Science.gov

Sample records for airborne hyperspectral images

  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. Citrus greening detection using airborne hyperspectral and multispectral imaging techniques

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging can provide unique spectral signatures for diseased vegetation. Airborne multispectral and hyperspectral imaging can be used to detect potentially infected trees over a large area for rapid detection of infected zones. This paper proposes a method to detect the citrus greening...

  3. Citrus greening disease detection using airborne multispectral and hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging can provide unique spectral signatures for diseased vegetation. Airborne hyperspectral imaging can be used to detect potentially infected trees over a large area for rapid detection of infected zones. Ground inspection and management can be focused on these infected zones rath...

  4. 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.

  5. 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.

  6. 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.

  7. Airborne midwave and longwave infrared hyperspectral imaging of gases

    NASA Astrophysics Data System (ADS)

    Gagnon, Marc-André; Tremblay, Pierre; Savary, Simon; Duval, Marc; Farley, Vincent; Chamberland, Martin

    2014-05-01

    Characterization of gas clouds are challenging situations to address due to the large and uneven distribution of these fast moving entities. Whether gas characterization is carried out for gas leaks surveys or environmental monitoring purposes, explosives and/or toxic chemicals are often involved. In such situations, airborne measurements present distinct advantages over ground based-techniques since large areas can be covered efficiently from a safe distance. In order to illustrate the potential of airborne thermal infrared hyperspectral imaging for gas cloud characterization, measurements were carried out above smokestacks and a ground-based gas release experiment. Quantitative airborne chemical images of carbon monoxide (CO) and ethylene (C2H4) were obtained from measurements carried out using a midwave (MWIR, 3-5 μm) and a longwave (LWIR, 8-12 μm) airborne infrared hyperspectral sensor respectively. Scattering effects were observed in the MWIR experiments on smokestacks as a result of water condensation upon rapid cool down of the hot emission gases. Airborne measurements were carried out using both mapping and targeting acquisition modes. The later provides unique time-dependent information such as the gas cloud direction and velocity.

  8. Airborne midwave and longwave infrared hyperspectral imaging of gases

    NASA Astrophysics Data System (ADS)

    Gagnon, Marc-André; Tremblay, Pierre; Savary, Simon; Duval, Marc; Farley, Vincent; Chamberland, Martin

    2014-11-01

    Characterization of gas clouds are challenging situations to address due to the large and uneven distribution of these fast moving entities. Whether gas characterization is carried out for gas leaks surveys or environmental monitoring purposes, explosives and/or toxic chemicals are often involved. In such situations, airborne measurements present distinct advantages over ground based-techniques since large areas can be covered efficiently from a safe distance. In order to illustrate the potential of airborne thermal infrared hyperspectral imaging for gas cloud characterization, measurements were carried out above smokestacks and a ground-based gas release experiment. Quantitative airborne chemical images of carbon monoxide (CO) and ethylene (C2H4) were obtained from measurements carried out using a midwave (MWIR, 3-5 μm) and a longwave (LWIR, 8-12 μm) airborne infrared hyperspectral sensor respectively. Scattering effects were observed in the MWIR experiments on smokestacks as a result of water condensation upon rapid cool down of the hot emission gases. Airborne measurements were carried out using both mapping and targeting acquisition modes. The later provides unique time-dependent information such as the gas cloud direction and velocity.

  9. Airborne midwave and longwave infrared hyperspectral imaging of gases

    NASA Astrophysics Data System (ADS)

    Gagnon, Marc-André; Tremblay, Pierre; Savary, Simon; Duval, Marc; Farley, Vincent; Guyot, Éric; Chamberland, Martin

    2014-10-01

    Characterization of gas clouds are challenging situations to address due to the large and uneven distribution of these fast moving entities. Whether gas characterization is carried out for gas leaks surveys or environmental monitoring purposes, explosives and/or toxic chemicals are often involved. In such situations, airborne measurements present distinct advantages over ground based-techniques since large areas can be covered efficiently from a safe distance. In order to illustrate the potential of airborne thermal infrared hyperspectral imaging for gas cloud characterization, measurements were carried out above smokestacks and a ground-based gas release experiment. Quantitative airborne chemical images of carbon monoxide (CO) and ethylene (C2H4) were obtained from measurements carried out using a midwave (MWIR, 3-5 μm) and a longwave (LWIR, 8-12 μm) airborne infrared hyperspectral sensor respectively. Scattering effects were observed in the MWIR experiments on smokestacks as a result of water condensation upon rapid cool down of the hot emission gases. Airborne measurements were carried out using both mapping and targeting acquisition modes. The later provides unique time-dependent information such as the gas cloud direction and velocity.

  10. 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 ...

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Measured performance of an airborne Fourier-transform hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Otten, Leonard John, III; Meigs, Andrew D.; Sellar, R. Glenn; Rafert, Bruce

    1996-11-01

    A new hyperspectral imager has recently been developed by Kestrel Corporation for use in light aircraft platforms. The instrument provides 256 spectral channels with 87 cm-1 spectral bandwidth over the 450 nm to 1000 nm portion of the spectrum. Operated as a pushbroom imager, the FTVHSI has been shown to have a IFOV of 0.75 mrad, and a FOV of 0.23 rad. The sensor includes an internal spectral/radiometric calibration source, a self contained spectrally resolved downwelling sensor, and complete line of sight and GPS positioning information. The instrument is now operating from a Cessna TU-206 single engine aircraft.

  16. Development of the second generation Hyperspectral Airborne Terrestrial Imager (HATI): HATI - 2500

    NASA Astrophysics Data System (ADS)

    Sandor-Leahy, S.; Thordarson, S.; Baldauf, B.; Figueroa, M.; Helmlinger, M.; Miller, H.; Reynolds, T.; Shepanski, J.

    2010-08-01

    Northrop Grumman Aerospace Systems (NGAS) has a long legacy developing and fielding hyperspectral sensors, including airborne and space based systems covering the visible through Long Wave Infrared (LWIR) wavelength ranges. Most recently NGAS has developed the Hyperspectral Airborne Terrestrial Instrument (HATI) family of hyperspectral sensors, which are compact airborne hyperspectral imagers designed to fly on a variety of platforms and be integrated with other sensors in NGAS's instrument suite. The current sensor under development is the HATI-2500, a full range Visible Near Infrared (VNIR) through Short Wave Infrared (SWIR) instrument covering the 0.4 - 2.5 micron wavelength range with high spectral resolution (3nm). The system includes a framing camera integrated with a GPS/INS to provide high-resolution multispectral imagery and precision geolocation. Its compact size and flexible acquisition parameters allow HATI-2500 to be integrated on a large variety of aerial platforms. This paper describes the HATI-2500 sensor and subsystems and its expected performance specifications.

  17. Diffused Matrix Format: A New Storage and Processing Format for Airborne Hyperspectral Sensor Images

    PubMed Central

    Martínez, Pablo; Cristo, Alejandro; Koch, Magaly; Pérez, Rosa Mª.; Schmid, Thomas; Hernández, Luz M.

    2010-01-01

    At present, hyperspectral images are mainly obtained with airborne sensors that are subject to turbulences while the spectrometer is acquiring the data. Therefore, geometric corrections are required to produce spatially correct images for visual interpretation and change detection analysis. This paper analyzes the data acquisition process of airborne sensors. The main objective is to propose a new data format called Diffused Matrix Format (DMF) adapted to the sensor's characteristics including its spectral and spatial information. The second objective is to compare the accuracy of the quantitative maps derived by using the DMF data structure with those obtained from raster images based on traditional data structures. Results show that DMF processing is more accurate and straightforward than conventional image processing of remotely sensed data with the advantage that the DMF file structure requires less storage space than other data formats. In addition the data processing time does not increase when DMF is used. PMID:22399919

  18. 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...

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Mapping Waterhyacinth Infestations Using Airborne Hyperspectral Imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Waterhyacinth [Eichhornia crassipes (Mart.) Solms] is an exotic aquatic weed that often invades and clogs waterways in many tropical and subtropical regions of the world. The objective of this study was to evaluate airborne hyperspectral imagery and different image classification techniques for mapp...

  6. An oil film information retrieval method overcoming the influence of sun glitter, based on AISA+ airborne hyper-spectral image

    NASA Astrophysics Data System (ADS)

    Zhan, Yuanzeng; Mao, Tianming; Gong, Fang; Wang, Difeng; Chen, Jianyu

    2010-10-01

    As an effective survey tool for oil spill detection, the airborne hyper-spectral sensor affords the potentiality for retrieving the quantitative information of oil slick which is useful for the cleanup of spilled oil. But many airborne hyper-spectral images are affected by sun glitter which distorts radiance values and spectral ratios used for oil slick detection. In 2005, there's an oil spill event leaking at oil drilling platform in The South China Sea, and an AISA+ airborne hyper-spectral image recorded this event will be selected for studying in this paper, which is affected by sun glitter terribly. Through a spectrum analysis of the oil and water samples, two features -- "spectral rotation" and "a pair of fixed points" can be found in spectral curves between crude oil film and water. Base on these features, an oil film information retrieval method which can overcome the influence of sun glitter is presented. Firstly, the radiance of the image is converted to normal apparent reflectance (NormAR). Then, based on the features of "spectral rotation" (used for distinguishing oil film and water) and "a pair of fixed points" (used for overcoming the effect of sun glitter), NormAR894/NormAR516 is selected as an indicator of oil film. Finally, by using a threshold combined with the technologies of image filter and mathematic morphology, the distribution and relative thickness of oil film are retrieved.

