconds. Data are captured using SpectralCube software (Specim) and analysed using ENVI and IDL (ITT Visual Information Solutions). Several food applications are presented. The strength of individual absorbance bands enables the distribution of particular components to be assessed. Examples are shown for detection of added gluten in wheat flour and to study the effect of processing conditions on fat distribution in chips/French fries. More detailed quantitative calibrations have been developed to study evolution of the moisture distribution in baguettes during storage at different humidities, to assess freshness of fish using measurements of whole cod and fillets, and for prediction of beef quality by identification and separate measurement of lean and fat regions.

  10. Selectable Hyperspectral Airborne Remote-sensing Kit (SHARK) on the Vision II turbine rotorcraft UAV over the Florida Keys

    NASA Astrophysics Data System (ADS)

    Holasek, R. E.; Nakanishi, K.; Swartz, B.; Zacaroli, R.; Hill, B.; Naungayan, J.; Herwitz, S.; Kavros, P.; English, D. C.

    2013-12-01

    As part of the NASA ROSES program, the NovaSol Selectable Hyperspectral Airborne Remote-sensing Kit (SHARK) was flown as the payload on the unmanned Vision II helicopter. The goal of the May 2013 data collection was to obtain high resolution visible and near-infrared (visNIR) hyperspectral data of seagrasses and coral reefs in the Florida Keys. The specifications of the SHARK hyperspectral system and the Vision II turbine rotorcraft will be described along with the process of integrating the payload to the vehicle platform. The minimal size, weight, and power (SWaP) specifications of the SHARK system is an ideal match to the Vision II helicopter and its flight parameters. One advantage of the helicopter over fixed wing platforms is its inherent ability to take off and land in a limited area and without a runway, enabling the UAV to be located in close proximity to the experiment areas and the science team. Decisions regarding integration times, waypoint selection, mission duration, and mission frequency are able to be based upon the local environmental conditions and can be modified just prior to take off. The operational procedures and coordination between the UAV pilot, payload operator, and scientist will be described. The SHARK system includes an inertial navigation system and digital elevation model (DEM) which allows image coordinates to be calculated onboard the aircraft in real-time. Examples of the geo-registered images from the data collection will be shown. SHARK mounted below VTUAV. SHARK deployed on VTUAV over water.

  11. Hyperspectral imaging for safety inspection of food and agricultural products

    NASA Astrophysics Data System (ADS)

    Lu, Renfu; Chen, Yud-Ren

    1999-01-01

    Development of effective food inspection systems is critical in successful implementation of the hazard analysis and critical control points (HACCP) program. Hyperspectral imaging or imaging spectroscopy, which combines techniques of imaging and spectroscopy to acquire spatial and spectral information simultaneously, has great potential in food quality and safety inspection. This paper reviewed the basic principle and features of hyperspectral imaging and its hardware and software implementation. The potential areas of application for hyperspectral imaging in food quality and safety inspection were identified and its limitations were discussed. A hyperspectral imaging system developed for research in food quality and safety inspection was described. Experiments were performed to acquire hyperspectral images from four classes of poultry carcasses: normal, cadaver, septicemia, and tumor. Noticeable differences in the spectra of the relative reflectance and its second difference in the wavelengths between 430 nm and 900 nm were observed between wholesome and unwholesome carcasses. Differences among the three classes of unwholesome carcasses were also observed from their respective spectra. These results showed that hyperspectral imaging can be an effective tool for safety inspection of poultry carcasses.

  12. Biodiversity Mapping in a Tropical West African Forest with Airborne Hyperspectral Data

    PubMed Central

    Vaglio Laurin, Gaia; Chan, Jonathan Cheung-Wai; Chen, Qi; Lindsell, Jeremy A.; Coomes, David A.; Guerriero, Leila; Frate, Fabio Del; Miglietta, Franco; Valentini, Riccardo

    2014-01-01

    Tropical forests are major repositories of biodiversity, but are fast disappearing as land is converted to agriculture. Decision-makers need to know which of the remaining forests to prioritize for conservation, but the only spatial information on forest biodiversity has, until recently, come from a sparse network of ground-based plots. Here we explore whether airborne hyperspectral imagery can be used to predict the alpha diversity of upper canopy trees in a West African forest. The abundance of tree species were collected from 64 plots (each 1250 m2 in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated. An airborne spectrometer measured reflectances of 186 bands in the visible and near-infrared spectral range at 1 m2 resolution. The standard deviations of these reflectance values and their first-order derivatives were calculated for each plot from the c. 1250 pixels of hyperspectral information within them. Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest). The regression model fitted the data well (pseudo-R2 = 84.9%), and we show that standard deviations of green-band reflectances and infra-red region derivatives had the strongest explanatory powers. Our work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales. PMID:24937407

  13. Hyperspectral image projector for advanced sensor characterization

    NASA Astrophysics Data System (ADS)

    Brown, S. W.; Rice, J. P.; Neira, J. E.; Bousquet, R.; Johnson, B. C.

    2006-08-01

    In this work, we describe radiometric platforms able to produce realistic spectral distributions and spatial scenes for the development of application-specific metrics to quantify the performance of sensors and systems. Using these platforms, sensor and system performance may be quantified in terms of the accuracy of measurements of standardized sets of complex source distributions. The same platforms can also serve as a basis for algorithm testing and instrument comparison. The platforms consist of spectrally tunable light sources (STS's) coupled with spatially programmable projection systems. The resultant hyperspectral image projectors (HIP) can generate complex spectral distributions with high spectral fidelity; that is, scenes with realistic spectral content. Using the same fundamental technology, platforms can be developed for the ultraviolet, visible, and infrared regions. These radiometric platforms will facilitate advanced sensor characterization testing, enabling a pre-flight validation of the pre-flight calibration.

  14. Unsupervised hyperspectral image analysis using independent component analysis (ICA)

    SciTech Connect

    S. S. Chiang; I. W. Ginsberg

    2000-06-30

    In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the designed learning algorithm is able to converge to non-orthogonal independent components. This is particularly useful in hyperspectral image analysis since many materials extracted from a hyperspectral image may have similar spectral signatures and may not be orthogonal. The AVIRIS experiments have demonstrated that the proposed ICA provides an effective unsupervised technique for hyperspectral image classification.

  15. Black Beauty's Rainbow: Hyperspectral Imaging of Northwest Africa 7034

    NASA Astrophysics Data System (ADS)

    Cannon, K. M.; Mustard, J. F.; Agee, C. B.; Wilson, J. H.; Greenberger, R. N.

    2014-07-01

    Hyperspectral imaging is used to characterize the first basaltic breccia from Mars, Northwest Africa 7034. Initial results show the spectral character of NWA 7034 is unlike other SNC meteorites and may be more representative of average martian crust.

  16. Mapping tree health using airborne full-waveform laser scans and hyperspectral imagery: a case study for floodplain eucalypt forest

    NASA Astrophysics Data System (ADS)

    Shendryk, I.; Tulbure, M. G.; Broich, M.

    2014-12-01

    Barmah-Millewa Forest (BMF), the largest River Red Gum forest in the world, located in south-eastern Australia is suffering from severe dieback, thus diminishing its ecological and economical value. Previous research showed that dieback is a good predictor of the forest health and stressed the need for BMF health mapping and change monitoring. In this respect, airborne laser scanning and hyperspectral imaging offer extensive spatial and spectral coverage of measurements and represent an ideal tool for forest health mapping at individual tree scale. The aim of this project is to quantify the health of individual, structurally complex floodplain eucalypt trees by integrating airborne hyperspectral imagery, full-waveform laser scans and field measurements. An aerial survey, conducted in May 2014, was designed to provide a representative sample of BMF tree health. The positioning of 17 flight lines aimed to capture the heterogeneity of the forest health and flood frequency. Preliminary analysis of the aerial remote sensing data with regards to chlorophyll concentrations, dieback levels and canopy densities allowed us to target our field campaign (conducted in June 2014). Field measurements included accurate position measurements, LAI, visual assessment, spectral measurement and mensuration of individual trees in 30 m2 plots. For detection of individual tree trunks from airborne laser scans we used a novel approach based on Euclidean distance clustering, taking advantage of the intensity and pulse width difference between woody and leaf tree compartments. The detected trunks were used to seed a minimum cut algorithm for tree crown delineation. In situ measurements confirmed the high structural diversity of the forest and allowed the calibration of the tree detection algorithm. An overall accuracy of the tree detection of 54% and 67% was achieved for trees with circumference over 40 cm and over 100 cm respectively. As a further step, 3D point clusters representing

  17. Infrared hyperspectral imaging sensor for gas detection

    NASA Astrophysics Data System (ADS)

    Hinnrichs, Michele

    2000-11-01

    A small light weight man portable imaging spectrometer has many applications; gas leak detection, flare analysis, threat warning, chemical agent detection, just to name a few. With support from the US Air Force and Navy, Pacific Advanced Technology has developed a small man portable hyperspectral imaging sensor with an embedded DSP processor for real time processing that is capable of remotely imaging various targets such as gas plums, flames and camouflaged targets. Based upon their spectral signature the species and concentration of gases can be determined. This system has been field tested at numerous places including White Mountain, CA, Edwards AFB, and Vandenberg AFB. Recently evaluation of the system for gas detection has been performed. This paper presents these results. The system uses a conventional infrared camera fitted with a diffractive optic that images as well as disperses the incident radiation to form spectral images that are collected in band sequential mode. Because the diffractive optic performs both imaging and spectral filtering, the lens system consists of only a single element that is small, light weight and robust, thus allowing man portability. The number of spectral bands are programmable such that only those bands of interest need to be collected. The system is entirely passive, therefore, easily used in a covert operation. Currently Pacific Advanced Technology is working on the next generation of this camera system that will have both an embedded processor as well as an embedded digital signal processor in a small hand held camera configuration. This will allow the implementation of signal and image processing algorithms for gas detection and identification in real time. This paper presents field test data on gas detection and identification as well as discuss the signal and image processing used to enhance the gas visibility. Flow rates as low as 0.01 cubic feet per minute have been imaged with this system.

  18. Gas plume quantification in downlooking hyperspectral longwave infrared images

    NASA Astrophysics Data System (ADS)

    Turcotte, Caroline S.; Davenport, Michael R.

    2010-10-01

    Algorithms have been developed to support quantitative analysis of a gas plume using down-looking airborne hyperspectral long-wave infrared (LWIR) imagery. The resulting gas quantification "GQ" tool estimates the quantity of one or more gases at each pixel, and estimates uncertainty based on factors such as atmospheric transmittance, background clutter, and plume temperature contrast. GQ uses gas-insensitive segmentation algorithms to classify the background very precisely so that it can infer gas quantities from the differences between plume-bearing pixels and similar non-plume pixels. It also includes MODTRAN-based algorithms to iteratively assess various profiles of air temperature, water vapour, and ozone, and select the one that implies smooth emissivity curves for the (unknown) materials on the ground. GQ then uses a generalized least-squares (GLS) algorithm to simultaneously estimate the most likely mixture of background (terrain) material and foreground plume gases. Cross-linking of plume temperature to the estimated gas quantity is very non-linear, so the GLS solution was iteratively assessed over a range of plume temperatures to find the best fit to the observed spectrum. Quantification errors due to local variations in the camera-topixel distance were suppressed using a subspace projection operator. Lacking detailed depth-maps for real plumes, the GQ algorithm was tested on synthetic scenes generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software. Initial results showed pixel-by-pixel gas quantification errors of less than 15% for a Freon 134a plume.

  19. Unmixing hyperspectral images using Markov random fields

    SciTech Connect

    Eches, Olivier; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2011-03-14

    This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) are estimated by the proposed algorithm. Due to physical constraints, the abundances have to satisfy positivity and sum-to-one constraints. The image is divided into homogeneous distinct regions having the same statistical properties for the abundance coefficients. The spatial dependencies within each class are modeled thanks to Potts-Markov random fields. Within a Bayesian framework, prior distributions for the abundances and the associated hyperparameters are introduced. A reparametrization of the abundance coefficients is proposed to handle the physical constraints (positivity and sum-to-one) inherent to hyperspectral imagery. The parameters (abundances), hyperparameters (abundance mean and variance for each class) and the classification map indicating the classes of all pixels in the image are inferred from the resulting joint posterior distribution. To overcome the complexity of the joint posterior distribution, Markov chain Monte Carlo methods are used to generate samples asymptotically distributed according to the joint posterior of interest. Simulations conducted on synthetic and real data are presented to illustrate the performance of the proposed algorithm.

  20. Directly Estimating Endmembers for Compressive Hyperspectral Images

    PubMed Central

    Xu, Hongwei; Fu, Ning; Qiao, Liyan; Peng, Xiyuan

    2015-01-01

    The large volume of hyperspectral images (HSI) generated creates huge challenges for transmission and storage, making data compression more and more important. Compressive Sensing (CS) is an effective data compression technology that shows that when a signal is sparse in some basis, only a small number of measurements are needed for exact signal recovery. Distributed CS (DCS) takes advantage of both intra- and inter- signal correlations to reduce the number of measurements needed for multichannel-signal recovery. HSI can be observed by the DCS framework to reduce the volume of data significantly. The traditional method for estimating endmembers (spectral information) first recovers the images from the compressive HSI and then estimates endmembers via the recovered images. The recovery step takes considerable time and introduces errors into the estimation step. In this paper, we propose a novel method, by designing a type of coherent measurement matrix, to estimate endmembers directly from the compressively observed HSI data via convex geometry (CG) approaches without recovering the images. Numerical simulations show that the proposed method outperforms the traditional method with better estimation speed and better (or comparable) accuracy in both noisy and noiseless cases. PMID:25905699

  1. Development of practical thermal infrared hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

    Wang, Jianyu; Li, Chunlai; Lv, Gang; Yuan, Liyin; Liu, Enguang; Jin, Jian; Ji, Hongzhen

    2014-11-01

    As an optical remote sensing equipment, the thermal infrared hyperspectral imager operates in the thermal infrared spectral band and acquires about 180 wavebands in range of 8.0~12.5μm. The field of view of this imager is 13° and the spatial resolution is better than 1mrad. Its noise equivalent temperature difference (NETD) is less than 0.2K@300K(average). 1 The influence of background radiation of the thermal infrared hyperspectral imager,and a simulation model of simplified background radiation is builded. 2 The design and implementationof the Cryogenic Optics. 3 Thermal infrared focal plane array (FPA) and special dewar component for the thermal infrared hyperspectral imager. 4 Parts of test results of the thermal infrared hyperspectral imager.The hyperspectral imaging system is China's first success in developing this type of instrument, whose flight validation experiments have already been embarked on. The thermal infrared hyperspectral data acquired will play an important role in fields such as geological exploration and air pollutant identification.

  2. Supercontinuum-source-based facility for evaluation of hyperspectral imagers

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yu; Yamada, Yoshiro; Ishii, Juntaro

    2013-10-01

    The next Japanese earth observing hyperspectral/multispectral imager mission, the HISUI (Hyper-spectral Imager SUIte) mission, is currently underway. In order to guarantee the hyperspectral images with a high spatial and wavelength resolution, it is necessary to evaluate the difference of the spectral sensitivities among the detector devices arrayed twodimensionally and correct spectral and spatial misregistrations and the effect of stray light. Since there are tens of thousands of detectors in the two-dimensional-array sensor, they have to be evaluated in parallel, instead of point by point, with the special technique for hyperspectral imagers. Hence, the new calibration system which has high radiance with the spatial uniformity and widely tunable wavelength range is required instead of conventional lamp systems which have poor power to calibrate arrayed devices at once. In this presentation, a supercontinuum-source-based system for calibration of hyperspectral imagers and its preliminary performance are described. Supercontinuum light is white light with continuous and broad spectra, which is generated by nonlinear optical effects of ultrashort pulse lasers in photonic crystal fibers. Using the system, the relative spectral responsivity and spectral misregistration of the hyperspectral imager, which is consist of a polychromator and twodimensionally arrayed CCD, are measured.

  3. Comparison of airborne multispectral and hyperspectral imagery for estimating grain sorghum yield

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Both multispectral and hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare the differences between these two types of imagery for assessing crop growth and yields. The objective of this study was to compare airb...

  4. A hyperspectral imaging prototype for online quality evaluation of pickling cucumbers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging prototype was developed for online evaluation of external and internal quality of pickling cucumbers. The prototype had several new, unique features including simultaneous reflectance and transmittance imaging and inline, real time calibration of hyperspectral images of each ...

  5. A new morphological anomaly detection algorithm for hyperspectral images and its GPU implementation

    NASA Astrophysics Data System (ADS)

    Paz, Abel; Plaza, Antonio

    2011-10-01

    Anomaly detection is considered a very important task for hyperspectral data exploitation. It is now routinely applied in many application domains, including defence and intelligence, public safety, precision agriculture, geology, or forestry. Many of these applications require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. However, with the recent explosion in the amount and dimensionality of hyperspectral imagery, this problem calls for the incorporation of parallel computing techniques. In the past, clusters of computers have offered an attractive solution for fast anomaly detection in hyperspectral data sets already transmitted to Earth. However, these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time, i.e., at the same time as the data is collected by the sensor. An exciting new development in the field of commodity computing is the emergence of commodity graphics processing units (GPUs), which can now bridge the gap towards on-board processing of remotely sensed hyperspectral data. In this paper, we develop a new morphological algorithm for anomaly detection in hyperspectral images along with an efficient GPU implementation of the algorithm. The algorithm is implemented on latest-generation GPU architectures, and evaluated with regards to other anomaly detection algorithms using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in the WTC complex. The proposed GPU implementation achieves real-time performance in the considered case study.

  6. Improved Scanners for Microscopic Hyperspectral Imaging

    NASA Technical Reports Server (NTRS)

    Mao, Chengye

    2009-01-01

    Improved scanners to be incorporated into hyperspectral microscope-based imaging systems have been invented. Heretofore, in microscopic imaging, including spectral imaging, it has been customary to either move the specimen relative to the optical assembly that includes the microscope or else move the entire assembly relative to the specimen. It becomes extremely difficult to control such scanning when submicron translation increments are required, because the high magnification of the microscope enlarges all movements in the specimen image on the focal plane. To overcome this difficulty, in a system based on this invention, no attempt would be made to move either the specimen or the optical assembly. Instead, an objective lens would be moved within the assembly so as to cause translation of the image at the focal plane: the effect would be equivalent to scanning in the focal plane. The upper part of the figure depicts a generic proposed microscope-based hyperspectral imaging system incorporating the invention. The optical assembly of this system would include an objective lens (normally, a microscope objective lens) and a charge-coupled-device (CCD) camera. The objective lens would be mounted on a servomotor-driven translation stage, which would be capable of moving the lens in precisely controlled increments, relative to the camera, parallel to the focal-plane scan axis. The output of the CCD camera would be digitized and fed to a frame grabber in a computer. The computer would store the frame-grabber output for subsequent viewing and/or processing of images. The computer would contain a position-control interface board, through which it would control the servomotor. There are several versions of the invention. An essential feature common to all versions is that the stationary optical subassembly containing the camera would also contain a spatial window, at the focal plane of the objective lens, that would pass only a selected portion of the image. In one version

  7. Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

    NASA Astrophysics Data System (ADS)

    Warren, Mark A.; Taylor, Benjamin H.; Grant, Michael G.; Shutler, Jamie D.

    2014-03-01

    Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

  8. A linear mixture analysis-based compression for hyperspectral image analysis

    SciTech Connect

    C. I. Chang; I. W. Ginsberg

    2000-06-30

    In this paper, the authors present a fully constrained least squares linear spectral mixture analysis-based compression technique for hyperspectral image analysis, particularly, target detection and classification. Unlike most compression techniques that directly deal with image gray levels, the proposed compression approach generates the abundance fractional images of potential targets present in an image scene and then encodes these fractional images so as to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in the abundance fractional images, the loss of information may have very little impact on image analysis. In some occasions, it even improves analysis performance. Airborne visible infrared imaging spectrometer (AVIRIS) data experiments demonstrate that it can effectively detect and classify targets while achieving very high compression ratios.

  9. Infrared hyperspectral imaging polarimeter using birefringent prisms.

    PubMed

    Craven-Jones, Julia; Kudenov, Michael W; Stapelbroek, Maryn G; Dereniak, Eustace L

    2011-03-10

    A compact short-wavelength and middle-wavelength infrared hyperspectral imaging polarimeter (IHIP) is introduced. The sensor includes a pair of sapphire Wollaston prisms and several high-order retarders to form an imaging Fourier transform spectropolarimeter. The Wollaston prisms serve as a birefringent interferometer with reduced sensitivity to vibration versus an unequal path interferometer, such as a Michelson. Polarimetric data are acquired through the use of channeled spectropolarimetry to modulate the spectrum with the Stokes parameter information. The collected interferogram is Fourier filtered and reconstructed to recover the spatially and spectrally varying Stokes vector data across the image. The IHIP operates over a ±5° field of view and implements a dual-scan false signature reduction technique to suppress polarimetric aliasing artifacts. In this paper, the optical layout and operation of the IHIP sensor are presented in addition to the radiometric, spectral, and polarimetric calibration techniques used with the system. Spectral and spectropolarimetric results from the laboratory and outdoor tests with the instrument are also presented. PMID:21394189

  10. Infrared hyperspectral imaging polarimeter using birefringent prisms.

    PubMed

    Craven-Jones, Julia; Kudenov, Michael W; Stapelbroek, Maryn G; Dereniak, Eustace L

    2011-03-10

    A compact short-wavelength and middle-wavelength infrared hyperspectral imaging polarimeter (IHIP) is introduced. The sensor includes a pair of sapphire Wollaston prisms and several high-order retarders to form an imaging Fourier transform spectropolarimeter. The Wollaston prisms serve as a birefringent interferometer with reduced sensitivity to vibration versus an unequal path interferometer, such as a Michelson. Polarimetric data are acquired through the use of channeled spectropolarimetry to modulate the spectrum with the Stokes parameter information. The collected interferogram is Fourier filtered and reconstructed to recover the spatially and spectrally varying Stokes vector data across the image. The IHIP operates over a ±5° field of view and implements a dual-scan false signature reduction technique to suppress polarimetric aliasing artifacts. In this paper, the optical layout and operation of the IHIP sensor are presented in addition to the radiometric, spectral, and polarimetric calibration techniques used with the system. Spectral and spectropolarimetric results from the laboratory and outdoor tests with the instrument are also presented.

  11. Bayesian fusion of hyperspectral astronomical images

    NASA Astrophysics Data System (ADS)

    Jalobeanu, André; Petremand, Matthieu; Collet, Christophe

    2011-03-01

    The new integral-field spectrograph MUSE will acquire hyperspectral images of the deep sky, requiring huge amounts of raw data to be processed, posing a challenge to modern algorithms and technologies. In order to achieve the required sensitivity to observe very faint objects, many observations need to be reconstructed and co-added into a single data cube. In this paper, we propose a new fusion method to combine all raw observations while removing most of the instrumental and observational artifacts such as blur or cosmic rays. Thus, the results can be accurately and consistently analyzed by astronomers. We use a Bayesian framework allowing for optimal data fusion and uncertainty estimation. The knowledge of the instrument allows to write the direct problem (data acquisition on the detector matrix) and then to invert it through Bayesian inference, assuming a smoothness prior for the data cube to be reconstructed. Compared to existing methods, the originality of the new technique is in the propagation of errors throughout the fusion pipeline and the ability to deal with various acquisition parameters for each input image. For this paper, we focus on small-size, simulated astronomical observations with varying parameters to validate the image formation model, the reconstruction algorithm and the predicted uncertainties.

  12. Camouflage target reconnaissance based on hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

    Hua, Wenshen; Guo, Tong; Liu, Xun

    2015-08-01

    Efficient camouflaged target reconnaissance technology makes great influence on modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. Hyperspectral target detection and classification technology are utilized to achieve single class and multi-class camouflaged targets reconnaissance respectively. Constrained energy minimization (CEM), a widely used algorithm in hyperspectral target detection, is employed to achieve one class camouflage target reconnaissance. Then, support vector machine (SVM), a classification method, is proposed to achieve multi-class camouflage target reconnaissance. Experiments have been conducted to demonstrate the efficiency of the proposed method.

  13. Hyperspectral image analysis using artificial color

    NASA Astrophysics Data System (ADS)

    Fu, Jian; Caulfield, H. John; Wu, Dongsheng; Tadesse, Wubishet

    2010-03-01

    By definition, HSC (HyperSpectral Camera) images are much richer in spectral data than, say, a COTS (Commercial-Off-The-Shelf) color camera. But data are not information. If we do the task right, useful information can be derived from the data in HSC images. Nature faced essentially the identical problem. The incident light is so complex spectrally that measuring it with high resolution would provide far more data than animals can handle in real time. Nature's solution was to do irreversible POCS (Projections Onto Convex Sets) to achieve huge reductions in data with minimal reduction in information. Thus we can arrange for our manmade systems to do what nature did - project the HSC image onto two or more broad, overlapping curves. The task we have undertaken in the last few years is to develop this idea that we call Artificial Color. What we report here is the use of the measured HSC image data projected onto two or three convex, overlapping, broad curves in analogy with the sensitivity curves of human cone cells. Testing two quite different HSC images in that manner produced the desired result: good discrimination or segmentation that can be done very simply and hence are likely to be doable in real time with specialized computers. Using POCS on the HSC data to reduce the processing complexity produced excellent discrimination in those two cases. For technical reasons discussed here, the figures of merit for the kind of pattern recognition we use is incommensurate with the figures of merit of conventional pattern recognition. We used some force fitting to make a comparison nevertheless, because it shows what is also obvious qualitatively. In our tasks our method works better.

  14. Raman Hyperspectral Imaging of Microfossils: Potential Pitfalls

    PubMed Central

    Olcott Marshall, Alison

    2013-01-01

    Abstract Initially, Raman spectroscopy was a specialized technique used by vibrational spectroscopists; however, due to rapid advancements in instrumentation and imaging techniques over the last few decades, Raman spectrometers are widely available at many institutions, allowing Raman spectroscopy to become a widespread analytical tool in mineralogy and other geological sciences. Hyperspectral imaging, in particular, has become popular due to the fact that Raman spectroscopy can quickly delineate crystallographic and compositional differences in 2-D and 3-D at the micron scale. Although this rapid growth of applications to the Earth sciences has provided great insight across the geological sciences, the ease of application as the instruments become increasingly automated combined with nonspecialists using this techique has resulted in the propagation of errors and misunderstandings throughout the field. For example, the literature now includes misassigned vibration modes, inappropriate spectral processing techniques, confocal depth of laser penetration incorrectly estimated into opaque crystalline solids, and a misconstrued understanding of the anisotropic nature of sp2 carbons. Key Words: Raman spectroscopy—Raman imaging—Confocal Raman spectroscopy—Disordered sp2 carbons—Hematite—Microfossils. Astrobiology 13, 920–931. PMID:24088070

  15. Visible-Infrared Hyperspectral Image Projector

    NASA Technical Reports Server (NTRS)

    Bolcar, Matthew

    2013-01-01

    The VisIR HIP generates spatially-spectrally complex scenes. The generated scenes simulate real-world targets viewed by various remote sensing instruments. The VisIR HIP consists of two subsystems: a spectral engine and a spatial engine. The spectral engine generates spectrally complex uniform illumination that spans the wavelength range between 380 nm and 1,600 nm. The spatial engine generates two-dimensional gray-scale scenes. When combined, the two engines are capable of producing two-dimensional scenes with a unique spectrum at each pixel. The VisIR HIP can be used to calibrate any spectrally sensitive remote-sensing instrument. Tests were conducted on the Wide-field Imaging Interferometer Testbed at NASA s Goddard Space Flight Center. The device is a variation of the calibrated hyperspectral image projector developed by the National Institute of Standards and Technology in Gaithersburg, MD. It uses Gooch & Housego Visible and Infrared OL490 Agile Light Sources to generate arbitrary spectra. The two light sources are coupled to a digital light processing (DLP(TradeMark)) digital mirror device (DMD) that serves as the spatial engine. Scenes are displayed on the DMD synchronously with desired spectrum. Scene/spectrum combinations are displayed in rapid succession, over time intervals that are short compared to the integration time of the system under test.

  16. Geographical classification of apple based on hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Guo, Zhiming; Huang, Wenqian; Chen, Liping; Zhao, Chunjiang; Peng, Yankun

    2013-05-01

    Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

  17. Standoff midwave infrared hyperspectral imaging of ship plumes

    NASA Astrophysics Data System (ADS)

    Gagnon, Marc-André; Gagnon, Jean-Philippe; Tremblay, Pierre; Savary, Simon; Farley, Vincent; Guyot, Éric; Lagueux, Philippe; Chamberland, Martin; Marcotte, Frédérick

    2016-05-01

    Characterization of ship plumes is very challenging due to the great variety of ships, fuel, and fuel grades, as well as the extent of a gas plume. In this work, imaging of ship plumes from an operating ferry boat was carried out using standoff midwave (3-5 μm) infrared hyperspectral imaging. Quantitative chemical imaging of combustion gases was achieved by fitting a radiative transfer model. Combustion efficiency maps and mass flow rates are presented for carbon monoxide (CO) and carbon dioxide (CO2). The results illustrate how valuable information about the combustion process of a ship engine can be successfully obtained using passive hyperspectral remote sensing imaging.

  18. Hyperspectral imaging in the infrared using LIFTIRS. Revision 1

    SciTech Connect

    Bennett, C.L.; Carter, M.R.; Fields, D.J.

    1995-10-01

    In this article the ideal performance for various possible designs for imaging spectrometers is discussed. Recent characterization measurements made with LIFTIRS, the Livermore Imaging Fourier Transform InfraRed Spectrometer are also presented. Hyperspectral imagers, characterized by having a large number of spectral channels, enable definitive identification and quantitative measurement of the composition of objects in the field of view. Infrared hyperspectral imagers are particularly useful for remote chemical analysis, since almost all molecules have characteristic rotation-vibration spectra in the infrared, and a broad portion of the so-called fingerprint region of the infrared spectrum lies where the atmosphere is relatively transparent, between 8 and 13 {micro}m.

  19. Thermal infrared hyperspectral imaging from vehicle-carried instrumentation

    NASA Astrophysics Data System (ADS)

    Kirkland, Laurel E.; Herr, Kenneth C.; Adams, Paul M.; McAfee, John; Salisbury, John

    2002-09-01

    Stand-off identification in the field using thermal infrared spectrometers (hyperspectral) is a maturing technique for gases and aerosols. However, capabilities to identify solid-phase materials on the surface lag substantially, particularly for identification in the field without benefit of ground truth (e.g. for "denied areas"). Spectral signatures of solid phase materials vary in complex and non-intuitive ways, including non-linear variations with surface texture, particle size, and intimate mixing. Also, in contrast to airborne or satellite measurements, reflected downwelling radiance strongly affects the signature measured by field spectrometers. These complex issues can confound interpretations or cause a misidentification in the field. Problems that remain particularly obstinate are (1) low ambiguity identification when there is no accompanying ground truth (e.g. measurements of denied areas, or Mars surface by the 2003 Mars lander spectrometer); (2) real- or near real-time identification, especially when a low ambiguity answer is critical; (3) identification of intimate mixtures (e.g. two fine powders mixed together) and targets composed of very small particles (e.g. aerosol fallout dust, some tailings); and (4) identification of non-diffuse targets (e.g. smooth coatings such as paint and desert varnish), particularly when measured at a high emission angle. In most studies that focus on gas phase targets or specific manmade targets, the solid phase background signatures are called "clutter" and are thrown out. Here we discuss our field spectrometer images measured of test targets that were selected to include a range of particle sizes, diffuse, non-diffuse, high, and low reflectance materials. This study was designed to identify and improve understanding of the issues that complicate stand-off identification in the field, with a focus on developing identification capabilities to proceed without benefit of ground truth. This information allows both improved

  20. Classification of Korla fragrant pears using NIR hyperspectral imaging analysis

    NASA Astrophysics Data System (ADS)

    Rao, Xiuqin; Yang, Chun-Chieh; Ying, Yibin; Kim, Moon S.; Chao, Kuanglin

    2012-05-01

    Korla fragrant pears are small oval pears characterized by light green skin, crisp texture, and a pleasant perfume for which they are named. Anatomically, the calyx of a fragrant pear may be either persistent or deciduous; the deciduouscalyx fruits are considered more desirable due to taste and texture attributes. Chinese packaging standards require that packed cases of fragrant pears contain 5% or less of the persistent-calyx type. Near-infrared hyperspectral imaging was investigated as a potential means for automated sorting of pears according to calyx type. Hyperspectral images spanning the 992-1681 nm region were acquired using an EMCCD-based laboratory line-scan imaging system. Analysis of the hyperspectral images was performed to select wavebands useful for identifying persistent-calyx fruits and for identifying deciduous-calyx fruits. Based on the selected wavebands, an image-processing algorithm was developed that targets automated classification of Korla fragrant pears into the two categories for packaging purposes.

  1. Textural Analysis of Hyperspectral Images for Improving Contaminant Detection Accuracy

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Previous studies demonstrated a hyperspectral imaging system has a potential for poultry fecal contaminant detection by measuring reflectance intensity. The simple image ratio at 565 and 517-nm images with optimal thresholding was able to detect fecal contaminants on broiler carcasses with high acc...

  2. Pulse tube cooler for flight hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Chan, C. K.; Clancy, Pamela; Godden, John

    A new version of TRW's miniature pulse tube cooler system maintains the short wave infrared-focal plane array (SWIR-FPA) (with wavelength spectrum of 0.9-2.5 μm in the hyperspectral imaging spectrometer for the Hyperion Instrument) interface at a temperature of 110 K. The cooler provides the nominally required cooling load of 0.84W to the FPA via a cold thermal strap, at 72% stroke consuming 14.7 W of electrical power, when the heat reject temperature is at 300 K. This cooler can operate up to 90% stroke, having 1.5 W cooling load, thus having 79% performance margin for the Hyperion mission. Before the installation and operation of the cooler onto the instrument, both the mechanical and the electronics assemblies underwent the environmental tests of launch vibration, thermal vacuum cycling, and burn-in. The cooler performance in terms of mechanical efficiency, electronics efficiency, load lines, temperature stability, self-induced vibrational force reduction, ripple current reduction, and magnetic radiated emission was measured and are reported here.

  3. Food inspection using hyperspectral imaging and SVDD

    NASA Astrophysics Data System (ADS)

    Uslu, Faruk Sukru; Binol, Hamidullah; Bal, Abdullah

    2016-05-01

    Nowadays food inspection and evaluation is becoming significant public issue, therefore robust, fast, and environmentally safe methods are studied instead of human visual assessment. Optical sensing is one of the potential methods with the properties of being non-destructive and accurate. As a remote sensing technology, hyperspectral imaging (HSI) is being successfully applied by researchers because of having both spatial and detailed spectral information about studied material. HSI can be used to inspect food quality and safety estimation such as meat quality assessment, quality evaluation of fish, detection of skin tumors on chicken carcasses, and classification of wheat kernels in the food industry. In this paper, we have implied an experiment to detect fat ratio in ground meat via Support Vector Data Description which is an efficient and robust one-class classifier for HSI. The experiments have been implemented on two different ground meat HSI data sets with different fat percentage. Addition to these implementations, we have also applied bagging technique which is mostly used as an ensemble method to improve the prediction ratio. The results show that the proposed methods produce high detection performance for fat ratio in ground meat.

  4. Hyperspectral Imaging of fecal contamination on chickens

    NASA Technical Reports Server (NTRS)

    2003-01-01

    ProVision Technologies, a NASA research partnership center at Sternis Space Center in Mississippi, has developed a new hyperspectral imaging (HSI) system that is much smaller than the original large units used aboard remote sensing aircraft and satellites. The new apparatus is about the size of a breadbox. Health-related applications of HSI include scanning chickens during processing to help prevent contaminated food from getting to the table. ProVision is working with Sanderson Farms of Mississippi and the U.S. Department of Agriculture. ProVision has a record in its spectral library of the unique spectral signature of fecal contamination, so chickens can be scanned and those with a positive reading can be separated. HSI sensors can also determine the quantity of surface contamination. Research in this application is quite advanced, and ProVision is working on a licensing agreement for the technology. The potential for future use of this equipment in food processing and food safety is enormous.