  7. New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images

    NASA Astrophysics Data System (ADS)

    Xie, Huan; Luo, Xin; Xu, Xiong; Tong, Xiaohua; Jin, Yanmin; Pan, Haiyan; Zhou, Bingzhong

    2014-01-01

    Extracting surface land-cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic tasks is to identify and map surface water boundaries. Spectral water indexes have been successfully used in the extraction of water bodies in multispectral images. However, directly applying a water index method to hyperspectral images disregards the abundant spectral information and involves difficulty in selecting appropriate spectral bands. It is also a challenge for a spectral water index to distinguish water from shadowed regions. The purpose of this study is therefore to develop an index that is suitable for water extraction by the use of hyperspectral images, and with the capability to mitigate the effects of shadow and low-albedo surfaces, especially in urban areas. Thus, we introduce a new hyperspectral difference water index (HDWI) to improve the water classification accuracy in areas that include shadow over water, shadow over other ground surfaces, and low-albedo ground surfaces. We tested the new method using PHI-2, HyMAP, and ROSIS hyperspectral images of Shanghai, Munich, and Pavia. The performance of the water index was compared with the normalized difference water index (NDWI) and the Mahalanobis distance classifier (MDC). With all three test images, the accuracy of HDWI was significantly higher than that of NDWI and MDC. Therefore, HDWI can be used for extracting water with a high degree of accuracy, especially in urban areas, where shadow caused by high buildings is an important source of classification error.

  8. Unsupervised hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Jiao, Xiaoli; Chang, Chein-I.

    2007-09-01

    Two major issues encountered in unsupervised hyperspectral image classification are (1) how to determine the number of spectral classes in the image and (2) how to find training samples that well represent each of spectral classes without prior knowledge. A recently developed concept, Virtual dimensionality (VD) is used to estimate the number of spectral classes of interest in the image data. This paper proposes an effective algorithm to generate an appropriate training set via a recently developed Prioritized Independent Component Analysis (PICA). Two sets of hyperspectral data, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Cuprite data and HYperspectral Digital Image Collection Experiment (HYDICE) data are used for experiments and performance analysis for the proposed method.

  9. 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.

  10. Airborne hyperspectral imaging for sensing phosphorus concentration in the Lake Okeechobee drainage basin

    NASA Astrophysics Data System (ADS)

    Bogrekci, Ismail; Lee, Won Suk; Jordan, Jonathan D.

    2005-05-01

    Eutrophication disturbs the ecological balance in the Lake Okeechobee due to high concentration of phosphorus emanated from the regions in the lake's drainage basin. Ability of measuring phosphorus (P) concentrations of water in the Lake Okeechobee itself is very important. Furthermore, monitoring P in its drainage basins is crucial in order to find the cause of P loading and contributing regions. Also, inexpensive real-time sensing capability for a large area in a short time would help scientist, government agents, and civilians to understand the causes, spot the high-risk areas, and develop management practices for restoring the natural equilibrium. In order to measure P concentrations in the Lake Okeechobee drainage basin, airborne hyperspectral images were taken from five representative target sites by deploying a modified queen air twin engine aircraft. Each flight line covered a swath of approximately 365 m wide. Spatial resolution was about 1 m. Spectral range covered was between 412.65 and 991.82 nm with an approximate of 5 nm spectral resolution. Ground truthing was conducted to collect soil and vegetation samples, GPS coordinates of each location, and reflectance measurement of each sample. On the ground, spectral reflectance was measured using a handheld spectrometer in 400-2500 nm. The samples were sent to a laboratory for chemical analysis. Also diffuse reflectance of the samples was measured in a laboratory setting using a spectrophotometer with an integrating sphere. Images were geocorrected and rectified to reduce geometric effect. Calibration of images was conducted to obtain actual reflectance of the target area. Score, SAM (Spectral Angle Mapping), SFF (Spectral Feature Fitting) were computed for spectral matching with image derived spectral library.

  11. ESTIMATING WITHIN-FIELD VARIATIONS IN SOIL PROPERTIES FROM AIRBORNE HYPERSPECTRAL IMAGES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The ability of hyperspectral image (HSI) data to provide estimates of soil electrical conductivity (EC) and soil fertility levels without requiring extensive field data collection was investigated. Bare soil images were acquired using a prism grating pushbroom scanner in April 2000 and May 2001 for ...

  12. [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

  13. Spectral angle mapper (SAM) based citrus greening disease detection using airborne hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Over the past two decades, hyperspectral (HS) imaging has provided remarkable performance in ground object classification and disease identification, due to its high spectral resolution. In this paper, a novel method named “extended spectral angle mapping (ESAM)” is proposed to detect citrus greenin...

  14. 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.

  15. 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.

  16. Airborne hyperspectral detection of small changes.

    PubMed

    Eismann, Michael T; Meola, Joseph; Stocker, Alan D; Beaven, Scott G; Schaum, Alan P

    2008-10-01

    Hyperspectral change detection offers a promising approach to detect objects and features of remotely sensed areas that are too difficult to find in single images, such as slight changes in land cover and the insertion, deletion, or movement of small objects, by exploiting subtle differences in the imagery over time. Methods for performing such change detection, however, must effectively maintain invariance to typically larger image-to-image changes in illumination and environmental conditions, as well as misregistration and viewing differences between image observations, while remaining sensitive to small differences in scene content. Previous research has established predictive algorithms to overcome such natural changes between images, and these approaches have recently been extended to deal with space-varying changes. The challenges to effective change detection, however, are often exacerbated in an airborne imaging geometry because of the limitations in control over flight conditions and geometry, and some of the recent change detection algorithms have not been demonstrated in an airborne setting. We describe the airborne implementation and relative performance of such methods. We specifically attempt to characterize the effects of spatial misregistration on change detection performance, the efficacy of class-conditional predictors in an airborne setting, and extensions to the change detection approach, including physically motivated shadow transition classifiers and matched change filtering based on in-scene atmospheric normalization. PMID:18830283

  17. Simulation of Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Richsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.

    2004-01-01

    A software package generates simulated hyperspectral imagery for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport, as well as reflections from surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, "ground truth" is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces, as well as the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for, and a supplement to, field validation data.

  18. Hyperspectral image processing

    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...

  19. 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...

  20. [Inversion of vegetation canopy's chlorophyll content based on airborne hyperspectral image].

    PubMed

    Li, Ming-Ze; Zhao, Xiao-Hong; Liu, Yue; Lu, Wei; Dong, Shuai; Meng, Lu

    2013-01-01

    By using the airborne hyperspectral remote sensing data of Liangshui National Nature Reserve in Yichun of Heilongjiang Province, Northeast China, 15 spectral parameters including red edge area, triangular vegetation index, and normalized difference vegetation index, etc. were extracted, and in combining with 5 geographical parameters including slope, aspect, elevation, canopy density and total vegetation coverage, and by using SPAD-502, the vegetation canopy's relative chlorophyll content in the reserve were measured, with the correlations of the leaf spectral reflectivity, its first-order derivative and other deformations with the SPAD value analyzed. A prediction model for relative chlorophyll content was established by adopting the kernel-based partial least-squares regression, and a quantitative estimation of the vegetation canopy's relative chlorophyll content in the study area was carried out with the established model. The results showed that the model performed best when the sections were three and the principle components were ten. The co-efficient of determination of the model was R2 = 0.855, the mean absolute percent error was 9.6%, and the prediction precision was 89.7%. PMID:23718007

  1. 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

  2. Design of an airborne Fourier transform visible hyperspectral imaging system for light aircraft environmental remote sensing

    NASA Astrophysics Data System (ADS)

    Otten, Leonard John, III; Butler, Eugene W.; Rafert, Bruce; Sellar, R. Glenn

    1995-06-01

    Kestrel Corporation and the Florida Institute of Technology have designed, and are now manufacturing, a Fourier transform visible hyperspectral imager system for use in a single engine light aircraft. The system is composed of a Sagnac-based interferometer optical subsystem, a data management system, and an aircraft attitude and current position sybsystem. The system is designed to have better than 5 nm spectral resolution at 450 nm, operates over the 440 nm to 1150 nm spectral band and has a 2D spatial resolution of 0.8 mrad. An internal calibration source is recorded with every frame of data to retain radiometric accuracy. The entire system fits into a Cessna 206 and uses a conventional downward looking view port located in the baggage compartment. During operation, data are collected at a rate of 15 Mbytes per second and stored direct to a disk array. Data storage has been sized to accommodate 56 minutes of observations. Designed for environmental mapping, this Fourier transform imager has uses in emergency response and military operations.

  3. 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.

  4. Fiber optic snapshot hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Mansur, David J.; Rentz Dupuis, Julia; Vaillancourt, Robert

    2012-06-01

    OPTRA is developing a snapshot hyperspectral imager (HSI) employing a fiber optic bundle and dispersive spectrometer. The fiber optic bundle converts a broadband spatial image to an array of fiber columns which serve as multiple entrance slits to a prism spectrometer. The dispersed spatially resolved spectra are then sampled by a two-dimensional focal plane array (FPA) at a greater than 30 Hz update rate, thereby qualifying the system as snapshot. Unlike snapshot HSI systems based on computed tomography or coded apertures, our approach requires only the remapping of the FPA frame into hyperspectral cubes rather than a complex reconstruction. Our system has high radiometric efficiency and throughput supporting sufficient signal to noise for hyperspectral imaging measurements made over very short integration times (< 33 ms). The overall approach is compact, low cost, and contains no moving parts, making it ideal for unmanned airborne surveillance. In this paper we present a preliminary design for the fiber optic snapshot HSI system.

  5. Evaluating Airborne Hyperspectral imagery for mapping waterhyacinth infestations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Waterhyacinth [Eichhornia crassipes (Mart.) Solms] is an exotic aquatic weed that often invades and clogs waterways in many tropical and subtropical regions of the world. The objective of this study was to evaluate airborne hyperspectral imagery and different image classification techniques for mapp...

  6. 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.

  7. Comparative analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and Hyperspectral Thermal Emission Spectrometer (HyTES) longwave infrared (LWIR) hyperspectral data for geologic mapping

    NASA Astrophysics Data System (ADS)

    Kruse, Fred A.

    2015-05-01

    Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and spatially coincident Hyperspectral Thermal Emission Spectrometer (HyTES) data were used to map geology and alteration for a site in northern Death Valley, California and Nevada, USA. AVIRIS, with 224 bands at 10 nm spectral resolution over the range 0.4 - 2.5 μm at 3-meter spatial resolution were converted to reflectance using an atmospheric model. HyTES data with 256 bands at approximately 17 nm spectral resolution covering the 8 - 12 μm range at 4-meter spatial resolution were converted to emissivity using a longwave infrared (LWIR) radiative transfer atmospheric compensation model and a normalized temperature-emissivity separation approach. Key spectral endmembers were separately extracted for each wavelength region and identified, and the predominant material at each pixel was mapped for each range using Mixture-Tuned-Matched Filtering (MTMF), a partial unmixing approach. AVIRIS mapped iron oxides, clays, mica, and silicification (hydrothermal alteration); and the difference between calcite and dolomite. HyTES separated and mapped several igneous phases (not possible using AVIRIS), silicification, and validated separation of calcite from dolomite. Comparison of the material maps from the different modes, however, reveals complex overlap, indicating that multiple materials/processes exist in many areas. Combined and integrated analyses were performed to compare individual results and more completely characterize occurrences of multiple materials. Three approaches were used 1) integrated full-range analysis, 2) combined multimode classification, and 3) directed combined analysis in geologic context. Results illustrate that together, these two datasets provide an improved picture of the distribution of geologic units and subsequent alteration.

  8. 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

  9. Airborne hyperspectral imaging for the detection of powdery mildew in wheat

    NASA Astrophysics Data System (ADS)

    Franke, Jonas; Mewes, Thorsten; Menz, Gunter

    2008-08-01

    Plant stresses, in particular fungal diseases, show a high variability in spatial and temporal dimension with respect to their impact on the host. Recent "Precision Agriculture"-techniques allow for a spatially and temporally adjusted pest control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stressdetection techniques such as random monitoring do not meet demands of such optimally placed management actions. The prerequisite is an accurate sensor-based detection of stress symptoms. The present study focuses on a remotely sensed detection of the fungal disease powdery mildew (Blumeria graminis) in wheat, Europe's main crop. In a field experiment, the potential of hyperspectral data for an early detection of stress symptoms was tested. A sophisticated endmember selection procedure was used and, additionally, a linear spectral mixture model was applied to a pixel spectrum with known characteristics, in order to derive an endmember representing 100% powdery mildew-infected wheat. Regression analyses of matched fraction estimates of this endmember and in-field-observed powdery mildew severities showed promising results (r=0.82 and r2=0.67).

  10. Handheld hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Saari, Heikki; Aallos, Ville-Veikko; Holmlund, Christer; Malinen, Jouko; Mäkynen, Jussi

    2010-04-01

    VTT Technical Research Centre of Finland has developed a new low cost hand-held staring hyperspectral imager for applications previously blocked by high cost of the instrumentation. The system is compatible with standard video and microscope lenses. The instrument can record 2D spatial images at several wavelength bands simultaneously. The concept of the hyperspectral imager has been published in SPIE Proc. 7474. The prototype fits in an envelope of 100 mm x 60 mm x 40 mm and its weight is ca. 300 g. The benefits of the new device compared to Acousto-Optic Tunable filter (AOTF) or Liquid Crystal Tunable Filter (LCTF) devices are small size and weight, speed of wavelength tuning, high optical throughput, independence of polarization state of incoming light and capability to record three wavelengths simultaneously. The operational wavelength range with Silicon-based CCD or CMOS sensors is 200 - 1100 nm and spectral resolution is 2 - 10 nm @ FWHM. Similar IR imagers can be built using InGaAs, InSb or MCT imaging sensors. The spatial resolution of the prototype is 480 x 750 pixels. It contains control system and memory for the image data acquisition. It operates either autonomously recording hyperspectral data cubes continuously or controlled by a laptop computer. The prototype was configured as a hyperspectral microscope for the spectral range 400 - 700 nm. The design of the hyperspectral imager, characterization results and sample measurement results are presented.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. Planetary Hyperspectral Imager (PHI)

    NASA Technical Reports Server (NTRS)

    Silvergate, Peter

    1996-01-01

    A hyperspectral imaging spectrometer was breadboarded. Key innovations were use of a sapphire prism and single InSb focal plane to cover the entire spectral range, and a novel slit optic and relay optics to reduce thermal background. Operation over a spectral range of 450 - 4950 nm (approximately 3.5 spectral octaves) was demonstrated. Thermal background reduction by a factor of 8 - 10 was also demonstrated.

  3. 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.

  4. Hyperspectral Imager-Tracker

    NASA Technical Reports Server (NTRS)

    Agurok, Llya

    2013-01-01

    The Hyperspectral Imager-Tracker (HIT) is a technique for visualization and tracking of low-contrast, fast-moving objects. The HIT architecture is based on an innovative and only recently developed concept in imaging optics. This innovative architecture will give the Light Prescriptions Innovators (LPI) HIT the possibility of simultaneously collecting the spectral band images (hyperspectral cube), IR images, and to operate with high-light-gathering power and high magnification for multiple fast- moving objects. Adaptive Spectral Filtering algorithms will efficiently increase the contrast of low-contrast scenes. The most hazardous parts of a space mission are the first stage of a launch and the last 10 kilometers of the landing trajectory. In general, a close watch on spacecraft operation is required at distances up to 70 km. Tracking at such distances is usually associated with the use of radar, but its milliradian angular resolution translates to 100- m spatial resolution at 70-km distance. With sufficient power, radar can track a spacecraft as a whole object, but will not provide detail in the case of an accident, particularly for small debris in the onemeter range, which can only be achieved optically. It will be important to track the debris, which could disintegrate further into more debris, all the way to the ground. Such fragmentation could cause ballistic predictions, based on observations using high-resolution but narrow-field optics for only the first few seconds of the event, to be inaccurate. No optical imager architecture exists to satisfy NASA requirements. The HIT was developed for space vehicle tracking, in-flight inspection, and in the case of an accident, a detailed recording of the event. The system is a combination of five subsystems: (1) a roving fovea telescope with a wide 30 field of regard; (2) narrow, high-resolution fovea field optics; (3) a Coude optics system for telescope output beam stabilization; (4) a hyperspectral

  5. Hyperspectral image analysis. A tutorial.

    PubMed

    Amigo, José Manuel; Babamoradi, Hamid; Elcoroaristizabal, Saioa

    2015-10-01

    This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case. PMID:26481986

  6. 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...

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil hyperspectral reflectance imagery was obtained from an airborne imaging spectrometer (400 to 2450 nm with ~10 nm resolution, 2.5 m spatial resolution) flown over six tilled (bare soil) agricultural fields on the Eastern Shore of the Chesapeake Bay (Queen Anne’s county, MD). Surface soil samples...

  8. 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.

  9. Software for Simulation of Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Richtsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.

    2002-01-01

    A package of software generates simulated hyperspectral images for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport as well as surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, 'ground truth' is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces and the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for and a supplement to field validation data.

  10. Medical hyperspectral imaging: a review

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Fei, Baowei

    2014-01-01

    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.

  11. 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

  12. 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...

  13. Satellite Hyperspectral Imaging Simulation

    NASA Technical Reports Server (NTRS)

    Zanoni, Vicki; Stanley, Tom; Blonski, Slawomir; Cao, Changyong; Gasser, Jerry; Ryan, Robert

    1999-01-01

    Simulation of generic pushbroom satellite hyperspectral sensors have been performed to evaluate the potential performance and validation techniques for satellite systems such as COIS(NEMO), Warfighter-1(OrbView-4) and Hyperion(EO-1). The simulations start with a generation of synthetic scenes from material maps of studied terrain. Scene-reflected radiance is corrected for atmospheric effects and convolved with sensor spectral response using MODTRAN 4 radiance and transmissions calculations. Scene images are further convolved with point spread functions derived from Optical Transfer Functions (OTF's) of the sensor system. Photon noise and etectorr/electronics noise are added to the simulated images, which are also finally quantized to the sensor bit resolution. Studied scenes include bridges and straight roads used for evaluation of sensor spatial resolution, as well as fields of minerals, vegetation and manmade materials used for evaluation of sensor radiometric response and sensitivity. The scenes are simulated with various seasons and weather conditions. Signal-to-noise ratios and expected performance are estimated for typical satellite system specifications and are discussed for all the scenes.

  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. A Synergistic Approach to Atmospheric Compensation of Neon's Airborne Hyperspectral Imagery Utilizing an Airborne Solar Spectral Irradiance Radiometer

    NASA Astrophysics Data System (ADS)

    Wright, L.; Karpowicz, B. M.; Kindel, B. C.; Schmidt, S.; Leisso, N.; Kampe, T. U.; Pilewskie, P.