  5. Comparative study on atmospheric correction methods of visible and near-infrared hyperspectral image

    NASA Astrophysics Data System (ADS)

    He, Qian; Wu, Jingli; Wang, Guangping; Liu, Chang; Tao, Tao

    2015-03-01

    Currently, common atmospheric correction methods usually based on the statistical information of image itself for relative reflectance calculation, or make use of the radiative transfer model and meteorological parameters for accurate calculations. In order to compare the advantages and disadvantages of these methods, we carried out some atmospheric correction experiments based on AVIRIS Airborne Visible and Near-Infrared hyperspectral data. It proved that, the statistical method is simple and convenient, but not wide adaptability, that can only get the relative reflectance; while the radiative transfer model method is very complex and require the support of auxiliary information, but it can get the precise absolute reflectance of surface features.

  6. In vivo and in vitro hyperspectral imaging of cervical neoplasia

    NASA Astrophysics Data System (ADS)

    Wang, Chaojian; Zheng, Wenli; Bu, Yanggao; Chang, Shufang; Tong, Qingping; Zhang, Shiwu; Xu, Ronald X.

    2014-02-01

    Cervical cancer is a prevalent disease in many developing countries. Colposcopy is the most common approach for screening cervical intraepithelial neoplasia (CIN). However, its clinical efficacy heavily relies on the examiner's experience. Spectroscopy is a potentially effective method for noninvasive diagnosis of cervical neoplasia. In this paper, we introduce a hyperspectral imaging technique for noninvasive detection and quantitative analysis of cervical neoplasia. A hyperspectral camera is used to collect the reflectance images of the entire cervix under xenon lamp illumination, followed by standard colposcopy examination and cervical tissue biopsy at both normal and abnormal sites in different quadrants. The collected reflectance data are calibrated and the hyperspectral signals are extracted. Further spectral analysis and image processing works are carried out to classify tissue into different types based on the spectral characteristics at different stages of cervical intraepithelial neoplasia. The hyperspectral camera is also coupled with a lab microscope to acquire the hyperspectral transmittance images of the pathological slides. The in vivo and the in vitro imaging results are compared with clinical findings to assess the accuracy and efficacy of the method.

  7. a Diversified Deep Belief Network for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Zhong, P.; Gong, Z. Q.; Schönlieb, C.

    2016-06-01

    In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image classification task cannot provide enough training samples for a supervised deep model, such as convolutional neural networks (CNNs), this work turns to investigate the deep belief networks (DBNs), which allow unsupervised training. The DBN trained over limited training samples usually has many "dead" (never responding) or "potential over-tolerant" (always responding) latent factors (neurons), which decrease the DBN's description ability and thus finally decrease the hyperspectral image classification performance. This work proposes a new diversified DBN through introducing a diversity promoting prior over the latent factors during the DBN pre-training and fine-tuning procedures. The diversity promoting prior in the training procedures will encourage the latent factors to be uncorrelated, such that each latent factor focuses on modelling unique information, and all factors will be summed up to capture a large proportion of information and thus increase description ability and classification performance of the diversified DBNs. The proposed method was evaluated over the well-known real-world hyperspectral image dataset. The experiments demonstrate that the diversified DBNs can obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.

  8. Diagnosis method of cucumber downy mildew with NIR hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Tian, Youwen; Li, Tianlai; Zhang, Lin; Zhang, Xiaodong

    2011-11-01

    This study was carried out to develop a hyperspectral imaging system in the near infrared (NIR) region (900-1700 nm) to diagnose cucumber downy mildew. Hyperspectral images were acquired from each diseased cucumber leaf samples with downy mildew and then their spectral data were extracted. Spectral data were analyzed using principal component analysis (PCA) to reduce the high dimensionality of the data and for selecting some important wavelengths. Out of 256 wavelengths, only two wavelengths (1426 and 1626nm) of first PC were selected as the optimum wavelengths for the diagnosis of cucumber downy mildew. The data analysis showed that it is possible to diagnose cucumber downy mildew with few numbers of wavelengths on the basis of their statistical image features and histogram features. The results revealed the potentiality of NIR hyperspectral imaging as an objective and non-destructive method for the authentication and diagnosis of cucumber downy mildew.

  9. Hyperspectral retinal imaging with a spectrally tunable light source

    NASA Astrophysics Data System (ADS)

    Francis, Robert P.; Zuzak, Karel J.; Ufret-Vincenty, Rafael

    2011-03-01

    Hyperspectral retinal imaging can measure oxygenation and identify areas of ischemia in human patients, but the devices used by current researchers are inflexible in spatial and spectral resolution. We have developed a flexible research prototype consisting of a DLP®-based spectrally tunable light source coupled to a fundus camera to quickly explore the effects of spatial resolution, spectral resolution, and spectral range on hyperspectral imaging of the retina. The goal of this prototype is to (1) identify spectral and spatial regions of interest for early diagnosis of diseases such as glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy (DR); and (2) define required specifications for commercial products. In this paper, we describe the challenges and advantages of using a spectrally tunable light source for hyperspectral retinal imaging, present clinical results of initial imaging sessions, and describe how this research can be leveraged into specifying a commercial product.

  10. [Nondestructive discrimination of waxed apples based on hyperspectral imaging technology].

    PubMed

    Gao, Jun-Feng; Zhang, Hai-Liang; Kong, Wen-Wen; He, Yong

    2013-07-01

    The potential of hyperspectral imaging technology was evaluated for discriminating three types of waxed apples. Three types of apples smeared with fruit wax, with industrial wax, and not waxed respectively were imaged by a hyperspectral imaging system with a spectral range of 308-1 024 nm. ENVI software processing platform was used for extracting hyperspectral image object of diffuse reflection spectral response characteristics. Eighty four of 126 apple samples were selected randomly as calibration set and the rest were prediction set. After different preprocess, the related mathematical models were established by using the partial least squares (PLS), the least squares support vector machine (LS-SVM) and BP neural network methods and so on. The results showed that the model of MSC-SPA-LSSVM was the best to discriminate three kinds of waxed apples with 100%, 100% and 92.86% correct prediction respectively.

  11. Mapping Ungulate Habitats in Yellowstone National Park with Airborne Hyperspectral Data

    NASA Technical Reports Server (NTRS)

    Terrie, Gregory; Warner, Amanda; Spruce, Joseph

    2001-01-01

    Mapping vegetation habitats of ungulates (e.g., bison, elk, and deer) is critical to the development of efficient wildlife management and monitoring practices in Yellowstone National Park. Image endmembers were chosen using the ENVI minimum noise fraction, pixel purity index, N-dimensional visualizer approach. The spectral angle mapper algorithm was used to classify the image. This process was applied to low altitude AVIRIS and Probe-1 hyperspectral imagery of the Lamar River/Soda Butte Creek confluence to map several ungulate habitats (e.g., grasses, sedge, sage, aspen, willow, and cottonwood. The results are being compared to field measurements and large-scale color infrared aerial photography to assess mapping accuracy. The use of AVIRIS and Probe-1 data enabled the examination of hyperspectral data collected at different spatial and spectral resolutions.

  12. Mapping beech ( Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Cho, Moses Azong; Skidmore, Andrew K.; Sobhan, Istiak

    2009-06-01

    Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest ( Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI ( i, j) = ( Ri - Rj)/( Ri + Rj), where Ri and Rj = reflectance in any two bands) were evaluated using calibration ( n = 33) and test ( n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756-820 nm) and the water absorption feature centred at 1200 nm (1172-1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R2 = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH

  13. Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

    NASA Astrophysics Data System (ADS)

    Alonzo, Mike Gerard

    Urban trees are strategically important in a city's effort to mitigate their carbon footprint, heat island effects, air pollution, and stormwater runoff. Currently, the most common method for quantifying urban forest structure and ecosystem function is through field plot sampling. However, taking intensive structural measurements on private properties throughout a city is difficult, and the outputs from sample inventories are not spatially explicit. The overarching goal of this dissertation is to develop methods for mapping urban forest structure and function using fused hyperspectral imagery and waveform lidar data at the individual tree crown scale. Urban forest ecosystem services estimated using the USDA Forest Service's i-Tree Eco (formerly UFORE) model are based largely on tree species and leaf area index (LAI). Accordingly, tree species were mapped in my Santa Barbara, California study area for 29 species comprising >80% of canopy. Crown-scale discriminant analysis methods were introduced for fusing Airborne Visible Infrared Imaging Spectrometry (AVIRIS) data with a suite of lidar structural metrics (e.g., tree height, crown porosity) to maximize classification accuracy in a complex environment. AVIRIS imagery was critical to achieving an overall species-level accuracy of 83.4% while lidar data was most useful for improving the discrimination of small and morphologically unique species. LAI was estimated at both the field-plot scale using laser penetration metrics and at the crown scale using allometry. Agreement of the former with photographic estimates of gap fraction and the latter with allometric estimates based on field measurements was examined. Results indicate that lidar may be used reasonably to measure LAI in an urban environment lacking in continuous canopy and characterized by high species diversity. Finally, urban ecosystem services such as carbon storage and building energy-use modification were analyzed through combination of aforementioned

  14. Concept and integration of an on-line quasi-operational airborne hyperspectral remote sensing system

    NASA Astrophysics Data System (ADS)

    Schilling, Hendrik; Lenz, Andreas; Gross, Wolfgang; Perpeet, Dominik; Wuttke, Sebastian; Middelmann, Wolfgang

    2013-10-01

    Modern mission characteristics require the use of advanced imaging sensors in reconnaissance. In particular, high spatial and high spectral resolution imaging provides promising data for many tasks such as classification and detecting objects of military relevance, such as camouflaged units or improvised explosive devices (IEDs). Especially in asymmetric warfare with highly mobile forces, intelligence, surveillance and reconnaissance (ISR) needs to be available close to real-time. This demands the use of unmanned aerial vehicles (UAVs) in combination with downlink capability. The system described in this contribution is integrated in a wing pod for ease of installation and calibration. It is designed for the real-time acquisition and analysis of hyperspectral data. The main component is a Specim AISA Eagle II hyperspectral sensor, covering the visible and near-infrared (VNIR) spectral range with a spectral resolution up to 1.2 nm and 1024 pixel across track, leading to a ground sampling distance below 1 m at typical altitudes. The push broom characteristic of the hyperspectral sensor demands an inertial navigation system (INS) for rectification and georeferencing of the image data. Additional sensors are a high resolution RGB (HR-RGB) frame camera and a thermal imaging camera. For on-line application, the data is preselected, compressed and transmitted to the ground control station (GCS) by an existing system in a second wing pod. The final result after data processing in the GCS is a hyperspectral orthorectified GeoTIFF, which is filed in the ERDAS APOLLO geographical information system. APOLLO allows remote access to the data and offers web-based analysis tools. The system is quasi-operational and was successfully tested in May 2013 in Bremerhaven, Germany.

  15. Hyperspectral Imaging Sensors and the Marine Coastal Zone

    NASA Technical Reports Server (NTRS)

    Richardson, Laurie L.

    2000-01-01

    Hyperspectral imaging sensors greatly expand the potential of remote sensing to assess, map, and monitor marine coastal zones. Each pixel in a hyperspectral image contains an entire spectrum of information. As a result, hyperspectral image data can be processed in two very different ways: by image classification techniques, to produce mapped outputs of features in the image on a regional scale; and by use of spectral analysis of the spectral data embedded within each pixel of the image. The latter is particularly useful in marine coastal zones because of the spectral complexity of suspended as well as benthic features found in these environments. Spectral-based analysis of hyperspectral (AVIRIS) imagery was carried out to investigate a marine coastal zone of South Florida, USA. Florida Bay is a phytoplankton-rich estuary characterized by taxonomically distinct phytoplankton assemblages and extensive seagrass beds. End-member spectra were extracted from AVIRIS image data corresponding to ground-truth sample stations and well-known field sites. Spectral libraries were constructed from the AVIRIS end-member spectra and used to classify images using the Spectral Angle Mapper (SAM) algorithm, a spectral-based approach that compares the spectrum, in each pixel of an image with each spectrum in a spectral library. Using this approach different phytoplankton assemblages containing diatoms, cyanobacteria, and green microalgae, as well as benthic community (seagrasses), were mapped.

  16. Hybrid tenso-vectorial compressive sensing for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Li, Qun; Bernal, Edgar A.

    2016-05-01

    Hyperspectral imaging has a wide range of applications relying on remote material identification, including astronomy, mineralogy, and agriculture; however, due to the large volume of data involved, the complexity and cost of hyperspectral imagers can be prohibitive. The exploitation of redundancies along the spatial and spectral dimensions of a hyperspectral image of a scene has created new paradigms that overcome the limitations of traditional imaging systems. While compressive sensing (CS) approaches have been proposed and simulated with success on already acquired hyperspectral imagery, most of the existing work relies on the capability to simultaneously measure the spatial and spectral dimensions of the hyperspectral cube. Most real-life devices, however, are limited to sampling one or two dimensions at a time, which renders a significant portion of the existing work unfeasible. We propose a new variant of the recently proposed serial hybrid vectorial and tensorial compressive sensing (HCS-S) algorithm that, like its predecessor, is compatible with real-life devices both in terms of the acquisition and reconstruction requirements. The newly introduced approach is parallelizable, and we abbreviate it as HCS-P. Together, HCS-S and HCS-P comprise a generalized framework for hybrid tenso-vectorial compressive sensing, or HCS for short. We perform a detailed analysis that demonstrates the uniqueness of the signal reconstructed by both the original HCS-S and the proposed HCS-P algorithms. Last, we analyze the behavior of the HCS reconstruction algorithms in the presence of measurement noise, both theoretically and experimentally.

  17. Advanced hyperspectral imaging solutions for near real-time target detection

    NASA Astrophysics Data System (ADS)

    Weatherbee, Oliver; Janaskie, Justin; Hyvärinen, Timo

    2012-09-01

    AISA hyperspectral imagers have been utilized in airborne applications for various defense related Intelligence, Surveillance and Reconnaissance (ISR) applications. In expanding the utility and capabilities of hyperspectral imagers for defense related applications, the implementation in a ground scanning configuration for check-point and forensic purposes has been achieved. System specifications, design, and operational considerations for a fully automated, near real-time target detection capability are presented. The system utilizes modularized software architecture, combining C++ command, capture, calibration, and messaging functions with drop-in IDL exploitation module for detection algorithm and target set flexibility. Performance capability against known defense related targets of interest have been tested, verified, and are presented utilizing full 400-2450nm spectral range provided by combined AisaEAGLE and AisaHAWK hyperspectral imagers. Initial results are also described for a new extended InGaAs system, covering 585-1630nm to provide a similar capability for integrations which have size, weight, and power restrictions.

  18. Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots

    EPA Science Inventory

    Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. ...

  19. a New Control Points Based Geometric Correction Algorithm for Airborne Push Broom Scanner Images Without On-Board Data

    NASA Astrophysics Data System (ADS)

    Strakhov, P.; Badasen, E.; Shurygin, B.; Kondranin, T.

    2016-06-01

    Push broom scanners, such as video spectrometers (also called hyperspectral sensors), are widely used in the present. Usage of scanned images requires accurate geometric correction, which becomes complicated when imaging platform is airborne. This work contains detailed description of a new algorithm developed for processing of such images. The algorithm requires only user provided control points and is able to correct distortions caused by yaw, flight speed and height changes. It was tested on two series of airborne images and yielded RMS error values on the order of 7 meters (3-6 source image pixels) as compared to 13 meters for polynomial-based correction.

  20. LED lighting for use in multispectral and hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Lighting for machine vision and hyperspectral imaging is an important component for collecting high quality imagery. However, it is often given minimal consideration in the overall design of an imaging system. Tungsten-halogens lamps are the most common source of illumination for broad spectrum appl...

  1. Phase correction algorithms for a snapshot hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

    Chan, Victoria C.; Kudenov, Michael; Dereniak, Eustace

    2015-09-01

    We present image processing algorithms that improve spatial and spectral resolution on the Snapshot Hyperspectral Imaging Fourier Transform (SHIFT) spectrometer. Final measurements are stored in the form of threedimensional datacubes containing the scene's spatial and spectral information. We discuss calibration procedures, review post-processing methods, and present preliminary results from proof-of-concept experiments.

  2. Identification of seedling cabbages and weeds using hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Target detectionis one of research focues for precision chemical application. This study developed a method to identify seedling cabbages and weeds using hyperspectral spectral imaging. In processing the image data, with ENVI software, after dimension reduction, noise reduction, de-correlation for h...

  3. The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area

    NASA Astrophysics Data System (ADS)

    Richter, Ronny; Reu, Björn; Wirth, Christian; Doktor, Daniel; Vohland, Michael

    2016-10-01

    The success of remote sensing approaches to assess tree species diversity in a heterogeneously mixed forest stand depends on the availability of both appropriate data and suitable classification algorithms. To separate the high number of in total ten broadleaf tree species in a small structured floodplain forest, the Leipzig Riverside Forest, we introduce a majority based classification approach for Discriminant Analysis based on Partial Least Squares (PLS-DA), which was tested against Random Forest (RF) and Support Vector Machines (SVM). The classifier performance was tested on different sets of airborne hyperspectral image data (AISA DUAL) that were acquired on single dates in August and September and also stacked to a composite product. Shadowed gaps and shadowed crown parts were eliminated via spectral mixture analysis (SMA) prior to the pixel-based classification. Training and validation sets were defined spectrally with the conditioned Latin hypercube method as a stratified random sampling procedure. In the validation, PLS-DA consistently outperformed the RF and SVM approaches on all datasets. The additional use of spectral variable selection (CARS, "competitive adaptive reweighted sampling") combined with PLS-DA further improved classification accuracies. Up to 78.4% overall accuracy was achieved for the stacked dataset. The image recorded in August provided slightly higher accuracies than the September image, regardless of the applied classifier.

  4. Radiometric calibration of multiple Earth observation sensors using airborne hyperspectral data at the Newell County rangeland test site

    NASA Astrophysics Data System (ADS)

    Teillet, Phil M.; Fedosejevs, Gunar; Gauthier, Robert P.; Shin, Raymond T.; O'Neill, Norman T.; Thome, Kurtis J.; Biggar, Stuart F.; Ripley, Herb T.; Meygret, Aime

    1999-09-01

    A single data set of spatially extensive hyperspectral imagery is used to carry out vicarious calibrations for multiple Earth observation sensors. Results are presented based on a data acquisition campaign at the newell County rangeland test site in Alberta in October 1998, which included ground-based measurements, satellite imagery, and airborne casi hyperspectral data. This paper present new calibration monitoring obtained for NOAA-14 AVHRR, OrbView-2 SeaWiFS, SPOT-4 VGT, Landsat-5 TM, and SPOT-2 HRV.

  5. Unsupervised hierarchical partitioning of hyperspectral images: application to marine algae identification

    NASA Astrophysics Data System (ADS)

    Chen, B.; Chehdi, K.; De Oliveria, E.; Cariou, C.; Charbonnier, B.

    2015-10-01

    In this paper a new unsupervised top-down hierarchical classification method to partition airborne hyperspectral images is proposed. The unsupervised approach is preferred because the difficulty of area access and the human and financial resources required to obtain ground truth data, constitute serious handicaps especially over large areas which can be covered by airborne or satellite images. The developed classification approach allows i) a successive partitioning of data into several levels or partitions in which the main classes are first identified, ii) an estimation of the number of classes automatically at each level without any end user help, iii) a nonsystematic subdivision of all classes of a partition Pj to form a partition Pj+1, iv) a stable partitioning result of the same data set from one run of the method to another. The proposed approach was validated on synthetic and real hyperspectral images related to the identification of several marine algae species. In addition to highly accurate and consistent results (correct classification rate over 99%), this approach is completely unsupervised. It estimates at each level, the optimal number of classes and the final partition without any end user intervention.

  6. Performance of Three Reflectance Calibration Methods for Airborne Hyperspectral Spectrometer Data

    PubMed Central

    Miura, Tomoaki; Huete, Alfredo R.

    2009-01-01

    In this study, the performances and accuracies of three methods for converting airborne hyperspectral spectrometer data to reflectance factors were characterized and compared. The “reflectance mode (RM)” method, which calibrates a spectrometer against a white reference panel prior to mounting on an aircraft, resulted in spectral reflectance retrievals that were biased and distorted. The magnitudes of these bias errors and distortions varied significantly, depending on time of day and length of the flight campaign. The “linear-interpolation (LI)” method, which converts airborne spectrometer data by taking a ratio of linearly-interpolated reference values from the preflight and post-flight reference panel readings, resulted in precise, but inaccurate reflectance retrievals. These reflectance spectra were not distorted, but were subject to bias errors of varying magnitudes dependent on the flight duration length. The “continuous panel (CP)” method uses a multi-band radiometer to obtain continuous measurements over a reference panel throughout the flight campaign, in order to adjust the magnitudes of the linear-interpolated reference values from the preflight and post-flight reference panel readings. Airborne hyperspectral reflectance retrievals obtained using this method were found to be the most accurate and reliable reflectance calibration method. The performances of the CP method in retrieving accurate reflectance factors were consistent throughout time of day and for various flight durations. Based on the dataset analyzed in this study, the uncertainty of the CP method has been estimated to be 0.0025 ± 0.0005 reflectance units for the wavelength regions not affected by atmospheric absorptions. The RM method can produce reasonable results only for a very short-term flight (e.g., < 15 minutes) conducted around a local solar noon. The flight duration should be kept shorter than 30 minutes for the LI method to produce results with reasonable accuracies

  7. Performance of three reflectance calibration methods for airborne hyperspectral spectrometer data.

    PubMed

    Miura, Tomoaki; Huete, Alfredo R

    2009-01-01

    In this study, the performances and accuracies of three methods for converting airborne hyperspectral spectrometer data to reflectance factors were characterized and compared. The "reflectance mode (RM)" method, which calibrates a spectrometer against a white reference panel prior to mounting on an aircraft, resulted in spectral reflectance retrievals that were biased and distorted. The magnitudes of these bias errors and distortions varied significantly, depending on time of day and length of the flight campaign. The "linear-interpolation (LI)" method, which converts airborne spectrometer data by taking a ratio of linearly-interpolated reference values from the preflight and post-flight reference panel readings, resulted in precise, but inaccurate reflectance retrievals. These reflectance spectra were not distorted, but were subject to bias errors of varying magnitudes dependent on the flight duration length. The "continuous panel (CP)" method uses a multi-band radiometer to obtain continuous measurements over a reference panel throughout the flight campaign, in order to adjust the magnitudes of the linear-interpolated reference values from the preflight and post-flight reference panel readings. Airborne hyperspectral reflectance retrievals obtained using this method were found to be the most accurate and reliable reflectance calibration method. The performances of the CP method in retrieving accurate reflectance factors were consistent throughout time of day and for various flight durations. Based on the dataset analyzed in this study, the uncertainty of the CP method has been estimated to be 0.0025 ± 0.0005 reflectance units for the wavelength regions not affected by atmospheric absorptions. The RM method can produce reasonable results only for a very short-term flight (e.g., < 15 minutes) conducted around a local solar noon. The flight duration should be kept shorter than 30 minutes for the LI method to produce results with reasonable accuracies. An important

  8. Fast, electrically tunable filters for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Liberman, V.; Parameswaran, L.; Gear, C.; Cabral, A.; Rothschild, M.

    2014-06-01

    Tunable, narrow-wavelength spectral filters with a ms response in the mid-wave/long-wave infrared (MW/LWIR) are an enabling technology for hyperspectral imaging systems. Few commercial off-the-shelf (COTS) components for this application exist, including filter wheels, movable gratings, and Fabry-Perot (FP) etalon-based devices. These devices can be bulky, fragile and often do not have the required response speed. Here, we present a fundamentally different approach for tunable reflective IR filters, based on coupling subwavelength plasmonic antenna arrays with liquid crystals (LCs). Our device operates in reflective mode and derives its narrow bandwidth from diffractive coupling of individual antenna elements. The wavelength tunability of the device arises from electrically-induced re-orientation of the LC material in intimate contact with antenna array. This re-orientation, in turn, induces a change in the local dielectric environment of the antenna array, leading to a wavelength shift. We will first present results of full-field optimization of micron-size antenna geometries to account for complex 3D LC anisotropy. We have fabricated these antenna arrays on IR-transparent CaF2 substrates utilizing electron beam lithography, and have demonstrated tunability using 5CB, a commercially available LC. However, the design can be extended to high-birefringence liquid crystals for an increased tuning range. Our initial results demonstrate <60% peak reflectance in the 4- 6 μm wavelength range with a tunability of 0.2 μm with re-orientation of the surface alignment layers. Preliminary electrical switching has been demonstrated and is being optimized.

  9. Hyperspectral imaging with a liquid crystal polarization interferometer.

    PubMed

    Hegyi, Alex; Martini, Joerg

    2015-11-01

    A novel hyperspectral imaging system has been developed that takes advantage of the tunable path delay between orthogonal polarization states of a liquid crystal variable retarder. The liquid crystal is placed in the optical path of an imaging system and the path delay between the polarization states is varied, causing an interferogram to be generated simultaneously at each pixel. A data set consisting of a series of images is recorded while varying the path delay; Fourier transforming the data set with respect to the path delay yields the hyperspectral data-cube. The concept is demonstrated with a prototype imager consisting of a liquid crystal variable retarder integrated into a commercial 640x480 pixel CMOS camera. The prototype can acquire a full hyperspectral data-cube in 0.4 s, and is sensitive to light over a 400 nm to 1100 nm range with a dispersion-dependent spectral resolution of 450 cm(-1) to 660 cm(-1). Similar to Fourier transform spectroscopy, the imager is spatially and spectrally multiplexed and therefore achieves high optical throughput. Additionally, the common-path nature of the polarization interferometer yields a vibration-insensitive device. Our concept allows for the spectral resolution, imaging speed, and spatial resolution to be traded off in software to optimally address a given application. The simplicity, compactness, potential low cost, and software adaptability of the device may enable a disruptive class of hyperspectral imaging systems with a broad range of applications. PMID:26561143

  10. Extended SWIR imaging sensors for hyperspectral imaging applications

    NASA Astrophysics Data System (ADS)

    Weber, A.; Benecke, M.; Wendler, J.; Sieck, A.; Hübner, D.; Figgemeier, H.; Breiter, R.

    2016-05-01

    AIM has developed SWIR modules including FPAs based on liquid phase epitaxy (LPE) grown MCT usable in a wide range of hyperspectral imaging applications. Silicon read-out integrated circuits (ROIC) provide various integration and readout modes including specific functions for spectral imaging applications. An important advantage of MCT based detectors is the tunable band gap. The spectral sensitivity of MCT detectors can be engineered to cover the extended SWIR spectral region up to 2.5μm without compromising in performance. AIM developed the technology to extend the spectral sensitivity of its SWIR modules also into the VIS. This has been successfully demonstrated for 384x288 and 1024x256 FPAs with 24μm pitch. Results are presented in this paper. The FPAs are integrated into compact dewar cooler configurations using different types of coolers, like rotary coolers, AIM's long life split linear cooler MCC030 or extreme long life SF100 Pulse Tube cooler. The SWIR modules include command and control electronics (CCE) which allow easy interfacing using a digital standard interface. The development status and performance results of AIM's latest MCT SWIR modules suitable for hyperspectral systems and applications will be presented.

  11. Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation.

    PubMed

    Dong, Weisheng; Fu, Fazuo; Shi, Guangming; Cao, Xun; Wu, Jinjian; Li, Guangyu; Li, Guangyu

    2016-05-01

    Hyperspectral imaging has many applications from agriculture and astronomy to surveillance and mineralogy. However, it is often challenging to obtain high-resolution (HR) hyperspectral images using existing hyperspectral imaging techniques due to various hardware limitations. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene. The estimation of the HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the prior knowledge of the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary representing prototype reflectance spectra vectors of the scene is first learned from the input LR image. Specifically, an efficient non-negative dictionary learning algorithm using the block-coordinate descent optimization technique is proposed. Then, the sparse codes of the desired HR hyperspectral image with respect to learned hyperspectral basis are estimated from the pair of LR and HR reference images. To improve the accuracy of non-negative sparse coding, a clustering-based structured sparse coding method is proposed to exploit the spatial correlation among the learned sparse codes. The experimental results on both public datasets and real LR hypspectral images suggest that the proposed method substantially outperforms several existing HR hyperspectral image recovery techniques in the literature in terms of both objective quality metrics and computational efficiency. PMID:27019486

  12. Spatial-scanning hyperspectral imaging probe for bio-imaging applications

    NASA Astrophysics Data System (ADS)

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2016-03-01

    The three common methods to perform hyperspectral imaging are the spatial-scanning, spectral-scanning, and snapshot methods. However, only the spectral-scanning and snapshot methods have been configured to a hyperspectral imaging probe as of today. This paper presents a spatial-scanning (pushbroom) hyperspectral imaging probe, which is realized by integrating a pushbroom hyperspectral imager with an imaging probe. The proposed hyperspectral imaging probe can also function as an endoscopic probe by integrating a custom fabricated image fiber bundle unit. The imaging probe is configured by incorporating a gradient-index lens at the end face of an image fiber bundle that consists of about 50 000 individual fiberlets. The necessary simulations, methodology, and detailed instrumentation aspects that are carried out are explained followed by assessing the developed probe's performance. Resolution test targets such as United States Air Force chart as well as bio-samples such as chicken breast tissue with blood clot are used as test samples for resolution analysis and for performance validation. This system is built on a pushbroom hyperspectral imaging system with a video camera and has the advantage of acquiring information from a large number of spectral bands with selectable region of interest. The advantages of this spatial-scanning hyperspectral imaging probe can be extended to test samples or tissues residing in regions that are difficult to access with potential diagnostic bio-imaging applications.

  13. Estimation of tissue optical parameters with hyperspectral imaging and spectral unmixing

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Chen, Zhuo G.; Fei, Baowei

    2015-03-01

    Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.

  14. Estimation of Tissue Optical Parameters with Hyperspectral Imaging and Spectral Unmixing

    PubMed Central

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2015-01-01

    Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model. PMID:26855467

  15. Models of formation and some algorithms of hyperspectral image processing

    NASA Astrophysics Data System (ADS)

    Achmetov, R. N.; Stratilatov, N. R.; Yudakov, A. A.; Vezenov, V. I.; Eremeev, V. V.

    2014-12-01

    Algorithms and information technologies for processing Earth hyperspectral imagery are presented. Several new approaches are discussed. Peculiar properties of processing the hyperspectral imagery, such as multifold signal-to-noise reduction, atmospheric distortions, access to spectral characteristics of every image point, and high dimensionality of data, were studied. Different measures of similarity between individual hyperspectral image points and the effect of additive uncorrelated noise on these measures were analyzed. It was shown that these measures are substantially affected by noise, and a new measure free of this disadvantage was proposed. The problem of detecting the observed scene object boundaries, based on comparing the spectral characteristics of image points, is considered. It was shown that contours are processed much better when spectral characteristics are used instead of energy brightness. A statistical approach to the correction of atmospheric distortions, which makes it possible to solve the stated problem based on analysis of a distorted image in contrast to analytical multiparametric models, was proposed. Several algorithms used to integrate spectral zonal images with data from other survey systems, which make it possible to image observed scene objects with a higher quality, are considered. Quality characteristics of hyperspectral data processing were proposed and studied.

  16. Advances in Hyperspectral and Multispectral Image Fusion and Spectral Unmixing

    NASA Astrophysics Data System (ADS)

    Lanaras, C.; Baltsavias, E.; Schindler, K.

    2015-08-01

    In this work, we jointly process high spectral and high geometric resolution images and exploit their synergies to (a) generate a fused image of high spectral and geometric resolution; and (b) improve (linear) spectral unmixing of hyperspectral endmembers at subpixel level w.r.t. the pixel size of the hyperspectral image. We assume that the two images are radiometrically corrected and geometrically co-registered. The scientific contributions of this work are (a) a simultaneous approach to image fusion and hyperspectral unmixing, (b) enforcing several physically plausible constraints during unmixing that are all well-known, but typically not used in combination, and (c) the use of efficient, state-of-the-art mathematical optimization tools to implement the processing. The results of our joint fusion and unmixing has the potential to enable more accurate and detailed semantic interpretation of objects and their properties in hyperspectral and multispectral images, with applications in environmental mapping, monitoring and change detection. In our experiments, the proposed method always improves the fusion compared to competing methods, reducing RMSE between 4% and 53%.

  17. Hyperspectral imaging and quantitative analysis for prostate cancer detection

    PubMed Central

    Akbari, Hamed; Halig, Luma V.; Schuster, David M.; Osunkoya, Adeboye; Master, Viraj; Nieh, Peter T.; Chen, Georgia Z.

    2012-01-01

    Abstract. Hyperspectral imaging (HSI) is an emerging modality for various medical applications. Its spectroscopic data might be able to be used to noninvasively detect cancer. Quantitative analysis is often necessary in order to differentiate healthy from diseased tissue. We propose the use of an advanced image processing and classification method in order to analyze hyperspectral image data for prostate cancer detection. The spectral signatures were extracted and evaluated in both cancerous and normal tissue. Least squares support vector machines were developed and evaluated for classifying hyperspectral data in order to enhance the detection of cancer tissue. This method was used to detect prostate cancer in tumor-bearing mice and on pathology slides. Spatially resolved images were created to highlight the differences of the reflectance properties of cancer versus those of normal tissue. Preliminary results with 11 mice showed that the sensitivity and specificity of the hyperspectral image classification method are 92.8% to 2.0% and 96.9% to 1.3%, respectively. Therefore, this imaging method may be able to help physicians to dissect malignant regions with a safe margin and to evaluate the tumor bed after resection. This pilot study may lead to advances in the optical diagnosis of prostate cancer using HSI technology. PMID:22894488

  18. Advances in Spectral-Spatial Classification of Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

    2012-01-01

    Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

  19. Development of an imaging hyperspectral camera using the ultraviolet and visible wavelength AOTF

    NASA Astrophysics Data System (ADS)

    Kurosaki, Hirohisa; Shingu, Hirokimi; Enkyo, Shigeharu; Suzuki, Takao; Tanioka, Kenkichi; Takefuji, Yoshiyasu

    2003-04-01

    A spectroscopic camera has been developed which has spectral resolution of less than 1.5nm in the ultraviolet (UV) and visible wavelength bands (320-580 nm). Its main components are a specially coated UV objective lens, a UV Acousto-Optic Tunable Filter (AOTF) with a thermo-electric cooling system, and a imaging system based on a high-gain avalanche rushing amorphous photoconductor (HARP) developed by NHK Science and Technical Research Laboratories. Research is currently under way to develop the hyperspectral camera into a sensor package for airborne and ultimately space-based remote sensing applications. This paper presents the basic principle and configuration of the hyperspectral camera, and gives details of tests to measure its performance. The results of spectral resolution tests analyzing very close two spectra from a helium-discharge lamp demonstrate the camera's high spectral resolution performance. Full color and spectral images obtained by a spectrometry experiment are also presented to demonstrate the camera's hyperspectral capabilities.

  20. Fault tolerance of SVM algorithm for hyperspectral image

    NASA Astrophysics Data System (ADS)

    Cui, Yabo; Yuan, Zhengwu; Wu, Yuanfeng; Gao, Lianru; Zhang, Hao

    2015-10-01

    One of the most important tasks in analyzing hyperspectral image data is the classification process[1]. In general, in order to enhance the classification accuracy, a data preprocessing step is usually adopted to remove the noise in the data before classification. But for the time-sensitive applications, we hope that even the data contains noise the classifier can still appear to execute correctly from the user's perspective, such as risk prevention and response. As the most popular classifier, Support Vector Machine (SVM) has been widely used for hyperspectral image classification and proved to be a very promising technique in supervised classification[2]. In this paper, two experiments are performed to demonstrate that for the hyperspectral data with noise, if the noise of the data is within a certain range, SVM algorithm is still able to execute correctly from the user's perspective.

  1. Spectral-Spatial Classification of Hyperspectral Images Using Hierarchical Optimization

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Tilton, James C.

    2011-01-01

    A new spectral-spatial method for hyperspectral data classification is proposed. For a given hyperspectral image, probabilistic pixelwise classification is first applied. Then, hierarchical step-wise optimization algorithm is performed, by iteratively merging neighboring regions with the smallest Dissimilarity Criterion (DC) and recomputing class labels for new regions. The DC is computed by comparing region mean vectors, class labels and a number of pixels in the two regions under consideration. The algorithm is converged when all the pixels get involved in the region merging procedure. Experimental results are presented on two remote sensing hyperspectral images acquired by the AVIRIS and ROSIS sensors. The proposed approach improves classification accuracies and provides maps with more homogeneous regions, when compared to previously proposed classification techniques.