    2014-12-01

    A wide variety of critical information regarding bioclimate, biodiversity, and biogeochemistry is embedded in airborne hyperspectral imagery. Most, if not all of the primary signal relies upon first deriving the surface reflectance of land cover and vegetation from measured hyperspectral radiance. This places stringent requirements on terrain, and atmospheric compensation algorithms to accurately derive surface reflectance properties. An observatory designed to measure bioclimate, biodiversity, and biogeochemistry variables from surface reflectance must take great care in developing an approach which chooses algorithms with the highest accuracy, along with providing those algorithms with data necessary to describe the physical mechanisms that affect the measured at sensor radiance. The Airborne Observation Platform (AOP) part of the National Ecological Observatory Network (NEON) is developing such an approach. NEON is a continental-scale ecological observation platform designed to collect and disseminate data to enable the understanding and forecasting of the impacts of climate change, land use change, and invasive species on ecology. The instrumentation package used by the AOP includes a visible and shortwave infrared hyperspectral imager, waveform LiDAR, and high resolution (RGB) digital camera. In addition to airborne measurements, ground-based CIMEL sun photometers will be used to help characterize atmospheric aerosol loading, and ground validation measurements with field spectrometers will be made at select NEON sites. While the core instrumentation package provides critical information to derive surface reflectance of land surfaces and vegetation, the addition of a Solar Spectral Irradiance Radiometer (SSIR) is being investigated as an additional source of data to help identify and characterize atmospheric aerosol, and cloud contributions contributions to the radiance measured by the hyperspectral imager. The addition of the SSIR provides the opportunity to

  16. 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.

  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. 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.

  1. 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.

  2. Hyperspectral Image Classification using a Self-Organizing Map

    NASA Technical Reports Server (NTRS)

    Martinez, P.; Gualtieri, J. A.; Aguilar, P. L.; Perez, R. M.; Linaje, M.; Preciado, J. C.; Plaza, A.

    2001-01-01

    The use of hyperspectral data to determine the abundance of constituents in a certain portion of the Earth's surface relies on the capability of imaging spectrometers to provide a large amount of information at each pixel of a certain scene. Today, hyperspectral imaging sensors are capable of generating unprecedented volumes of radiometric data. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), for example, routinely produces image cubes with 224 spectral bands. This undoubtedly opens a wide range of new possibilities, but the analysis of such a massive amount of information is not an easy task. In fact, most of the existing algorithms devoted to analyzing multispectral images are not applicable in the hyperspectral domain, because of the size and high dimensionality of the images. The application of neural networks to perform unsupervised classification of hyperspectral data has been tested by several authors and also by us in some previous work. We have also focused on analyzing the intrinsic capability of neural networks to parallelize the whole hyperspectral unmixing process. The results shown in this work indicate that neural network models are able to find clusters of closely related hyperspectral signatures, and thus can be used as a powerful tool to achieve the desired classification. The present work discusses the possibility of using a Self Organizing neural network to perform unsupervised classification of hyperspectral images. In sections 3 and 4, the topology of the proposed neural network and the training algorithm are respectively described. Section 5 provides the results we have obtained after applying the proposed methodology to real hyperspectral data, described in section 2. Different parameters in the learning stage have been modified in order to obtain a detailed description of their influence on the final results. Finally, in section 6 we provide the conclusions at which we have arrived.

  3. Ore minerals textural characterization by hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Bonifazi, Giuseppe; Picone, Nicoletta; Serranti, Silvia

    2013-02-01

    The utilization of hyperspectral detection devices, for natural resources mapping/exploitation through remote sensing techniques, dates back to the early 1970s. From the first devices utilizing a one-dimensional profile spectrometer, HyperSpectral Imaging (HSI) devices have been developed. Thus, from specific-customized devices, originally developed by Governmental Agencies (e.g. NASA, specialized research labs, etc.), a lot of HSI based equipment are today available at commercial level. Parallel to this huge increase of hyperspectral systems development/manufacturing, addressed to airborne application, a strong increase also occurred in developing HSI based devices for "ground" utilization that is sensing units able to play inside a laboratory, a processing plant and/or in an open field. Thanks to this diffusion more and more applications have been developed and tested in this last years also in the materials sectors. Such an approach, when successful, is quite challenging being usually reliable, robust and characterised by lower costs if compared with those usually associated to commonly applied analytical off- and/or on-line analytical approaches. In this paper such an approach is presented with reference to ore minerals characterization. According to the different phases and stages of ore minerals and products characterization, and starting from the analyses of the detected hyperspectral firms, it is possible to derive useful information about mineral flow stream properties and their physical-chemical attributes. This last aspect can be utilized to define innovative process mineralogy strategies and to implement on-line procedures at processing level. The present study discusses the effects related to the adoption of different hardware configurations, the utilization of different logics to perform the analysis and the selection of different algorithms according to the different characterization, inspection and quality control actions to apply.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Estimating leaf area index and aboveground biomass of an invasive weed (yellow starthistle, Centaurea solstitalis L.) using airborne hyperspectral data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral remote sensed data was obtained via a Compact Airborne Spectrographic Imager (CASI) and used to estimate leaf area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (Centaurea solstitialis L.). In parallel, 34 ground-based field plots were used t...

  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. Retrieval of cloud optical properties using airborne hyperspectral cameras during the VOCALS campaign.

    NASA Astrophysics Data System (ADS)

    Labrador, L.; Vaughan, G.

    2009-09-01

    A set of two hyperspectral imaging sensors have been used to analyze the optical properties of stratocumulus cloud off the coast of Northern Chile within the framework of the VAMOS Ocean Clouds Atmosphere Land Study (VOCALS) during September-October 2008. The SPECIM Aisa Eagle & Hawk are tandem pushbroom-type hyperspectral imagers scanning in the 400-970 and 970-2500 nm range, respectively. The instruments were mounted onboard the National Environmental Research Council's (NERC) Dornier DO-228 aircraft, based in Arica, northern Chile during the campaign. An area approximately 600 x 200 km was surveyed off the northern coast of Chile and a total of 14 science flights were carried out where hyperspectral data were successfully collected over the stratocumulus deck at altitudes varying between 10000 and 15000 ft. Cloud optical properties, such as cloud optical thickness, cloud effective radius and liquid water path can be retrieved which can then be compared with space-borne hyperspectral imagers' retrievals. Atmospheric corrections have been applied to enable the comparison between the different type of sensors and the analysis requires, amongst other, solving the back-scattering problems associated with off-nadir views. The high resolution, both spatial and temporal, of these airborne sensors makes them ideal to validate satellite retrievals of cloud optical properties.

  13. 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.

  14. 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.

  15. 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...

  16. Hyperspectral imaging using compressed sensing

    NASA Astrophysics Data System (ADS)

    Ramirez I., Gabriel Eduardo; Manian, Vidya B.

    2012-06-01

    Compressed sensing (CS) has attracted a lot of attention in recent years as a promising signal processing technique that exploits a signal's sparsity to reduce its size. It allows for simple compression that does not require a lot of additional computational power, and would allow physical implementation at the sensor using spatial light multiplexers using Texas Instruments (TI) digital micro-mirror device (DMD). The DMD can be used as a random measurement matrix, reflecting the image off the DMD is the equivalent of an inner product between the images individual pixels and the measurement matrix. CS however is asymmetrical, meaning that the signals recovery or reconstruction from the measurements does require a higher level of computation. This makes the prospect of working with the compressed version of the signal in implementations such as detection or classification much more efficient. If an initial analysis shows nothing of interest, the signal need not be reconstructed. Many hyper-spectral image applications are precisely focused on these areas, and would greatly benefit from a compression technique like CS that could help minimize the light sensor down to a single pixel, lowering costs associated with the cameras while reducing the large amounts of data generated by all the bands. The present paper will show an implementation of CS using a single pixel hyper-spectral sensor, and compare the reconstructed images to those obtained through the use of a regular sensor.

  17. 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.

  18. 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

  19. 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).

  20. 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.

  1. Hyperspectral forest monitoring and imaging implications

    NASA Astrophysics Data System (ADS)

    Goodenough, David G.; Bannon, David

    2014-05-01

    The forest biome is vital to the health of the earth. Canada and the United States have a combined forest area of 4.68 Mkm2. The monitoring of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of improved information products to land managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory (major forest species), forest health, foliar biochemistry, biomass, and aboveground carbon. Operationally there is a requirement for a mix of airborne and satellite approaches. This paper surveys some methods and results in hyperspectral sensing of forests and discusses the implications for space initiatives with hyperspectral sensing

  2. 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.

  3. 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.

  4. 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

  5. Vessel contrast enhancement in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Bjorgan, Asgeir; Denstedt, Martin; Milanič, Matija; Paluchowski, Lukasz A.; Randeberg, Lise L.

    2015-03-01

    Imaging of vessel structures can be useful for investigation of endothelial function, angiogenesis and hyper-vascularization. This can be challenging for hyperspectral tissue imaging due to photon scattering and absorption in other parts of the tissue. Real-time processing techniques for enhancement of vessel contrast in hyperspectral tissue images were investigated. Wavelet processing and an inverse diffusion model were employed, and compared to band ratio metrics and statistical methods. A multiscale vesselness filter was applied for further enhancement. The results show that vessel structures in hyperspectral images can be enhanced and characterized using a combination of statistical, numerical and more physics informed models.

  6. Progressive band processing for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Schultz, Robert C.

    Hyperspectral imaging has emerged as an image processing technique in many applications. The reason that hyperspectral data is called hyperspectral is mainly because the massive amount of information provided by the hundreds of spectral bands that can be used for data analysis. However, due to very high band-to-band correlation much information may be also redundant. Consequently, how to effectively and best utilize such rich spectral information becomes very challenging. One general approach is data dimensionality reduction which can be performed by data compression techniques, such as data transforms, and data reduction techniques, such as band selection. This dissertation presents a new area in hyperspectral imaging, to be called progressive hyperspectral imaging, which has not been explored in the past. Specifically, it derives a new theory, called Progressive Band Processing (PBP) of hyperspectral data that can significantly reduce computing time and can also be realized in real-time. It is particularly suited for application areas such as hyperspectral data communications and transmission where data can be communicated and transmitted progressively through spectral or satellite channels with limited data storage. Most importantly, PBP allows users to screen preliminary results before deciding to continue with processing the complete data set. These advantages benefit users of hyperspectral data by reducing processing time and increasing the timeliness of crucial decisions made based on the data such as identifying key intelligence information when a required response time is short.

  7. Satellite Hyperspectral Imaging Simulation

    NASA Technical Reports Server (NTRS)

    Zanoni, Vicki; Stanley, Tom; Blonski, Slawomir; Cao, Changyong; Gasser, Jerry; Ryan, Robert; Zanoni, Vicki; Stanley, Tom

    1999-01-01

    Simulations of generic pushbroom satellite hyperspetral sensors have been performed to evaluate the potential performance and validation techniques for satellite systems such as COIS (NEMO), Warfighter-1 (OrbView-4), and Hyperion (EO-1). The simulaitons start with a generation of synthetic scenes form material maps of studied terrain. Scene-reflected radiance is corrected for atmospheric effects and convolved with sensor spectral response uwing MODTRAN 4 radiance and transmission calculations. Scene images are further convolved with point spread functions derived from Optical Transfer Functions (OTF's) of the sensor system. Photon noise and detector/electronics noise are added to the simulated images, which are also finally quantized to the sensor bit resolution. Studied scenes include bridges and straight roads used for evaluation of sensor spatial resolution, as well as fields of minerals, vegetation, and manmade materials used for evaluation of sensor radiometric response and sensitivity. The scenes are simulated with various seasons and weather conditions. Signal-to-noise ratos and expected performance are estimated for typica satellite system specifications and are discussed for all the scenes.

  8. 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

  9. 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.

  10. 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...

  11. 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...

  12. Spectral Unmixing of airborne hyperspectral imagery for mapping giant reed infestations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Spectral unmixing techniques applied to hyperspectral imagery were examined for mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern United States and northern Mexico. Airborne hyperspectral imagery with 102...

  13. 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.

  14. 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.

  15. 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

  16. The design and the development of a hyperspectral and multispectral airborne mapping system

    NASA Astrophysics Data System (ADS)

    Gorsevski, Pece V.; Gessler, Paul E.

    Flexible and cost-effective tools for rapid image acquisition and natural resource mapping are needed by land managers. This paper describes the hardware and software architecture of a low-cost system that can be deployed on a light aircraft for rapid data acquisition. The Hyperspectral and Multispectral Cameras for Airborne Mapping (HAMCAM) was designed and developed in the Geospatial Laboratory for Environmental Dynamics at the University of Idaho as a student-learning tool, and to enhance the existing curriculum currently offered. The system integrates a hyperspectral sensor with four multispectral cameras, an Inertial Navigation System (INS), a Wide Area Augmentation System (WAAS)-capable Global Positioning System (GPS), a data acquisition computer, and custom software for running the sensors in a variety of different modes. The outputs include very high resolution imagery obtained in four adjustable visible and near-infrared bands from the multispectral imager. The hyperspectral sensor acquires 240 spectral bands along 2.7 nm intervals within the 445-900 nm range. The INS provides aircraft pitch, roll and yaw information for rapid geo-registration of the imagery. This paper will discuss the challenges associated with the development of the system and the integration of components and software for implementation of this system for natural resource management applications. In addition, sample imagery acquired by the sensor will be presented.

  17. Miniaturization of sub-meter resolution hyperspectral imagers on unmanned aerial systems

    NASA Astrophysics Data System (ADS)

    Hill, Samuel L.; Clemens, Peter

    2014-05-01

    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 (< 150 m). Despite this trend, there are several key parameters affecting the use of traditional hyperspectral instruments in UAS with payloads less than 0.5 kg (~1lb) 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 that provides full hyperspectral resolution and traditional hyperspectral capabilities without sacrificing performance to accommodate the decreasing SWaP of smaller and smaller UAS platforms.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. Urban land-cover classification based on airborne hyperspectral data and field observation

    NASA Astrophysics Data System (ADS)

    Yamazaki, Fumio; Hara, Konomi; Liu, Wen

    2014-10-01

    Using a dataset from the 2013 IEEE data fusion contest, a basic study to classify urban land-cover was carried out. The spectral reflectance characteristics of surface materials were investigated from the airborne hyperspectral (HS) data acquired by CASI-1500 imager over Houston, Texas, USA. The HS data include 144 spectral bands in the visible to near-infrared (380 nm to 1050 nm) regions. A multispectral (MS) image acquired by WorldView-2 satellite was also introduced in order to compare it with the HS image. A field measurement in the Houston's test site was carried out using a handheld spectroradiometer by the present authors. The reflectance of surface materials obtained by the measurement was also compared with the pseudo-reflectance of the HS data and they showed good agreement. Finally a principal component analysis was conducted for the HS and MS data and the result was discussed.

  4. First Use of an Airborne Thermal Infrared Hyperspectral Scanner for Compositional Mapping

    NASA Technical Reports Server (NTRS)

    Kirkland, Laurel; Herr, Kenneth; Keim, Eric; Adams, Paul; Salisbury, John; Hackwell, John; Treiman, Allan

    2002-01-01

    In May 1999, the airborne thermal infrared hyperspectral imaging system, Spatially Enhanced Broadband Array Spectrograph System (SEBASS), was flown over Mon-non Mesa, NV, to provide the first test of such a system for geological mapping. Several types of carbonate deposits were identified using the 11.25 microns band. However, massive calcrete outcrops exhibited weak spectral contrast, which was confirmed by field and laboratory measurements. Because the weathered calcrete surface appeared relatively smooth in hand specimen, this weak spectral contrast was unexpected. Here we show that microscopic roughness not readily apparent to the eye has introduced both a cavity effect and volume scattering to reduce spectral contrast. The macroroughness of crevices and cobbles may also have a significant cavity effect. The diminished spectral contrast is important because it places higher signal-to-noise ratio (SNR) requirements for spectroscopic detection and identification. This effect should be factored into instrumentation planning and interpretations, especially interpretations without benefit of ground truth. SEBASS had the required high SNR and spectral resolution to allow us to demonstrate for the first time the ability of an airborne hyperspectral thermal infrared scanner to detect and identify spectrally subtle materials.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. An optimized hybrid encode based compression algorithm for hyperspectral image

    NASA Astrophysics Data System (ADS)

    Wang, Cheng; Miao, Zhuang; Feng, Weiyi; He, Weiji; Chen, Qian; Gu, Guohua

    2013-12-01

    Compression is a kernel procedure in hyperspectral image processing due to its massive data which will bring great difficulty in date storage and transmission. In this paper, a novel hyperspectral compression algorithm based on hybrid encoding which combines with the methods of the band optimized grouping and the wavelet transform is proposed. Given the characteristic of correlation coefficients between adjacent spectral bands, an optimized band grouping and reference frame selection method is first utilized to group bands adaptively. Then according to the band number of each group, the redundancy in the spatial and spectral domain is removed through the spatial domain entropy coding and the minimum residual based linear prediction method. Thus, embedded code streams are obtained by encoding the residual images using the improved embedded zerotree wavelet based SPIHT encode method. In the experments, hyperspectral images collected by the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) were used to validate the performance of the proposed algorithm. The results show that the proposed approach achieves a good performance in reconstructed image quality and computation complexity.The average peak signal to noise ratio (PSNR) is increased by 0.21~0.81dB compared with other off-the-shelf algorithms under the same compression ratio.

  10. [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

  11. Real-time snapshot hyperspectral imaging endoscope

    PubMed Central

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

    2011-01-01

    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

  12. Airborne Visible / Infrared Imaging Spectrometer AVIS: Design, Characterization and Calibration

    PubMed Central

    Oppelt, Natascha; Mauser, Wolfram

    2007-01-01

    The Airborne Visible / Infrared imaging Spectrometer AVIS is a hyperspectral imager designed for environmental monitoring purposes. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several experiments between 1999 and 2007. We describe the instrument design and present the results of laboratory characterization and calibration of the system's second generation, AVIS-2, which is currently being operated. The processing of the data is described and examples of remote sensing reflectance data are presented.

  13. 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.

  14. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

    NASA Astrophysics Data System (ADS)

    Cho, Moses Azong; Skidmore, Andrew; Corsi, Fabio; van Wieren, Sipke E.; Sobhan, Istiak

    2007-12-01

    The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration ( n = 30) and test ( n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m -2) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m -2), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m -2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m -2) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m -2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.

  15. 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.

  16. Compressive Hyperspectral Imaging With Side Information

    NASA Astrophysics Data System (ADS)

    Yuan, Xin; Tsai, Tsung-Han; Zhu, Ruoyu; Llull, Patrick; Brady, David; Carin, Lawrence

    2015-09-01

    A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements.The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns a dictionary {\\em in situ} from the measurements via global-local shrinkage priors. By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality. A prototype camera is built using a liquid-crystal-on-silicon modulator. Experimental reconstructions of hyperspectral datacubes from both simulated and real compressed measurements demonstrate the efficacy of the proposed inversion algorithm, the feasibility of the camera and the benefit of side information.