  2. Hyperspectral Imaging and Related Field Methods: Building the Science

    NASA Technical Reports Server (NTRS)

    Goetz, Alexander F. H.; Steffen, Konrad; Wessman, Carol

    1999-01-01

    The proposal requested funds for the computing power to bring hyperspectral image processing into undergraduate and graduate remote sensing courses. This upgrade made it possible to handle more students in these oversubscribed courses and to enhance CSES' summer short course entitled "Hyperspectral Imaging and Data Analysis" provided for government, industry, university and military. Funds were also requested to build field measurement capabilities through the purchase of spectroradiometers, canopy radiation sensors and a differential GPS system. These instruments provided systematic and complete sets of field data for the analysis of hyperspectral data with the appropriate radiometric and wavelength calibration as well as atmospheric data needed for application of radiative transfer models. The proposed field equipment made it possible to team-teach a new field methods course, unique in the country, that took advantage of the expertise of the investigators rostered in three different departments, Geology, Geography and Biology.

  3. Land cover mapping in Latvia using hyperspectral airborne and simulated Sentinel-2 data

    NASA Astrophysics Data System (ADS)

    Jakovels, Dainis; Filipovs, Jevgenijs; Brauns, Agris; Taskovs, Juris; Erins, Gatis

    2016-08-01

    Land cover mapping in Latvia is performed as part of the Corine Land Cover (CLC) initiative every six years. The advantage of CLC is the creation of a standardized nomenclature and mapping protocol comparable across all European countries, thereby making it a valuable information source at the European level. However, low spatial resolution and accuracy, infrequent updates and expensive manual production has limited its use at the national level. As of now, there is no remote sensing based high resolution land cover and land use services designed specifically for Latvia which would account for the country's natural and land use specifics and end-user interests. The European Space Agency launched the Sentinel-2 satellite in 2015 aiming to provide continuity of free high resolution multispectral satellite data thereby presenting an opportunity to develop and adapted land cover and land use algorithm which accounts for national enduser needs. In this study, land cover mapping scheme according to national end-user needs was developed and tested in two pilot territories (Cesis and Burtnieki). Hyperspectral airborne data covering spectral range 400-2500 nm was acquired in summer 2015 using Airborne Surveillance and Environmental Monitoring System (ARSENAL). The gathered data was tested for land cover classification of seven general classes (urban/artificial, bare, forest, shrubland, agricultural/grassland, wetlands, water) and sub-classes specific for Latvia as well as simulation of Sentinel-2 satellite data. Hyperspectral data sets consist of 122 spectral bands in visible to near infrared spectral range (356-950 nm) and 100 bands in short wave infrared (950-2500 nm). Classification of land cover was tested separately for each sensor data and fused cross-sensor data. The best overall classification accuracy 84.2% and satisfactory classification accuracy (more than 80%) for 9 of 13 classes was obtained using Support Vector Machine (SVM) classifier with 109 band

  4. Thermal Infrared Spectral Imager for Airborne Science Applications

    NASA Technical Reports Server (NTRS)

    Johnson, William R.; Hook, Simon J.; Mouroulis, Pantazis; Wilson, Daniel W.; Gunapala, Sarath D.; Hill, Cory J.; Mumolo, Jason M.; Eng, Bjorn T.

    2009-01-01

    An airborne thermal hyperspectral imager is under development which utilizes the compact Dyson optical configuration and quantum well infrared photo detector (QWIP) focal plane array. The Dyson configuration uses a single monolithic prism-like grating design which allows for a high throughput instrument (F/1.6) with minimal ghosting, stray-light and large swath width. The configuration has the potential to be the optimal imaging spectroscopy solution for lighter-than-air (LTA) vehicles and unmanned aerial vehicles (UAV) due to its small form factor and relatively low power requirements. The planned instrument specifications are discussed as well as design trade-offs. Calibration testing results (noise equivalent temperature difference, spectral linearity and spectral bandwidth) and laboratory emissivity plots from samples are shown using an operational testbed unit which has similar specifications as the final airborne system. Field testing of the testbed unit was performed to acquire plots of apparent emissivity for various known standard minerals (such as quartz). A comparison is made using data from the ASTER spectral library.

  5. Detecting red blotch disease in grape leaves using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Mehrubeoglu, Mehrube; Orlebeck, Keith; Zemlan, Michael J.; Autran, Wesley

    2016-05-01

    Red blotch disease is a viral disease that affects grapevines. Symptoms appear as irregular blotches on grape leaves with pink and red veins on the underside of the leaves. Red blotch disease causes a reduction in the accumulation of sugar in grapevines affecting the quality of grapes and resulting in delayed harvest. Detecting and monitoring this disease early is important for grapevine management. This work focuses on the use of hyperspectral imaging for detection and mapping red blotch disease in grape leaves. Grape leaves with known red blotch disease have been imaged with a portable hyperspectral imaging system both on and off the vine to investigate the spectral signature of red blotch disease as well as to identify the diseased areas on the leaves. Modified reflectance calculated at spectral bands corresponding to 566 nm (green) and 628 nm (red), and modified reflectance ratios computed at two sets of bands (566 nm / 628 nm, 680 nm / 738 nm) were selected as effective features to differentiate red blotch from healthy-looking and dry leaf. These two modified reflectance and two ratios of modified reflectance values were then used to train the support vector machine classifier in a supervised learning scheme. Once the SVM classifier was defined, two-class classification was achieved for grape leaf hyperspectral images. Identification of the red blotch disease on grape leaves as well as mapping different stages of the disease using hyperspectral imaging are presented in this paper.

  6. Study on full-polarization hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

    Wei, Xiangyu; Zhou, Qiang; Zhong, Tenghui; Li, Yubo

    2014-02-01

    Since full-polarization parameter measurement can not be well combined with hyperspectral imaging technology yet , a new full-polarization hyperspectral imaging measurement structure using a dual optical path system was investigated. We utilized the hyperspectral1 interference imaging technology and polarization modulation technology based on electro-optic effect in our research. The polarization information, spectral information and spatial image information were acquired at the same time, which means the simultaneous measurement of hyperspectral information and full-polarization parameter was achieved. In this artical, the principle of the full-polarization parameter measurement was introduced at first. Then the experiment setup was shown and the optical elements were illustrated. Also,the detailed formula derivation steps of the full-Stokes vector was given. At last, some computer simulation data and experimental results were given. Through the combination of spectral imaging and full-polarization parameter measurement, the detecting information of the object is greatly enriched. This work will definitely be helpful to many optical remote sensing technology areas such as resources survey, environmental monitoring and military reconnaissan.

  7. Study on classification of pork quality using hyperspectral imaging technique

    NASA Astrophysics Data System (ADS)

    Zeng, Shan; Bai, Jun; Wang, Haibin

    2015-12-01

    The relative problems' research of chilled meat, thawed meat and spoiled meat discrimination by hyperspectral image technique were proposed, such the section of feature wavelengths, et al. First, based on 400 ~ 1000nm range hyperspectral image data of testing pork samples, by K-medoids clustering algorithm based on manifold distance, we select 30 important wavelengths from 753 wavelengths, and thus select 8 feature wavelengths (454.4, 477.5, 529.3, 546.8, 568.4, 580.3, 589.9 and 781.2nm) based on the discrimination value. Then 8 texture features of each image under 8 feature wavelengths were respectively extracted by two-dimensional Gabor wavelets transform as pork quality feature. Finally, we build a pork quality classification model using the fuzzy C-mean clustering algorithm. Through the experiment of extracting feature wavelengths, we found that although the hyperspectral images between adjacent bands have a strong linear correlation, they show a significant non-linear manifold relationship from the entire band. K-medoids clustering algorithm based on manifold distance used in this paper for selecting the characteristic wavelengths, which is more reasonable than traditional principal component analysis (PCA). Through the classification result, we conclude that hyperspectral imaging technology can distinguish among chilled meat, thawed meat and spoiled meat accurately.

  8. Hyperspectral imaging system for whole corn ear surface inspection

    NASA Astrophysics Data System (ADS)

    Yao, Haibo; Kincaid, Russell; Hruska, Zuzana; Brown, Robert L.; Bhatnagar, Deepak; Cleveland, Thomas E.

    2013-05-01

    Aflatoxin is a mycotoxin produced mainly by Aspergillus flavus (A.flavus) and Aspergillus parasitiucus fungi that grow naturally in corn. Very serious health problems such as liver damage and lung cancer can result from exposure to high toxin levels in grain. Consequently, many countries have established strict guidelines for permissible levels in consumables. Conventional chemical-based analytical methods used to screen for aflatoxin such as thin-layer chromatography (TLC) and high performance liquid chromatography (HPLC) are time consuming, expensive, and require the destruction of samples as well as proper training for data interpretation. Thus, it has been a continuing effort within the research community to find a way to rapidly and non-destructively detect and possibly quantify aflatoxin contamination in corn. One of the more recent developments in this area is the use of spectral technology. Specifically, fluorescence hyperspectral imaging offers a potential rapid, and non-invasive method for contamination detection in corn infected with toxigenic A.flavus spores. The current hyperspectral image system is designed for scanning flat surfaces, which is suitable for imaging single or a group of corn kernels. In the case of a whole corn cob, it is preferred to be able to scan the circumference of the corn ear, appropriate for whole ear inspection. This paper discusses the development of a hyperspectral imaging system for whole corn ear imaging. The new instrument is based on a hyperspectral line scanner using a rotational stage to turn the corn ear.

  9. Context Modeler for Wavelet Compression of Spectral Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Kiely, Aaron; Xie, Hua; Klimesh, matthew; Aranki, Nazeeh

    2010-01-01

    A context-modeling sub-algorithm has been developed as part of an algorithm that effects three-dimensional (3D) wavelet-based compression of hyperspectral image data. The context-modeling subalgorithm, hereafter denoted the context modeler, provides estimates of probability distributions of wavelet-transformed data being encoded. These estimates are utilized by an entropy coding subalgorithm that is another major component of the compression algorithm. The estimates make it possible to compress the image data more effectively than would otherwise be possible. The following background discussion is prerequisite to a meaningful summary of the context modeler. This discussion is presented relative to ICER-3D, which is the name attached to a particular compression algorithm and the software that implements it. The ICER-3D software is summarized briefly in the preceding article, ICER-3D Hyperspectral Image Compression Software (NPO-43238). Some aspects of this algorithm were previously described, in a slightly more general context than the ICER-3D software, in "Improving 3D Wavelet-Based Compression of Hyperspectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. In turn, ICER-3D is a product of generalization of ICER, another previously reported algorithm and computer program that can perform both lossless and lossy wavelet-based compression and decompression of gray-scale-image data. In ICER-3D, hyperspectral image data are decomposed using a 3D discrete wavelet transform (DWT). Following wavelet decomposition, mean values are subtracted from spatial planes of spatially low-pass subbands prior to encoding. The resulting data are converted to sign-magnitude form and compressed. In ICER-3D, compression is progressive, in that compressed information is ordered so that as more of the compressed data stream is received, successive reconstructions of the hyperspectral image data are of successively higher overall fidelity.

  10. Snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS).

    PubMed

    Gao, Liang; Smith, R Theodore; Tkaczyk, Tomasz S

    2012-01-01

    We present a snapshot hyperspectral retinal camera with the Image Mapping Spectrometer (IMS) for eye imaging applications. The resulting system is capable of simultaneously acquiring 48 spectral channel images in the range 470 nm-650 nm with frame rate at 5.2 fps. The spatial sampling of each measured spectral scene is 350 × 350 pixels. The advantages of this snapshot device are elimination of the eye motion artifacts and pixel misregistration problems in traditional scanning-based hyperspectral retinal cameras, and real-time imaging of oxygen saturation dynamics with sub-second temporal resolution. The spectral imaging performance is demonstrated in a human retinal imaging experiment in vivo. The absorption spectral signatures of oxy-hemoglobin and macular pigments were successfully acquired by using this device. PMID:22254167

  11. CASI/SASI airborne hyperspectral remote sensing anomaly extraction of metallogenic prediction research in Gansu Beishan South Beach area

    NASA Astrophysics Data System (ADS)

    Che, Yongfei; Zhao, Yingjun

    2014-11-01

    Hyperspectral remote sensing has one of the technical advantages atlas. The known deposits of Gansu Beishan South Beach deposits as the study area, based on the theory of wall rock alteration, using airborne hyperspectral remote sensing data (CASI/SASI), extracted mineralization alteration information and analysis. Based on airborne hyperspectral remote sensing mineral mapping results in the study area, Combining analysising of possible mineral formation fluid properties, spatial distribution characteristics and time evolution with analysising of mineral formation environment (lithology and tectonic environment), construction of the South Beach gold deposit location model, the deposit location model as a guide, comprehensive analysis of mineralization geological background and surface geochemical data, delineated mineralization favorable areas. The field investigation showed that signs of altered development of strong in the delineation of the mineralization favorable areas and metallogenic potential of better, is worth paying attention to the prospecting target area. Further explanation that the hyperspectral remote sensing can provide accurate and reliable information for the prospecting, and is worthy of further mining the ore prospecting potential.

  12. Portable hyperspectral imager for assessment of skin disorders: preliminary measurements

    NASA Astrophysics Data System (ADS)

    Beach, James M.; Lanoue, Mark A.; Brabham, Kori; Khoobehi, Bahram

    2005-04-01

    Oxygenation of the facial skin was evaluated in rosacea using a hyperspectral camera. A portable imaging system utilizing crossed-polarization optics for illumination and recording is described. Relative oxygen saturation was determined from rosacea features and compared with normal skin. Saturation maps and light absorption spectra showed a significant increase in the oxygen saturation of the blood in rosacea-affected skin.

  13. Recent Advances in Techniques for Hyperspectral Image Processing

    NASA Technical Reports Server (NTRS)

    Plaza, Antonio; Benediktsson, Jon Atli; Boardman, Joseph W.; Brazile, Jason; Bruzzone, Lorenzo; Camps-Valls, Gustavo; Chanussot, Jocelyn; Fauvel, Mathieu; Gamba, Paolo; Gualtieri, Anthony; Marconcini, Mattia; Tilton, James C.; Trianni, Giovanna

    2009-01-01

    Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than 30 years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the highdimensional nature of the data, and to integrate the spatial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific applications, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms

  14. Hyperspectral image representation and processing with binary partition trees.

    PubMed

    Valero, Silvia; Salembier, Philippe; Chanussot, Jocelyn

    2013-04-01

    The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the BPT succeeds in presenting: 1) the decomposition of the image in terms of coherent regions, and 2) the inclusion relations of the regions in the scene. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.

  15. Visible Hyperspectral Imaging for Standoff Detection of Explosives on Surfaces

    SciTech Connect

    Bernacki, Bruce E.; Blake, Thomas A.; Mendoza, Albert; Johnson, Timothy J.

    2010-11-01

    There is an ever-increasing need to be able to detect the presence of explosives, preferably from standoff distances. This paper presents an application of visible hyperspectral imaging using anomaly, polarization and spectral identification approaches for the standoff detection (13 meters) of nitroaromatic explosives on realistic painted surfaces based upon the colorimetric differences between tetryl and TNT which are enhanced by solar irradiation.

  16. Detection of lettuce discoloration using hyperspectral reflectance imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to classify the discoloration of lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectra...

  17. Emissivity retrieval from indoor hyperspectral imaging of mineral grains

    NASA Astrophysics Data System (ADS)

    Yousefi, Bardia; Sojasi, Saeed; Ibarra Castanedo, Clemente; Beaudoin, Georges; Huot, François; Maldague, Xavier P. V.; Chamberland, Martin; Lalonde, Erik

    2016-05-01

    The proposed approach addresses the problem of retrieving the emissivity of hyperspectral data in the spectroscopic imageries from indoor experiments. This methodology was tested on experimental data that have been recorded with hyperspectral images working in visible/near infrared and long-wave infrared bands. The proposed technique provides a framework for computing down-welling spectral radiance applying non-negative matrix factorization (NMF) analysis. It provides the necessary means for the non-uniform correction of active thermographical experiments. The obtained results indicate promising accuracy. In addition, the application of the proposed technique is not limited to non-uniform heating spectroscopy but to uniform spectroscopy as well.

  18. Methodology for hyperspectral image classification using novel neural network

    SciTech Connect

    Subramanian, S., Gat, N., Sheffield, M.,; Barhen, J.; Toomarian, N.

    1997-04-01

    A novel feed forward neural network is used to classify hyperspectral data from the AVIRIS sector. The network applies an alternating direction singular value decomposition technique to achieve rapid training times (few seconds per class). Very few samples (10-12) are required for training. 100% accurate classification is obtained using test data sets. The methodology combines this rapid training neural network together with data reduction and maximal feature separation techniques such as principal component analysis and simultaneous diagonalization of covariance matrices, for rapid and accurate classification of large hyperspectral images. The results are compared to those of standard statistical classifiers. 21 refs., 3 figs., 5 tabs.

  19. Embedded GPU implementation of anomaly detection for hyperspectral images

    NASA Astrophysics Data System (ADS)

    Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Yang, Bin; Chen, Zhengchao

    2015-10-01

    Anomaly detection is one of the most important techniques for remotely sensed hyperspectral data interpretation. Developing fast processing techniques for anomaly detection has received considerable attention in recent years, especially in analysis scenarios with real-time constraints. In this paper, we develop an embedded graphics processing units based parallel computation for streaming background statistics anomaly detection algorithm. The streaming background statistics method can simulate real-time anomaly detection, which refer to that the processing can be performed at the same time as the data are collected. The algorithm is implemented on NVIDIA Jetson TK1 development kit. The experiment, conducted with real hyperspectral data, indicate the effectiveness of the proposed implementations. This work shows the embedded GPU gives a promising solution for high-performance with low power consumption hyperspectral image applications.

  20. Hyperspectral imaging in medicine: image pre-processing problems and solutions in Matlab.

    PubMed

    Koprowski, Robert

    2015-11-01

    The paper presents problems and solutions related to hyperspectral image pre-processing. New methods of preliminary image analysis are proposed. The paper shows problems occurring in Matlab when trying to analyse this type of images. Moreover, new methods are discussed which provide the source code in Matlab that can be used in practice without any licensing restrictions. The proposed application and sample result of hyperspectral image analysis.

  1. Hyperspectral imaging in medicine: image pre-processing problems and solutions in Matlab.

    PubMed

    Koprowski, Robert

    2015-11-01

    The paper presents problems and solutions related to hyperspectral image pre-processing. New methods of preliminary image analysis are proposed. The paper shows problems occurring in Matlab when trying to analyse this type of images. Moreover, new methods are discussed which provide the source code in Matlab that can be used in practice without any licensing restrictions. The proposed application and sample result of hyperspectral image analysis. PMID:25676816

  2. [Design of hyperspectral imaging system based on LCTF].

    PubMed

    Zhang, Dong-ying; Hong, Jin; Tang, Wei-ping; Yang, Wei-feng; Luo, Jun; Qiao, Yan-li; Zhang, Xie

    2008-10-01

    A new compact lightweight imaging system for hyperspectral imaging is described. The system can be thought of as the substitute for traditional mechanical filter-wheel sensor. The system is based on different techniques. It uses an electronic controlled LCTF(liquid crystal tunable filter) which provided rapid and vibrationless selection of any wavelength in the visible to IR range. The imaging system consisted of an optic lens, a CRI VariSpec LCTF and a Dalsa 1M30 camera. First the outline of this system setup is presented, then the optics designed is introduced, next the working principle of LCTF is described in details. A field experiment with the imaging system loaded on an airship was carried out and collected hyperspectral solid image. The images obtained had higher spectral and spatial resolution. Some parts of the 540-600 nm components of the 16-band image cube were also shown. Finally, the data acquired were rough processed to get reflection spectrum(from 420 to 720 nm) of three targets. It is concluded that the experiment has proved that the imaging system is effective in obtaining hyperspectral data. The image captured by the system can be applied to spectral estimation, spectra based classification and spectral based analysis. PMID:19123429

  3. Extracting Roof Parameters and Heat Bridges Over the City of Oldenburg from Hyperspectral, Thermal, and Airborne Laser Scanning Data

    NASA Astrophysics Data System (ADS)

    Bannehr, L.; Luhmann, Th.; Piechel, J.; Roelfs, T.; Schmidt, An.

    2011-09-01

    Remote sensing methods are used to obtain different kinds of information about the state of the environment. Within the cooperative research project HiReSens, funded by the German BMBF, a hyperspectral scanner, an airborne laser scanner, a thermal camera, and a RGB-camera are employed on a small aircraft to determine roof material parameters and heat bridges of house tops over the city Oldenburg, Lower Saxony. HiReSens aims to combine various geometrical highly resolved data in order to achieve relevant evidence about the state of the city buildings. Thermal data are used to obtain the energy distribution of single buildings. The use of hyperspectral data yields information about material consistence of roofs. From airborne laser scanning data (ALS) digital surface models are inferred. They build the basis to locate the best orientations for solar panels of the city buildings. The combination of the different data sets offers the opportunity to capitalize synergies between differently working systems. Central goals are the development of tools for the collection of heat bridges by means of thermal data, spectral collection of roofs parameters on basis of hyperspectral data as well as 3D-capture of buildings from airborne lasers scanner data. Collecting, analyzing and merging of the data are not trivial especially not when the resolution and accuracy is aimed in the domain of a few decimetre. The results achieved need to be regarded as preliminary. Further investigations are still required to prove the accuracy in detail.

  4. Automatic Denoising and Unmixing in Hyperspectral Image Processing

    NASA Astrophysics Data System (ADS)

    Peng, Honghong

    This thesis addresses two important aspects in hyperspectral image processing: automatic hyperspectral image denoising and unmixing. The first part of this thesis is devoted to a novel automatic optimized vector bilateral filter denoising algorithm, while the remainder concerns nonnegative matrix factorization with deterministic annealing for unsupervised unmixing in remote sensing hyperspectral images. The need for automatic hyperspectral image processing has been promoted by the development of potent hyperspectral systems, with hundreds of narrow contiguous bands, spanning the visible to the long wave infrared range of the electromagnetic spectrum. Due to the large volume of raw data generated by such sensors, automatic processing in the hyperspectral images processing chain is preferred to minimize human workload and achieve optimal result. Two of the mostly researched processing for such automatic effort are: hyperspectral image denoising, which is an important preprocessing step for almost all remote sensing tasks, and unsupervised unmixing, which decomposes the pixel spectra into a collection of endmember spectral signatures and their corresponding abundance fractions. Two new methodologies are introduced in this thesis to tackle the automatic processing problems described above. Vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios. Typical vector bilateral filtering usage does not employ parameters that have been determined to satisfy optimality criteria. This thesis also introduces an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means. The approach is based on posing the filtering problem as one of nonlinear estimation and minimizing the Stein

  5. Postfire soil burn severity mapping with hyperspectral image unmixing

    USGS Publications Warehouse

    Robichaud, P.R.; Lewis, S.A.; Laes, D.Y.M.; Hudak, A.T.; Kokaly, R.F.; Zamudio, J.A.

    2007-01-01

    Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r2 = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r2 = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes. ?? 2006 Elsevier Inc. All rights reserved.

  6. Identification of inflammation sites in arthritic joints using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Paluchowski, Lukasz A.; Milanic, Matija; Bjorgan, Asgeir; Grandaunet, Berit; Dhainaut, Alvilde; Hoff, Mari; Randeberg, Lise L.

    2014-03-01

    Inflammatory arthritic diseases have prevalence between 2 and 3% and may lead to joint destruction and deformation resulting in a loss of function. Patient's quality of life is often severely affected as the disease attacks hands and finger joints. Pathology involved in arthritis includes angiogenesis, hyper-vascularization, hyper-metabolism and relative hypoxia. We have employed hyperspectral imaging to study the hemodynamics of affected- and non-affected joints and tissue. Two hyperspectral, push-broom cameras were used (VNIR-1600, SWIR-320i, Norsk Elektro Optikk AS, Norway). Optical spectra (400nm - 1700nm) of high spectral resolution were collected from 15 patients with visible symptoms of arthritic rheumatic diseases in at least one joint. The control group consisted of 10 healthy individuals. Concentrations of dominant chromophores were calculated based on analytical calculations of light transport in tissue. Image processing was used to analyze hyperspectral data and retrieve information, e.g. blood concentration and tissue oxygenation maps. The obtained results indicate that hyperspectral imaging can be used to quantify changes within affected joints and surrounding tissue. Further improvement of this method will have positive impact on diagnosis of arthritic joints at an early stage. Moreover it will enable development of fast, noninvasive and noncontact diagnostic tool of arthritic joints

  7. Hyperspectral image classification by collaboration of spatial and spectral information

    NASA Astrophysics Data System (ADS)

    Yan, Yu-zhou; Zhao, Yongqiang; Xue, Hui-feng; Kou, Xiao-dong; Liu, Yuanzheng

    2009-07-01

    The classification of hyperspectral image data has drawn much attention in recent years. Consequently, it contains not only spectral information of objects, but also spatial arrangement of objects. The most established Hyperspectral classifiers are based on the observed spectral signal, and ignore the spatial relations among observations. Information captured in neighboring locations may provide useful supplementary knowledge for analysis. To combine the spectral and spatial information in the classification process, in this paper, a Multidimensional Local Spatial Autocorrelation (MLSA) is proposed for hyperspectral image data. Based on this measure, a collaborative classification method is proposed, which integrates the spectral and spatial autocorrelation during the decision-making process. The trials of our experiment are conducted on two scenes, one from HYDICE 210-band imagery collected over an area that contains a diverse range of terrain features and the other is toy car hyperspectral image captured at Instrumentation and Sensing Laboratory (ISL) at Beltsville Agricultural Research Center. Quantitative measures of local consistency (smoothness) and global labeling, along with class maps, demonstrate the benefits of applying this method for unsupervised and supervised classification.

  8. Passive shortwave infrared technology and hyperspectral imaging for maritime applications

    NASA Astrophysics Data System (ADS)

    Judd, K. Peter; Waterman, James R.; Nichols, J. M.

    2010-04-01

    We present image data and discuss naval sensing applications of SWIR and Hyperspectral SWIR imaging in littoral and marine environments under various light conditions. These environments prove to be challenging for persistent surveillance applications as light levels may vary over several orders of magnitude within and from scene to scene. Additional difficulties include imaging over long water paths where marine haze and turbulence tend to degrade radiation transmission, and discrimination of low contrast objects under low-light and night imaging. Image data obtained from two separate passive sensor systems, both of which are built around an RVS large format (1280 x 1024) InGaAs FPA with high dynamic range and low noise electronics, are presented. The SWIR camera imager is equipped with a custom 300 mm focal length f/2 narrow field-of-view (6° diagonal) refractive telescope. The Hyperspectral imager has a custom selectable 900/1800 mm focal length telescope with corresponding 1.55°/0.79° field-of-view and fnumbers of 3/6 respectively. The sensor uses 1280 pixels in the spatial direction and a window of 192 are used for the spectral and operates at a nominal frame rate of 120 Hz. To assess field performance of the SWIR/Hyperspectral imagers, comparison is made to output from a scientific grade VNIR camera and two state-of-the-art low-light sensors.

  9. Detecting citrus canker by hyperspectral reflectance imaging and PCA-based image classification method

    NASA Astrophysics Data System (ADS)

    Qin, Jianwei; Burks, Thomas F.; Kim, Moon S.; Chao, Kuanglin; Ritenour, Mark A.

    2008-04-01

    Citrus canker is one of the most devastating diseases that threaten citrus crops. Technologies that can efficiently identify citrus canker would assure fruit quality and safety and enhance the competitiveness and profitability of the citrus industry. This research was aimed to investigate the potential of using hyperspectral imaging technique for detecting canker lesions on citrus fruit. A portable hyperspectral imaging system consisting of an automatic sample handling unit, a light source, and a hyperspectral imaging unit was developed for citrus canker detection. The imaging system was used to acquire reflectance images from citrus samples in the wavelength range between 400 nm and 900 nm. Ruby Red grapefruits with normal and various diseased skin conditions including canker, copper burn, greasy spot, wind scar, cake melanose, and specular melanose were tested. Hyperspectral reflectance images were analyzed using principal component analysis (PCA) to compress the 3-D hyperspectral image data and extract useful image features that could be used to discriminate cankerous samples from normal and other diseased samples. Image processing and classification algorithms were developed based upon the transformed images of PCA. The overall accuracy for canker detection was 92.7%. This research demonstrated that hyperspectral imaging technique could be used for discriminating citrus canker from other confounding diseases.

  10. Analysis of hyperspectral scattering images using a moment method for apple firmness prediction

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This article reports on using a moment method to extract features from the hyperspectral scattering profiles for apple fruit firmness prediction. Hyperspectral scattering images between 500 nm and 1000 nm were acquired online, using a hyperspectral scattering system, for ‘Golden Delicious’, ’Jonagol...

  11. High spatial resolution image restoration from subpixel-shifted hyperspectral images

    NASA Astrophysics Data System (ADS)

    Su, Lijuan; Zhou, Shubo; Yuan, Yan

    2015-01-01

    The spatial resolution of hyperspectral imaging systems is constrained by a spatial-spectral resolution tradeoff and current technique limitations. However, spatial resolution is a critical feature for applications that require high spatial resolution and utilization of details. We present a method of restoring high-resolution (HR) images from a set of low-resolution (LR) hyperspectral data cubes with subpixel shifts across different bands. A new observation model is introduced to demonstrate LR hyperspectral images at different bands and an HR image that covers all these bands. A regularized super-resolution (SR) algorithm is then implemented to solve the problem. Experiments of the proposed algorithm and existing SR algorithms are performed and the results are evaluated. The results demonstrate the feasibility of the proposed SR method. Moreover, the image fusion results also demonstrate that the restored image is suitable for enhancing the spatial resolution of entire hyperspectral data cubes.

  12. Hyperspectral scanning white light interferometry based on compressive imaging

    NASA Astrophysics Data System (ADS)

    Azari, Mohammad; Habibi, Nasim; Abolbashari, Mehrdad; Farahi, Faramarz

    2016-02-01

    We have developed a compressive hyperspectral imaging system that is based on single-pixel camera architecture. We have incorporated the developed system in a scanning white-light interferometer (SWLI) and showed that replacing SWLI's CCD-based camera by the compressive hyperspectral imaging system, we have access to high-resolution multispectral images of interferometer's fringes. Using these multi-spectral images, the system is capable of simultaneous spectroscopy of the surface, which can be used, for example, to eliminate the effect of surface contamination and providing new spectral information for fringe signal analysis which could be used to reduce the need for vertical scan, therefore making height measurement more tolerant to object's position.

  13. [Molecular hyperspectral imaging (MHSI) system and application in biochemical medicine].

    PubMed

    Liu, Hong-Ying; Li, Qing-Li; Wang, Yi-Ting; Liu, Jin-Gao; Xue, Yong-Qi

    2011-10-01

    A novel molecular hyperspectral imaging (MHSI) system based on AOTF (acousto-optic tunable filters) was presented. The system consists of microscope, AOTF-based spectrometer, matrix CCD, image collection card and computer. The spectral range of the MHSI is from 550 to 1 000 nm. The spectral resolution is less than 2 nm, and the spatial resolution is about 0.3 microm. This paper has also presented that spectral curves extracted from the corrected hyperspectral data of the sample, which have been preprocessed by the gray correction coefficient, can more truly represent biochemical characteristic of the sample. The system can supply not only single band images in the visible range, but also spectrum curve of random pixel of sample image. This system can be widely used in various fields of biomedicine, clinical medicine, material science and microelectronics. PMID:22250515

  14. Towards real-time medical diagnostics using hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

    Bjorgan, Asgeir; Randeberg, Lise L.

    2015-07-01

    Hyperspectral imaging provides non-contact, high resolution spectral images which has a substantial diagnostic potential. This can be used for e.g. diagnosis and early detection of arthritis in finger joints. Processing speed is currently a limitation for clinical use of the technique. A real-time system for analysis and visualization using GPU processing and threaded CPU processing is presented. Images showing blood oxygenation, blood volume fraction and vessel enhanced images are among the data calculated in real-time. This study shows the potential of real-time processing in this context. A combination of the processing modules will be used in detection of arthritic finger joints from hyperspectral reflectance and transmittance data.

  15. A hyperspectral image analysis workbench for environmental science applications

    SciTech Connect

    Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.; Slater, J.C.

    1992-10-01

    A significant challenge to the information sciences is to provide more powerful and accessible means to exploit the enormous wealth of data available from high-resolution imaging spectrometry, or ``hyperspectral`` imagery, for analysis, for mapping purposes, and for input to environmental modeling applications. As an initial response to this challenge, Argonne`s Advanced Computer Applications Center has developed a workstation-based prototype software workbench which employs Al techniques and other advanced approaches to deduce surface characteristics and extract features from the hyperspectral images. Among its current capabilities, the prototype system can classify pixels by abstract surface type. The classification process employs neural network analysis of inputs which include pixel spectra and a variety of processed image metrics, including image ``texture spectra`` derived from fractal signatures computed for subimage tiles at each wavelength.

  16. A hyperspectral image analysis workbench for environmental science applications

    SciTech Connect

    Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.; Slater, J.C.

    1992-01-01

    A significant challenge to the information sciences is to provide more powerful and accessible means to exploit the enormous wealth of data available from high-resolution imaging spectrometry, or hyperspectral'' imagery, for analysis, for mapping purposes, and for input to environmental modeling applications. As an initial response to this challenge, Argonne's Advanced Computer Applications Center has developed a workstation-based prototype software workbench which employs Al techniques and other advanced approaches to deduce surface characteristics and extract features from the hyperspectral images. Among its current capabilities, the prototype system can classify pixels by abstract surface type. The classification process employs neural network analysis of inputs which include pixel spectra and a variety of processed image metrics, including image texture spectra'' derived from fractal signatures computed for subimage tiles at each wavelength.

  17. Target detection in hyperspectral Imaging using logistic regression

    NASA Astrophysics Data System (ADS)

    Lo, Edisanter; Ientilucci, Emmett

    2016-05-01

    Target detection is an important application in hyperspectral imaging. Conventional algorithms for target detection assume that the pixels have a multivariate normal distribution. The pixels in most images do not have multivariate normal distributions. The logistic regression model, which does not require the assumption of multivariate normal distribution, is proposed in this paper as a target detection algorithm. Experimental results show that the logistic regression model can work well in target detection.

  18. Hyperspectral image visualization using t-distributed stochastic neighbor embedding

    NASA Astrophysics Data System (ADS)

    Zhang, Biyin; Yu, Xin

    2015-12-01

    Hyperspectral image visualization reduces high-dimensional spectral bands to three color channels, which are sought in order to explain well the nonlinear data characteristics that are hidden in the high-dimensional spectral bands. Despite the surge in the linear visualization techniques, the development of nonlinear visualization has been limited. The paper presents a new technique for visualization of hyperspectral image using t-distributed stochastic neighbor embedding, called VHI-tSNE, which learns a nonlinear mapping between the high-dimensional spectral space and the three-dimensional color space. VHI-tSNE transforms hyperspectral data into bilateral probability similarities, and employs a heavy-tailed distribution in three-dimensional color space to alleviate the crowding problem and optimization problem in SNE technique. We evaluate the performance of VHI-tSNE in experiments on several hyperspectral imageries, in which we compare it to the performance of other state-of-art techniques. The results of experiments demonstrated the strength of the proposed technique.

  19. Study on the Methods of Detecting Cucumber Downy Mildew Using Hyperspectral Imaging Technology

    NASA Astrophysics Data System (ADS)

    Tian, Youwen; Zhang, Lin

    Hyperspectral imaging technology, which can integrate the advantages of spectral detection and image detection, meets the need of detecting the cucumber diseases fast and nondestructively. In this paper, hyperspectral imaging technology is adopted to detect the cucumber downy mildew fast and nondestructively. Firstly, hyperspectral images of cucumber leaves infected downy mildew are acquired by the hyperspectral image acquisition system. And optimum wavelengths are collected by the principal component analysis to get the featured images. Then the image fusion technology is adopted to combine collected images with the featured images to form new images by pixel-level image fusion. Finally, the methods of the image enhancement, binarization, corrosion and dilatation treatments are carried out, so the cucumber downy mildew is detected. The result shows that the accuracy rate of the algorithm for detecting cucumber disease can reach nearly 90%. Studies have shown that hyperspectral imaging technology can be used to detect cucumber downy mildew.