  17. Tongue Tumor Detection in Medical Hyperspectral Images

    PubMed Central

    Liu, Zhi; Wang, Hongjun; Li, Qingli

    2012-01-01

    A hyperspectral imaging system to measure and analyze the reflectance spectra of the human tongue with high spatial resolution is proposed for tongue tumor detection. To achieve fast and accurate performance for detecting tongue tumors, reflectance data were collected using spectral acousto-optic tunable filters and a spectral adapter, and sparse representation was used for the data analysis algorithm. Based on the tumor image database, a recognition rate of 96.5% was achieved. The experimental results show that hyperspectral imaging for tongue tumor diagnosis, together with the spectroscopic classification method provide a new approach for the noninvasive computer-aided diagnosis of tongue tumors. PMID:22368462

  18. 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

  19. Phase congruency assesses hyperspectral image quality

    NASA Astrophysics Data System (ADS)

    Shao, Xiaopeng; Zhong, Cheng

    2012-10-01

    Blind image quality assessment (QA) is a tough task especially for hyperspectral imagery which is degraded by noise, distortion, defocus, and other complex factors. Subjective hyperspectral imagery QA methods are basically measured the degradation of image from human perceptual visual quality. As the most important image quality measurement features, noise and blur, determined the image quality greatly, are employed to predict the objective hyperspectral imagery quality of each band. We demonstrate a novel no-reference hyperspectral imagery QA model based on phase congruency (PC), which is a dimensionless quantity and provides an absolute measure of the significance of feature point. First, Log Gabor wavelet is used to calculate the phase congruency of frequencies of each band image. The relationship between noise and PC can be derived from above transformation under the assumption that noise is additive. Second, PC focus measure evaluation model is proposed to evaluate blur caused by different amounts of defocus. The ratio and mean factors of edge blur level and noise is defined to assess the quality of each band image. This image QA method obtains excellent correlation with subjective image quality score without any reference. Finally, the PC information is utilized to improve the quality of some bands images.

  20. Hyperspectral image feature extraction accelerated by GPU

    NASA Astrophysics Data System (ADS)

    Qu, HaiCheng; Zhang, Ye; Lin, Zhouhan; Chen, Hao

    2012-10-01

    PCA (principal components analysis) algorithm is the most basic method of dimension reduction for high-dimensional data1, which plays a significant role in hyperspectral data compression, decorrelation, denoising and feature extraction. With the development of imaging technology, the number of spectral bands in a hyperspectral image is getting larger and larger, and the data cube becomes bigger in these years. As a consequence, operation of dimension reduction is more and more time-consuming nowadays. Fortunately, GPU-based high-performance computing has opened up a novel approach for hyperspectral data processing6. This paper is concerning on the two main processes in hyperspectral image feature extraction: (1) calculation of transformation matrix; (2) transformation in spectrum dimension. These two processes belong to computationally intensive and data-intensive data processing respectively. Through the introduction of GPU parallel computing technology, an algorithm containing PCA transformation based on eigenvalue decomposition 8(EVD) and feature matching identification is implemented, which is aimed to explore the characteristics of the GPU parallel computing and the prospects of GPU application in hyperspectral image processing by analysing thread invoking and speedup of the algorithm. At last, the result of the experiment shows that the algorithm has reached a 12x speedup in total, in which some certain step reaches higher speedups up to 270 times.

  1. Experimental results from an airborne static Fourier transform imaging spectrometer.

    PubMed

    Ferrec, Yann; Taboury, Jean; Sauer, Hervé; Chavel, Pierre; Fournet, Pierre; Coudrain, Christophe; Deschamps, Joël; Primot, Jérôme

    2011-10-20

    A high étendue static Fourier transform spectral imager has been developed for airborne use. This imaging spectrometer, based on a Michelson interferometer with rooftop mirrors, is compact and robust and benefits from a high collection efficiency. Experimental airborne images were acquired in the visible domain. The processing chain to convert raw images to hyperspectral data is described, and airborne spectral images are presented. These experimental results show that the spectral resolution is close to the one expected, but also that the signal to noise ratio is limited by various phenomena (jitter, elevation fluctuations, and one parasitic image). We discuss the origin of those limitations and suggest solutions to circumvent them. PMID:22015418

  2. 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

  3. 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.

  4. Hyperspectral imaging of melanocytic lesions.

    PubMed

    Gaudi, Sudeep; Meyer, Rebecca; Ranka, Jayshree; Granahan, James C; Israel, Steven A; Yachik, Theodore R; Jukic, Drazen M

    2014-02-01

    Hyperspectral imaging (HSI) allows the identification of objects through the analysis of their unique spectral signatures. Although first developed many years ago for use in terrestrial remote sensing, this technology has more recently been studied for application in the medical field. With preliminary data favoring a role for HSI in distinguishing normal and lesional skin tissues, we sought to investigate the potential use of HSI as a diagnostic aid in the classification of atypical Spitzoid neoplasms, a group of lesions that often leave dermatopathologists bewildered. One hundred and two hematoxylin and eosin-stained tissue samples were divided into 1 of 4 diagnostic categories (Spitz nevus, Spitz nevus with unusual features, atypical Spitzoid neoplasm, and Spitzoid malignant melanoma) and 1 of 2 control groups (benign melanocytic nevus and malignant melanoma). A region of interest was selected from the dermal component of each sample, thereby maximizing the examination of melanocytes. Tissue samples were examined at ×400 magnification using a spectroscopy system interfaced with a light microscope. The absorbance patterns of wavelengths from 385 to 880 nm were measured and then analyzed within and among groups. All tissue groups demonstrated 3 common absorbance spectra at 496, 533, and 838 nm. Each sample group contained at least one absorption point that was unique to that group. The Spitzoid malignant melanoma category had the highest number of total and unique absorption points for any sample group. The data were then clustered into 12 representative spectral classes. Although each of the sample groups contained all 12 spectral vectors, they did so in differing proportions. These preliminary results reveal differences in the spectral signatures of the Spitzoid lesions examined in this study. Further investigation into a role for HSI in classifying atypical Spitzoid neoplasms is encouraged. PMID:24247577

  5. 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.

  6. 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.

  7. Research on hyperspectral polarization imaging technique

    NASA Astrophysics Data System (ADS)

    Zhao, Haibo; Feng, Lei; Zhou, Yu; Wang, Zheng; Lin, Xuling

    2015-08-01

    The summary of hyperspectral polarization remote sensing detection is presented, including the characteristics and mechanism of polarization detection, the expression of polarization light and the detection method. The present research of hyperspectral polarization remote sensing is introduced. A novel method of hyperspectral polarization imaging technique is discussed, which is based on static modulation adding with the double refraction crystal. The static modulation is composed of one polarizer and two retarders. The double refraction crystal is used to generate interference image. The four Stokes vectors and spectral information can be detected only by one measurement. The method of static modulation is introduced in detail and is simulated by computer. The experimental system is also established in laboratory. The basic concept of the technique is verified. The simulation error of DOP (polarization degree detection) is about 1%. The experimental error of DOP is less than 5%. The merits of the novel system are no moving parts, compactness and no electrical element.

  8. 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...

  9. 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.

  10. Hyperspectral imaging with liquid-crystal tunable filter for biological and agricultural assessment

    NASA Astrophysics Data System (ADS)

    Mao, Chengye; Heitschmidt, Jerry

    1999-01-01

    A hyperspectral imaging system has been developed to provide the capability of both airborne and ground/laboratory data acquisitions. The system consists of modular front imaging optics with a liquid crystal tunable filter (LCTF), a CCD video camera, a frame grabber and a portable computer system. The spectral range is form 450 nm to 750 nm with a 10 nm bandpass for each band acquired. The system can capture different spectral images at a rate up to 14 images per second. Hyperspectral imaging with an LCTF provides a new method for hyperspectral image acquisition. The system allows the user to define a wavelength sequence of up to thirty-two spectrums specifically required for individual application, and can quickly switch from the current wavelength to the next during automated image acquisition. Hyperspectral images of crop fields, vegetation, fruits, and meat were successfully captured during laboratory experiments and airborne image acquisition. The constructed spectral image cube not only shows the spatial features of the target, but also reveals the individual pixels with unique spectral signatures. The imaging system with LCTF is, therefore, very useful in biological and agricultural assessment for detecting variations in crop fields, or defects in samples and products.

  11. 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.

  12. 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.

  13. 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...

  14. Comparison of support vector machine-based processing chains for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Rojas, Marta; Dópido, Inmaculada; Plaza, Antonio; Gamba, Paolo

    2010-08-01

    Many different approaches have been proposed in recent years for remotely sensed hyperspectral image classification. Despite the variety of techniques designed to tackle the aforementioned problem, the definition of standardized processing chains for hyperspectral image classification is a difficult objective, which may ultimately depend on the application being addressed. Generally speaking, a hyperspectral image classification chain may be defined from two perspectives: 1) the provider's viewpoint, and 2) the user's viewpoint, where the first part of the chain comprises activities such as data calibration and geo-correction aspects, while the second part of the chain comprises information extraction processes from the collected data. The modules in the second part of the chain (which constitutes our main focus in this paper) should be ideally flexible enough to be accommodated not only to different application scenarios, but also to different hyperspectral imaging instruments with varying characteristics, and spatial and spectral resolutions. In this paper, we evaluate the performance of different processing chains resulting from combinations of modules for dimensionality reduction, feature extraction/ selection, image classification, and spatial post-processing. The support vector machine (SVM) classifier is adopted as a baseline due to its ability to classify hyperspectral data sets using limited training samples. A specific classification scenario is investigated, using a reference hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in Indiana, USA.

  15. 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.

  16. 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.

  17. 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

  18. Hyperspectral image classifier based on beach spectral feature

    NASA Astrophysics Data System (ADS)

    Liang, Zhang; Lianru, Gao; Bing, Zhang

    2014-03-01

    The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches.

  19. Hyperspectral imaging using RGB color for foodborne pathogen detection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 sp...

  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. Applying linear spectral unmixing to airborne hyperspectral imagery for mapping crop yield variability.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study evaluated linear spectral unmixing techniques for mapping the variation in crop yield. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery recorded from one grain sorghum field and a cotton field. A pair of plant and soil spect...

  3. 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.

  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. Radiometric characterization of hyperspectral imagers using multispectral sensors

    NASA Astrophysics Data System (ADS)

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

    2009-08-01

    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 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 work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on the 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. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of MODIS. 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 bands as well as similar agreement between results that employ the different MODIS sensors as a reference.

  6. NASA image-based geological expert system development project for hyperspectral image analysis

    NASA Technical Reports Server (NTRS)

    Chiou, W. C., Sr.