  20. Hyperspectral laser-induced autofluorescence imaging of dental caries

    NASA Astrophysics Data System (ADS)

    Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

    2012-01-01

    Dental caries is a disease characterized by demineralization of enamel crystals leading to the penetration of bacteria into the dentine and pulp. Early detection of enamel demineralization resulting in increased enamel porosity, commonly known as white spots, is a difficult diagnostic task. Laser induced autofluorescence was shown to be a useful method for early detection of demineralization. The existing studies involved either a single point spectroscopic measurements or imaging at a single spectral band. In the case of spectroscopic measurements, very little or no spatial information is acquired and the measured autofluorescence signal strongly depends on the position and orientation of the probe. On the other hand, single-band spectral imaging can be substantially affected by local spectral artefacts. Such effects can significantly interfere with automated methods for detection of early caries lesions. In contrast, hyperspectral imaging effectively combines the spatial information of imaging methods with the spectral information of spectroscopic methods providing excellent basis for development of robust and reliable algorithms for automated classification and analysis of hard dental tissues. In this paper, we employ 405 nm laser excitation of natural caries lesions. The fluorescence signal is acquired by a state-of-the-art hyperspectral imaging system consisting of a high-resolution acousto-optic tunable filter (AOTF) and a highly sensitive Scientific CMOS camera in the spectral range from 550 nm to 800 nm. The results are compared to the contrast obtained by near-infrared hyperspectral imaging technique employed in the existing studies on early detection of dental caries.

  1. Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

    PubMed

    Mo, Changyeun; Kim, Giyoung; Lim, Jongguk; Kim, Moon S; Cho, Hyunjeong; Cho, Byoung-Kwan

    2015-01-01

    Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400-1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557-701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce. PMID:26610510

  2. Hyperspectral image-based methods for spectral diversity

    NASA Astrophysics Data System (ADS)

    Sotomayor, Alejandro; Medina, Ollantay; Chinea, J. D.; Manian, Vidya

    2015-05-01

    Hyperspectral images are an important tool to assess ecosystem biodiversity. To obtain more precise analysis of biodiversity indicators that agree with indicators obtained using field data, analysis of spectral diversity calculated from images have to be validated with field based diversity estimates. The plant species richness is one of the most important indicators of biodiversity. This indicator can be measured in hyperspectral images considering the Spectral Variation Hypothesis (SVH) which states that the spectral heterogeneity is related to spatial heterogeneity and thus to species richness. The goal of this research is to capture spectral heterogeneity from hyperspectral images for a terrestrial neo tropical forest site using Vector Quantization (VQ) method and then use the result for prediction of plant species richness. The results are compared with that of Hierarchical Agglomerative Clustering (HAC). The validation of the process index is done calculating the Pearson correlation coefficient between the Shannon entropy from actual field data and the Shannon entropy computed in the images. One of the advantages of developing more accurate analysis tools would be the extension of the analysis to larger zones. Multispectral image with a lower spatial resolution has been evaluated as a prospective tool for spectral diversity.

  3. Hyperspectral imaging of bruises in the SWIR spectral region

    NASA Astrophysics Data System (ADS)

    Randeberg, Lise L.; Hernandez-Palacios, Julio

    2012-02-01

    Optical diagnostics of bruised skin might provide important information for characterization and age determination of such injuries. Hyperspectral imaging is one of the optical techniques that have been employed for bruise characterization. This technique combines high spatial and spectral resolution and makes it possible to study both chromophore signatures and -distributions in an injury. Imaging and spectroscopy in the visible spectral range have resulted in increased knowledge about skin bruises. So far the SWIR region has not been explored for this application. The main objective of the current study was to characterize bruises in the SWIR wavelength range. Hyperspectral images in the SWIR (950-2500nm ) and VNIR (400-850nm) spectral range were collected from 3 adult volunteers with bruises of known age. Data were collected over a period of 8 days. The data were analyzed using spectroscopic techniques and statistical image analysis. Preliminary results from the pilot study indicate that SWIR hyperspectral imaging might be an important supplement to imaging in the visible part of the spectrum. The technique emphasizes local edema and gives a possibility to visualize features that cannot easily be seen in the visible part of the spectrum.

  4. Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging

    PubMed Central

    Mo, Changyeun; Kim, Giyoung; Lim, Jongguk; Kim, Moon S.; Cho, Hyunjeong; Cho, Byoung-Kwan

    2015-01-01

    Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400–1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557–701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce. PMID:26610510

  5. Airborne Laser Scanning and Image Processing Techniques for Archaeological Prospection

    NASA Astrophysics Data System (ADS)

    Faltýnová, M.; Nový, P.

    2014-06-01

    Aerial photography was, for decades, an invaluable tool for archaeological prospection, in spite of the limitation of this method to deforested areas. The airborne laser scanning (ALS) method can be nowadays used to map complex areas and suitable complement earlier findings. This article describes visualization and image processing methods that can be applied on digital terrain models (DTMs) to highlight objects hidden in the landscape. Thanks to the analysis of visualized DTM it is possible to understand the landscape evolution including the differentiation between natural processes and human interventions. Different visualization methods were applied on a case study area. A system of parallel tracks hidden in a forest and its surroundings - part of old route called "Devil's Furrow" near the town of Sázava was chosen. The whole area around well known part of Devil's Furrow has not been prospected systematically yet. The data from the airborne laser scanning acquired by the Czech Office for Surveying, Mapping and Cadastre was used. The average density of the point cloud was approximately 1 point/m2 The goal of the project was to visualize the utmost smallest terrain discontinuities, e.g. tracks and erosion furrows, which some were not wholly preserved. Generally we were interested in objects that are clearly not visible in DTMs displayed in the form of shaded relief. Some of the typical visualization methods were tested (shaded relief, aspect and slope image). To get better results we applied image-processing methods that were successfully used on aerial photographs or hyperspectral images in the past. The usage of different visualization techniques on one site allowed us to verify the natural character of the southern part of Devil's Furrow and find formations up to now hidden in the forests.

  6. Lesion detection in magnetic resonance brain images by hyperspectral imaging algorithms

    NASA Astrophysics Data System (ADS)

    Xue, Bai; Wang, Lin; Li, Hsiao-Chi; Chen, Hsian Min; Chang, Chein-I.

    2016-05-01

    Magnetic Resonance (MR) images can be considered as multispectral images so that MR imaging can be processed by multispectral imaging techniques such as maximum likelihood classification. Unfortunately, most multispectral imaging techniques are not particularly designed for target detection. On the other hand, hyperspectral imaging is primarily developed to address subpixel detection, mixed pixel classification for which multispectral imaging is generally not effective. This paper takes advantages of hyperspectral imaging techniques to develop target detection algorithms to find lesions in MR brain images. Since MR images are collected by only three image sequences, T1, T2 and PD, if a hyperspectral imaging technique is used to process MR images it suffers from the issue of insufficient dimensionality. To address this issue, two approaches to nonlinear dimensionality expansion are proposed, nonlinear correlation expansion and nonlinear band ratio expansion. Once dimensionality is expanded hyperspectral imaging algorithms are readily applied. The hyperspectral detection algorithm to be investigated for lesion detection in MR brain is the well-known subpixel target detection algorithm, called Constrained Energy Minimization (CEM). In order to demonstrate the effectiveness of proposed CEM in lesion detection, synthetic images provided by BrainWeb are used for experiments.

  7. Hyperspectral Image Turbulence Measurements of the Atmosphere

    NASA Technical Reports Server (NTRS)

    Lane, Sarah E.; West, Leanne L.; Gimmestad, Gary G.; Kireev, Stanislav; Smith, William L., Sr.; Burdette, Edward M.; Daniels, Taumi; Cornman, Larry

    2012-01-01

    A Forward Looking Interferometer (FLI) sensor has the potential to be used as a means of detecting aviation hazards in flight. One of these hazards is mountain wave turbulence. The results from a data acquisition activity at the University of Colorado s Mountain Research Station will be presented here. Hyperspectral datacubes from a Telops Hyper-Cam are being studied to determine if evidence of a turbulent event can be identified in the data. These data are then being compared with D&P TurboFT data, which are collected at a much higher time resolution and broader spectrum.

  8. MITAS: multisensor imaging technology for airborne surveillance

    NASA Astrophysics Data System (ADS)

    Thomas, John D.

    1991-08-01

    MITAS, a unique and low-cost solution to the problem of collecting and processing multisensor imaging data for airborne surveillance operations has been developed, MITAS results from integrating the established and proven real-time video processing, target tracking, and sensor management software of TAU with commercially available image exploitation and map processing software. The MITAS image analysis station (IAS) supports airborne day/night reconnaissance and surveillance missions involving low-altitude collection platforms employing a suite of sensors to perform reconnaissance functions against a variety of ground and sea targets. The system will detect, locate, and recognize threats likely to be encountered in support of counternarcotic operations and in low-intensity conflict areas. The IAS is capable of autonomous, near real-time target exploitation and has the appropriate communication links to remotely located IAS systems for more extended analysis of sensor data. The IAS supports the collection, fusion, and processing of three main imaging sensors: daylight imagery (DIS), forward looking infrared (FLIR), and infrared line scan (IRLS). The MITAS IAS provides support to all aspects of the airborne surveillance mission, including sensor control, real-time image enhancement, automatic target tracking, sensor fusion, freeze-frame capture, image exploitation, target data-base management, map processing, remote image transmission, and report generation.

  9. Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

    NASA Astrophysics Data System (ADS)

    Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.

    2014-03-01

    This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to hyperspectral imaging.

  10. Airborne test results for smart pushbroom imaging system with optoelectronic image correction

    NASA Astrophysics Data System (ADS)

    Tchernykh, Valerij; Dyblenko, Serguei; Janschek, Klaus; Seifart, Klaus; Harnisch, Bernd

    2004-02-01

    Smart pushbroom imaging system (SMARTSCAN) solves the problem of image correction for satellite pushbroom cameras which are disturbed by satellite attitude instability effects. Satellite cameras with linear sensors are particularly sensitive to attitude errors, which cause considerable image distortions. A novel solution of distortions correction is presented, which is based on the real-time recording of the image motion in the focal plane of the satellite camera. This allows using such smart pushbroom cameras (multi-/hyperspectral) even on moderately stabilised satellites, e.g. small sat's, LEO comsat's. The SMARTSCAN concept uses in-situ measurements of the image motion with additional CCD-sensors in the focal plane and real-time image processing of these measurements by an onboard Joint Transform Optical Correlator. SMARTSCAN has been successfully demonstrated with breadboard models for the Optical Correlator and a Smart Pushbroom Camera at laboratory level (satellite motion simulator on base of a 5 DOF industrial robot) and by an airborne flight demonstration in July 2002. The paper describes briefly the principle of operation of the system and gives a description of the hardware model are provided. Detailed results of the airborne tests and performance analysis are given as well as detailed tests description.

  11. Hyperspectral imaging for detection of cholesterol in human skin

    NASA Astrophysics Data System (ADS)

    Milanič, Matija; Bjorgan, Asgeir; Larsson, Marcus; Marraccini, Paolo; Strömberg, Tomas; Randeberg, Lise L.

    2015-03-01

    Hypercholesterolemia is characterized by high levels of cholesterol in the blood and is associated with an increased risk of atherosclerosis and coronary heart disease. Early detection of hypercholesterolemia is necessary to prevent onset and progress of cardiovascular disease. Optical imaging techniques might have a potential for early diagnosis and monitoring of hypercholesterolemia. In this study, hyperspectral imaging was investigated for this application. The main aim of the study was to identify spectral and spatial characteristics that can aid identification of hypercholesterolemia in facial skin. The first part of the study involved a numerical simulation of human skin affected by hypercholesterolemia. A literature survey was performed to identify characteristic morphological and physiological parameters. Realistic models were prepared and Monte Carlo simulations were performed to obtain hyperspectral images. Based on the simulations optimal wavelength regions for differentiation between normal and cholesterol rich skin were identified. Minimum Noise Fraction transformation (MNF) was used for analysis. In the second part of the study, the simulations were verified by a clinical study involving volunteers with elevated and normal levels of cholesterol. The faces of the volunteers were scanned by a hyperspectral camera covering the spectral range between 400 nm and 720 nm, and characteristic spectral features of the affected skin were identified. Processing of the images was done after conversion to reflectance and masking of the images. The identified features were compared to the known cholesterol levels of the subjects. The results of this study demonstrate that hyperspectral imaging of facial skin can be a promising, rapid modality for detection of hypercholesterolemia.

  12. Detection of Tephra Layers in Antarctic Sediment Cores with Hyperspectral Imaging

    PubMed Central

    Aymerich, Ismael F.; Oliva, Marc; Giralt, Santiago; Martín-Herrero, Julio

    2016-01-01

    Tephrochronology uses recognizable volcanic ash layers (from airborne pyroclastic deposits, or tephras) in geological strata to set unique time references for paleoenvironmental events across wide geographic areas. This involves the detection of tephra layers which sometimes are not evident to the naked eye, including the so-called cryptotephras. Tests that are expensive, time-consuming, and/or destructive are often required. Destructive testing for tephra layers of cores from difficult regions, such as Antarctica, which are useful sources of other kinds of information beyond tephras, is always undesirable. Here we propose hyperspectral imaging of cores, Self-Organizing Map (SOM) clustering of the preprocessed spectral signatures, and spatial analysis of the classified images as a convenient, fast, non-destructive method for tephra detection. We test the method in five sediment cores from three Antarctic lakes, and show its potential for detection of tephras and cryptotephras. PMID:26815202

  13. Detection of Tephra Layers in Antarctic Sediment Cores with Hyperspectral Imaging.

    PubMed

    Aymerich, Ismael F; Oliva, Marc; Giralt, Santiago; Martín-Herrero, Julio

    2016-01-01

    Tephrochronology uses recognizable volcanic ash layers (from airborne pyroclastic deposits, or tephras) in geological strata to set unique time references for paleoenvironmental events across wide geographic areas. This involves the detection of tephra layers which sometimes are not evident to the naked eye, including the so-called cryptotephras. Tests that are expensive, time-consuming, and/or destructive are often required. Destructive testing for tephra layers of cores from difficult regions, such as Antarctica, which are useful sources of other kinds of information beyond tephras, is always undesirable. Here we propose hyperspectral imaging of cores, Self-Organizing Map (SOM) clustering of the preprocessed spectral signatures, and spatial analysis of the classified images as a convenient, fast, non-destructive method for tephra detection. We test the method in five sediment cores from three Antarctic lakes, and show its potential for detection of tephras and cryptotephras.

  14. Detection of Tephra Layers in Antarctic Sediment Cores with Hyperspectral Imaging.

    PubMed

    Aymerich, Ismael F; Oliva, Marc; Giralt, Santiago; Martín-Herrero, Julio

    2016-01-01

    Tephrochronology uses recognizable volcanic ash layers (from airborne pyroclastic deposits, or tephras) in geological strata to set unique time references for paleoenvironmental events across wide geographic areas. This involves the detection of tephra layers which sometimes are not evident to the naked eye, including the so-called cryptotephras. Tests that are expensive, time-consuming, and/or destructive are often required. Destructive testing for tephra layers of cores from difficult regions, such as Antarctica, which are useful sources of other kinds of information beyond tephras, is always undesirable. Here we propose hyperspectral imaging of cores, Self-Organizing Map (SOM) clustering of the preprocessed spectral signatures, and spatial analysis of the classified images as a convenient, fast, non-destructive method for tephra detection. We test the method in five sediment cores from three Antarctic lakes, and show its potential for detection of tephras and cryptotephras. PMID:26815202

  15. High-sensitivity hyperspectral imager for biomedical video diagnostic applications

    NASA Astrophysics Data System (ADS)

    Leitner, Raimund; Arnold, Thomas; De Biasio, Martin

    2010-04-01

    Video endoscopy allows physicians to visually inspect inner regions of the human body using a camera and only minimal invasive optical instruments. It has become an every-day routine in clinics all over the world. Recently a technological shift was done to increase the resolution from PAL/NTSC to HDTV. But, despite a vast literature on invivo and in-vitro experiments with multi-spectral point and imaging instruments that suggest that a wealth of information for diagnostic overlays is available in the visible spectrum, the technological evolution from colour to hyper-spectral video endoscopy is overdue. There were two approaches (NBI, OBI) that tried to increase the contrast for a better visualisation by using more than three wavelengths. But controversial discussions about the real benefit of a contrast enhancement alone, motivated a more comprehensive approach using the entire spectrum and pattern recognition algorithms. Up to now the hyper-spectral equipment was too slow to acquire a multi-spectral image stack at reasonable video rates rendering video endoscopy applications impossible. Recently, the availability of fast and versatile tunable filters with switching times below 50 microseconds made an instrumentation for hyper-spectral video endoscopes feasible. This paper describes a demonstrator for hyper-spectral video endoscopy and the results of clinical measurements using this demonstrator for measurements after otolaryngoscopic investigations and thorax surgeries. The application investigated here is the detection of dysplastic tissue, although hyper-spectral video endoscopy is of course not limited to cancer detection. Other applications are the detection of dysplastic tissue or polyps in the colon or the gastrointestinal tract.

  16. A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection

    NASA Astrophysics Data System (ADS)

    Pike, Robert; Patton, Samuel K.; Lu, Guolan; Halig, Luma V.; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2014-03-01

    Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images.

  17. A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection

    PubMed Central

    Pike, Robert; Patton, Samuel K.; Lu, Guolan; Halig, Luma V.; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2014-01-01

    Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images. PMID:25426272

  18. Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety

    PubMed Central

    Huang, Hui; Liu, Li; Ngadi, Michael O.

    2014-01-01

    Hyperspectral imaging which combines imaging and spectroscopic technology is rapidly gaining ground as a non-destructive, real-time detection tool for food quality and safety assessment. Hyperspectral imaging could be used to simultaneously obtain large amounts of spatial and spectral information on the objects being studied. This paper provides a comprehensive review on the recent development of hyperspectral imaging applications in food and food products. The potential and future work of hyperspectral imaging for food quality and safety control is also discussed. PMID:24759119

  19. [Decomposition of Interference Hyperspectral Images Using Improved Morphological Component Analysis].

    PubMed

    Wen, Jia; Zhao, Jun-suo; Wang, Cai-ling; Xia, Yu-li

    2016-01-01

    As the special imaging principle of the interference hyperspectral image data, there are lots of vertical interference stripes in every frames. The stripes' positions are fixed, and their pixel values are very high. Horizontal displacements also exist in the background between the frames. This special characteristics will destroy the regular structure of the original interference hyperspectral image data, which will also lead to the direct application of compressive sensing theory and traditional compression algorithms can't get the ideal effect. As the interference stripes signals and the background signals have different characteristics themselves, the orthogonal bases which can sparse represent them will also be different. According to this thought, in this paper the morphological component analysis (MCA) is adopted to separate the interference stripes signals and background signals. As the huge amount of interference hyperspectral image will lead to glow iterative convergence speed and low computational efficiency of the traditional MCA algorithm, an improved MCA algorithm is also proposed according to the characteristics of the interference hyperspectral image data, the conditions of iterative convergence is improved, the iteration will be terminated when the error of the separated image signals and the original image signals are almost unchanged. And according to the thought that the orthogonal basis can sparse represent the corresponding signals but cannot sparse represent other signals, an adaptive update mode of the threshold is also proposed in order to accelerate the computational speed of the traditional MCA algorithm, in the proposed algorithm, the projected coefficients of image signals at the different orthogonal bases are calculated and compared in order to get the minimum value and the maximum value of threshold, and the average value of them is chosen as an optimal threshold value for the adaptive update mode. The experimental results prove that

  20. Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data

    PubMed Central

    Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R.

    2015-01-01

    Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite’s Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds. PMID:26378538

  1. The challenges of analysing blood stains with hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Kuula, J.; Puupponen, H.-H.; Rinta, H.; Pölönen, I.

    2014-06-01

    Hyperspectral imaging is a potential noninvasive technology for detecting, separating and identifying various substances. In the forensic and military medicine and other CBRNE related use it could be a potential method for analyzing blood and for scanning other human based fluids. For example, it would be valuable to easily detect whether some traces of blood are from one or more persons or if there are some irrelevant substances or anomalies in the blood. This article represents an experiment of separating four persons' blood stains on a white cotton fabric with a SWIR hyperspectral camera and FT-NIR spectrometer. Each tested sample includes standardized 75 _l of 100 % blood. The results suggest that on the basis of the amount of erythrocytes in the blood, different people's blood might be separable by hyperspectral analysis. And, referring to the indication given by erythrocytes, there might be a possibility to find some other traces in the blood as well. However, these assumptions need to be verified with wider tests, as the number of samples in the study was small. According to the study there also seems to be several biological, chemical and physical factors which affect alone and together on the hyperspectral analyzing results of blood on fabric textures, and these factors need to be considered before making any further conclusions on the analysis of blood on various materials.

  2. Infrared hyperspectral upconversion imaging using spatial object translation.

    PubMed

    Kehlet, Louis Martinus; Sanders, Nicolai; Tidemand-Lichtenberg, Peter; Dam, Jeppe Seidelin; Pedersen, Christian

    2015-12-28

    In this paper hyperspectral imaging in the mid-infrared wavelength region is realised using nonlinear frequency upconversion. The infrared light is converted to the near-infrared region for detection with a Si-based CCD camera. The object is translated in a predefined grid by motorized actuators and an image is recorded for each position. A sequence of such images is post-processed into a series of monochromatic images in a wavelength range defined by the phasematch condition and numerical aperture of the upconversion system. A standard USAF resolution target and a polystyrene film are used to impart spatial and spectral information unto the source. PMID:26832059

  3. Winter wheat growth spatial variation monitoring through hyperspectral remote sensing image

    NASA Astrophysics Data System (ADS)

    Song, Xiaoyu; Li, Ting; Wang, Jihua; Gu, Xiaohe; Xu, Xingang

    2015-10-01

    This work aims at quantifying the winter wheat growth spatial heterogeneity captured by hyperspectral airborne images. The field experiment was conducted in 2001 and 2002 and airborne hyperspectral remote-sensing data was acquired at noon on 11 April 2001 using an operational modular imaging spectrometer (OMIS). Totally 12 winter fields which covered by both dense and sparse winter wheat canopies were selected to analysis the winter wheat growth heterogeneity. The experimental semi-variograms for bands covered from invisible to mid-infrared were computed for each field then the theoretical models were be fitted with least squares algorithm for spherical model, exponential model. The optimization model was selected after evaluated by R-square. Three key terms in each model, the sill, the range, and nugget variance were then calculated from the models. The study results show that the sill, range and nugget for same field wheat were varied with the wavelength from blue to mid infrared bands. Although wheat growth in different fields showed different spatial heterogeneity, they all showed an obvious sill pattern. The minimum of mean range value was 7.52 m for mid-infrared bands while the maximum value was 91.71 m for visible bands. The minimum of mean sill value ranged from 1.46 for visible bands to 39.76 for NIR bands, the minimum of mean nugget value ranged from 0.06 for visible bands to5.45 for mid-infrared bands. This study indicate that remote sensing image is important for crop growth spatial heterogeneity study. But it is necessary to explore the effect of different wavelength of image data on crop growth semi-variogram estimation and find out which band data could be used to estimate crop semi-variogram reliably.

  4. Miniaturized hyperspectral imager calibration and UAV flight campaigns

    NASA Astrophysics Data System (ADS)

    Saari, Heikki; Pölönen, Ilkka; Salo, Heikki; Honkavaara, Eija; Hakala, Teemu; Holmlund, Christer; Mäkynen, Jussi; Mannila, Rami; Antila, Tapani; Akujärvi, Altti

    2013-10-01

    VTT Technical Research Centre of Finland has developed Tunable Fabry-Perot Interferometer (FPI) based miniaturized hyperspectral imager which can be operated from light weight Unmanned Aerial Vehicles (UAV). The concept of the hyperspectral imager has been published in the SPIE Proc. 7474, 8174 and 8374. This instrument requires dedicated laboratory and on-board calibration procedures which are described. During summer 2012 extensive UAV Hyperspectral imaging campaigns in the wavelength range 400 - 900 nm at resolution range 10 - 40 nm @ FWHM were performed to study forest inventory, crop biomass and nitrogen distributions and environmental status of natural water applications. The instrument includes spectral band limiting filters which can be used for the on-board wavelength scale calibration by scanning the FPI pass band center wavelength through the low and high edge of the operational wavelength band. The procedure and results of the calibration tests will be presented. A short summary of the performed extensive UAV imaging campaign during summer 2012 will be presented.

  5. Sublingual vein extraction algorithm based on hyperspectral tongue imaging technology.

    PubMed

    Li, Qingli; Wang, Yiting; Liu, Hongying; Guan, Yana; Xu, Liang

    2011-04-01

    Among the parts of the human tongue surface, the sublingual vein is one of the most important ones which may have pathological relationship with some diseases. To analyze this information quantitatively, one primitive work is to extract sublingual veins accurately from tongue body. In this paper, a hyperspectral tongue imaging system instead of a digital camera is used to capture sublingual images. A hidden Markov model approach is presented to extract the sublingual veins from the hyperspectral sublingual images. This approach characterizes the spectral correlation and the band-to-band variability using a hidden Markov process, where the model parameters are estimated by the spectra of the pixel vectors forming the observation sequences. The proposed algorithm, the pixel-based sublingual vein segmentation algorithm, and the spectral angle mapper algorithm are tested on a total of 150 scenes of hyperspectral sublingual veins images to evaluate the performance of the new method. The experimental results demonstrate that the proposed algorithm can extract the sublingual veins more accurately than the traditional algorithms and can perform well even in a noisy environment. PMID:21030208

  6. Performance metrics for an airborne imaging system

    NASA Astrophysics Data System (ADS)

    Dayton, David C.; Gonglewski, John D.

    2004-11-01

    A series of airborne imaging experiments have been conducted on the island of Maui and at North Oscura Peak in New Mexico. Two platform altitudes were considered 3000 meters and 600 meters, both with a slant range to the target up to 10000 meters. The airborne imaging platform was a Twin Otter aircraft, which circled ground target sites. The second was a fixed platform on a mountain peak overlooking a valley 600 meters below. The experiments were performed during the day using solar illuminated target buildings. Imaging system performance predictions were calculated using standard atmospheric turbulence models, and aircraft boundary layer models. Several different measurement approaches were then used to estimate the actual system performance, and make comparisons with the calculations.

  7. Hyperspectral optical imaging of two different species of lepidoptera

    NASA Astrophysics Data System (ADS)

    Medina, José Manuel; Nascimento, Sérgio Miguel Cardoso; Vukusic, Pete

    2011-05-01

    In this article, we report a hyperspectral optical imaging application for measurement of the reflectance spectra of photonic structures that produce structural colors with high spatial resolution. The measurement of the spectral reflectance function is exemplified in the butterfly wings of two different species of Lepidoptera: the blue iridescence reflected by the nymphalid Morpho didius and the green iridescence of the papilionid Papilio palinurus. Color coordinates from reflectance spectra were calculated taking into account human spectral sensitivity. For each butterfly wing, the observed color is described by a characteristic color map in the chromaticity diagram and spreads over a limited volume in the color space. The results suggest that variability in the reflectance spectra is correlated with different random arrangements in the spatial distribution of the scales that cover the wing membranes. Hyperspectral optical imaging opens new ways for the non-invasive study and classification of different forms of irregularity in structural colors.

  8. Objective color classification of ecstasy tablets by hyperspectral imaging.

    PubMed

    Edelman, Gerda; Lopatka, Martin; Aalders, Maurice

    2013-07-01

    The general procedure followed in the examination of ecstasy tablets for profiling purposes includes a color description, which depends highly on the observers' perception. This study aims to provide objective quantitative color information using visible hyperspectral imaging. Both self-manufactured and illicit tablets, created with different amounts of known colorants were analyzed. We derived reflectance spectra from hyperspectral images of these tablets, and successfully determined the most likely colorant used in the production of all self-manufactured tablets and four of five illicit tablets studied. Upon classification, the concentration of the colorant was estimated using a photon propagation model and a single reference measurement of a tablet of known concentration. The estimated concentrations showed a high correlation with the actual values (R(2) = 0.9374). The achieved color information, combined with other physical and chemical characteristics, can provide a powerful tool for the comparison of tablet seizures, which may reveal their origin.

  9. Hyperspectral imaging: a useful technology for transportation analysis

    NASA Astrophysics Data System (ADS)

    Gomez, Richard B.

    2002-09-01

    We address hyperspectral imaging (HSI) technology and its attendant key issue of spectral libraries to enable the exploitation of hyperspectral images for transportation applications. Five key applications are reviewed here: detection/identification of submerged aquatic vegetation in navigable waterways, detection/tracking of oil spills, extracting/assessing road characteristics, mapping impervious surfaces, and the detection/identification of vehicles. Central to all these applications is the need for a comprehensive spectral library in which various reflective spectra are correlated with physical surfaces and environments encountered in transportation. Much of this critical work is being funded by the Department of Transportation through four university consortia, each specializing in one of the key transportation areas of: transportation flows; infrastructure; environmental assessment; and safety, hazards, and disaster assessment for transportation lifelines.

  10. Hyperspectral imaging for differentiation of foreign materials from pinto beans

    NASA Astrophysics Data System (ADS)

    Mehrubeoglu, Mehrube; Zemlan, Michael; Henry, Sam

    2015-09-01

    Food safety and quality in packaged products are paramount in the food processing industry. To ensure that packaged products are free of foreign materials, such as debris and pests, unwanted materials mixed with the targeted products must be detected before packaging. A portable hyperspectral imaging system in the visible-to-NIR range has been used to acquire hyperspectral data cubes from pinto beans that have been mixed with foreign matter. Bands and band ratios have been identified as effective features to develop a classification scheme for detection of foreign materials in pinto beans. A support vector machine has been implemented with a quadratic kernel to separate pinto beans and background (Class 1) from all other materials (Class 2) in each scene. After creating a binary classification map for the scene, further analysis of these binary images allows separation of false positives from true positives for proper removal action during packaging.

  11. Mineral identification in hyperspectral imaging using Sparse-PCA

    NASA Astrophysics Data System (ADS)

    Yousefi, Bardia; Sojasi, Saeed; Ibarra Castanedo, Clemente; Beaudoin, Georges; Huot, François; Maldague, Xavier P. V.; Chamberland, Martin; Lalonde, Erik

    2016-05-01

    Hyperspectral imaging has been considerably developed during the recent decades. The application of hyperspectral imagery and infrared thermography, particularly for the automatic identification of minerals from satellite images, has been the subject of several interesting researches. In this study, a method is presented for the automated identification of the mineral grains typically used from satellite imagery and adapted for analyzing collected sample grains in a laboratory environment. For this, an approach involving Sparse Principle Components Analysis (SPCA) based on spectral abundance mapping techniques (i.e. SAM, SID, NormXCorr) is proposed for extraction of the representative spectral features. It develops an approximation of endmember as a reference spectrum process through the highest sparse principle component of the pure mineral grains. Subsequently, the features categorized by kernel Extreme Learning Machine (Kernel- ELM) classify and identify the mineral grains in a supervised manner. Classification is conducted in the binary scenario and the results indicate the dependency to the training spectra.

  12. Validation of Satellite Ammonia Retrievals using Airborne Hyperspectral Thermal-Infrared Spectrometry

    NASA Astrophysics Data System (ADS)

    Tratt, D. M.; Hall, J. L.; Chang, C. S.; Qian, J.; Clarisse, L.; Van Damme, M.; Clerbaux, C.; Hurtmans, D.; Coheur, P.

    2011-12-01

    We demonstrate the utility of a new airborne sensor with the requisite spatial, spectral, and radiometric resolution to characterize "point" sources of ammonia emission. Flights were conducted over California's San Joaquin Valley, which is a region of intensive agriculture and animal husbandry that has been identified as one of the single largest sources of atmospheric free ammonia worldwide. Airborne data acquisition operations were coordinated with daytime overpasses of the Infrared Atmospheric Sounding Interferometer (IASI) aboard the European Space Agency's MetOp-A platform. IASI is capable of measuring total columns of ammonia and the primary purpose of this investigation was to compare and validate the IASI ammonia product against high-spatial-resolution airborne retrievals acquired contemporaneously over the same footprint. The ~12-km pixel size of the IASI satellite measurements cannot resolve the local-scale variability of ammonia abundance and consequently cannot characterize the often compact source emissions. The nominal 2-m pixel size of the airborne data revealed variability of ammonia concentration at several different scales within the IASI footprint. At this pixel size, well-defined plumes issuing from individual dairy facilities could be imaged and their dispersion characteristics resolved. Retrieved ammonia concentrations in excess of 50 ppb were inferred for some of the strongest discrete plumes.

  13. Imaging of blood cells based on snapshot Hyper-Spectral Imaging systems

    NASA Astrophysics Data System (ADS)

    Robison, Christopher J.; Kolanko, Christopher; Bourlai, Thirimachos; Dawson, Jeremy M.

    2015-05-01

    Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering coregistered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera; attached to a microscope at varying objective powers and illumination intensity. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyper-spectral data cube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting.

  14. [Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

    PubMed

    Li, Xun-lan; Yi, Shi-lai; He, Shao-lan; Lü, Qiang; Xie, Rang-jin; Zheng, Yong-qiang; Deng, Lie

    2015-09-01

    Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and

  15. [Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology].

    PubMed

    Li, Xun-lan; Yi, Shi-lai; He, Shao-lan; Lü, Qiang; Xie, Rang-jin; Zheng, Yong-qiang; Deng, Lie

    2015-09-01

    Existing methods for the identification of pummelo cultivars are usually time-consuming and costly, and are therefore inconvenient to be used in cases that a rapid identification is needed. This research was aimed at identifying different pummelo cultivars by hyperspectral imaging technology which can achieve a rapid and highly sensitive measurement. A total of 240 leaf samples, 60 for each of the four cultivars were investigated. Samples were divided into two groups such as calibration set (48 samples of each cultivar) and validation set (12 samples of each cultivar) by a Kennard-Stone-based algorithm. Hyperspectral images of both adaxial and abaxial surfaces of each leaf were obtained, and were segmented into a region of interest (ROI) using a simple threshold. Spectra of leaf samples were extracted from ROI. To remove the absolute noises of the spectra, only the date of spectral range 400~1000 nm was used for analysis. Multiplicative scatter correction (MSC) and standard normal variable (SNV) were utilized for data preprocessing. Principal component analysis (PCA) was used to extract the best principal components, and successive projections algorithm (SPA) was used to extract the effective wavelengths. Least squares support vector machine (LS-SVM) was used to obtain the discrimination model of the four different pummelo cultivars. To find out the optimal values of σ2 and γ which were important parameters in LS-SVM modeling, Grid-search technique and Cross-Validation were applied. The first 10 and 11 principal components were extracted by PCA for the hyperspectral data of adaxial surface and abaxial surface, respectively. There were 31 and 21 effective wavelengths selected by SPA based on the hyperspectral data of adaxial surface and abaxial surface, respectively. The best principal components and the effective wavelengths were used as inputs of LS-SVM models, and then the PCA-LS-SVM model and the SPA-LS-SVM model were built. The results showed that 99.46% and

  16. Hyperspectral imaging technique for determination of pork freshness attributes

    NASA Astrophysics Data System (ADS)

    Li, Yongyu; Zhang, Leilei; Peng, Yankun; Tang, Xiuying; Chao, Kuanglin; Dhakal, Sagar

    2011-06-01

    Freshness of pork is an important quality attribute, which can vary greatly in storage and logistics. The specific objectives of this research were to develop a hyperspectral imaging system to predict pork freshness based on quality attributes such as total volatile basic-nitrogen (TVB-N), pH value and color parameters (L*,a*,b*). Pork samples were packed in seal plastic bags and then stored at 4°C. Every 12 hours. Hyperspectral scattering images were collected from the pork surface at the range of 400 nm to 1100 nm. Two different methods were performed to extract scattering feature spectra from the hyperspectral scattering images. First, the spectral scattering profiles at individual wavelengths were fitted accurately by a three-parameter Lorentzian distribution (LD) function; second, reflectance spectra were extracted from the scattering images. Partial Least Square Regression (PLSR) method was used to establish prediction models to predict pork freshness. The results showed that the PLSR models based on reflectance spectra was better than combinations of LD "parameter spectra" in prediction of TVB-N with a correlation coefficient (r) = 0.90, a standard error of prediction (SEP) = 7.80 mg/100g. Moreover, a prediction model for pork freshness was established by using a combination of TVB-N, pH and color parameters. It could give a good prediction results with r = 0.91 for pork freshness. The research demonstrated that hyperspectral scattering technique is a valid tool for real-time and nondestructive detection of pork freshness.

  17. A hyperspectral image data exploration workbench for environmental science applications

    SciTech Connect

    Woyna, M.A.; Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.

    1994-08-01

    The Hyperspectral Image Data Exploration Workbench (HIDEW) software system has been developed by Argonne National Laboratory to enable analysts at Unix workstations to conveniently access and manipulate high-resolution imagery data for analysis, mapping purposes, and input to environmental modeling applications. HIDEW is fully object-oriented, including the underlying database. This system was developed as an aid to site characterization work and atmospheric research projects.

  18. Space-based hyperspectral imaging spectroradiometer for coastal studies

    NASA Astrophysics Data System (ADS)

    Puschell, Jeffery J.; Silny, John; Cook, Lacy; Champion, Shaun; Schiller, Stephen; La Komski, David; Nastal, Jamie; Malone, Neil; Davis, Curtiss

    2011-11-01

    Resolving the complexity of coastal and estuarine waters requires high spatial resolution, hyperspectral imaging spectroradiometry. Hyperspectral measurements also provide capability for measuring bathymetry and bottom types in optically shallow water and for detailed cross calibration with other instruments in polar and geosynchronous orbit. This paper reports on recent design studies for a hyperspectral Coastal Imager (CI - pronounced "sea") that measures key data products from sun synchronous orbit. These products include water-leaving radiances in the near-ultraviolet, visible and near-infrared for separation of absorbing and scattering coastal water constituents and for calculation of chlorophyll fluorescence. In addition, CI measures spectral radiances in the near-infrared and shortwave infrared for atmospheric corrections while also measuring cloud radiances without saturation to enable more accurate removal of instrument stray light effects. CI provides contiguous spectral coverage from 380 to 2500 nm at 20 m GIFOV at nadir across 5000+ km2 scenes with spectral sampling, radiometric sensitivity and calibration performance needed to meet the demanding requirements of coastal imaging. This paper describes the CI design, including concepts of operation for data collection, calibration (radiometric, spectral and spatial), onboard processing and data transmission to Earth. Performance characteristics for the instrument and all major subsystems including the optics, focal plane assemblies, onboard calibration, onboard processing and thermal subsystem are presented along with performance predictions for instrument sensitivity and calibration. Initial estimates of size, mass, power and data rate are presented.

  19. Spatial and spectral performance of a chromotomosynthetic hyperspectral imaging system.

    PubMed

    Bostick, Randall L; Perram, Glen P

    2012-03-01

    The spatial and spectral resolutions achievable by a prototype rotating prism chromotomosynthetic imaging (CTI) system operating in the visible spectrum are described. The instrument creates hyperspectral imagery by collecting a set of 2D images with each spectrally projected at a different rotation angle of the prism. Mathematical reconstruction techniques that have been well tested in the field of medical physics are used to reconstruct the data to produce the 3D hyperspectral image. The instrument operates with a 100 mm focusing lens in the spectral range of 400-900 nm with a field of view of 71.6 mrad and angular resolution of 0.8-1.6 μrad. The spectral resolution is 0.6 nm at the shortest wavelengths, degrading to over 10 nm at the longest wavelengths. Measurements using a point-like target show that performance is limited by chromatic aberration. The system model is slightly inaccurate due to poor estimation of detector spatial resolution, this is corrected based on results improving model performance. As with traditional dispersion technology, calibration of the transformed wavelength axis is required, though with this technology calibration improves both spectral and spatial resolution. While this prototype does not operate at high speeds, components exist which will allow for CTI systems to generate hyperspectral video imagery at rates greater than 100 Hz. PMID:22462909

  20. Spatial and spectral performance of a chromotomosynthetic hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

    Bostick, Randall L.; Perram, Glen P.

    2012-03-01

    The spatial and spectral resolutions achievable by a prototype rotating prism chromotomosynthetic imaging (CTI) system operating in the visible spectrum are described. The instrument creates hyperspectral imagery by collecting a set of 2D images with each spectrally projected at a different rotation angle of the prism. Mathematical reconstruction techniques that have been well tested in the field of medical physics are used to reconstruct the data to produce the 3D hyperspectral image. The instrument operates with a 100 mm focusing lens in the spectral range of 400-900 nm with a field of view of 71.6 mrad and angular resolution of 0.8-1.6 μrad. The spectral resolution is 0.6 nm at the shortest wavelengths, degrading to over 10 nm at the longest wavelengths. Measurements using a point-like target show that performance is limited by chromatic aberration. The system model is slightly inaccurate due to poor estimation of detector spatial resolution, this is corrected based on results improving model performance. As with traditional dispersion technology, calibration of the transformed wavelength axis is required, though with this technology calibration improves both spectral and spatial resolution. While this prototype does not operate at high speeds, components exist which will allow for CTI systems to generate hyperspectral video imagery at rates greater than 100 Hz.

  1. Using TerraSAR-X and hyperspectral airborne data to monitor surface deformation and physical properties of the Barrow permafrost landscape, Alask

    NASA Astrophysics Data System (ADS)

    Haghshenas-Haghighi, M.; Motagh, M.; Heim, B.; Sachs, T.; Kohnert, K.; Streletskiy, D. A.

    2014-12-01

    In this study, we assess seasonal subsidence/heaving due to thawing/freezing of the permafrost in Barrow (71.3 N, 156.5 W) at the northernmost point of Alaska. The topographic relief in this area is low. Thick Permafrost underlies the entire area, with large ice volumes in its upper layer. With a large collection of field measurements during the past decades at the Barrow Environmental Observatory (BEO), it is an ideal site for permafrost investigation. There are long term systematic geocryological investigations within the Global Terrestrial Network (GTN-P) of the Circumpolar Active Layer Monitoring (CALM) programme. We use 28 TerraSAR-X images, acquired between December 2012 and December 2013 and analyze them using the Small BAseline Subset (SBAS) technique to extract time-series of ground surface deformation. We also analyze hyperspectral images acquired by the airborne AISA sensor over Barrow area, within the AIRMETH2013 programme, to assess physical characteristics such as vegetation biomass and density, surface moisture, and water bodies. Finally, we combine the information derived from both InSAR and hyperspectral analysis, with field measurements to investigate the link between physical characteristics of the permafrost and surface displacement.

  2. Performance portability study of an automatic target detection and classification algorithm for hyperspectral image analysis using OpenCL

    NASA Astrophysics Data System (ADS)

    Bernabe, Sergio; Igual, Francisco D.; Botella, Guillermo; Garcia, Carlos; Prieto-Matias, Manuel; Plaza, Antonio

    2015-10-01

    Recent advances in heterogeneous high performance computing (HPC) have opened new avenues for demanding remote sensing applications. Perhaps one of the most popular algorithm in target detection and identification is the automatic target detection and classification algorithm (ATDCA) widely used in the hyperspectral image analysis community. Previous research has already investigated the mapping of ATDCA on graphics processing units (GPUs) and field programmable gate arrays (FPGAs), showing impressive speedup factors that allow its exploitation in time-critical scenarios. Based on these studies, our work explores the performance portability of a tuned OpenCL implementation across a range of processing devices including multicore processors, GPUs and other accelerators. This approach differs from previous papers, which focused on achieving the optimal performance on each platform. Here, we are more interested in the following issues: (1) evaluating if a single code written in OpenCL allows us to achieve acceptable performance across all of them, and (2) assessing the gap between our portable OpenCL code and those hand-tuned versions previously investigated. Our study includes the analysis of different tuning techniques that expose data parallelism as well as enable an efficient exploitation of the complex memory hierarchies found in these new heterogeneous devices. Experiments have been conducted using hyperspectral data sets collected by NASA's Airborne Visible Infra- red Imaging Spectrometer (AVIRIS) and the Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensors. To the best of our knowledge, this kind of analysis has not been previously conducted in the hyperspectral imaging processing literature, and in our opinion it is very important in order to really calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.

  3. Infrared hyperspectral imaging results from vapor plume experiments

    SciTech Connect

    Bennett, C.L.; Carter, M.R.; Fields, D.J.

    1995-04-17

    In this article, recent measurements made with LIFTIRS, the Livermore Imaging Fourier Transform InfraRed Spectrometer, are presented. The experience gained with this instrument has produced a variety of insights into the tradeoffs between signal to noise ratio (SNR), spectral resolution and temporal resolution for time multiplexed Fourier transform imaging spectrometers. This experience has also clarified the practical advantages and disadvantages of Fourier transform hyperspectral imaging spectrometers regarding adaptation to varying measurement requirements on SNR vs. spectral resolution, spatial resolution and temporal resolution.

  4. a Class-Outlier Approach for Environnemental Monitoring Using Uav Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Hemissi, S.; Riadh Farah, I.

    2015-04-01

    In several remote sensing applications, detecting exceptional/irregular regions (i.e, pixels) with respect to the whole dataset homogeneity is regarded as a very interested issue. Currently, this is limited to the pre-processing step aiming to eliminate the cloud or noisy pixels. In this paper, we propose to extend the coverage area and to tackle this issue by regarding the irregular/exceptional pixels as outliers. The main purpose is the adaptation of the class outlier mining concept in order to find abnormal and irregular pixels in hyperspectral images. This should be done taking into account the class labels and the relative uncertainty of collected data. To reach this goal, the Class Outliers: DistanceBased (CODB) algorithm is enhanced to take into account the multivariate high-dimensional data and the concomitant partially available knowledge of our data. This is mainly done by using belief theory and a learnable task-specific similarity measure. To validate our approach, we apply it for vegetation inspection and normality monitoring. For experimental purposes, the Airborne Prism Experiment (APEX) data, set acquired during an APEX flight campaign in June 2011, was used. Moreover, a collection of simulated hyperspectral images and spectral indices, providing a quantitative indicator of vegetation health, were generated for this purpose. The encouraging obtained results can be used to monitor areas where vegetation may be stressed, as a proxy to detect potential drought.

  5. Hyperspectral image segmentation using a cooperative nonparametric approach

    NASA Astrophysics Data System (ADS)

    Taher, Akar; Chehdi, Kacem; Cariou, Claude

    2013-10-01

    In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.

  6. Identification of staphylococcus species with hyperspectral microscope imaging and classification algrorithms

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral microscope imaging is presented as a rapid and efficient tool to classify foodborne bacteria species. The spectral data were obtained from five different species of Staphylococcus spp. with a hyperspectral microscope imaging system that provided a maximum of 89 contiguous spectral imag...

  7. Drawbacks of using linear mixture modeling on hyperspectral images

    NASA Astrophysics Data System (ADS)

    Rodricks, Neena; Kirkland, Laurel E.

    2004-10-01

    Hyperspectral spectroscopy can be used remotely to measure emitted radiation from minerals and rocks at a series of narrow and continuous wavelength bands resulting in a continuous spectrum for each pixel, thereby providing ample spectral information to identify and distinguish spectrally unique materials. Linear mixture modeling ("spectral unmixing"), a commonly used method, is based on the theory that the radiance in the thermal infrared region (8-12 μm) from a multi-mineral surface can be modeled as a linear combination of the endmembers. A linear mixture model can thus potentially model the minerals present on planetary surfaces. It works by scaling the endmember spectra so that the sum of the scaled endmember spectra matches the measured spectrum with the smallest "error" (difference). But one of the drawbacks of this established method is that mathematically, a fit with an inverted spectrum is valid, which effectively returns a negative abundance of a material. Current models usually address the problem by elimination of endmembers that have negative scale factors. Eliminating the negative abundance problem is not a major issue when the endmembers are known. However, identifying unknown target composition (like on Mars) can be a problem. The goal of this study is to improve the understanding and find a subsequent solution of the negative abundance problem for Mars analog field data obtained from airborne and ground spectrometers. We are using a well-defined library of spectra to test the accuracy of hyperspectral analysis for the identification of minerals on planetary surfaces.

  8. Landsat radiometric continuity using airborne imaging spectrometry

    NASA Astrophysics Data System (ADS)

    McCorkel, J.; Angal, A.; Thome, K.; Cook, B.

    2015-12-01

    NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) includes a scanning lidar, an imaging spectrometer and a thermal camera. The Visible Near-Infrared (VNIR) Imaging Spectrometer acquires high resolution spectral measurements (1.5 nm resolution) from 0.4 to 1.0 µm. The SIRCUS-based calibration facility at NASA's Goddard Space Flight Center was used to measure the absolute spectral response (ASR) of the G-LiHT's imaging spectrometer. Continuously tunable lasers coupled to an integrating sphere facilitated a radiance-based calibration for the detectors in the reflective solar bands. The transfer of the SIRCUS-based laboratory calibration of G-LiHT's Imaging Spectrometer to the Landsat sensors (Landsat 7 ETM+ and Landsat 8 OLI) is demonstrated using simultaneous overpasses over the Red Lake Playa and McClaw's Playa sites during the commissioning phase of Landsat 8 in March 2013. Solar Lunar Absolute Imaging Spectrometer (SOLARIS) is the calibration demonstration system for the reflected solar instrument of CLARREO. A portable version of SOLARIS, known as Suitcase SOLARIS, also calibrated using a SIRCUS-based setup, was deployed for ground measurements as a part of both the field campaigns. Simultaneous measurements of SOLARIS allow cross-comparison with G-LiHT and Landsat sensors. The transfer of the lab-based calibration of G-LiHT to Landsat sensors show that the sensors agree within 5% with a 1-3% calibration uncertainty of G-LiHT's Imaging Spectrometer.

  9. Hyperspectral image analysis for CARS, SRS, and Raman data

    PubMed Central

    Karuna, Arnica; Borri, Paola; Langbein, Wolfgang

    2015-01-01

    In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masia et al. 1 The HIA introduced a method to factorize the hyperspectral data into the product of component concentrations and spectra for quantitative analysis of the chemical composition of the sample. The enhancements shown here comprise (1) a spatial weighting to reduce the spatial variation of the spectral error, which improves the retrieval of the chemical components with significant local but small global concentrations; (2) a new selection criterion for the spectra used when applying sparse sampling2 to speed up sequential hyperspectral imaging; and (3) a filter for outliers in the data using singular value decomposition, suited e.g. to suppress motion artifacts. We demonstrate the enhancements on coherent anti‐Stokes Raman scattering, stimulated Raman scattering, and spontaneous Raman data. We provide the HIA software as executable for public use. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd. PMID:27478301

  10. Reconstruction of hyperspectral image using matting model for classification

    NASA Astrophysics Data System (ADS)

    Xie, Weiying; Li, Yunsong; Ge, Chiru

    2016-05-01

    Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.

  11. Detection of early plant stress responses in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Behmann, Jan; Steinrücken, Jörg; Plümer, Lutz

    2014-07-01

    Early stress detection in crop plants is highly relevant, but hard to achieve. We hypothesize that close range hyperspectral imaging is able to uncover stress related processes non-destructively in the early stages which are invisible to the human eye. We propose an approach which combines unsupervised and supervised methods in order to identify several stages of progressive stress development from series of hyperspectral images. Stress of an entire plant is detected by stress response levels at pixel scale. The focus is on drought stress in barley (Hordeum vulgare). Unsupervised learning is used to separate hyperspectral signatures into clusters related to different stages of stress response and progressive senescence. Whereas all such signatures may be found in both, well watered and drought stressed plants, their respective distributions differ. Ordinal classification with Support Vector Machines (SVM) is used to quantify and visualize the distribution of progressive stages of senescence and to separate well watered from drought stressed plants. For each senescence stage a distinctive set of most relevant Vegetation Indices (VIs) is identified. The method has been applied on two experiments involving potted barley plants under well watered and drought stress conditions in a greenhouse. Drought stress is detected up to ten days earlier than using NDVI. Furthermore, it is shown that some VIs have overall relevance, while others are specific to particular senescence stages. The transferability of the method to the field is illustrated by an experiment on maize (Zea mays).

  12. Semi-supervised feature learning for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Zhang, Pengfei; Cao, Liujuan; Wang, Cheng; Li, Jonathan

    2016-03-01

    Hyperspectral image has high-dimensional Spectral-spatial features, those features with some noisy and redundant information. Since redundant features can have significant adverse effect on learning performance. So efficient and robust feature selection methods are make the best of labeled and unlabeled points to extract meaningful features and eliminate noisy ones. On the other hand, obtaining sufficient accurate labeled data is either impossible or expensive. In order to take advantage of both precious labeled and unlabeled data points, in this paper, we propose a new semisupervised feature selection method, Firstly, we use labeled points are to enlarge the margin between data points from different classes; Secondly, we use unlabeled points to find the local structure of the data space; Finally, we compare our proposed algorithm with Fisher score, PCA and Laplacian score on HSI classification. Experimental results on benchmark hyperspectral data sets demonstrate the efficiency and effectiveness of our proposed algorithm.

  13. Quantum cascade laser-based hyperspectral imaging of biological tissue

    NASA Astrophysics Data System (ADS)

    Kröger, Niels; Egl, Alexander; Engel, Maria; Gretz, Norbert; Haase, Katharina; Herpich, Iris; Kränzlin, Bettina; Neudecker, Sabine; Pucci, Annemarie; Schönhals, Arthur; Vogt, Jochen; Petrich, Wolfgang

    2014-11-01

    The spectroscopy of analyte-specific molecular vibrations in tissue thin sections has opened up a path toward histopathology without the need for tissue staining. However, biomedical vibrational imaging has not yet advanced from academic research to routine histopathology due to long acquisition times for the microscopic hyperspectral images and/or cost and availability of the necessary equipment. Here we show that the combination of a fast-tuning quantum cascade laser with a microbolometer array detector allows for a rapid image acquisition and bares the potential for substantial cost reduction. A 3.1×2.8 mm2 unstained thin section of mouse jejunum has been imaged in the 9.2 to 9.7 μm wavelength range (spectral resolution ˜1 cm-1) within 5 min with diffraction limited spatial resolution. The comparison of this hyperspectral imaging approach with standard Fourier transform infrared imaging or mapping of the identical sample shows a reduction in acquisition time per wavenumber interval and image area by more than one or three orders of magnitude, respectively.

  14. Airborne multispectral and hyperspectral remote sensing: Examples of applications to the study of environmental and engineering problems

    SciTech Connect

    Bianchi, R.; Marino, C.M.

    1997-10-01

    The availability of a new aerial survey capability carried out by the CNR/LARA (National Research Council - Airborne Laboratory for the Environmental Research) by a new spectroradiometer AA5000 MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) on board a CASA 212/200 aircraft, enable the scientists to obtain innovative data sets, for different approach to the definitions and the understanding of a variety of environmental and engineering problems. The 102 MIVIS channels spectral bandwidths are chosen to meet the needs of scientific research for advanced applications of remote sensing data. In such configuration MIVIS can offer significant contributions to problem solving in wide sectors such as geologic exploration, agricultural crop studies, forestry, land use mapping, idrogeology, oceanography and others. LARA in 1994-96 has been active over different test-sites in joint-venture with JPL, Pasadena, different European Institutions and Italian University and Research Institutes. These aerial surveys allow the national and international scientific community to approach the use of Hyperspectral Remote Sensing in environmental problems of very large interest. The sites surveyed in Italy, France and Germany include a variety of targets such as quarries, landfills, karst cavities areas, landslides, coastlines, geothermal areas, etc. The deployments gathered up to now more than 300 GBytes of MIVIS data in more than 30 hours of VLDS data recording. The purpose of this work is to present and to comment the procedures and the results at research and at operational level of the past campaigns with special reference to the study of environmental and engineering problems.

  15. Detection of abandoned mines/caves using airborne LWIR hyperspectral data

    NASA Astrophysics Data System (ADS)

    Shen, Sylvia S.; Roettiger, Kurt A.

    2012-09-01

    The detection of underground structures, both natural and man-made, continues to be an important requirement in both the military/intelligence and civil communities. There are estimates that as many as 70,000 abandoned mines/caves exist across the nation. These mines represent significant hazards to public health and safety, and they are of concern to Government agencies at the local, state, and federal levels. NASA is interested in the detection of caves on Mars and the Moon in anticipation of future manned space missions. And, the military/ intelligence community is interested in detecting caves, mines, and other underground structures that may be used to conceal the production of weapons of mass destruction or to harbor insurgents or other persons of interest by the terrorists. Locating these mines/caves scattered over millions of square miles is an enormous task, and limited resources necessitate the development of an efficient and effective broad area search strategy using remote sensing technologies. This paper describes an internally-funded research project of The Aerospace Corporation (Aerospace) to assess the feasibility of using airborne hyperspectral data to detect abandoned cave/mine entrances in a broad-area search application. In this research, we have demonstrated the potential utility of using thermal contrast between the cave/mine entrance and the ambient environment as a discriminatory signature. We have also demonstrated the use of a water vapor absorption line at12.55 μm and a quartz absorption feature at 9.25 μm as discriminatory signatures. Further work is required to assess the broader applicability of these signatures.

  16. Spatial regularization for the unmixing of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Bauer, Sebastian; Neumann, Florian; Puente León, Fernando

    2015-05-01

    For demanding sorting tasks, the acquisition and processing of color images does not provide sufficient information for the successful discrimination between the different object classes that are to be sorted. An alternative to integrating three spectral regions of visible light to the three color channels is to sample the spectrum at up to several hundred, evenly-spaced points and acquire so-called hyperspectral images. Such images provide a complete image of the scene at each considered wavelength and contain much more information about the composition of the different materials. Hyperspectral images can also be acquired in spectral regions neighboring visible light such as, e.g., the ultraviolet (UV) and near-infrared (NIR) region. From a mathematical point of view, it is possible to extract the spectra of the pure materials and the amount to which these spectra contribute to material mixtures. This process is called spectral unmixing. Spectral unmixing based on the mostly used linear mixing model is a difficult task due to model ambiguities and distorting factors such as noise. Until a few years ago, the most inherent property of hyperspectral images, that is to say, the abundance correlation between neighboring pixels, was not used in unmixing algorithms. Only recently, researchers started to incorporate spatial information into the unmixing process, which by now is known to improve the unmixing results. In this paper, we will introduce two new methods and study the effect of these two and two already described methods on spectral unmixing, especially on their ability to account for edges and other shapes in the abundance maps.

  17. Mapping tree health using airborne laser scans and hyperspectral imagery: a case study for a floodplain eucalypt forest

    NASA Astrophysics Data System (ADS)

    Shendryk, Iurii; Tulbure, Mirela; Broich, Mark; McGrath, Andrew; Alexandrov, Sergey; Keith, David

    2016-04-01

    Airborne laser scanning (ALS) and hyperspectral imaging (HSI) are two complementary remote sensing technologies that provide comprehensive structural and spectral characteristics of forests over large areas. In this study we developed two algorithms: one for individual tree delineation utilizing ALS and the other utilizing ALS and HSI to characterize health of delineated trees in a structurally complex floodplain eucalypt forest. We conducted experiments in the largest eucalypt, river red gum forest in the world, located in the south-east of Australia that experienced severe dieback over the past six decades. For detection of individual trees from ALS we developed a novel bottom-up approach based on Euclidean distance clustering to detect tree trunks and random walks segmentation to further delineate tree crowns. Overall, our algorithm was able to detect 67% of tree trunks with diameter larger than 13 cm. We assessed the accuracy of tree delineations in terms of crown height and width, with correct delineation of 68% of tree crowns. The increase in ALS point density from ~12 to ~24 points/m2 resulted in tree trunk detection and crown delineation increase of 11% and 13%, respectively. Trees with incorrectly delineated crowns were generally attributed to areas with high tree density along water courses. The accurate delineation of trees allowed us to classify the health of this forest using machine learning and field-measured tree crown dieback and transparency ratios, which were good predictors of tree health in this forest. ALS and HSI derived indices were used as predictor variables to train and test object-oriented random forest classifier. Returned pulse width, intensity and density related ALS indices were the most important predictors in the tree health classifications. At the forest level in terms of tree crown dieback, 77% of trees were classified as healthy, 14% as declining and 9% as dying or dead with 81% mapping accuracy. Similarly, in terms of tree

  18. Evaluation of a hyperspectral image database for demosaicking purposes

    NASA Astrophysics Data System (ADS)

    Larabi, Mohamed-Chaker; Süsstrunk, Sabine

    2011-01-01

    We present a study on the the applicability of hyperspectral images to evaluate color filter array (CFA) design and the performance of demosaicking algorithms. The aim is to simulate a typical digital still camera processing pipe-line and to compare two different scenarios: evaluate the performance of demosaicking algorithms applied to raw camera RGB values before color rendering to sRGB, and evaluate the performance of demosaicking algorithms applied on the final sRGB color rendered image. The second scenario is the most frequently used one in literature because CFA design and algorithms are usually tested on a set of existing images that are already rendered, such as the Kodak Photo CD set containing the well-known lighthouse image. We simulate the camera processing pipe-line with measured spectral sensitivity functions of a real camera. Modeling a Bayer CFA, we select three linear demosaicking techniques in order to perform the tests. The evaluation is done using CMSE, CPSNR, s-CIELAB and MSSIM metrics to compare demosaicking results. We find that the performance, and especially the difference between demosaicking algorithms, is indeed significant depending if the mosaicking/demosaicking is applied to camera raw values as opposed to already rendered sRGB images. We argue that evaluating the former gives a better indication how a CFA/demosaicking combination will work in practice, and that it is in the interest of the community to create a hyperspectral image dataset dedicated to that effect.

  19. Tongue color analysis and discrimination based on hyperspectral images.

    PubMed

    Li, Qingli; Liu, Zhi

    2009-04-01

    Human tongue is one of the important organs of the body, which carries abound of information of the health status. Among the various information on tongue, color is the most important factor. Most existing methods carry out pixel-wise or RGB color space classification in a tongue image captured with color CCD cameras. However, these conversional methods impede the accurate analysis on the subjects of tongue surface because of the less information of this kind of images. To address problems in RGB images, a pushbroom hyperspectral tongue imager is developed and its spectral response calibration method is discussed. A new approach to analyze tongue color based on spectra with spectral angle mapper is presented. In addition, 200 hyperspectral tongue images from the tongue image database were selected on which the color recognition is performed with the new method. The results of experiment show that the proposed method has good performance in terms of the rates of correctness for color recognition of tongue coatings and substances. The overall rate of correctness for each color category was 85% of tongue substances and 88% of tongue coatings with the new method. In addition, this algorithm can trace out the color distribution on the tongue surface which is very helpful for tongue disease diagnosis. The spectrum of organism can be used to retrieve organism colors more accurately. This new color analysis approach is superior to the traditional method especially in achieving meaningful areas of substances and coatings of tongue. PMID:19157779

  20. Transillumination hyperspectral imaging for histopathological examination of excised tissue

    NASA Astrophysics Data System (ADS)

    Vasefi, Fartash; Najiminaini, Mohamadreza; Ng, Eldon; Chamson-Reig, Astrid; Kaminska, Bozena; Brackstone, Muriel; Carson, Jeffrey

    2011-08-01

    Angular domain spectroscopic imaging (ADSI) is a novel technique for the detection and characterization of optical contrast in turbid media based on spectral characteristics. The imaging system employs a silicon micromachined angular filter array to reject scattered light traversing a specimen and an imaging spectrometer to capture and discriminate the largely remaining quasiballistic light based on spatial position and wavelength. The imaging modality results in hyperspectral shadowgrams containing two-dimensional (2D) spatial maps of spectral information. An ADSI system was constructed and its performance was evaluated in the near-infrared region on tissue-mimicking phantoms. Image-based spectral correlation analysis using transmission spectra and first order derivatives revealed that embedded optical targets could be resolved. The hyperspectral images obtained with ADSI were observed to depend on target concentration, target depth, and scattering level of the background medium. A similar analysis on a muscle and tumor sample dissected from a mouse resulted in spatially dependent optical transmission spectra that were distinct, which suggested that ADSI may find utility in classifying tissues in biomedical applications.

  1. Hyperspectral image segmentation using spatial-spectral graphs

    NASA Astrophysics Data System (ADS)

    Gillis, David B.; Bowles, Jeffrey H.

    2012-06-01

    Spectral graph theory has proven to be a useful tool in the analysis of high-dimensional data sets. Recall that, mathematically, a graph is a collection of objects (nodes) and connections between them (edges); a weighted graph additionally assigns numerical values (weights) to the edges. Graphs are represented by their adjacency whose elements are the weights between the nodes. Spectral graph theory uses the eigendecomposition of the adjacency matrix (or, more generally, the Laplacian of the graph) to derive information about the underlying graph. In this paper, we develop a spectral method based on the 'normalized cuts' algorithm to segment hyperspectral image data (HSI). In particular, we model an image as a weighted graph whose nodes are the image pixels, and edges defined as connecting spatial neighbors; the edge weights are given by a weighted combination of the spatial and spectral distances between nodes. We then use the Laplacian of the graph to recursively segment the image. The advantages of our approach are that, first, the graph structure naturally incorporates both the spatial and spectral information present in HSI; also, by using only spatial neighbors, the adjacency matrix is highly sparse; as a result, it is possible to apply our technique to much larger images than previous techniques. In the paper, we present the details of our algorithm, and include experimental results from a variety of hyperspectral images.

  2. Standoff Hyperspectral Imaging of Explosives Residues Using Broadly Tunable External Cavity Quantum Cascade Laser Illumination

    SciTech Connect

    Bernacki, Bruce E.; Phillips, Mark C.

    2010-05-01

    We describe experimental results on the detection of explosives residues using active hyperspectral imaging by illumination of the target surface using an external cavity quantum cascade laser (ECQCL) and imaging using a room temperature microbolometer camera. The active hyperspectral imaging technique forms an image hypercube by recording one image for each tuning step of the ECQCL. The resulting hyperspectral image contains the full absorption spectrum produced by the illumination laser at each pixel in the image which can then be used to identify the explosive type and relative quantity using spectral identification approaches developed initially in the remote sensing community.

  3. Evaluation of the spatial distribution, in percent coverage of the oil spilled during the Trecate blow-out, based on the analysis of airborne hyperspectral MIVIS data

    SciTech Connect

    Bianchi, R.; Cavalli, R.M.; Marino, C.M.

    1996-07-01

    On February 1994 a large area close to the Trecate town has been interested by an oil blow-out from an AGIP rig located within the Ticino Regional Park. One month later an airborne survey has been carried out in the framework of the CNR Lara Project, by utilizing the Daedalus AA5000 MIVIS spectrometer with 102 channels from Visible to Thermal Infrared. Different authors stress, for oil slicks discrimination, the utility of laser and microwaves based techniques, but the high spatial and spectral MIVIS resolutions can improve the detection of the relative coverage by spilled oil. This task has been performed by applying hyperspectral unmixing methods to the MIVIS calibrated data, obtaining an oil fractional image with respect to other chosen end-members. The analysis has shown a good agreement between the results of the unconstrained unmixing technique applied to MIVIS data and the ground truths, offering a tool useful to quantify in a synoptic overview the effects of oil spills over land, by relating the ppm of oil with the oil hyperspectral information gathered by MIVIS.

  4. Hyperspectral imaging system for disease scanning on banana plants

    NASA Astrophysics Data System (ADS)

    Ochoa, Daniel; Cevallos, Juan; Vargas, German; Criollo, Ronald; Romero, Dennis; Castro, Rodrigo; Bayona, Oswaldo

    2016-05-01

    Black Sigatoka (BS) is a banana plant disease caused by the fungus Mycosphaerella fijiensis. BS symptoms can be observed at late infection stages. By that time, BS has probably spread to other plants. In this paper, we present our current work on building an hyper-spectral (HS) imaging system aimed at in-vivo detection of BS pre-symptomatic responses in banana leaves. The proposed imaging system comprises a motorized stage, a high-sensitivity VIS-NIR camera and an optical spectrograph. To capture images of the banana leaf, the stage's speed and camera's frame rate must be computed to reduce motion blur and to obtain the same resolution along both spatial dimensions of the resulting HS cube. Our continuous leaf scanning approach allows imaging leaves of arbitrary length with minimum frame loss. Once the images are captured, a denoising step is performed to improve HS image quality and spectral profile extraction.

  5. Next generation miniature simultaneous multi-hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

    Hinnrichs, Michele; Gupta, Neelam

    2014-03-01

    The concept for a hyperspectral imaging system using a Fabry-Perot tunable filter (FPTF) array that is fabricated using "miniature optical electrical mechanical system" (MOEMS) technology. [1] Using an array of FPTF as an approach to hyperspectral imaging relaxes wavelength tuning requirements considerably because of the reduced portion of the spectrum that is covered by each element in the array. In this paper, Pacific Advanced Technology and ARL present the results of a concept design and performed analysis of a MOEMS based tunable Fabry-Perot array (FPTF) to perform simultaneous multispectral and hyperspectral imaging with relatively high spatial resolution. The concept design was developed with support of an Army SBIR Phase I program The Fabry-Perot tunable MOEMS filter array was combined with a miniature optics array and a focal plane array of 1024 x 1024 pixels to produce 16 colors every frame of the camera. Each color image has a spatial resolution of 256 x 256 pixels with an IFOV of 1.7 mrads and FOV of 25 degrees. The spectral images are collected simultaneously allowing high resolution spectral-spatial-temporal information in each frame of the camera, thus enabling the implementation of spectral-temporal-spatial algorithms in real-time to provide high sensitivity for the detection of weak signals in a high clutter background environment with low sensitivity to camera motion. The challenge in the design was the independent actuation of each Fabry Perot element in the array allowing for individual tuning. An additional challenge was the need to maximize the fill factor to improve the spatial coverage with minimal dead space. This paper will only address the concept design and analysis of the Fabry-Perot tunable filter array. A previous paper presented at SPIE DSS in 2012 explained the design of the optical array.

  6. Single aflatoxin contaminated corn kernel analysis with fluorescence hyperspectral image

    NASA Astrophysics Data System (ADS)

    Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Ononye, Ambrose; Brown, Robert L.; Cleveland, Thomas E.

    2010-04-01

    Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.

  7. Hyperspectral imaging in the infrared using LIFTIRS

    SciTech Connect

    Bennett, C.L.; Carter, M.R.; Fields, D.J.

    1995-07-01

    In this article, recent characterization measurements made with LIFTIRS, the Livermore Imaging Fourier Transform InfraRed Spectrometer, are presented. A discussion is also presented of the relative merits of the various alternative designs for imaging spectrometers.

  8. Image enhancement based on in vivo hyperspectral gastroscopic images: a case study.

    PubMed

    Gu, Xiaozhou; Han, Zhimin; Yao, Liqing; Zhong, Yunshi; Shi, Qiang; Fu, Ye; Liu, Changsheng; Wang, Xiguang; Xie, Tianyu

    2016-10-01

    Hyperspectral imaging (HSI) has been recognized as a powerful tool for noninvasive disease detection in the gastrointestinal field. However, most of the studies on HSI in this field have involved ex vivo biopsies or resected tissues. We proposed an image enhancement method based on in vivo hyperspectral gastroscopic images. First, we developed a flexible gastroscopy system capable of obtaining in vivo hyperspectral images of different types of stomach disease mucosa. Then, depending on a specific object, an appropriate band selection algorithm based on dependence of information was employed to determine a subset of spectral bands that would yield useful spatial information. Finally, these bands were assigned to be the color components of an enhanced image of the object. A gastric ulcer case study demonstrated that our method yields higher color tone contrast, which enhanced the displays of the gastric ulcer regions, and that it will be valuable in clinical applications. PMID:27206742

  9. Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging

    PubMed Central

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2015-01-01

    Complete surgical removal of tumor tissue is essential for postoperative prognosis after surgery. Intraoperative tumor imaging and visualization are an important step in aiding surgeons to evaluate and resect tumor tissue in real time, thus enabling more complete resection of diseased tissue and better conservation of healthy tissue. As an emerging modality, hyperspectral imaging (HSI) holds great potential for comprehensive and objective intraoperative cancer assessment. In this paper, we explored the possibility of intraoperative tumor detection and visualization during surgery using HSI in the wavelength range of 450 nm - 900 nm in an animal experiment. We proposed a new algorithm for glare removal and cancer detection on surgical hyperspectral images, and detected the tumor margins in five mice with an average sensitivity and specificity of 94.4% and 98.3%, respectively. The hyperspectral imaging and quantification method have the potential to provide an innovative tool for image-guided surgery. PMID:26523083

  10. Quantitative wavelength analysis and image classification for intraoperative cancer diagnosis with hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2015-03-01

    Complete surgical removal of tumor tissue is essential for postoperative prognosis after surgery. Intraoperative tumor imaging and visualization are an important step in aiding surgeons to evaluate and resect tumor tissue in real time, thus enabling more complete resection of diseased tissue and better conservation of healthy tissue. As an emerging modality, hyperspectral imaging (HSI) holds great potential for comprehensive and objective intraoperative cancer assessment. In this paper, we explored the possibility of intraoperative tumor detection and visualization during surgery using HSI in the wavelength range of 450 nm - 900 nm in an animal experiment. We proposed a new algorithm for glare removal and cancer detection on surgical hyperspectral images, and detected the tumor margins in five mice with an average sensitivity and specificity of 94.4% and 98.3%, respectively. The hyperspectral imaging and quantification method have the potential to provide an innovative tool for image-guided surgery.

  11. Embedded Bone Fragment Detection in Chicken Fillets using Transmittance Image Enhancement and Hyperspectral Reflectance Imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper is concerned with the detection of bone fragments embedded in compressed de-boned skinless chicken breast fillets by enhancing single-band transmittance images generated by back-lighting and exploiting spectral information from hyperspectral reflectance images. Optical imaging of chicken ...