    1985-01-01

    The NASA image-based geological expert system was applied to analyze remotely sensed hyperspectral image data. The major objective is for geologists to identify the earth surface mineral properties directly from the airborne and spaceborne imaging spectrometer data. With certain constraints, it is shown that the system can identify correctly different classes of mineral. It has the built-in learning paradigm to enhance the confidence factor of mineral identification. A very powerful natural language system was incorporated as the user-friendly front end, and the concurrent processing efficiency of the frame-based knowledge representation in the hypercube microsupercomputer simulation was tested.

  7. 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

  8. Hyperspectral image data compression based on DSP

    NASA Astrophysics Data System (ADS)

    Fan, Jiming; Zhou, Jiankang; Chen, Xinhua; Shen, Weimin

    2010-11-01

    The huge data volume of hyperspectral image challenges its transportation and store. It is necessary to find an effective method to compress the hyperspectral image. Through analysis and comparison of current various algorithms, a mixed compression algorithm based on prediction, integer wavelet transform and embedded zero-tree wavelet (EZW) is proposed in this paper. We adopt a high-powered Digital Signal Processor (DSP) of TMS320DM642 to realize the proposed algorithm. Through modifying the mixed algorithm and optimizing its algorithmic language, the processing efficiency of the program was significantly improved, compared the non-optimized one. Our experiment show that the mixed algorithm based on DSP runs much faster than the algorithm on personal computer. The proposed method can achieve the nearly real-time compression with excellent image quality and compression performance.

  9. Geometric correction of synchronous scanned Operational Modular Imaging Spectrometer II hyperspectral remote sensing images using spatial positioning data of an inertial navigation system

    NASA Astrophysics Data System (ADS)

    Zhou, Xiaohu; Neubauer, Franz; Zhao, Dong; Xu, Shichao

    2015-01-01

    The high-precision geometric correction of airborne hyperspectral remote sensing image processing was a hard nut to crack, and conventional methods of remote sensing image processing by selecting ground control points to correct the images are not suitable in the correction process of airborne hyperspectral image. The optical scanning system of an inertial measurement unit combined with differential global positioning system (IMU/DGPS) is introduced to correct the synchronous scanned Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing images. Posture parameters, which were synchronized with the OMIS II, were first obtained from the IMU/DGPS. Second, coordinate conversion and flight attitude parameters' calculations were conducted. Third, according to the imaging principle of OMIS II, mathematical correction was applied and the corrected image pixels were resampled. Then, better image processing results were achieved.

  10. Infrared hyperspectral imaging for chemical vapour detection

    NASA Astrophysics Data System (ADS)

    Ruxton, K.; Robertson, G.; Miller, W.; Malcolm, G. P. A.; Maker, G. T.; Howle, C. R.

    2012-10-01

    Active hyperspectral imaging is a valuable tool in a wide range of applications. One such area is the detection and identification of chemicals, especially toxic chemical warfare agents, through analysis of the resulting absorption spectrum. This work presents a selection of results from a prototype midwave infrared (MWIR) hyperspectral imaging instrument that has successfully been used for compound detection at a range of standoff distances. Active hyperspectral imaging utilises a broadly tunable laser source to illuminate the scene with light at a range of wavelengths. While there are a number of illumination methods, the chosen configuration illuminates the scene by raster scanning the laser beam using a pair of galvanometric mirrors. The resulting backscattered light from the scene is collected by the same mirrors and focussed onto a suitable single-point detector, where the image is constructed pixel by pixel. The imaging instrument that was developed in this work is based around an IR optical parametric oscillator (OPO) source with broad tunability, operating in the 2.6 to 3.7 μm (MWIR) and 1.5 to 1.8 μm (shortwave IR, SWIR) spectral regions. The MWIR beam was primarily used as it addressed the fundamental absorption features of the target compounds compared to the overtone and combination bands in the SWIR region, which can be less intense by more than an order of magnitude. We show that a prototype NCI instrument was able to locate hydrocarbon materials at distances up to 15 metres.

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

    PubMed Central

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

    2014-01-01

    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. PMID:25328640

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. Advances in hyperspectral LWIR pushbroom imagers

    NASA Astrophysics Data System (ADS)

    Holma, Hannu; Mattila, Antti-Jussi; Hyvärinen, Timo; Weatherbee, Oliver

    2011-06-01

    Two long-wave infrared (LWIR) hyperspectral imagers have been under extensive development. The first one utilizes a microbolometer focal plane array (FPA) and the second one is based on an Mercury Cadmium Telluride (MCT) FPA. Both imagers employ a pushbroom imaging spectrograph with a transmission grating and on-axis optics. The main target has been to develop high performance instruments with good image quality and compact size for various industrial and remote sensing application requirements. A big challenge in realizing these goals without considerable cooling of the whole instrument is to control the instrument radiation. The challenge is much bigger in a hyperspectral instrument than in a broadband camera, because the optical signal from the target is spread spectrally, but the instrument radiation is not dispersed. Without any suppression, the instrument radiation can overwhelm the radiation from the target even by 1000 times. The means to handle the instrument radiation in the MCT imager include precise instrument temperature stabilization (but not cooling), efficient optical background suppression and the use of background-monitoring-on-chip (BMC) method. This approach has made possible the implementation of a high performance, extremely compact spectral imager in the 7.7 to 12.4 μm spectral range. The imager performance with 84 spectral bands and 384 spatial pixels has been experimentally verified and an excellent NESR of 14 mW/(m2srμm) at 10 μm wavelength with a 300 K target has been achieved. This results in SNR of more than 700. The LWIR imager based on a microbolometer detector array, first time introduced in 2009, has been upgraded. The sensitivity of the imager has improved drastically by a factor of 3 and SNR by about 15 %. It provides a rugged hyperspectral camera for chemical imaging applications in reflection mode in laboratory and industry.

  17. 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.

  18. 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

  19. 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).

  20. 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.

  1. 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.

  2. Comparison of Airborne Multispectral and Hyperspectral Imagery for Yield Estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multispectral and hyperspectral imagery is being used to monitor crop conditions and map yield variability. However, limited research has been conducted to compare the differences between these two types of imagery for assessing crop growth and yield. The objective of this study was to compare airbo...

  3. Real-time lossy compression of hyperspectral images using iterative error analysis on graphics processing units

    NASA Astrophysics Data System (ADS)

    Sánchez, Sergio; Plaza, Antonio

    2012-06-01

    Hyperspectral image compression is an important task in remotely sensed Earth Observation as the dimensionality of this kind of image data is ever increasing. This requires on-board compression in order to optimize the donwlink connection when sending the data to Earth. A successful algorithm to perform lossy compression of remotely sensed hyperspectral data is the iterative error analysis (IEA) algorithm, which applies an iterative process which allows controlling the amount of information loss and compression ratio depending on the number of iterations. This algorithm, which is based on spectral unmixing concepts, can be computationally expensive for hyperspectral images with high dimensionality. In this paper, we develop a new parallel implementation of the IEA algorithm for hyperspectral image compression on graphics processing units (GPUs). The proposed implementation is tested on several different GPUs from NVidia, and is shown to exhibit real-time performance in the analysis of an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) data sets collected over different locations. The proposed algorithm and its parallel GPU implementation represent a significant advance towards real-time onboard (lossy) compression of hyperspectral data where the quality of the compression can be also adjusted in real-time.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. Range-gated intensified spectrographic imager: an instrument for active hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Simard, Jean-Robert; Mathieu, Pierre; Fournier, Georges R.; Larochelle, Vincent; Babey, Stephen K.

    2000-09-01

    Hyperspectral imaging has demonstrated impressive capabilities in airborne surveys, particularly for mineral and biomass characterizations. Based on this success, it is believed that other applications like search and rescue operations, and detection/identification of various ground military targets could greatly benefit from this technology. The strength of hyperspectral imaging comes from the access to another dimension of information: the spectral content of the detected return signal for each spatial pixel. In the case of conventional hyperspectral imaging, the return signal depicts the spectral reflectance of the day irradiance from the scene within the field of view of each pixel. However, by inserting a range-gated intensifier into a hyperspectral camera and by combining the camera with selected pulsed lasers, it becomes possible to relate the returned spectral information to specific light/matter interactions like induced fluorescence. This new technique may be referred to as 'active hyperspectral imaging.' Among its advantages, this approach is independent of the ambient lighting conditions and can be customized in excitation wavelengths. Moreover, by using a range-gated intensified camera, it is possible to survey limited area with a significant increase in signal-to-noise ratio. A camera of this type has been built by our group in collaboration with private industry and is described in this paper. The internal design of the camera is discussed, new issues concerning the calibration of the camera are depicted and a model based on signal-to-noise ratio analysis is presented. From the fluorescent characteristics of surrogate land mines measured in the laboratory, this model is used to predict the capabilities of detecting surface-laid mines from an aerial platform based scenario.

  9. 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

  10. 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

  11. Instrumental error in chromotomosynthetic hyperspectral imaging.

    PubMed

    Bostick, Randall L; Perram, Glen P

    2012-07-20

    Chromotomosynthetic imaging (CTI) is a method of convolving spatial and spectral information that can be reconstructed into a hyperspectral image cube using the same transforms employed in medical tomosynthesis. A direct vision prism instrument operating in the visible (400-725 nm) with 0.6 mrad instantaneous field of view (IFOV) and 0.6-10 nm spectral resolution has been constructed and characterized. Reconstruction of hyperspectral data cubes requires an estimation of the instrument component properties that define the forward transform. We analyze the systematic instrumental error in collected projection data resulting from prism spectral dispersion, prism alignment, detector array position, and prism rotation angle. The shifting and broadening of both the spectral lineshape function and the spatial point spread function in the reconstructed hyperspectral imagery is compared with experimental results for monochromatic point sources. The shorter wavelength (λ<500 nm) region where the prism has the highest spectral dispersion suffers mostly from degradation of spectral resolution in the presence of systematic error, while longer wavelengths (λ>600 nm) suffer mostly from a shift of the spectral peaks. The quality of the reconstructed hyperspectral imagery is most sensitive to the misalignment of the prism rotation mount. With less than 1° total angular error in the two axes of freedom, spectral resolution was degraded by as much as a factor of 2 in the blue spectral region. For larger errors than this, spectral peaks begin to split into bimodal distributions, and spatial point response functions are reconstructed in rings with radii proportional to wavelength and spatial resolution. PMID:22858961

  12. 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., III; 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.