  12. Assessment of chlorophyll-a concentration in the Gulf of Riga using hyperspectral airborne and simulated Sentinel-3 OLCI data

    NASA Astrophysics Data System (ADS)

    Jakovels, Dainis; Brauns, Agris; Filipovs, Jevgenijs; Taskovs, Juris; Fedorovicha, Dagnija; Paavel, Birgot; Ligi, Martin; Kutser, Tiit

    2016-08-01

    Remote sensing has proved to be an accurate and reliable tool in clear water environments like oceans or the Mediterranean Sea. However, the current algorithms and methods usually fail on optically complex waters like coastal and inland waters. The whole Baltic Sea can be considered as optically complex coastal waters. Remote assessment of water quality parameters (eg., chlorophyll-a concentration) is of interest for monitoring of marine environment, but hasn't been used as a routine approach in Latvia. In this study, two simultaneous hyperspectral airborne data and in situ measurement campaigns were performed in the Gulf of Riga near the River Daugava mouth in summer 2015 to simulate Sentinel-3 data and test existing algorithms for retrieval of Level 2 Water products. Comparison of historical data showed poor overall correlation between in situ measurements and MERIS chlorophyll-a data products. Better correlation between spectral chl-a data products and in situ water sampling measurements was achieved during simultaneous airborne and field campaign resulting in R2 up to 0.94 for field spectral data, R2 of 0.78 for airborne data. Test of all two band ratio combinations showed that R2 could be improved from 0.63 to 0.94 for hyperspectral airborne data choosing 712 and 728 nm bands instead of 709 and 666 nm, and R2 could be improved from 0.61 to 0.83 for simulated Sentinel-3 OLCI data choosing Oa10 and Oa8 bands instead of Oa11 and Oa8. Repeated campaigns are planned during spring and summer blooms 2016 in the Gulf of Riga to get larger data set for validation and evaluate repeatability. The main challenges remain to acquire as good data as possible within rapidly changing environment and often cloudy weather conditions.

  13. Multimodal confocal hyperspectral imaging microscopy with wavelength sweeping source

    NASA Astrophysics Data System (ADS)

    Kim, Young-Duk; Do, Dukho; Yoo, Hongki; Gweon, DaeGab

    2015-02-01

    There exist microscopes that are able to obtain the chemical properties of a sample, because there are some cases in which it is difficult to find out causality of a phenomenon by using only the structural information of a sample. Obtaining the chemical properties of a sample is important in biomedical imaging, because most biological phenomena include changes in the chemical properties of the sample. Hyperspectral imaging (HSI) is one of the popular imaging methods for characterizing materials and biological samples by measuring the reflectance or emission spectrum of the sample. Because all materials have a unique reflectance spectrum, it is possible to analyze material properties and detect changes in the chemical properties of a sample by measuring the spectral changes with respect to the original spectrum. Because of its ability to measure the spectrum of a sample, HSI is widely used in materials identification applications such as aerial reconnaissance and is the subject of various studies in microscopy. Although there are many advantages to using the method, conventional HSI has some limitations because of its complex configuration and slow speed. In this research we propose a new type of multimodal confocal hyperspectral imaging microscopy with fast image acquisition and a simple configuration that is capable of both confocal and HSI microscopies.

  14. Hyperspectral imaging for detection of arthritis: feasibility and prospects

    NASA Astrophysics Data System (ADS)

    Milanic, Matija; Paluchowski, Lukasz A.; Randeberg, Lise L.

    2015-09-01

    Rheumatoid arthritis (RA) is a disease that frequently leads to joint destruction. It has a high incidence rate worldwide, and the disease significantly reduces patients' quality of life. Detecting and treating inflammatory arthritis before structural damage to the joint has occurred is known to be essential for preventing patient disability and pain. Existing diagnostic technologies are expensive, time consuming, and require trained personnel to collect and interpret data. Optical techniques might be a fast, noninvasive alternative. Hyperspectral imaging (HSI) is a noncontact optical technique which provides both spectral and spatial information in one measurement. In this study, the feasibility of HSI in arthritis diagnostics was explored by numerical simulations and optimal imaging parameters were identified. Hyperspectral reflectance and transmission images of RA and normal human joint models were simulated using the Monte Carlo method. The spectral range was 600 to 1100 nm. Characteristic spatial patterns for RA joints and two spectral windows with transmission were identified. The study demonstrated that transmittance images of human joints could be used as one parameter for discrimination between arthritic and unaffected joints. The presented work shows that HSI is a promising imaging modality for the diagnostics and follow-up monitoring of arthritis in small joints.

  15. POULTRY SKIN TUMOR DETECTION IN HYPERSPECTRAL REFLECTANCE IMAGES BY COMBINING CLASSIFIERS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper presents a new method for detecting poultry skin tumors in hyperspectral reflectance images. We employ the principal component analysis (PCA), discrete wavelet transform (DWT), and kernel discriminant analysis (KDA) to extract the independent feature sets in hyperspectral reflectance imag...

  16. Differentiation of deciduous-calyx Korla fragrant pears using NIR hyperspectral imaging analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Near-infrared hyperspectral imaging was investigated as a potential method for automatic sorting of pears according to their calyx type. The hyperspectral images were analyzed and wavebands at 1190 nm and 1199 nm were selected for differentiating deciduous-calyx fruits from persistent-calyx ones. A ...

  17. Application of hyperspectral imaging for characterization of intramuscular fat distribution in beef

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this study, a hyperspectral imaging system in the spectral region of 400–1000 nm was used for visualization and determination of intramuscular fat concentration in beef samples. Hyperspectral images were acquired for beef samples, and spectral information was then extracted from each single sampl...

  18. MCT-based SWIR hyperspectral imaging system for evaluation of biological samples

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging has been shown to be a powerful tool for nondestructive evaluation of biological samples. We recently developed a new line-scan-based shortwave infrared (SWIR) hyperspectral imaging system. Critical sensing components of the system include a SWIR spectrograph, an MCT (HgCdTe) a...

  19. Hyperspectral microscope imaging methods to classify gram-positive and gram-negative foodborne pathogenic bacteria

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An acousto-optic tunable filter-based hyperspectral microscope imaging method has potential for identification of foodborne pathogenic bacteria from microcolony rapidly with a single cell level. We have successfully developed the method to acquire quality hyperspectral microscopic images from variou...

  20. Penetration depth measurement of near-infrared hyperspectral imaging light for milk powder

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The increasingly common application of near-infrared (NIR) hyperspectral imaging technique to the analysis of food powders has led to the need for optical characterization of samples. This study was aimed at exploring the feasibility of quantifying penetration depth of NIR hyperspectral imaging ligh...

  1. Hyperspectral imaging-based classification and wavebands selection for internal defect detection of pickling cucumbers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging is useful for detecting internal defect of pickling cucumbers. The technique, however, is not yet suitable for high-speed online implementation due to the challenges for analyzing large-scale hyperspectral images. This research was aimed to select the optimal wavebands from the...

  2. HICO and RAIDS Experiment Payload - Hyperspectral Imager for the Coastal Ocean

    NASA Technical Reports Server (NTRS)

    Corson, Mike

    2009-01-01

    HICO and RAIDS Experiment Payload - Hyperspectral Imager For The Coastal Ocean (HREP-HICO) will operate a visible and near-infrared (VNIR) Maritime Hyperspectral Imaging (MHSI) system, to detect, identify and quantify coastal geophysical features from the International Space Station.

  3. Super-resolution of hyperspectral images using sparse representation and Gabor prior

    NASA Astrophysics Data System (ADS)

    Patel, Rakesh C.; Joshi, Manjunath V.

    2016-04-01

    Super-resolution (SR) as a postprocessing technique is quite useful in enhancing the spatial resolution of hyperspectral (HS) images without affecting its spectral resolution. We present an approach to increase the spatial resolution of HS images by making use of sparse representation and Gabor prior. The low-resolution HS observations consisting of large number of bands are represented as a linear combination of a small number of basis images using principal component analysis (PCA), and the significant components are used in our work. We first obtain initial estimates of SR on this reduced dimension by using compressive sensing-based method. Since SR is an ill-posed problem, the final solution is obtained by using a regularization framework. The novelty of our approach lies in: (1) estimation of optimal point spread function in the form of decimation matrix, and (2) using a new prior called "Gabor prior" to super-resolve the significant PCA components. Experiments are conducted on two different HS datasets namely, 31-band natural HS image set collected under controlled laboratory environment and a set of 224-band real HS images collected by airborne visible/infrared imaging spectrometer remote sensing sensor. Visual inspections and quantitative comparison confirm that our method enhances spatial information without introducing significant spectral distortion. Our conclusions include: (1) incorporate the sensor characteristics in the form of estimated decimation matrix for SR, and (2) preserve various frequencies in super-resolved image by making use of Gabor prior.

  4. Hyperspectral imaging of microalgae using two-photon excitation.

    SciTech Connect

    Sinclair, Michael B.; Melgaard, David Kennett; Reichardt, Thomas A.; Timlin, Jerilyn Ann; Garcia, Omar Fidel; Luk, Ting Shan; Jones, Howland D. T.; Collins, Aaron M.

    2010-10-01

    A considerable amount research is being conducted on microalgae, since microalgae are becoming a promising source of renewable energy. Most of this research is centered on lipid production in microalgae because microalgae produce triacylglycerol which is ideal for biodiesel fuels. Although we are interested in research to increase lipid production in algae, we are also interested in research to sustain healthy algal cultures in large scale biomass production farms or facilities. The early detection of fluctuations in algal health, productivity, and invasive predators must be developed to ensure that algae are an efficient and cost-effective source of biofuel. Therefore we are developing technologies to monitor the health of algae using spectroscopic measurements in the field. To do this, we have proposed to spectroscopically monitor large algal cultivations using LIDAR (Light Detection And Ranging) remote sensing technology. Before we can deploy this type of technology, we must first characterize the spectral bio-signatures that are related to algal health. Recently, we have adapted our confocal hyperspectral imaging microscope at Sandia to have two-photon excitation capabilities using a chameleon tunable laser. We are using this microscope to understand the spectroscopic signatures necessary to characterize microalgae at the cellular level prior to using these signatures to classify the health of bulk samples, with the eventual goal of using of LIDAR to monitor large scale ponds and raceways. By imaging algal cultures using a tunable laser to excite at several different wavelengths we will be able to select the optimal excitation/emission wavelengths needed to characterize algal cultures. To analyze the hyperspectral images generated from this two-photon microscope, we are using Multivariate Curve Resolution (MCR) algorithms to extract the spectral signatures and their associated relative intensities from the data. For this presentation, I will show our two

  5. Object detection in rural areas using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Ozturk, Safak; Emre Esin, Yunus; Artan, Yusuf

    2015-10-01

    Object detection has gained considerable interest in remote sensing community with a broad range of applications including the remote monitoring of building development in rural areas. Many earlier studies on this task performed their analysis using either multispectral satellite imagery or color images obtained via an aerial vehicle. In recent years, hyperspectral imaging (HSI) has emerged as an alternative technique for remote monitoring of building developments. Unlike other imaging techniques, HSI provides a continuous spectral signature of the objects in the field of view (FOV) which facilitates the separation among different objects. In general, spectral signature similarity between objects often causes a significant amount of false alarm (FA) rate that adversely effects the overall accuracy of these systems. In order to reduce the high rate of FA posed by the pixel-wise classification, we propose a novel rural building detection method that utilizes both spatial information and spectral signature of the pixels. Proposed technique consists of three parts; a spectral signature classifier, watershed based superpixel map and an oriented-gradient filters based object detector. In our analysis, we have evaluated the performance of proposed approach using hyperspectral image dataset obtained at various elevation levels, namely 500 meters and 3000 meters. NEO HySpex VNIR-1800 camera is used for 182 band hyperspectral data acquisition. First 155 band is used due to the atmospheric effects on the last bands. Performance comparison between the proposed technique and the pixel-wise spectral classifier indicates a reduction in sensitivity rate but a notable increase in specificity and overall accuracy rates. Proposed method yields sensitivity, specificity, accuracy rate of 0.690, 0.997 and 0.992, respectively, whereas pixel-wise classification yields sensitivity, specificity, and accuracy rate of 0.758, 0.983, 0.977, respectively. Note that the sensitivity reduction is

  6. Comparative analysis of different implementations of a parallel algorithm for automatic target detection and classification of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Paz, Abel; Plaza, Antonio; Plaza, Javier

    2009-08-01

    Automatic target detection in hyperspectral images is a task that has attracted a lot of attention recently. In the last few years, several algoritms have been developed for this purpose, including the well-known RX algorithm for anomaly detection, or the automatic target detection and classification algorithm (ATDCA), which uses an orthogonal subspace projection (OSP) approach to extract a set of spectrally distinct targets automatically from the input hyperspectral data. Depending on the complexity and dimensionality of the analyzed image scene, the target/anomaly detection process may be computationally very expensive, a fact that limits the possibility of utilizing this process in time-critical applications. In this paper, we develop computationally efficient parallel versions of both the RX and ATDCA algorithms for near real-time exploitation of these algorithms. In the case of ATGP, we use several distance metrics in addition to the OSP approach. The parallel versions are quantitatively compared in terms of target detection accuracy, using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center in New York, five days after the terrorist attack of September 11th, 2001, and also in terms of parallel performance, using a massively Beowulf cluster available at NASA's Goddard Space Flight Center in Maryland.

  7. Improvement of Short-Wave InfraRed Hyperspectral Imaging by Direct Polarization Measurements

    NASA Astrophysics Data System (ADS)

    Leblanc, G. E.; Allux, S.

    2010-12-01

    Hyperspectral imaging is susceptible to a myriad of atmospheric effects that cause undesirable effects when applying advanced processing techniques. Principally, scattering of incoming solar energy and the resulting “haze” produced has a considerable influence on the resulting quality of the data acquired in the ShortWave InfraRed (SWIR) region (850nm to 2500nm) of the electromagnetic spectrum. To alleviate this condition we have devised a polarization system for the Flight Research Laboratory’s (FRL) airborne SWIR hyperspectral imaging system and have collected data from various sources via ground-based and airborne environments. The Polarized SWIR (PSWIR) system we have devised incorporates a linear 90° polarization filter, within a framework that has been attached to FRL’s SWIR system. The polarizer lens is aligned to the 0° axis of the optical slit and can be rotated a full 360°. For the ground-based data collection, the PSWIR was mounted on a z-axis 360° rotation mount which allowed for scanning within the vertical plane as the imager is a push-broom imager that requires motion of the system in order to obtain spatial information. Data was acquired at various polarization orientations in steps of 45° from 0° through to 180°. The data was then analysed using Principle Component Analysis (PCA) and results show that substantial improvement signal-to-noise in higher-order PC’s are obtained in the PSWIR system compared to the SWIR system without polarization. Further, the higher order PC’s derived from the PSWIR system once compared to the SWIR system alone, lead us to conclude that many “artefacts” often ascribed to sensor problems can, in fact, be attributed to unresolved scatter issues in the data. The entire system was then installed aboard FRL’s Twin Otter aircraft and flown over various target materials of interest (man-made, vegetation, soils...) several times over the course of 3-weeks in July/August 2010. As a result of analysing

  8. Parallel optimization of pixel purity index algorithm for massive hyperspectral images in cloud computing environment

    NASA Astrophysics Data System (ADS)

    Chen, Yufeng; Wu, Zebin; Sun, Le; Wei, Zhihui; Li, Yonglong

    2016-04-01

    With the gradual increase in the spatial and spectral resolution of hyperspectral images, the size of image data becomes larger and larger, and the complexity of processing algorithms is growing, which poses a big challenge to efficient massive hyperspectral image processing. Cloud computing technologies distribute computing tasks to a large number of computing resources for handling large data sets without the limitation of memory and computing resource of a single machine. This paper proposes a parallel pixel purity index (PPI) algorithm for unmixing massive hyperspectral images based on a MapReduce programming model for the first time in the literature. According to the characteristics of hyperspectral images, we describe the design principle of the algorithm, illustrate the main cloud unmixing processes of PPI, and analyze the time complexity of serial and parallel algorithms. Experimental results demonstrate that the parallel implementation of the PPI algorithm on the cloud can effectively process big hyperspectral data and accelerate the algorithm.

  9. Rapid hyperspectral imaging in the mid-infrared

    NASA Astrophysics Data System (ADS)

    Kröger, N.; Egl, A.; Engel, M.; Gretz, N.; Haase, K.; Herpich, I.; Neudecker, S.; Pucci, A.; Schönhals, A.; Petrich, W.

    2014-03-01

    Despite the successes of mid-infrared hyperspectral imaging in a research environment, progress in the migration of technology into the day-to-day clinical application is slow. Clinical acceptance may be improved if the spectroscopy would be faster and the infrared microscopes available at lower cost. Here we present first results of a fast, multi-scale mid-infrared microscopy setup which allows for the investigation of 10.6×11.7 mm2 and 2.8×3.1mm2 fields of view with a resolution of 23.0+/-3.5 μm and 9.4+/-1.8 μm, respectively. Tunable quantum cascade lasers in the wavenumber ranges of 1030-1090 cm-1 and 1160-1320 cm-1 serve as light sources. A vapor cell is used as a frequency reference during the rapid scanning. As far as the imaging is concerned, it is the high spectral power density of the quantum cascade laser which enables the use of a microbolometer array while still obtaining reasonable signal-to-noise ratios on each pixel. Hyperspectral images are taken in times which can be as low as 52s for the overall image acquisition including referencing.

  10. NIR hyperspectral imaging to evaluate degradation in captopril commercial tablets.

    PubMed

    França, Leandro de Moura; Pimentel, Maria Fernanda; Simões, Simone da Silva; Grangeiro, Severino; Prats-Montalbán, José M; Ferrer, Alberto

    2016-07-01

    Pharmaceutical quality control is important for improving the effectiveness, purity and safety of drugs, as well as for the prevention or control of drug degradation. In the present work, near infrared hyperspectral images (HSI-NIR) of tablets with different expiration dates were employed to evaluate the degradation of captopril into captopril disulfide in different layers, on the top and on the bottom surfaces of the tablets. Multivariate curve resolution (MCR) models were used to extract the concentration distribution maps from the hyperspectral images. Afterward, multivariate image techniques were applied to the concentration distribution maps (CDMs), to extract features and build models relating the main characteristics of the images to their corresponding manufacturing dates. Resolution methods followed by extracting features were able to estimate the tablet manufacture date with a prediction error of 120days. The model developed could be useful to evaluate whether a sample shows a degradation pattern consistent with the date of manufacturing or to detect abnormal behaviors in the natural degradation process of the sample. The information provided by the HIS-NIR is important for the development of the process (QbD), looking inside the formulation, revealing the behavior of the active pharmaceutical ingredient (API) during the product's shelf life. PMID:27163244

  11. DLP hyperspectral imaging for surgical and clinical utility

    NASA Astrophysics Data System (ADS)

    Zuzak, Karel J.; Francis, Robert P.; Wehner, Eleanor F.; Smith, Jack; Litorja, Maritoni; Allen, David W.; Tracy, Chad; Cadeddu, Jeffrey; Livingston, Edward

    2009-02-01

    We describe a novel digital light processing, DLP hyperspectral imaging system for visualizing chemical composition of in vivo tissues during surgical procedures non-invasively and at near video rate. The novelty of the DLP hyperspectral imaging system resides in (1) its ability to conform light to rapidly sweep through a series of preprogrammed spectral illuminations as simple as a set of contiguous bandpasses to any number of complex spectra, and (2) processing the reflected spectroscopic image data using unique supervised and unsupervised chemometric methods that color encode molecular content of tissue at each image detector pixel providing an optical biopsy. Spectral illumination of tissue is accomplished utilizing a DLP® based spectral illuminator incorporating a series of bandpass spectra and measuring the reflectance image with a CCD array detector. Wavelength dependent images are post processed with a multivariate least squares analysis method using known reference spectra of oxy- and deoxyhemoglobin. Alternatively, illuminating with complex reference spectra reduces the number of spectral images required for generating chemically relevant images color encoded for relative percentage of oxyhemoglobin are collected and displayed in real time near-video rate, (3 to 4) frames per second (fps). As a proof of principle application, a kidney of an anesthetized pig was imaged before and after renal vasculature occlusion showing the clamped kidney to be 61% of the unclamped kidney percentage of oxyhemoglobin. Using the "3-Shot" spectral illumination method and gathering data at (3 to 4) fps shows a non-linear exponential de-oxygenation of hemoglobin reaching steady state within 30 seconds post occlusion.

  12. Application of multivariate curve resolution alternating least squares (MCR-ALS) to remote sensing hyperspectral imaging.

    PubMed

    Zhang, Xin; Tauler, Romà

    2013-01-31

    The application of the MCR-ALS method is demonstrated on two simulated remote sensing spectroscopic images and on one experimental reference remote sensing spectroscopic image obtained by the Airborn Visible/Infrared Imaging Spectrometer (AVIRIS). By application of MCR-ALS, the spectra signatures of the pure constituents present in the image and their concentration distribution at a pixel level are estimated. Results obtained by MCR-ALS are compared to those obtained by other methods frequently used in the remote sensing spectroscopic imaging field like VCA and MVSA. In the case of the analysis of the experimental data set, the resolved pure spectra signatures were compared to reference spectra from USGS library for their identification. In all cases, results were also evaluated for the presence of rotational ambiguities using the MCR-BANDS method. The obtained results confirmed that the MCR-ALS method can be successfully used for remote sensing hyperspectral image resolution purposes. However, the amount of rotation ambiguity still present in the solutions obtained by this and other resolution methods (like VCA or MVSA) can still be large and it should be evaluated with care, trying to reduce its effects by selecting the more appropriate constraints. Only in this way it is possible to increase the reliability of the solutions provided by these methods and decrease the uncertainties associated to their use.

  13. Static hyperspectral imaging polarimeter for full linear Stokes parameters.

    PubMed

    Mu, Tingkui; Zhang, Chunmin; Jia, Chenling; Ren, Wenyi

    2012-07-30

    A compact, static hyperspectral imaging linear polarimeter (HILP) based on a Savart interferometer (SI) is conceptually described. It improves the existing SI by replacing front polarizer with two Wollaston prisms, and can simultaneously acquire four interferograms corresponding to four linearly polarized lights on a single CCD. The spectral dependence of linear Stokes parameters can be recovered with Fourier transformation. Since there is no rotating or moving parts, the system is relatively robust. The interference model of the HILP is proved. The performance of the system is demonstrated through a numerical simulation, and the methods for compensating the imperfection of the polarization elements are described. PMID:23038368

  14. Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

    PubMed

    Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin

    2015-12-01

    Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

  15. Classification of fecal contamination on leafy greens by hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Yang, Chun-Chieh; Jun, Won; Kim, Moon S.; Chao, Kaunglin; Kang, Sukwon; Chan, Diane E.; Lefcourt, Alan

    2010-04-01

    This paper reported the development of hyperspectral fluorescence imaging system using ultraviolet-A excitation (320-400 nm) for detection of bovine fecal contaminants on the abaxial and adaxial surfaces of romaine lettuce and baby spinach leaves. Six spots of fecal contamination were applied to each of 40 lettuce and 40 spinach leaves. In this study, the wavebands at 666 nm and 680 nm were selected by the correlation analysis. The two-band ratio, 666 nm / 680 nm, of fluorescence intensity was used to differentiate the contaminated spots from uncontaminated leaf area. The proposed method could accurately detect all of the contaminated spots.

  16. Melanoma detection using smartphone and multimode hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    MacKinnon, Nicholas; Vasefi, Fartash; Booth, Nicholas; Farkas, Daniel L.

    2016-04-01

    This project's goal is to determine how to effectively implement a technology continuum from a low cost, remotely deployable imaging device to a more sophisticated multimode imaging system within a standard clinical practice. In this work a smartphone is used in conjunction with an optical attachment to capture cross-polarized and collinear color images of a nevus that are analyzed to quantify chromophore distribution. The nevus is also imaged by a multimode hyperspectral system, our proprietary SkinSpect™ device. Relative accuracy and biological plausibility of the two systems algorithms are compared to assess aspects of feasibility of in-home or primary care practitioner smartphone screening prior to rigorous clinical analysis via the SkinSpect.

  17. Non-negative structural sparse representation for high resolution hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Meng, Guiyu; Li, Guangyu; Dong, Weisheng; Shi, Guangming

    2014-11-01

    High resolution hyperspectral images have important applications in many areas, such as anomaly detection, target recognition and image classification. Due to the limitation of the sensors, it is challenging to obtain high spatial resolution hyperspectral images. Recently, the methods that reconstruct high spatial resolution hyperspectral images from the pair of low resolution hyperspectral images and high resolution RGB image of the same scene have shown promising results. In these methods, sparse non-negative matrix factorization (SNNMF) technique was proposed to exploit the spectral correlations among the RGB and spectral images. However, only the spectral correlations were exploited in these methods, ignoring the abundant spatial structural correlations of the hyperspectral images. In this paper, we propose a novel algorithm combining the structural sparse representation and non-negative matrix factorization technique to exploit the spectral-spatial structure correlations and nonlocal similarity of the hyperspectral images. Compared with SNNMF, our method makes use of both the spectral and spatial redundancies of hyperspectral images, leading to better reconstruction performance. The proposed optimization problem is efficiently solved by using the alternating direction method of multipliers (ADMM) technique. Experiments on a public database show that our approach performs better than other state-of-the-art methods on the visual effect and in the quantitative assessment.

  18. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

    PubMed Central

    Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2016-01-01

    Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management. PMID:27656035

  19. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2016-03-01

    Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

  20. On the response function separability of hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

    Jemec, Jurij; Pernuš, Franjo; Likar, Boštjan; Bürmen, Miran

    2015-05-01

    Hyperspectral imaging systems effectively collect information across the spectral and two spatial dimensions by employing three main components: the front lens, the light-diffraction element and a camera. Imperfections in these components introduce spectral and spatial dependent distortions in the recorded hyperspectral image. These can be characterized by a 3D response function that is subsequently used to remove distortions and enhance the resolution of the recorded images by deconvolution. The majority of existing characterization methods assume spatial and spectral separability of the 3D response function. In this way, the complex problem of 3D response function characterization is reduced to independent characterizations of the three orthogonal response function components. However, if the 3D response function is non-separable, such characterization can lead to poor response function estimates, and hence inaccurate and distorted results of the subsequent deconvolution-based calibration and image enhancement. In this paper, we evaluate the influence of the spatial response function non-separability on the results of the calibration by deconvolution. For this purpose, a novel procedure for direct measurement of the 2D spatial response function is proposed along with a quantitative measure of the spatial response function non-separability. The quality of deconvolved images is assessed in terms of full width at half maximum (FWHM) and step edge overshoot magnitude observed in the deconvolved images of slanted edges, images of biological slides, and 1951 USAF resolution test chart. Results show that there are cases, when nonseparability of the system response function is significant and should be considered by the deconvolution-based calibration and image enhancement methods.

  1. Detecting pits in tart cherries by hyperspectral transmission imaging

    NASA Astrophysics Data System (ADS)

    Qin, Jianwei; Lu, Renfu

    2004-11-01

    The presence of pits in processed cherry products causes safety concerns for consumers and imposes potential liability for the food industry. The objective of this research was to investigate a hyperspectral transmission imaging technique for detecting the pit in tart cherries. A hyperspectral imaging system was used to acquire transmission images from individual cherry fruit for four orientations before and after pits were removed over the spectral region between 450 nm and 1,000 nm. Cherries of three size groups (small, intermediate, and large), each with two color classes (light red and dark red) were used for determining the effect of fruit orientation, size, and color on the pit detection accuracy. Additional cherries were studied for the effect of defect (i.e., bruises) on the pit detection. Computer algorithms were developed using the neural network (NN) method to classify the cherries with and without the pit. Two types of data inputs, i.e., single spectra and selected regions of interest (ROIs), were compared. The spectral region between 690 nm and 850 nm was most appropriate for cherry pit detection. The NN with inputs of ROIs achieved higher pit detection rates ranging from 90.6% to 100%, with the average correct rate of 98.4%. Fruit orientation and color had a small effect (less than 1%) on pit detection. Fruit size and defect affected pit detection and their effect could be minimized by training the NN with properly selected cherry samples.

  2. Calibration methodology and performance characterization of a polarimetric hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Holder, Joel G.; Martin, Jacob A.; Pitz, Jeremey; Pezzaniti, Joseph L.; Gross, Kevin C.

    2014-05-01

    Polarimetric hyperspectral imaging (P-HSI) has the potential to improve target detection, material identification, and background characterization over conventional hyperspectral imaging and polarimetric imaging. To fully exploit the spectro-polarimetric signatures captured by such an instrument, a careful calibration process is required to remove the spectrally- and polarimetrically-dependent system response (gain). Calibration of instruments operating in the long-wave infrared (LWIR, 8μm to 12 μm) is further complicated by the polarized spectral radiation generated within the instrument (offset). This paper presents a calibration methodology developed for a LWIR Telops Hyper-Cam modified for polarimetry by replacing the entrance window with a rotatable holographic wire-grid polarizer (4000 line/mm, ZnSe substrate, 350:1 extinction ratio). A standard Fourier-transform spectrometer (FTS) spectro-radiometric calibration is modified to include a Mueller-matrix approach to account for polarized transmission through and polarized selfemission from each optical interface. It is demonstrated that under the ideal polarizer assumption, two distinct blackbody measurements at polarizer angles of 0°, 45°, 90°, and 135° are sufficient to calibrate the system for apparent degree-of-linear-polarization (DoLP) measurements. Noise-equivalent s1, s2, and DoLP are quantified using a wide-area blackbody. A polarization-state generator is used to determine the Mueller deviation matrix. Finally, a realistic scene involving buildings, cars, sky radiance, and natural vegetation is presented.

  3. Identifying volcanic endmembers in hyperspectral images using spectral unmixing

    NASA Astrophysics Data System (ADS)

    Piscini, Alessandro; Carboni, Elisa; Del Frate, Fabio; Grainger, Roy Gordon

    2014-10-01

    Spectral unmixing technique is used in remote sensed data analysis for the determination of certain basis spectra called 'endmembers'. Once those spectra are found, the image cube can be 'unmixed' into fractional abundance of each material in each pixel. In the present work infrared spectra recorded by Infrared Atmospheric Sounding Interferometer (IASI) were used to characterize the emission from Grimsvotn volcanic eruption on 2011. In particular, a methodology based on spectral unmixing theory was used in order to extract the spectral signature of volcanic cloud constituents, such as ash and sulphur dioxide (SO2) and maps of their abundances in a IASI image were obtained. Taking the advantage of IASI broad spectral coverage the broadband signature in the Thermal Infrared (TIR) radiance spectra in the 1000-1410 cm-1 range associated with the presence of aerosols was obtained. Volcanic ash and SO2 spectral signatures were extracted, as well as those related to the simultaneous presence of ash, SO2 and cloud. The study proved that spectral unmixing, applied to Hyperspectral images, is able to identify volcanic aerosols and other species like SO2 despite a strong presence of meteorological clouds. Moreover, the analysis of hyperspectral datasets permitted to generate abundance maps for each endmember extracted. In particular, maps obtained for the test case of 2011 May, 23th put in evidence the separation between clouds of ejected SO2 and volcanic ash. The former dispersed at Northern latitudes, whilst the latter was situated at southern latitudes, South of Iceland.

  4. Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA

    PubMed Central

    Han, Zhongzhi; Wan, Jianhua; Deng, Limiao; Liu, Kangwei

    2016-01-01

    To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400–720nm. Radiation indexes extracted from each ROI are used as feature vectors. These indexes are individual band radiation index (RI), difference of consecutive spectral band radiation index (DRI), ratio of consecutive spectral band radiation index (RRI) and normalized DRI (NDRI). Another set of features called quantized histogram matrix (QHM) are extracted by applying quantization on the image histogram from these features. Based on these feature sets, improved kernel independent component analysis (iKICA) is used to select significant features. For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select the most significant wavelengths or features. Support vector machine (SVM) is used as the classifier. Experimental results show that the proposed methods are able to obtain robust and better classification performance with fewer number of spectral bands and simplify the design of computer vision systems. PMID:26820311

  5. Hyperspectral Image Target Detection Improvement Based on Total Variation.

    PubMed

    Yang, Shuo; Shi, Zhenwei

    2016-05-01

    For the hyperspectral target detection, the neighbors of a target pixel are very likely to be target pixels, and those of a background pixel are very likely to be background pixels. In order to utilize this spatial homogeneity or smoothness, based on total variation (TV), we propose a novel supervised target detection algorithm which uses a single target spectrum as the prior knowledge. TV can make the image smooth, and has been widely used in image denoising and restoration. The proposed algorithm uses TV to keep the spatial homogeneity or smoothness of the detection output. Meanwhile, a constraint is used to guarantee the spectral signature of the target unsuppressed. The final formulated detection model is an ℓ1-norm convex optimization problem. The split Bregman algorithm is used to solve our optimization problem, as it can solve the ℓ1-norm optimization problem efficiently. Two synthetic and two real hyperspectral images are used to do experiments. The experimental results demonstrate that the proposed algorithm outperforms the other algorithms for the experimental data sets. The experimental results also show that even when the target occupies only one pixel, the proposed algorithm can still obtain good results. This is because in such a case, the background is kept smooth, but at the same time, the algorithm allows for sharp edges in the detection output. PMID:27019489

  6. Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA.

    PubMed

    Han, Zhongzhi; Wan, Jianhua; Deng, Limiao; Liu, Kangwei

    2016-01-01

    To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400-720nm. Radiation indexes extracted from each ROI are used as feature vectors. These indexes are individual band radiation index (RI), difference of consecutive spectral band radiation index (DRI), ratio of consecutive spectral band radiation index (RRI) and normalized DRI (NDRI). Another set of features called quantized histogram matrix (QHM) are extracted by applying quantization on the image histogram from these features. Based on these feature sets, improved kernel independent component analysis (iKICA) is used to select significant features. For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select the most significant wavelengths or features. Support vector machine (SVM) is used as the classifier. Experimental results show that the proposed methods are able to obtain robust and better classification performance with fewer number of spectral bands and simplify the design of computer vision systems. PMID:26820311

  7. A novel highly parallel algorithm for linearly unmixing hyperspectral images

    NASA Astrophysics Data System (ADS)

    Guerra, Raúl; López, Sebastián.; Callico, Gustavo M.; López, Jose F.; Sarmiento, Roberto

    2014-10-01

    Endmember extraction and abundances calculation represent critical steps within the process of linearly unmixing a given hyperspectral image because of two main reasons. The first one is due to the need of computing a set of accurate endmembers in order to further obtain confident abundance maps. The second one refers to the huge amount of operations involved in these time-consuming processes. This work proposes an algorithm to estimate the endmembers of a hyperspectral image under analysis and its abundances at the same time. The main advantage of this algorithm is its high parallelization degree and the mathematical simplicity of the operations implemented. This algorithm estimates the endmembers as virtual pixels. In particular, the proposed algorithm performs the descent gradient method to iteratively refine the endmembers and the abundances, reducing the mean square error, according with the linear unmixing model. Some mathematical restrictions must be added so the method converges in a unique and realistic solution. According with the algorithm nature, these restrictions can be easily implemented. The results obtained with synthetic images demonstrate the well behavior of the algorithm proposed. Moreover, the results obtained with the well-known Cuprite dataset also corroborate the benefits of our proposal.

  8. NIR DLP hyperspectral imaging system for medical applications

    NASA Astrophysics Data System (ADS)

    Wehner, Eleanor; Thapa, Abhas; Livingston, Edward; Zuzak, Karel

    2011-03-01

    DLP® hyperspectral reflectance imaging in the visible range has been previously shown to quantify hemoglobin oxygenation in subsurface tissues, 1 mm to 2 mm deep. Extending the spectral range into the near infrared reflects biochemical information from deeper subsurface tissues. Unlike any other illumination method, the digital micro-mirror device, DMD, chip is programmable, allowing the user to actively illuminate with precisely predetermined spectra of illumination with a minimum bandpass of approximately 10 nm. It is possible to construct active spectral-based illumination that includes but is not limited to containing sharp cutoffs to act as filters or forming complex spectra, varying the intensity of light at discrete wavelengths. We have characterized and tested a pure NIR, 760 nm to 1600 nm, DLP hyperspectral reflectance imaging system. In its simplest application, the NIR system can be used to quantify the percentage of water in a subject, enabling edema visualization. It can also be used to map vein structure in a patient in real time. During gall bladder surgery, this system could be invaluable in imaging bile through fatty tissue, aiding surgeons in locating the common bile duct in real time without injecting any contrast agents.

  9. SETA-Hyperspectral Imaging Spectrometer for Marco Polo mission.

    NASA Astrophysics Data System (ADS)

    de Sanctis, M. Cristina; Filacchione, Gianrico; Capaccioni, Fabrizio; Piccioni, Giuseppe; Ammannito, Eleonora; Capria, M. Teresa; Coradini, Angioletta; Migliorini, Alessandra; Battistelli, Enrico; Preti, Giampaolo

    2010-05-01

    The Marco Polo NEO sample return M-class mission has been selected for assessment study within the ESA Cosmic Vision 2015-2025 program. The Marco Polo mission proposes to do a sample return mission to Near Earth Asteroid. With this mission we have the opportunity to return for study in Earth-based laboratories a direct sample of the earliest record of how our solar system formed. The landing site and sample selection will be the most important scientific decision to make during the course of the entire mission. The imaging spectrometer is a key instrument being capable to characterize the mineralogical composition of the entire asteroid and to analyze the of the landing site and the returned sample in its own native environment. SETA is a Hyperspectral Imaging Spectrometer able to perform imaging spectroscopy in the spectral range 400-3300 nm for a complete mapping of the target in order to characterize the mineral properties of the surface. The spectral sampling is of at least 20 nm and the spatial resolution of the order of meter. SETA shall be able to return a detailed determination of the mineralogical composition for the different geologic units as well as the overall surface mineralogy with a spatial resolution of the order of few meters. These compositional characterizations involve the analysis of spectral parameters that are diagnostic of the presence and composition of various mineral species and materials that may be present on the target body. Most of the interesting minerals have electronic and vibrational absorption features in their VIS-NIR reflectance spectra. The SETA design is based on a pushbroom imaging spectrometer operating in the 400-3300 nm range, using a 2D array HgCdTe detector. This kind of instrument allows a simultaneous measurement of a full spectrum taken across the field of view defined by the slit's axis (samples). The second direction (lines) of the hyperspectral image shall be obtained by using the relative motion of the orbiter

  10. Hyperspectral image preprocessing with bilateral filter for improving the classification accuracy of support vector machines

    NASA Astrophysics Data System (ADS)

    Sahadevan, Anand S.; Routray, Aurobinda; Das, Bhabani S.; Ahmad, Saquib

    2016-04-01

    Bilateral filter (BF) theory is applied to integrate spatial contextual information into the spectral domain for improving the accuracy of the support vector machine (SVM) classifier. The proposed classification framework is a two-stage process. First, an edge-preserved smoothing is carried out on a hyperspectral image (HSI). Then, the SVM multiclass classifier is applied on the smoothed HSI. One of the advantages of the BF-based implementation is that it considers the spatial as well as spectral closeness for smoothing the HSI. Therefore, the proposed method provides better smoothing in the homogeneous region and preserves the image details, which in turn improves the separability between the classes. The performance of the proposed method is tested using benchmark HSIs obtained from the airborne-visible-infrared-imaging-spectrometer (AVIRIS) and the reflective-optics-system-imaging-spectrometer (ROSIS) sensors. Experimental results demonstrate the effectiveness of the edge-preserved filtering in the classification of the HSI. Average accuracies (with 10% training samples) of the proposed classification framework are 99.04%, 98.11%, and 96.42% for AVIRIS-Salinas, ROSIS-Pavia University, and AVIRIS-Indian Pines images, respectively. Since the proposed method follows a combination of BF and the SVM formulations, it will be quite simple and practical to implement in real applications.

  11. Illumination-invariant face recognition in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Pan, Zhihong; Healey, Glenn E.; Prasad, Manish; Tromberg, Bruce J.

    2003-09-01

    We examine the performance of illumination-invariant face recognition in hyperspectral images on a database of 200 subjects. The images are acquired over the near-infrared spectral range of 0.7-1.0 microns. Each subject is imaged over a range of facial orientations and expressions. Faces are represented by local spectral information for several tissue types. Illumination variation is modeled by low-dimensional linear subspaces of reflected radiance spectra. One hundred outdoor illumination spectra measured at Boulder, Colorado are used to synthesize the radiance spectra for the face tissue types. Weighted invariant subspace projection over multiple tissue types is used for recognition. Illumination-invariant face recognition is tested for various face rotations as well as different facial expressions.

  12. Hyperspectral imaging applied to medical diagnoses and food safety

    NASA Astrophysics Data System (ADS)

    Carrasco, Oscar; Gomez, Richard B.; Chainani, Arun; Roper, William E.

    2003-08-01

    This paper analyzes the feasibility and performance of HSI systems for medical diagnosis as well as for food safety. Illness prevention and early disease detection are key elements for maintaining good health. Health care practitioners worldwide rely on innovative electronic devices to accurately identify disease. Hyperspectral imaging (HSI) is an emerging technique that may provide a less invasive procedure than conventional diagnostic imaging. By analyzing reflected and fluorescent light applied to the human body, a HSI system serves as a diagnostic tool as well as a method for evaluating the effectiveness of applied therapies. The safe supply and production of food is also of paramount importance to public health illness prevention. Although this paper will focus on imaging and spectroscopy in food inspection procedures -- the detection of contaminated food sources -- to ensure food quality, HSI also shows promise in detecting pesticide levels in food production (agriculture.)

  13. Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique

    NASA Astrophysics Data System (ADS)

    Guan, Yana; Li, Qingli; Wang, Yiting; Liu, Hongying; Zhu, Ziqiang

    2012-05-01

    White blood cells (WBC) are comparatively significant components in the human blood system, and they have a pathological relationship with some blood-related diseases. To analyze the disease information accurately, the most essential work is to segment WBCs. We propose a new method for pathological WBC segmentation based on a hyperspectral imaging system. This imaging system is used to capture WBC images, which is characterized by acquiring 1-D spectral information and 2-D spatial information for each pixel. A spectral information divergence algorithm is presented to segment pathological WBCs into four parts. In order to evaluate the performance of the new approach, K-means and spectral angle mapper-based segmental methods are tested in contrast on six groups of blood smears. Experimental results show that the presented method can segment pathological WBCs more accurately, regardless of their irregular shapes, sizes, and gray-values.

  14. New Method for Calibration for Hyperspectral Pushbroom Imaging Systems

    NASA Technical Reports Server (NTRS)

    Ryan, Robert; Olive, Dan; ONeal, Duane; Schere, Chris; Nixon, Thomas; May, Chengye; Ryan, Jim; Stanley, Tom; Witcher, Kern

    1999-01-01

    A new, easy-to-implement approach for achieving highly accurate spectral and radiometric calibration of array-based, hyperspectral pushbroom imagers is presented in this paper. The equivalence of the plane of the exit port of an integrating sphere to a Lambertian surface is utilized to provide a field-filling radiance source for the imager. Several different continuous wave lasers of various wavelengths and a quartz-tungsten-halogen lamp internally illuminate the sphere. The imager is positioned to "stare" into the port, and the resultant data cube is analyzed to determine wavelength calibrations, spectral widths of channels, radiometric characteristics, and signal-to-noise ratio, as well as an estimate of signal-to-noise performance in the field. The "smile" (geometric distortion of spectra) of the system can be quickly ascertained using this method. As the price and availability of solid state laser sources improve, this technique could gain wide acceptance.

  15. Methods for gas detection using stationary hyperspectral imaging sensors

    SciTech Connect

    Conger, James L.; Henderson, John R.

    2012-04-24

    According to one embodiment, a method comprises producing a first hyperspectral imaging (HSI) data cube of a location at a first time using data from a HSI sensor; producing a second HSI data cube of the same location at a second time using data from the HSI sensor; subtracting on a pixel-by-pixel basis the second HSI data cube from the first HSI data cube to produce a raw difference cube; calibrating the raw difference cube to produce a calibrated raw difference cube; selecting at least one desired spectral band based on a gas of interest; producing a detection image based on the at least one selected spectral band and the calibrated raw difference cube; examining the detection image to determine presence of the gas of interest; and outputting a result of the examination. Other methods, systems, and computer program products for detecting the presence of a gas are also described.

  16. A fast smoothing algorithm for post-processing of surface reflectance spectra retrieved from airborne imaging spectrometer data.

    PubMed

    Gao, Bo-Cai; Liu, Ming

    2013-10-14

    Surface reflectance spectra retrieved from remotely sensed hyperspectral imaging data using radiative transfer models often contain residual atmospheric absorption and scattering effects. The reflectance spectra may also contain minor artifacts due to errors in radiometric and spectral calibrations. We have developed a fast smoothing technique for post-processing of retrieved surface reflectance spectra. In the present spectral smoothing technique, model-derived reflectance spectra are first fit using moving filters derived with a cubic spline smoothing algorithm. A common gain curve, which contains minor artifacts in the model-derived reflectance spectra, is then derived. This gain curve is finally applied to all of the reflectance spectra in a scene to obtain the spectrally smoothed surface reflectance spectra. Results from analysis of hyperspectral imaging data collected with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data are given. Comparisons between the smoothed spectra and those derived with the empirical line method are also presented.

  17. Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging

    NASA Astrophysics Data System (ADS)

    Schubert, Till; Wenzel, Susanne; Roscher, Ribana; Stachniss, Cyrill

    2016-06-01

    The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the analysis of latent traces and extends the classical concept to the conservation of the crime scene for retrospective laboratory analysis. We examine specimen of blood, semen and saliva traces in several dilution steps, prepared on cardboard substrate. As our key result we successfully make latent traces visible up to dilution factor of 1:8000. We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the shortwave infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood classifier, assuming normally distributed data. Further, we use Random Forest as a competitive approach. The classifiers retrieve the exact positions of labelled trace preparation up to highest dilution and determine posterior probabilities. By modelling the classification task with a Markov Random Field we are able to integrate prior information about the spatial relation of neighboured pixel labels.

  18. Exploration of virtual dimensionality in hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Chang, Chein-I.

    2006-05-01

    Virtual dimensionality (VD) is a new concept which was developed to estimate the number of spectrally distinct signatures present in hyperspectral image data. Unlike intrinsic dimensionality which is mainly of theoretical interest, the VD is a very useful and practical notion. It is derived from the Neyman-Pearson detection theory. Unfortunately, its utility in hyperspectral data exploitation has yet to be explored. This paper presents several applications to which the VD is applied successfully. Since the VD is derived from a binary hypothesis testing problem for each spectral band, it can be used for band selection. When the test fails for a band, it indicates that there is a signal source in that particular band which must be selected. By the same token it can be further used for dimensionality reduction. For principal components analysis (PCA) or independent component analysis (ICA), the VD helps to determine the number of principal components or independent components are required for exploitation such as detection, classification, compression, etc. For unsupervised target detection and classification, the VD can be used to determine how many unwanted signal sources present in the image data so that they can be eliminated prior to detection and classification. For endmember extraction, the VD provides a good estimate of the number of endmembers needed to be extracted. All these applications are justified by experiments.

  19. Non-uniform system response detection for hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

    Castorena, Juan; Morrison, Jason; Paliwal, Jitendra; Erkinbaev, Chyngyz

    2015-11-01

    Near infrared (NIR) hyperspectral imaging (HSI) has established itself as a powerful non-destructive tool for the chemical analysis of heterogeneous samples. However, one of the main disadvantages of NIR HSI is that the technique suffers from instrumentation-related problems, which in turn affect the acquired images. In general, focal plane array (FPA) based hyperspectral systems are affected by spatial and spectral non-uniform response, the presence of defective sensors (e.g. dead or saturated sensors), and temporal and spatial (e.g. dark current) noise. Another issue is each new camera system needs to be calibrated to assess its specific responses to light. To correct for these issues, we used known standards to measure the response of the sensors and capture the location of the field of view and defective sensors using linear and quadratic models. The parameters of these models were then used as input features for classification of sensor responses using a k-means algorithm. The results conclude that linear models are insufficiently precise for calibration but estimate sufficiently accurately the system's response and functionality. Specifically, it was shown that the classification method discriminates non-responsive regions effectively.

  20. Hyperspectral image segmentation of the common bile duct

    NASA Astrophysics Data System (ADS)

    Samarov, Daniel; Wehner, Eleanor; Schwarz, Roderich; Zuzak, Karel; Livingston, Edward

    2013-03-01

    Over the course of the last several years hyperspectral imaging (HSI) has seen increased usage in biomedicine. Within the medical field in particular HSI has been recognized as having the potential to make an immediate impact by reducing the risks and complications associated with laparotomies (surgical procedures involving large incisions into the abdominal wall) and related procedures. There are several ongoing studies focused on such applications. Hyperspectral images were acquired during pancreatoduodenectomies (commonly referred to as Whipple procedures), a surgical procedure done to remove cancerous tumors involving the pancreas and gallbladder. As a result of the complexity of the local anatomy, identifying where the common bile duct (CBD) is can be difficult, resulting in comparatively high incidents of injury to the CBD and associated complications. It is here that HSI has the potential to help reduce the risk of such events from happening. Because the bile contained within the CBD exhibits a unique spectral signature, we are able to utilize HSI segmentation algorithms to help in identifying where the CBD is. In the work presented here we discuss approaches to this segmentation problem and present the results.

  1. Monitoring biofilm attachment on medical devices surfaces using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Le, Hanh N. D.; Hitchins, Victoria M.; Ilev, Ilko K.; Kim, Do-Hyun

    2014-02-01

    Microbial biofilm is a colony of single bacteria cells (planktonic) that attached to surfaces, attract other microorganisms to attach and grow, and together they build an extracellular matrix composed of polysaccharides, protein, and DNA. Eventually, some cells will detach and spread to other surface. Biofilm on medical devices can cause severe infection to all age ranges from infant to adult. Therefore, it is important to detect biofilm in a fast and efficient manner. Hyperspectral imaging was utilized for distinguishing wide area of biofilm coverage on various materials and on different textures of stainless steeltest coupons. Not only is the coverage of biofilm important, but also the shear stress of biofilm on the attached surfaces is significant. This study investigates the effects of shear stress on the adhesion of biofilms on common medical device surfaces such as glass, polycarbonate, polytetrafluoroethylene, and stainless steel with different textures. Biofilm was grown using Ps. aeruginosa and growth was monitored after 24 and 48 hours at 37° C. The coupons covered with biofilm were tilted at 45 degrees and 90 degrees for 30 seconds to induce shear stress and Hyperspectral images were taken. We hypothesize that stronger attachment on rough surface would be able to withstand greater shear stress compared to smooth surface.

  2. High-performance hyperspectral imaging using virtual slit optics

    NASA Astrophysics Data System (ADS)

    Behr, Bradford B.; Meade, Jeffrey T.; Hajian, Arsen R.; Cenko, Andrew T.

    2013-05-01

    The High Throughput Virtual Slit (or HTVS) is a new optical technology which can significantly increase the throughput and resolution of a dispersive spectrometer. The HTVS is able to preserve spectrometer étendue, mitigating photon losses normally associated with a slit. Originally implemented in multimode fiber-input spectrometers, HTVS has now been shown to be broadly applicable to a wide variety of spatially scanning hyperspectral imagers and standoff sensors, enhancing their performance and unlocking new application areas. In essence, the anamorphic elements of the HTVS optical system provide a means to decouple the spatial (iFOV) and spectral resolution of nearly any HSI system. In some scenarios, HTVS can be used to achieve better spectral resolution with the same input slit width. Alternatively, the slit can be widened (to increase the collected signal) while maintaining the same spectral resolution. This newfound flexibility in optimizing critical performance parameters not only improves the performance of HSI systems in existing remote sensing contexts, but also opens up numerous new application areas which were previously inaccessible to hyperspectral techniques. This method adds substantial value to existing HSI designs, particularly in applications involving targets with large spatial extent and requiring high spectral resolution (e.g. standoff Raman spectroscopy). We present recent experimental results from our prototype HTVS pushbroom imager and discuss case studies of standoff Raman detection of hazardous materials, passive detection of faint narrowband and monochromatic sources, and optimal disentangling of target spectral signatures from the solar spectrum under daytime illumination.

  3. Development of a Hyperspectral Imaging System for Online Quality Inspection of Pickling Cucumbers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper reports on the development of a hyperspectral imaging prototype for evaluation of external and internal quality of pickling cucumbers. The prototype consisted of a two-lane round belt conveyor, two illumination sources (one for reflectance and one for transmittance), and a hyperspectral i...

  4. Identification of early cancerous lesion of esophagus with endoscopic images by hyperspectral image technique (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Huang, Shih-Wei; Chen, Shih-Hua; Chen, Weichung; Wu, I.-Chen; Wu, Ming Tsang; Kuo, Chie-Tong; Wang, Hsiang-Chen

    2016-03-01

    This study presents a method to identify early esophageal cancer within endoscope using hyperspectral imaging technology. The research samples are three kinds of endoscopic images including white light endoscopic, chromoendoscopic, and narrow-band endoscopic images with different stages of pathological changes (normal, dysplasia, dysplasia - esophageal cancer, and esophageal cancer). Research is divided into two parts: first, we analysis the reflectance spectra of endoscopic images with different stages to know the spectral responses by pathological changes. Second, we identified early cancerous lesion of esophagus by principal component analysis (PCA) of the reflectance spectra of endoscopic images. The results of this study show that the identification of early cancerous lesion is possible achieve from three kinds of images. In which the spectral characteristics of NBI endoscopy images of a gray area than those without the existence of the problem the first two, and the trend is very clear. Therefore, if simply to reflect differences in the degree of spectral identification, chromoendoscopic images are suitable samples. The best identification of early esophageal cancer is using the NBI endoscopic images. Based on the results, the use of hyperspectral imaging technology in the early endoscopic esophageal cancer lesion image recognition helps clinicians quickly diagnose. We hope for the future to have a relatively large amount of endoscopic image by establishing a hyperspectral imaging database system developed in this study, so the clinician can take this repository more efficiently preliminary diagnosis.

  5. VNIR-SWIR-TIR hyperspectral airborne campaign for soil and sediment mapping in semi-arid south african environments

    NASA Astrophysics Data System (ADS)

    Milewski, Robert; Chabrillat, Sabine; Eisele, Andreas

    2016-04-01

    Airborne hyperspectral remote sensing techniques has been proven to offer efficient procedures for soil and sediment mineralogical mapping in arid areas on larger scales. Optical methods based on traditional remote sensing windows using the solar reflective spectral wavelength range from the visible-near infrared (VNIR: 0.4-1.1 μm) to the short-wave infrared region (SWIR: 1.1-2.5 μm) allow mapping of common soil properties such as iron oxides, textural characteristics and organic carbon. However, soil mapping in semi-arid environments using VNIR-SWIR is currently limited due to specific spectral characteristics. Challenges appear in such environments due to the common presence of sandy soils (coarse textured) which grain size distribution is driven by the dominant mineral, quartz (SiO2), and which lacks any distinctive Si-O bond related spectral features within the VNIR-SWIR. Furthermore, another challenge is represented by the common presence of other specific spectral features due to different salts (gypsum, halite) or coatings of different forms (cyanobacteria, iron-oxides and/or -oxyhydroxides) for which few studies exists or that oft prevent detection of any other potential spectral feature of e.g. soil organics. In this context, more methodological developments are needed to overcome current limitations of hyperspectral remote sensing for arid areas, and to extent its scope using the thermal infrared (TIR) wavelength region within the atmospheric window between 8 and 14 μm (longwave infrared). In 2015 an extensive VNIR-SWIR-TIR airborne hyperspectral dataset consisting of HySpex-VNIR, HySpex-SWIR (NEO) and Hyper-Cam (TELOPS) data has been acquired in various Namibian and South African landscapes part of the Dimap/GFZ campaign in the frame of the BMBF-SPACES Geoarchive project. Research goals are 1) to demonstrate the capabilities to extract information from such a dataset and 2) to demonstrate the potential of advanced hyperspectral remote sensing

  6. ASTRAL, a hyperspectral imaging DNA sequencer

    NASA Astrophysics Data System (ADS)

    O'Brien, Kevin M.; Wren, Jonathan; Davé, Varshal K.; Bai, Diane; Anderson, Richard D.; Rayner, Simon; Evans, Glen A.; Dabiri, Ali E.; Garner, Harold R.

    1998-05-01

    We are developing a prototype automatic DNA sequencer which utilizes polyacrylamide slab gels imaged through a novel optical detection system. The design of this prototype sequencer allows the ability to perform direct optical coupling over the entire read area of the gel and hyperspectrographic separation and detection of the fluorescence emission. The machine has no moving parts. All the major components incorporated in this prototype are all currently available "off the shelf," thus reducing equipment development time and decreasing costs. Software developed for data acquisition, analysis, and conversion to other standard formats facilitates compatibility.

  7. Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf Coast

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA+ hyperspectral imagery and image transformation and classificatio...

  8. Mapping Black Mangrove Along the South Texas Gulf Coast Using AISA+ Hyperspectral Imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne hyperspectral imagery and image compression and classification techniq...

  9. Improving 3D Wavelet-Based Compression of Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Klimesh, Matthew; Kiely, Aaron; Xie, Hua; Aranki, Nazeeh

    2009-01-01

    Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a

  10. Application of hyperspectral imaging in food safety inspection and control: a review.

    PubMed

    Feng, Yao-Ze; Sun, Da-Wen

    2012-01-01

    Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.

  11. Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging

    NASA Astrophysics Data System (ADS)

    Chaudhari, Abhijit J.; Darvas, Felix; Bading, James R.; Moats, Rex A.; Conti, Peter S.; Smith, Desmond J.; Cherry, Simon R.; Leahy, Richard M.

    2005-12-01

    For bioluminescence imaging studies in small animals, it is important to be able to accurately localize the three-dimensional (3D) distribution of the underlying bioluminescent source. The spectrum of light produced by the source that escapes the subject varies with the depth of the emission source because of the wavelength-dependence of the optical properties of tissue. Consequently, multispectral or hyperspectral data acquisition should help in the 3D localization of deep sources. In this paper, we describe a framework for fully 3D bioluminescence tomographic image acquisition and reconstruction that exploits spectral information. We describe regularized tomographic reconstruction techniques that use semi-infinite slab or FEM-based diffusion approximations of photon transport through turbid media. Singular value decomposition analysis was used for data dimensionality reduction and to illustrate the advantage of using hyperspectral rather than achromatic data. Simulation studies in an atlas-mouse geometry indicated that sub-millimeter resolution may be attainable given accurate knowledge of the optical properties of the animal. A fixed arrangement of mirrors and a single CCD camera were used for simultaneous acquisition of multispectral imaging data over most of the surface of the animal. Phantom studies conducted using this system demonstrated our ability to accurately localize deep point-like sources and show that a resolution of 1.5 to 2.2 mm for depths up to 6 mm can be achieved. We also include an in vivo study of a mouse with a brain tumour expressing firefly luciferase. Co-registration of the reconstructed 3D bioluminescent image with magnetic resonance images indicated good anatomical localization of the tumour.

  12. [Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

    PubMed

    Liu, De-hua; Zhang, Shu-juan; Wang, Bin; Yu, Ke-qiang; Zhao, Yan-ru; He, Yong

    2015-11-01

    Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect

  13. [Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

    PubMed

    Liu, De-hua; Zhang, Shu-juan; Wang, Bin; Yu, Ke-qiang; Zhao, Yan-ru; He, Yong

    2015-11-01

    Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect

  14. SPECTRAL SMILE CORRECTION IN CRISM HYPERSPECTRAL IMAGES

    NASA Astrophysics Data System (ADS)

    Ceamanos, X.; Doute, S.

    2009-12-01

    The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is affected by a common artifact in "push-broom" sensors, the so-called "spectral smile". As a consequence, both central wavelength and spectral width of the spectral response vary along the across-track dimension, thus giving rise to a shifting and smoothing of spectra (see Fig. 1 (left)). In fact, both effects are greater for spectra on the edges, while they are minimum for data acquired by central detectors, the so-called "sweet spot". The prior artifacts become particularly critical for Martian observations which contain steep spectra such as CO2 ice-rich polar images. Fig. 1 (right) shows the horizontal brightness gradient which appears in every band corresponding to a steep portion of spectra. The correction of CRISM spectral smile is addressed using a two-step method which aims at modifying data sensibly in order to mimic the optimal CRISM response. First, all spectra, which are previously interpolated by cubic splines, are resampled to the "sweet spot" wavelengths in order to overcome the spectra shift. Secondly, the non-uniform spectral width is overcome by mimicking an increase of spectral resolution thanks to a spectral sharpening. In order to minimize noise, only bands particularly suffering from smile are selected. First, bands corresponding to the outliers of the Minimum Noise Transformation (MNF) eigenvector, which corresponds to the MNF band related to smile (MNF-smile), are selected. Then, a spectral neighborhood Θi, which takes into account the local spectral convexity or concavity, is defined for every selected band in order to maximize spectral shape preservation. The proposed sharpening technique takes into account both the instrument parameters and the observed spectra. First, every reflectance value belonging to a Θi is reevaluated by a sharpening which depends on a ratio of the spectral width of the current detector and the "sweet spot" one. Then, the optimal degree of

  15. Near-infrared hyperspectral imaging for quality analysis of agricultural and food products

    NASA Astrophysics Data System (ADS)

    Singh, C. B.; Jayas, D. S.; Paliwal, J.; White, N. D. G.

    2010-04-01

    Agricultural and food processing industries are always looking to implement real-time quality monitoring techniques as a part of good manufacturing practices (GMPs) to ensure high-quality and safety of their products. Near-infrared (NIR) hyperspectral imaging is gaining popularity as a powerful non-destructive tool for quality analysis of several agricultural and food products. This technique has the ability to analyse spectral data in a spatially resolved manner (i.e., each pixel in the image has its own spectrum) by applying both conventional image processing and chemometric tools used in spectral analyses. Hyperspectral imaging technique has demonstrated potential in detecting defects and contaminants in meats, fruits, cereals, and processed food products. This paper discusses the methodology of hyperspectral imaging in terms of hardware, software, calibration, data acquisition and compression, and development of prediction and classification algorithms and it presents a thorough review of the current applications of hyperspectral imaging in the analyses of agricultural and food products.

  16. Compressed hyperspectral image sensing with joint sparsity reconstruction

    NASA Astrophysics Data System (ADS)

    Liu, Haiying; Li, Yunsong; Zhang, Jing; Song, Juan; Lv, Pei

    2011-10-01

    Recent compressed sensing (CS) results show that it is possible to accurately reconstruct images from a small number of linear measurements via convex optimization techniques. In this paper, according to the correlation analysis of linear measurements for hyperspectral images, a joint sparsity reconstruction algorithm based on interband prediction and joint optimization is proposed. In the method, linear prediction is first applied to remove the correlations among successive spectral band measurement vectors. The obtained residual measurement vectors are then recovered using the proposed joint optimization based POCS (projections onto convex sets) algorithm with the steepest descent method. In addition, a pixel-guided stopping criterion is introduced to stop the iteration. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed.

  17. Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification.

    PubMed

    Zhou, Yicong; Wei, Yantao

    2016-07-01

    This paper proposes a spectral-spatial feature learning (SSFL) method to obtain robust features of hyperspectral images (HSIs). It combines the spectral feature learning and spatial feature learning in a hierarchical fashion. Stacking a set of SSFL units, a deep hierarchical model called the spectral-spatial networks (SSN) is further proposed for HSI classification. SSN can exploit both discriminative spectral and spatial information simultaneously. Specifically, SSN learns useful high-level features by alternating between spectral and spatial feature learning operations. Then, kernel-based extreme learning machine (KELM), a shallow neural network, is embedded in SSN to classify image pixels. Extensive experiments are performed on two benchmark HSI datasets to verify the effectiveness of SSN. Compared with state-of-the-art methods, SSN with a deep hierarchical architecture obtains higher classification accuracy in terms of the overall accuracy, average accuracy, and kappa ( κ ) coefficient of agreement, especially when the number of the training samples is small.

  18. Application of the MAP estimation model to hyperspectral resolution image enhancement

    NASA Astrophysics Data System (ADS)

    Dong, Guangjun; Zhou, Haifang; Ji, Song; Shu, Rong

    2009-10-01

    This paper makes a study of maximum a posteriori (MAP) estimation method for enhancing the spatial resolution of a hyperspectral image using a higher resolution coincident panchromatic image. Here, the mathematical formulation of the proposed MAP method is described and the detail process step is introduced. Then, enhancement results using PHI hyperspectral image datasets are provided. In general, it is found that the MAP method is able to obtain high-resolution hyperspectral data. Experiment shows that the method is effective while the enhancement for conventional methods, like average estimation, is limited primarily to fuse spectral information.

  19. Detection of hypercholesterolemia using hyperspectral imaging of human skin

    NASA Astrophysics Data System (ADS)

    Milanic, Matija; Bjorgan, Asgeir; Larsson, Marcus; Strömberg, Tomas; Randeberg, Lise L.

    2015-07-01

    Hypercholesterolemia is characterized by high blood levels of cholesterol and is associated with increased risk of atherosclerosis and cardiovascular disease. Xanthelasma is a subcutaneous lesion appearing in the skin around the eyes. Xanthelasma is related to hypercholesterolemia. Identifying micro-xanthelasma can thereforeprovide a mean for early detection of hypercholesterolemia and prevent onset and progress of disease. The goal of this study was to investigate spectral and spatial characteristics of hypercholesterolemia in facial skin. Optical techniques like hyperspectral imaging (HSI) might be a suitable tool for such characterization as it simultaneously provides high resolution spatial and spectral information. In this study a 3D Monte Carlo model of lipid inclusions in human skin was developed to create hyperspectral images in the spectral range 400-1090 nm. Four lesions with diameters 0.12-1.0 mm were simulated for three different skin types. The simulations were analyzed using three algorithms: the Tissue Indices (TI), the two layer Diffusion Approximation (DA), and the Minimum Noise Fraction transform (MNF). The simulated lesions were detected by all methods, but the best performance was obtained by the MNF algorithm. The results were verified using data from 11 volunteers with known cholesterol levels. The face of the volunteers was imaged by a LCTF system (400- 720 nm), and the images were analyzed using the previously mentioned algorithms. The identified features were then compared to the known cholesterol levels of the subjects. Significant correlation was obtained for the MNF algorithm only. This study demonstrates that HSI can be a promising, rapid modality for detection of hypercholesterolemia.

  20. Spectral homogenization techniques for the hyperspectral image projector

    NASA Astrophysics Data System (ADS)

    Hillberry, Logan E.; Rice, Joseph P.

    2015-05-01

    In an effort to improve technology for performance testing and calibration of multispectral and hyperspectral imagers, the National Institute of Standards and Technology (NIST) has been developing a Hyperspectral Image Projector (HIP) capable of projecting dynamic scenes than include distinct, programmable spectra in each of its 1024x768 spatial pixels. The HIP is comprised of a spectral engine, which is a light source capable generating the spectra in the scene, coupled to a spatial engine, capable of projecting the spectra into the correct locations of the scene. In the prototype HIP, the light exiting the Visible-Near-Infrared (VNIR) / Short-Wavelength Infrared (SWIR) spectral engine is spectrally dispersed and needs to be spectrally homogenized before it enters the spatial engine. In this paper we describe the results from a study of several different techniques for performing this spectral homogenization. These techniques include an integrating sphere, a liquid light guide, a randomized fiber bundle, and an engineered diffuser, in various combinations. The spectral uniformity of projected HIP scenes is measured and analyzed using the spectral angle mapper (SAM) algorithm over the VNIR spectral range. The SAM provides a way to analyze the spectral uniformity independently from the radiometric uniformity. The goal of the homogenizer is a spectrally uniform and bright projected image. An integrating sphere provides the most spectrally uniform image, but at a great loss of light compared with the other methods. The randomized fiber bundle generally outperforms the liquid light guide in both spectral homogenization and brightness. Using an engineered diffuser with the randomized fiber bundle increases the spectral uniformity by a factor of five, with a decrease in brightness by a factor of five, compared with the randomized fiber bundle alone. The combination of an engineered diffuser with a randomized fiber bundle provides comparable spectral uniformity to the

  1. Excitation-scanning hyperspectral imaging system for microscopic and endoscopic applications

    NASA Astrophysics Data System (ADS)

    Mayes, Sam A.; Leavesley, Silas J.; Rich, Thomas C.

    2016-04-01

    Current microscopic and endoscopic technologies for cancer screening utilize white-light illumination sources. Hyper-spectral imaging has been shown to improve sensitivity while retaining specificity when compared to white-light imaging in both microscopy and in vivo imaging. However, hyperspectral imaging methods have historically suffered from slow acquisition times due to the narrow bandwidth of spectral filters. Often minutes are required to gather a full image stack. We have developed a novel approach called excitation-scanning hyperspectral imaging that provides 2-3 orders of magnitude increased signal strength. This reduces acquisition times significantly, allowing for live video acquisition. Here, we describe a preliminary prototype excitation-scanning hyperspectral imaging system that can be coupled with endoscopes or microscopes for hyperspectral imaging of tissues and cells. Our system is comprised of three subsystems: illumination, transmission, and imaging. The illumination subsystem employs light-emitting diode arrays to illuminate at different wavelengths. The transmission subsystem utilizes a unique geometry of optics and a liquid light guide. Software controls allow us to interface with and control the subsystems and components. Digital and analog signals are used to coordinate wavelength intensity, cycling and camera triggering. Testing of the system shows it can cycle 16 wavelengths at as fast as 1 ms per cycle. Additionally, more than 18% of the light transmits through the system. Our setup should allow for hyperspectral imaging of tissue and cells in real time.

  2. Spectral Regression Discriminant Analysis for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Pan, Y.; Wu, J.; Huang, H.; Liu, J.

    2012-08-01

    Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.

  3. Detecting liquid contamination on surfaces using hyperspectral imaging data

    NASA Astrophysics Data System (ADS)

    Warren, Russell E.; Cohn, David B.; Gagnon, Marc-André; Farley, Vincent

    2015-05-01

    Over the past two years we have developed a new approach for detecting and identifying the presence of liquid chemical contamination on surfaces using hyperspectral imaging data. This work requires an algorithm for unmixing the data to separate the liquid contamination component of the data from all other possible spectral effects, such as the illumination and reflectance spectra of the pure background. The contamination components from S and P polarized reflectance data are then used to estimate the complex refractive index. We retain the index estimates within spectral windows chosen for each of a set of candidate contaminant materials based on their optical extinction. Spectral estimates within those windows are characteristic of the liquid material, and can be passed on to an algorithm for chemical detection and identification. The resulting algorithm is insensitive to the composition of the surface material, and requires no prior measurements of the uncontaminated surface. In a series of field tests, data from the Telops Hyper-Cam sensor were used to develop and validate our approach. We discuss our hyperspectral unmixing and index estimation approaches, and show results from tests conducted at the Telops facility in Québec under a contract with the U.S. Army Edgewood Chemical Biological Center.

  4. A generalized representation-based approach for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Li, Jiaojiao; Li, Wei; Du, Qian; Li, Yunsong

    2016-05-01

    Sparse representation-based classifier (SRC) is of great interest recently for hyperspectral image classification. It is assumed that a testing pixel is linearly combined with atoms of a dictionary. Under this circumstance, the dictionary includes all the training samples. The objective is to find a weight vector that yields a minimum L2 representation error with the constraint that the weight vector is sparse with a minimum L1 norm. The pixel is assigned to the class whose training samples yield the minimum error. In addition, collaborative representation-based classifier (CRC) is also proposed, where the weight vector has a minimum L2 norm. The CRC has a closed-form solution; when using class-specific representation it can yield even better performance than the SRC. Compared to traditional classifiers such as support vector machine (SVM), SRC and CRC do not have a traditional training-testing fashion as in supervised learning, while their performance is similar to or even better than SVM. In this paper, we investigate a generalized representation-based classifier which uses Lq representation error, Lp weight norm, and adaptive regularization. The classification performance of Lq and Lp combinations is evaluated with several real hyperspectral datasets. Based on these experiments, recommendation is provide for practical implementation.

  5. Framelet-Based Sparse Unmixing of Hyperspectral Images.

    PubMed

    Zhang, Guixu; Xu, Yingying; Fang, Faming

    2016-04-01

    Spectral unmixing aims at estimating the proportions (abundances) of pure spectrums (endmembers) in each mixed pixel of hyperspectral data. Recently, a semi-supervised approach, which takes the spectral library as prior knowledge, has been attracting much attention in unmixing. In this paper, we propose a new semi-supervised unmixing model, termed framelet-based sparse unmixing (FSU), which promotes the abundance sparsity in framelet domain and discriminates the approximation and detail components of hyperspectral data after framelet decomposition. Due to the advantages of the framelet representations, e.g., images have good sparse approximations in framelet domain, and most of the additive noises are included in the detail coefficients, the FSU model has a better antinoise capability, and accordingly leads to more desirable unmixing performance. The existence and uniqueness of the minimizer of the FSU model are then discussed, and the split Bregman algorithm and its convergence property are presented to obtain the minimal solution. Experimental results on both simulated data and real data demonstrate that the FSU model generally performs better than the compared methods. PMID:26849863

  6. Towards a colony counting system using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Masschelein, B.; Robles-Kelly, A.; Blanch, C.; Tack, N.; Simpson-Young, B.; Lambrechts, A.