  13. Classification of the forest cover of Tver oblast using hyperspectral airborne imagery

    NASA Astrophysics Data System (ADS)

    Dmitriev, E. V.

    2014-12-01

    Recent research efforts have been focused on building a system of hyperspectral aerial sounding of forest vegetation on regional scales. The components of this system are developed using data obtained in the course of measurement campaigns in Tver forestry test sites. Hyperspectral airborne surveys are conducted using a Russian video spectrometer produced by the NPO Lepton company. The technique for recognizing ground-based objects is based on Bayesian classification principles with the feature space optimization. The choice of the most informative spectral channels is based on the step-up method. We propose an approach allowing the choice of channels to be more stable. We compare the classification of timber stands on the basis of hyperspectral imagery with ground-based data to demonstrate the consistency of the system developed.

  14. 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

  15. 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.

  16. Hyperspectral and multispectral imaging for evaluating food safety and quality

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Spectral imaging technologies have been developed rapidly during the past decade. This paper presents hyperspectral and multispectral imaging technologies in the area of food safety and quality evaluation, with an introduction, demonstration, and summarization of the spectral imaging techniques avai...

  17. Modified wavelet kernel methods for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Hsu, Pai-Hui; Huang, Xiu-Man

    2015-10-01

    Hyperspectral images have the capability of acquiring images of earth surface with several hundred of spectral bands. Providing such abundant spectral data should increase the abilities in classifying land use/cover type. However, due to the high dimensionality of hyperspectral data, traditional classification methods are not suitable for hyperspectral data classification. The common method to solve this problem is dimensionality reduction by using feature extraction before classification. Kernel methods such as support vector machine (SVM) and multiple kernel learning (MKL) have been successfully applied to hyperspectral images classification. In kernel methods applications, the selection of kernel function plays an important role. The wavelet kernel with multidimensional wavelet functions can find the optimal approximation of data in feature space for classification. The SVM with wavelet kernels (called WSVM) have been also applied to hyperspectral data and improve classification accuracy. In this study, wavelet kernel method combined multiple kernel learning algorithm and wavelet kernels was proposed for hyperspectral image classification. After the appropriate selection of a linear combination of kernel functions, the hyperspectral data will be transformed to the wavelet feature space, which should have the optimal data distribution for kernel learning and classification. Finally, the proposed methods were compared with the existing methods. A real hyperspectral data set was used to analyze the performance of wavelet kernel method. According to the results the proposed wavelet kernel methods in this study have well performance, and would be an appropriate tool for hyperspectral image classification.

  18. A Hyperspectral Imaging System for Quality Detection of Pickles

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging system in simultaneous reflectance (400-675 nm) and transmittance (675-1000 nm) modes was developed for detection of hollow or bloater damage on whole pickles. Hyperspectral reflectance and transmittance images were acquired from normal and bloated whole pickle samples collec...

  19. 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...

  20. Detection of hatching and table egg defects using hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging system was developed to detect problem hatching eggs (non-fertile or dead embryos) prior to or during early incubation and to detect table eggs with blood spots and cracked shells. All eggs were imaged using a hyperspectral camera system (wavelengths detected from 400-900mm) ...

  1. Spectral difference analysis and airborne imaging classification for citrus greening infected trees

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Citrus greening, also called Huanglongbing (HLB), became a devastating disease spread through citrus groves in Florida, since it was first found in 2005. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were acquired to detect citrus greening infected trees in 20...

  2. 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.

  3. 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

  4. 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.

  5. Reconfigurable Hardware for Compressing Hyperspectral Image Data

    NASA Technical Reports Server (NTRS)

    Aranki, Nazeeh; Namkung, Jeffrey; Villapando, Carlos; Kiely, Aaron; Klimesh, Matthew; Xie, Hua

    2010-01-01

    High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of

  6. Research on a project of the new computational hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Li, Huan; Zhou, Feng; Zhang, Zhi; Shi, Guang-ming

    2012-09-01

    This paper brings hyperspectral technology and compute image together, on the basis of geometrical optics theory and compressed sensing theory, put forward a new computational spectral Imaging technology. That raises two to four times on spatial resolution and double on spectral resolution compared conventional hyperspectral imagers. Owing to have finished compressing when getting the imaging signal, that could resolve the conflict between the mass of data bringing with high resolution and transfers and storage. The paper carries out a project to the new hyperspectral imager.

  7. Compressive Hyperspectral Imaging via Approximate Message Passing

    NASA Astrophysics Data System (ADS)

    Tan, Jin; Ma, Yanting; Rueda, Hoover; Baron, Dror; Arce, Gonzalo R.

    2016-03-01

    We consider a compressive hyperspectral imaging reconstruction problem, where three-dimensional spatio-spectral information about a scene is sensed by a coded aperture snapshot spectral imager (CASSI). The CASSI imaging process can be modeled as suppressing three-dimensional coded and shifted voxels and projecting these onto a two-dimensional plane, such that the number of acquired measurements is greatly reduced. On the other hand, because the measurements are highly compressive, the reconstruction process becomes challenging. We previously proposed a compressive imaging reconstruction algorithm that is applied to two-dimensional images based on the approximate message passing (AMP) framework. AMP is an iterative algorithm that can be used in signal and image reconstruction by performing denoising at each iteration. We employed an adaptive Wiener filter as the image denoiser, and called our algorithm "AMP-Wiener." In this paper, we extend AMP-Wiener to three-dimensional hyperspectral image reconstruction, and call it "AMP-3D-Wiener." Applying the AMP framework to the CASSI system is challenging, because the matrix that models the CASSI system is highly sparse, and such a matrix is not suitable to AMP and makes it difficult for AMP to converge. Therefore, we modify the adaptive Wiener filter and employ a technique called damping to solve for the divergence issue of AMP. Our approach is applied in nature, and the numerical experiments show that AMP-3D-Wiener outperforms existing widely-used algorithms such as gradient projection for sparse reconstruction (GPSR) and two-step iterative shrinkage/thresholding (TwIST) given a similar amount of runtime. Moreover, in contrast to GPSR and TwIST, AMP-3D-Wiener need not tune any parameters, which simplifies the reconstruction process.

  8. Thermal infrared spectral imager for airborne science applications

    NASA Astrophysics Data System (ADS)

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

    2009-05-01

    An airborne thermal hyperspectral imager is underdevelopment 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 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 emissivity for various known standard minerals (quartz). A comparison is made using data from the ASTER spectral library.

  9. Towards HyTES: an airborne thermal imaging spectroscopy instrument

    NASA Astrophysics Data System (ADS)

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

    2009-08-01

    An airborne thermal hyperspectral imager is underdevelopment 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 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 emissivity for various known standard minerals (quartz). A comparison is made using data from the ASTER spectral library.

  10. 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.

  11. Hyperspectral imaging from space: Warfighter-1

    NASA Astrophysics Data System (ADS)

    Cooley, Thomas; Seigel, Gary; Thorsos, Ivan

    1999-01-01

    The Air Force Research Laboratory Integrated Space Technology Demonstrations (ISTD) Program Office has partnered with Orbital Sciences Corporation (OSC) to complement the commercial satellite's high-resolution panchromatic imaging and Multispectral imaging (MSI) systems with a moderate resolution Hyperspectral imaging (HSI) spectrometer camera. The program is an advanced technology demonstration utilizing a commercially based space capability to provide unique functionality in remote sensing technology. This leveraging of commercial industry to enhance the value of the Warfighter-1 program utilizes the precepts of acquisition reform and is a significant departure from the old-school method of contracting for government managed large demonstration satellites with long development times and technology obsolescence concerns. The HSI system will be able to detect targets from the spectral signature measured by the hyperspectral camera. The Warfighter-1 program will also demonstrate the utility of the spectral information to theater military commanders and intelligence analysts by transmitting HSI data directly to a mobile ground station that receives and processes the data. After a brief history of the project origins, this paper will present the details of the Warfighter-1 system and expected results from exploitation of HSI data as well as the benefits realized by this collaboration between the Air Force and commercial industry.

  12. 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.

  13. 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.

  14. Estimating foliar nitrogen concentration with hyperspectral remote sensing image

    NASA Astrophysics Data System (ADS)

    Zhang, Xia; Zhang, Bing; Liu, Liangyun; Wang, Jihua

    2003-06-01

    The hyperspectral image used in this study was acquired by the airborne operative modular imaging spectrometer (OMIS) in Xiaotangshan area, Beijing, on April 26th, 2001. Accurate geometry correction and reflectance transformation was conducted on this image so that 43 image spectra were extracted to match with the canopy-level total nitrogen concentration (TN) of wheat precisely. By using methods of stepwise regression and spectrum feature analysis, characteristic bands and parameters were selected and developed for TN retrieval from the image spectra. Nitrogen distribution map was obtained by applying the best estimation equation to all wheat pixels. It turned out, the absorption depths and areas within spectral ranges 590-756nm,1096-1295nm and 1295-1642nm could be used to estimate TN. NDVI(NRCA1175.8,NRCA733.9) and NDVI(dr745,dr699.2) was the best estimator of TN (R2 = 0.8145 and 0.769