    2012-03-01

    Colony counting is a procedure used in microbiology laboratories for food quality monitoring, environmental management, etc. Its purpose is to detect the level of contamination due to the presence and growth of bacteria, yeasts and molds in a given product. Current automated counters require a tedious training and setup procedure per product and bacteria type and do not cope well with diversity. This contrasts with the setting at microbiology laboratories, where a wide variety of food and bacteria types have to be screened on a daily basis. To overcome the limitations of current systems, we propose the use of hyperspectral imaging technology and examine the spectral variations induced by factors such as illumination, bacteria type, food source and age and type of the agar. To this end, we perform experiments making use of two alternative hyperspectral processing pipelines and compare our classification results to those yielded by color imagery. Our results show that colony counting may be automated through the automatic recovery of the illuminant power spectrum and reflectance. This is consistent with the notion that the recovery of the illuminant should minimize the variations in the spectra due to reflections, shadows and other photometric artifacts. We also illustrate how, with the reflectance at hand, the colonies can be counted making use of classical segmentation and classification algorithms.

  7. Maximum margin metric learning based target detection for hyperspectral images

    NASA Astrophysics Data System (ADS)

    Dong, Yanni; Zhang, Liangpei; Zhang, Lefei; Du, Bo

    2015-10-01

    Target detection is one of the most important problems in hyperspectral image (HSI) processing. However, the classical algorithms depend on the specific statistical hypothesis test, and the algorithms may only perform well under certain conditions, e.g., the adaptive matched subspace detector algorithm assumes that the background covariance matrices do not include the target signatures, which seldom happens in the real world. How to develop a proper metric for measuring the separability between targets and backgrounds becomes the key in target detection. This paper proposes an efficient maximum margin metric learning (MMML) based target detection algorithm, which aims at exploring the limited samples in metric learning and transfers the metric learning problem for hyperspectral target detection into a maximum margin problem which can be optimized via a cutting plane method, and maximally separates the target samples from the background ones. The extensive experimental results with different HSIs demonstrate that the proposed method outperforms both the state-of-the-art target detection algorithms and the other classical metric learning methods.

  8. Aerosol, Cloud and Trace Gas Observations Derived from Airborne Hyperspectral Radiance and Direct Beam Measurements in Recent Field Campaigns

    NASA Technical Reports Server (NTRS)

    Redemann, J.; Flynn, C. J.; Shinozuka, Y.; Kacenelenbogen, M.; Segal-Rosenheimer, M.; LeBlanc, S.; Russell, P. B.; Livingston, J. M.; Schmid, B.; Dunagan, S. E.; Johnson, R. R.

    2014-01-01

    The AERONET (AErosol RObotic NETwork) ground-based suite of sunphotometers provides measurements of spectral aerosol optical depth (AOD), precipitable water and spectral sky radiance, which can be inverted to retrieve aerosol microphysical properties that are critical to assessments of aerosol-climate interactions. Because of data quality criteria and sampling constraints, there are significant limitations to the temporal and spatial coverage of AERONET data and their representativeness for global aerosol conditions. The 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) instrument, jointly developed by NASA Ames and PNNL with NASA Goddard collaboration, combines airborne sun tracking and AERONET-like sky scanning with spectroscopic detection. Being an airborne instrument, 4STAR has the potential to fill gaps in the AERONET data set. Dunagan et al. [2013] present results establishing the performance of the instrument, along with calibration, engineering flight test, and preliminary scientific field data. The 4STAR instrument operated successfully in the SEAC4RS [Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys] experiment in Aug./Sep. 2013 aboard the NASA DC-8 and in the DoE [Department of Energy]-sponsored TCAP [Two Column Aerosol Project, July 2012 & Feb. 2013] experiment aboard the DoE G-1 aircraft (Shinozuka et al., 2013), and acquired a wealth of data in support of mission objectives on all SEAC4RS and TCAP research flights. 4STAR provided direct beam measurements of hyperspectral AOD, columnar trace gas retrievals (H2O, O3, NO2; Segal-Rosenheimer et al., 2014), and the first ever airborne hyperspectral sky radiance scans, which can be inverted to yield the same products as AERONET ground-based observations. In addition, 4STAR measured zenith radiances underneath cloud decks for retrievals of cloud optical depth and effective diameter. In this presentation, we provide an overview of the new

  9. Airborne imaging sensors for environmental monitoring & surveillance in support of oil spills & recovery efforts

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R.; Jones, James; Frystacky, Heather; Coppin, Gaelle; Leavaux, Florian; Neyt, Xavier

    2011-11-01

    Collection of pushbroom sensor imagery from a mobile platform requires corrections using inertial measurement units (IMU's) and DGPS in order to create useable imagery for environmental monitoring and surveillance of shorelines in freshwater systems, coastal littoral zones and harbor areas. This paper describes a suite of imaging systems used during collection of hyperspectral imagery in northern Florida panhandle and Gulf of Mexico airborne missions to detect weathered oil in coastal littoral zones. Underlying concepts of pushbroom imagery, the needed corrections for directional changes using DGPS and corrections for platform yaw, pitch, and roll using IMU data is described as well as the development and application of optimal band and spectral regions associated with weathered oil. Pushbroom sensor and frame camera data collected in response to the recent Gulf of Mexico oil spill disaster is presented as the scenario documenting environmental monitoring and surveillance techniques using mobile sensing platforms. Data was acquired during the months of February, March, April and May of 2011. The low altitude airborne systems include a temperature stabilized hyperspectral imaging system capable of up to 1024 spectral channels and 1376 spatial across track pixels flown from 3,000 to 4,500 feet altitudes. The hyperspectral imaging system is collocated with a full resolution high definition video recorder for simultaneous HD video imagery, a 12.3 megapixel digital, a mapping camera using 9 inch film types that yields scanned aerial imagery with approximately 22,200 by 22,200 pixel multispectral imagery (~255 megapixel RGB multispectral images in order to conduct for spectral-spatial sharpening of fused multispectral, hyperspectral imagery. Two high spectral (252 channels) and radiometric sensitivity solid state spectrographs are used for collecting upwelling radiance (sub-meter pixels) with downwelling irradiance fiber optic attachment. These sensors are utilized for

  10. High speed measurement of corn seed viability using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Ambrose, Ashabahebwa; Kandpal, Lalit Mohan; Kim, Moon S.; Lee, Wang-Hee; Cho, Byoung-Kwan

    2016-03-01

    Corn is one of the most cultivated crops all over world as food for humans as well as animals. Optimized agronomic practices and improved technological interventions during planting, harvesting and post-harvest handling are critical to improving the quantity and quality of corn production. Seed germination and vigor are the primary determinants of high yield notwithstanding any other factors that may play during the growth period. Seed viability may be lost during storage due to unfavorable conditions e.g. moisture content and temperatures, or physical damage during mechanical processing e.g. shelling, or over heating during drying. It is therefore vital for seed companies and farmers to test and ascertain seed viability to avoid losses of any kind. This study aimed at investigating the possibility of using hyperspectral imaging (HSI) technique to discriminate viable and nonviable corn seeds. A group of corn samples were heat treated by using microwave process while a group of seeds were kept as control group (untreated). The hyperspectral images of corn seeds of both groups were captured between 400 and 2500 nm wave range. Partial least squares discriminant analysis (PLS-DA) was built for the classification of aged (heat treated) and normal (untreated) corn seeds. The model showed highest classification accuracy of 97.6% (calibration) and 95.6% (prediction) in the SWIR region of the HSI. Furthermore, the PLS-DA and binary images were capable to provide the visual information of treated and untreated corn seeds. The overall results suggest that HSI technique is accurate for classification of viable and non-viable seeds with non-destructive manner.

  11. Hyperspectral imaging for the detection of retinal disease

    NASA Astrophysics Data System (ADS)

    Harvey, Andrew R.; Lawlor, Joanne; McNaught, Andrew I.; Williams, John W.; Fletcher-Holmes, David W.

    2002-11-01

    Hyperspectral imaging (HSI) shows great promise for the detection and classification of several diseases, particularly in the fields of "optical biopsy" as applied to oncology, and functional retinal imaging in ophthalmology. In this paper, we discuss the application of HSI to the detection of retinal diseases and technological solutions that address some of the fundamental difficulties of spectral imaging within the eye. HSI of the retina offers a route to non-invasively deduce biochemical and metabolic processes within the retina. For example it shows promise for the mapping of retinal blood perfusion using spectral signatures of oxygenated and deoxygenated hemoglobin. Compared with other techniques using just a few spectral measurements, it offers improved classification in the presence of spectral cross-contamination by pigments and other components within the retina. There are potential applications for this imaging technique in the investigation and treatment of the eye complications of diabetes, and other diseases involving disturbances to the retinal, or optic-nerve-head circulation. It is well known that high-performance HSI requires high signal-to-noise ratios (SNR) whereas the application of any imaging technique within the eye must cope with the twin limitations of the small numerical aperture provided by the entrance pupil to the eye and the limit on the radiant power at the retina. We advocate the use of spectrally-multiplexed spectral imaging techniques (the traditional filter wheel is a traditional example). These approaches enable a flexible approach to spectral imaging, with wider spectral range, higher SNRs and lower light intensity at the retina than could be achieved using a Fourier-transform (FT) approach. We report the use of spectral imaging to provide calibrated spectral albedo images of healthy and diseased retinas and the use of this data for screening purposes. These images clearly demonstrate the ability to distinguish between

  12. Multi- and hyperspectral UAV imaging system for forest and agriculture applications

    NASA Astrophysics Data System (ADS)

    Mäkynen, Jussi; Saari, Heikki; Holmlund, Christer; Mannila, Rami; Antila, Tapani

    2012-06-01

    VTT Technical Research Centre of Finland has developed a Fabry-Perot Interferometer (FPI) based hyperspectral imager compatible with light weight UAV (Unmanned Aerial Vehicle) platforms (SPIE Proc. 74741, 8186B2). The FPI based hyperspectral imager was used in a UAV imaging campaign for forest and agriculture tests during the summer 2011 (SPIE Proc. 81743). During these tests high spatial resolution Color-Infrared (CIR) images and hyperspectral images were recorded on separate flights. The spectral bands of the CIR camera were 500 - 580 nm for the green band, 580 - 700 nm for the red band and 700 - 1000 nm for the near infrared band. For the summer 2012 flight campaign a new hyperspectral imager is currently being developed. A custom made CIR camera will also be used. The system which includes both the high spatial resolution Color-Infrared camera and a light weight hyperspectral imager can provide all necessary data with just one UAV flight over the target area. The new UAV imaging system contains a 4 Megapixel CIR camera which is used for the generation of the digital surface models and CIR mosaics. The hyperspectral data can be recorded in the wavelength range 500 - 900 nm at a resolution of 10 - 30 nm at FWHM. The resolution can be selected from approximate values of 10, 15, 20 or 30 nm at FWHM.

  13. Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

    PubMed

    Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin

    2015-12-01

    Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells. PMID:26554882

  14. Full field imaging based instantaneous hyperspectral absolute refractive index measurement

    SciTech Connect

    Baba, Justin S; Boudreaux, Philip R

    2012-01-01

    Multispectral refractometers typically measure refractive index (RI) at discrete monochromatic wavelengths via a serial process. We report on the demonstration of a white light full field imaging based refractometer capable of instantaneous multispectral measurement of absolute RI of clear liquid/gel samples across the entire visible light spectrum. The broad optical bandwidth refractometer is capable of hyperspectral measurement of RI in the range 1.30 1.70 between 400nm 700nm with a maximum error of 0.0036 units (0.24% of actual) at 414nm for a = 1.50 sample. We present system design and calibration method details as well as results from a system validation sample.

  15. Aliasing removing of hyperspectral image based on fractal structure matching

    NASA Astrophysics Data System (ADS)

    Wei, Ran; Zhang, Ye; Zhang, Junping

    2015-05-01

    Due to the richness on high frequency components, hyperspectral image (HSI) is more sensitive to distortion like aliasing. Many methods aiming at removing such distortion have been proposed. However, seldom of them are suitable to HSI, due to low spatial resolution characteristic of HSI. Fortunately, HSI contains plentiful spectral information, which can be exploited to overcome such difficulties. Motivated by this, we proposed an aliasing removing method for HSI. The major differences between proposed and current methods is that proposed algorithm is able to utilize fractal structure information, thus the dilemma originated from low-resolution of HSI is solved. Experiments on real HSI data demonstrated subjectively and objectively that proposed method can not only remove annoying visual effect brought by aliasing, but also recover more high frequency component.

  16. Probability Density and CFAR Threshold Estimation for Hyperspectral Imaging

    SciTech Connect

    Clark, G A

    2004-09-21

    The work reported here shows the proof of principle (using a small data set) for a suite of algorithms designed to estimate the probability density function of hyperspectral background data and compute the appropriate Constant False Alarm Rate (CFAR) matched filter decision threshold for a chemical plume detector. Future work will provide a thorough demonstration of the algorithms and their performance with a large data set. The LASI (Large Aperture Search Initiative) Project involves instrumentation and image processing for hyperspectral images of chemical plumes in the atmosphere. The work reported here involves research and development on algorithms for reducing the false alarm rate in chemical plume detection and identification algorithms operating on hyperspectral image cubes. The chemical plume detection algorithms to date have used matched filters designed using generalized maximum likelihood ratio hypothesis testing algorithms [1, 2, 5, 6, 7, 12, 10, 11, 13]. One of the key challenges in hyperspectral imaging research is the high false alarm rate that often results from the plume detector [1, 2]. The overall goal of this work is to extend the classical matched filter detector to apply Constant False Alarm Rate (CFAR) methods to reduce the false alarm rate, or Probability of False Alarm P{sub FA} of the matched filter [4, 8, 9, 12]. A detector designer is interested in minimizing the probability of false alarm while simultaneously maximizing the probability of detection P{sub D}. This is summarized by the Receiver Operating Characteristic Curve (ROC) [10, 11], which is actually a family of curves depicting P{sub D} vs. P{sub FA}parameterized by varying levels of signal to noise (or clutter) ratio (SNR or SCR). Often, it is advantageous to be able to specify a desired P{sub FA} and develop a ROC curve (P{sub D} vs. decision threshold r{sub 0}) for that case. That is the purpose of this work. Specifically, this work develops a set of algorithms and MATLAB

  17. ICER-3D: A Progressive Wavelet-Based Compressor for Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Kiely, A.; Klimesh, M.; Xie, H.; Aranki, N.

    2005-01-01

    ICER-3D is a progressive, wavelet-based compressor for hyperspectral images. ICER-3D is derived from the ICER image compressor. ICER-3D can provide lossless and lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The three-dimensional wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of hyperspectral data sets, while facilitating elimination of spectral ringing artifacts. Correlation is further exploited by a context modeler that effectively exploits spectral dependencies in the wavelet-transformed hyperspectral data. Performance results illustrating the benefits of these features are presented.

  18. Infrared adaptive spectral imagers for direct detection of spectral signatures and hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Goldstein, Neil; Fox, Marsha; Adler-Golden, Steven; Gregor, Brian

    2013-03-01

    Field test results are presented for a prototype long-wave adaptive imager that provides both hyperspectral imagery and contrast imagery based on the direct application of hyperspectral detection algorithms in hardware. Programmable spatial light modulators are used to provide both spectral and spatial resolution using a single element detector. Programmable spectral and spatial detection filters can be used to superimpose any possible analog spectral detection filter on the image. In this work, we demonstrate three modes of operation, including hyperspectral imagery, and one and two-dimensional imagery using a generalized matched filter for detection of a specific target gas within the scene.

  19. Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

    2016-03-01

    Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.

  20. Hyperspectral Imaging of Neoplastic Progression in a Mouse Model of Oral Carcinogenesis

    PubMed Central

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

    2016-01-01

    Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection. PMID:27656034

  1. Rapid prototyping of biomimetic vascular phantoms for hyperspectral reflectance imaging.

    PubMed

    Ghassemi, Pejhman; Wang, Jianting; Melchiorri, Anthony J; Ramella-Roman, Jessica C; Mathews, Scott A; Coburn, James C; Sorg, Brian S; Chen, Yu; Pfefer, T Joshua

    2015-01-01

    The emerging technique of rapid prototyping with three-dimensional (3-D) printers provides a simple yet revolutionary method for fabricating objects with arbitrary geometry. The use of 3-D printing for generating morphologically biomimetic tissue phantoms based on medical images represents a potentially major advance over existing phantom approaches. Toward the goal of image-defined phantoms, we converted a segmented fundus image of the human retina into a matrix format and edited it to achieve a geometry suitable for printing. Phantoms with vessel-simulating channels were then printed using a photoreactive resin providing biologically relevant turbidity, as determined by spectrophotometry. The morphology of printed vessels was validated by x-ray microcomputed tomography. Channels were filled with hemoglobin (Hb) solutions undergoing desaturation, and phantoms were imaged with a near-infrared hyperspectral reflectance imaging system. Additionally, a phantom was printed incorporating two disjoint vascular networks at different depths, each filled with Hb solutions at different saturation levels. Light propagation effects noted during these measurements—including the influence of vessel density and depth on Hb concentration and saturation estimates, and the effect of wavelength on vessel visualization depth—were evaluated. Overall, our findings indicated that 3-D-printed biomimetic phantoms hold significant potential as realistic and practical tools for elucidating light–tissue interactions and characterizing biophotonic system performance. PMID:26662064

  2. Rapid prototyping of biomimetic vascular phantoms for hyperspectral reflectance imaging

    NASA Astrophysics Data System (ADS)

    Ghassemi, Pejhman; Wang, Jianting; Melchiorri, Anthony J.; Ramella-Roman, Jessica C.; Mathews, Scott A.; Coburn, James C.; Sorg, Brian S.; Chen, Yu; Joshua Pfefer, T.

    2015-12-01

    The emerging technique of rapid prototyping with three-dimensional (3-D) printers provides a simple yet revolutionary method for fabricating objects with arbitrary geometry. The use of 3-D printing for generating morphologically biomimetic tissue phantoms based on medical images represents a potentially major advance over existing phantom approaches. Toward the goal of image-defined phantoms, we converted a segmented fundus image of the human retina into a matrix format and edited it to achieve a geometry suitable for printing. Phantoms with vessel-simulating channels were then printed using a photoreactive resin providing biologically relevant turbidity, as determined by spectrophotometry. The morphology of printed vessels was validated by x-ray microcomputed tomography. Channels were filled with hemoglobin (Hb) solutions undergoing desaturation, and phantoms were imaged with a near-infrared hyperspectral reflectance imaging system. Additionally, a phantom was printed incorporating two disjoint vascular networks at different depths, each filled with Hb solutions at different saturation levels. Light propagation effects noted during these measurements-including the influence of vessel density and depth on Hb concentration and saturation estimates, and the effect of wavelength on vessel visualization depth-were evaluated. Overall, our findings indicated that 3-D-printed biomimetic phantoms hold significant potential as realistic and practical tools for elucidating light-tissue interactions and characterizing biophotonic system performance.

  3. HIL range performance of notional hyperspectral imaging sensors

    NASA Astrophysics Data System (ADS)

    Hodgkin, Van A.; Howell, Christopher L.

    2016-05-01

    In the use of conventional broadband imaging systems, whether reflective or emissive, scene image contrasts are often so low that target discrimination is difficult or uncertain, and it is contrast that drives human-in-the-loop (HIL) sensor range performance. This situation can occur even when the spectral shapes of the target and background signatures (radiances) across the sensor waveband differ significantly from each other. The fundamental components of broadband image contrast are the spectral integrals of the target and background signatures, and this spectral integration can average away the spectral differences between scene objects. In many low broadband image contrast situations, hyperspectral imaging (HSI) can preserve a greater degree of the intrinsic scene spectral contrast for the display, and more display contrast means greater range performance by a trained observer. This paper documents a study using spectral radiometric signature modeling and the U.S. Army's Night Vision Integrated Performance Model (NV-IPM) to show how waveband selection by a notional HSI sensor using spectral contrast optimization can significantly increase HIL sensor range performance over conventional broadband sensors.

  4. A low cost thermal infrared hyperspectral imager for small satellites

    NASA Astrophysics Data System (ADS)

    Crites, S. T.; Lucey, P. G.; Wright, R.; Garbeil, H.; Horton, K. A.

    2011-06-01

    The traditional model for space-based earth observations involves long mission times, high cost, and long development time. Because of the significant time and monetary investment required, riskier instrument development missions or those with very specific scientific goals are unlikely to successfully obtain funding. However, a niche for earth observations exploiting new technologies in focused, short lifetime missions is opening with the growth of the small satellite market and launch opportunities for these satellites. These low-cost, short-lived missions provide an experimental platform for testing new sensor technologies that may transition to larger, more long-lived platforms. The low costs and short lifetimes also increase acceptable risk to sensors, enabling large decreases in cost using commercial off the shelf (COTS) parts and allowing early-career scientists and engineers to gain experience with these projects. We are building a low-cost long-wave infrared spectral sensor, funded by the NASA Experimental Project to Stimulate Competitive Research program (EPSCOR), to demonstrate the ways in which a university's scientific and instrument development programs can fit into this niche. The sensor is a low-mass, power efficient thermal hyperspectral imager with electronics contained in a pressure vessel to enable the use of COTS electronics, and will be compatible with small satellite platforms. The sensor, called Thermal Hyperspectral Imager (THI), is based on a Sagnac interferometer and uses an uncooled 320x256 microbolometer array. The sensor will collect calibrated radiance data at long-wave infrared (LWIR, 8-14 microns) wavelengths in 230-meter pixels with 20 wavenumber spectral resolution from a 400-km orbit.

  5. Airborne Multiangle SpectroPolarimeteric Imager (AirMSPI): Calibration and Comparison with Collocated Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Data

    NASA Astrophysics Data System (ADS)

    Seidel, F. C.; Diner, D. J.; Bruegge, C. J.; Rheingans, B. E.; Garay, M. J.; Daugherty, B. J.; Chipman, R. A.; Davis, A.

    2014-12-01

    The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) is a pushbroom multiangle spectropolarimetric camera with spectral bands near 355, 380, 445, 470, 555, 660, 865, and 935 nm. Flying on NASAs's high-altitude ER-2 aircraft since 2010, AirMSPI uses dual photoelastic modulator (PEM)-based technology to provide accurate measurements of the Stokes linear polarization parameters Q and U in the 470, 660, and 865 nm bands, providing unique observing capabilities for aerosol, cloud, and surface studies. We describe the methodologies used for radiometric and polarimetric calibration and characterization of the AirMSPI instrument, which make use of a combination of laboratory and vicarious techniques. A 1.65 m integrating sphere and overflights of Ivanpah Playa, NV are used for radiometric calibration. Radiometric cross-comparisons with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), also flying on the ER-2, are used to validate the radiometric scale. For polarimetric calibration, a well-calibrated Polarization State Generator is used to provide known polarimetric inputs. A high-extinction rotating wiregrid polarizer is used to derive polarimetric calibration coefficients for each pixel, and the results are then validated using partially polarized light generated using tilted glass plates. Examples of collocated multiangular, polarimetric imagery from AirMSPI and hyperspectral imagery from AVIRIS will be shown, presenting new opportunities for atmosphere and surface remote sensing.

  6. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for contaminant screening of leafy greens

    NASA Astrophysics Data System (ADS)

    Everard, Colm D.; Kim, Moon S.; Lee, Hoyoung

    2014-05-01

    The production of contaminant free fresh fruit and vegetables is needed to reduce foodborne illnesses and related costs. Leafy greens grown in the field can be susceptible to fecal matter contamination from uncontrolled livestock and wild animals entering the field. Pathogenic bacteria can be transferred via fecal matter and several outbreaks of E.coli O157:H7 have been associated with the consumption of leafy greens. This study examines the use of hyperspectral fluorescence imaging coupled with multivariate image analysis to detect fecal contamination on Spinach leaves (Spinacia oleracea). Hyperspectral fluorescence images from 464 to 800 nm were captured; ultraviolet excitation was supplied by two LED-based line light sources at 370 nm. Key wavelengths and algorithms useful for a contaminant screening optical imaging device were identified and developed, respectively. A non-invasive screening device has the potential to reduce the harmful consequences of foodborne illnesses.

  7. Mosaicing of Hyperspectral Images: The Application of a Spectrograph Imaging Device

    PubMed Central

    Moroni, Monica; Dacquino, Carlo; Cenedese, Antonio

    2012-01-01

    Hyperspectral monitoring of large areas (more than 10 km2) can be achieved via the use of a system employing spectrometers and CMOS cameras. A robust and efficient algorithm for automatically combining multiple, overlapping images of a scene to form a single composition (i.e., for the estimation of the point-to-point mapping between views), which uses only the information contained within the images themselves is described here. The algorithm, together with the 2D fast Fourier transform, provides an estimate of the displacement between pairs of images by accounting for rotations and changes of scale. The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system. This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification. The performances of the algorithm were evaluated using sample images and image sequences acquired during a proximal sensing field campaign conducted in San Teodoro (Olbia-Tempio—Sardinia). The hyperspectral cube closely corresponds to the mosaic. Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements. PMID:23112597

  8. An automatic blood cell segmentation method based on hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

    Wang, Qian; Li, Qingli; Liu, Hongying; Zhou, Mei; Guo, Fangmin

    2015-08-01

    Hyperspectral blood image has been utilized in biomedical field for a period of time. However, identifying and segmenting blood cells is still a tricky issue. Thus, this paper proposed a new method based on support vector machine (SVM) to solve this issue from hyperspectral images. Then post-processing of holes-filling and noise removing are performed on the segmented results to get completed cell. The experimental results proved the accuracy and accommodation for this new proposed method.

  9. Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

    PubMed Central

    Xiao, Rui; Gao, Junbin; Bossomaier, Terry

    2016-01-01

    A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression. PMID:27695102

  10. Assessment of effects of lossy compression of hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Su, Jonathan K.; Hsu, Su May; Orloff, Seth

    2004-08-01

    Hyperspectral imaging (HSI) sensors provide imagery with hundreds of spectral bands, typically covering VNIR and/or SWIR wavelengths. This high spectral resolution aids applications such as terrain classification and material identification, but it can also produce imagery that occupies well over 100 MB, which creates problems for storage and transmission. This paper investigates the effects of lossy compression on a representative HSI cube, with background classification serving as an example application. The compression scheme first performs principal components analysis spectrally, then discards many of the lower-importance principal-component (PC) images, and then applies JPEG2000 spatial compression to each of the individual retained PC images. The assessment of compression effects considers both general-purpose distortion measures, such as root mean square difference, and statistical tests for deciding whether compression causes significant degradations in classification. Experimental results demonstrate the effectiveness of proper PC-image rate allocation, which enabled compression at ratios of 100-340 without producing significant classification differences. Results also indicate that distortion might serve as a predictor of compression-induced changes in application performance.

  11. On combining spectral and spatial information of hyperspectral image for camouflaged target detecting

    NASA Astrophysics Data System (ADS)

    Hua, Wenshen; Liu, Xun; Yang, Jia

    2013-12-01

    Detecting enemy's targets and being undetectable play increasingly important roles in modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. As supervised classification requires prior knowledge which cannot be acquired easily, unsupervised classification usually is adopted to process hyperspectral images to detect camouflaged target. But one of its drawbacks—low detecting accuracy confines its application for camouflaged target detecting. Most research on the processing of hyperspectral image tends to focus exclusively on spectral domain and ignores spatial domain. However current hyperspectral image provides high spatial resolution which contains useful information for camouflaged target detecting. A new method combining spectral and spatial information is proposed to increase the detecting accuracy using unsupervised classification. The method has two steps. In the first step, a traditional unsupervised classifier (i.e. K-MEANS, ISODATA) is adopted to classify the hyperspectral image to acquire basic classifications or clusters. During the second step, a 3×3 model and spectral angle mapping are utilized to test the spatial character of the hyperspectral image. The spatial character is defined as spatial homogeneity and calculated by spectral angle mapping. Theory analysis and experiment shows the method is reasonable and efficient. Camouflaged targets are extracted from the background and different camouflaged targets are also recognized. And the proposed algorithm outperforms K-MEANS in terms of detecting accuracy, robustness and edge's distinction. This paper demonstrates the new method is meaningful to camouflaged targets detecting.

  12. Hyperspectral Imaging for Determining Pigment Contents in Cucumber Leaves in Response to Angular Leaf Spot Disease

    PubMed Central

    Zhao, Yan-Ru; Li, Xiaoli; Yu, Ke-Qiang; Cheng, Fan; He, Yong

    2016-01-01

    Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll (Chl), and carotenoid (Car) contents in cucumber leaves in response to angular leaf spot (ALS). Altogether, 196 hyperspectral images of cucumber leaves with five infection severities of ALS were captured by a hyperspectral imaging system in the range of 380–1,030 nm covering 512 wavebands. Mean spectrum were extracted from regions of interest (ROIs) in the hyperspectral images. Partial least square regression (PLSR) models were used to develop quantitative analysis between the spectra and the pigment contents measured by biochemical analyses. In addition, regression coefficients (RCs) in PLSR models were employed to select important wavelengths (IWs) for modelling. It was found that the PLSR models developed by the IWs provided the optimal measurement results with correlation coefficient (R) of prediction of 0.871 and 0.876 for Chl and Car contents, respectively. Finally, Chl and Car distributions in cucumber leaves with the ALS infection were mapped by applying the optimal models pixel-wise to the hyperspectral images. The results proved the feasibility of hyperspectral imaging for visualizing the pigment distributions in cucumber leaves in response to ALS. PMID:27283050

  13. Hyperspectral Imaging for Determining Pigment Contents in Cucumber Leaves in Response to Angular Leaf Spot Disease.

    PubMed

    Zhao, Yan-Ru; Li, Xiaoli; Yu, Ke-Qiang; Cheng, Fan; He, Yong

    2016-01-01

    Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll (Chl), and carotenoid (Car) contents in cucumber leaves in response to angular leaf spot (ALS). Altogether, 196 hyperspectral images of cucumber leaves with five infection severities of ALS were captured by a hyperspectral imaging system in the range of 380-1,030 nm covering 512 wavebands. Mean spectrum were extracted from regions of interest (ROIs) in the hyperspectral images. Partial least square regression (PLSR) models were used to develop quantitative analysis between the spectra and the pigment contents measured by biochemical analyses. In addition, regression coefficients (RCs) in PLSR models were employed to select important wavelengths (IWs) for modelling. It was found that the PLSR models developed by the IWs provided the optimal measurement results with correlation coefficient (R) of prediction of 0.871 and 0.876 for Chl and Car contents, respectively. Finally, Chl and Car distributions in cucumber leaves with the ALS infection were mapped by applying the optimal models pixel-wise to the hyperspectral images. The results proved the feasibility of hyperspectral imaging for visualizing the pigment distributions in cucumber leaves in response to ALS. PMID:27283050

  14. [Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

    PubMed

    Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei

    2015-11-01

    A novel method of combination of the chemometrics and the hyperspectral imaging techniques was presented to detect the temperatures of Ethylene-Vinyl Acetate copolymer (EVA) films in photovoltaic cells during the thermal encapsulation process. Four varieties of the EVA films which had been heated at the temperatures of 128, 132, 142 and 148 °C during the photovoltaic cells production process were used for investigation in this paper. These copolymer encapsulation films were firstly scanned by the hyperspectral imaging equipment (Spectral Imaging Ltd. Oulu, Finland). The scanning band range of hyperspectral equipemnt was set between 904.58 and 1700.01 nm. The hyperspectral dataset of copolymer films was randomly divided into two parts for the training and test purpose. Each type of the training set and test set contained 90 and 10 instances, respectively. The obtained hyperspectral images of EVA films were dealt with by using the ENVI (Exelis Visual Information Solutions, USA) software. The size of region of interest (ROI) of each obtained hyperspectral image of EVA film was set as 150 x 150 pixels. The average of reflectance hyper spectra of all the pixels in the ROI was used as the characteristic curve to represent the instance. There kinds of chemometrics methods including partial least squares regression (PLSR), multi-class support vector machine (SVM) and large margin nearest neighbor (LMNN) were used to correlate the characteristic hyper spectra with the encapsulation temperatures of of copolymer films. The plot of weighted regression coefficients illustrated that both bands of short- and long-wave near infrared hyperspectral data contributed to enhancing the prediction accuracy of the forecast model. Because the attained reflectance hyperspectral data of EVA materials displayed the strong nonlinearity, the prediction performance of linear modeling method of PLSR declined and the prediction precision only reached to 95%. The kernel-based forecast models were

  15. [Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

    PubMed

    Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei

    2015-11-01

    A novel method of combination of the chemometrics and the hyperspectral imaging techniques was presented to detect the temperatures of Ethylene-Vinyl Acetate copolymer (EVA) films in photovoltaic cells during the thermal encapsulation process. Four varieties of the EVA films which had been heated at the temperatures of 128, 132, 142 and 148 °C during the photovoltaic cells production process were used for investigation in this paper. These copolymer encapsulation films were firstly scanned by the hyperspectral imaging equipment (Spectral Imaging Ltd. Oulu, Finland). The scanning band range of hyperspectral equipemnt was set between 904.58 and 1700.01 nm. The hyperspectral dataset of copolymer films was randomly divided into two parts for the training and test purpose. Each type of the training set and test set contained 90 and 10 instances, respectively. The obtained hyperspectral images of EVA films were dealt with by using the ENVI (Exelis Visual Information Solutions, USA) software. The size of region of interest (ROI) of each obtained hyperspectral image of EVA film was set as 150 x 150 pixels. The average of reflectance hyper spectra of all the pixels in the ROI was used as the characteristic curve to represent the instance. There kinds of chemometrics methods including partial least squares regression (PLSR), multi-class support vector machine (SVM) and large margin nearest neighbor (LMNN) were used to correlate the characteristic hyper spectra with the encapsulation temperatures of of copolymer films. The plot of weighted regression coefficients illustrated that both bands of short- and long-wave near infrared hyperspectral data contributed to enhancing the prediction accuracy of the forecast model. Because the attained reflectance hyperspectral data of EVA materials displayed the strong nonlinearity, the prediction performance of linear modeling method of PLSR declined and the prediction precision only reached to 95%. The kernel-based forecast models were

  16. Lossless compression of hyperspectral images based on the prediction error block

    NASA Astrophysics Data System (ADS)

    Li, Yongjun; Li, Yunsong; Song, Juan; Liu, Weijia; Li, Jiaojiao

    2016-05-01

    A lossless compression algorithm of hyperspectral image based on distributed source coding is proposed, which is used to compress the spaceborne hyperspectral data effectively. In order to make full use of the intra-frame correlation and inter-frame correlation, the prediction error block scheme are introduced. Compared with the scalar coset based distributed compression method (s-DSC) proposed by E.Magli et al., that is , the bitrate of the whole block is determined by its maximum prediction error, and the s-DSC-classify scheme proposed by Song Juan that is based on classification and coset coding, the prediction error block scheme could reduce the bitrate efficiently. Experimental results on hyperspectral images show that the proposed scheme can offer both high compression performance and low encoder complexity and decoder complexity, which is available for on-board compression of hyperspectral images.

  17. Geometric correction method of rotary scanning hyperspectral image in agriculture application

    NASA Astrophysics Data System (ADS)

    Wan, Peng; Yang, Guijun; Xu, Bo; Feng, Haikuan; Yu, Haiyang

    2015-04-01

    In order to meet the demand of farmland plot experiments hyperspectral images acquisition, an equipment that incorporating an aerial lift vehicle with hyperspectral imager was proposed. In this manner, high spatial resolution (in millimeter) imageries were collected, which meets the need of spatial resolution on farm experiments, but also improves the efficiency of image acquisition. In allusion to the image circular geometric distortion which produced by telescopic arm rotation, an image rectification method that based on mounted position and orientation system was proposed. Experimental results shows that the image rectification method is effective.

  18. Airborne Demonstration of FPGA Implementation of Fast Lossless Hyperspectral Data Compression System

    NASA Technical Reports Server (NTRS)

    Keymeulen, D.; Aranki, N.; Bakhshi, A.; Luong, H.; Sartures, C.; Dolman, D.

    2014-01-01

    Efficient on-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact r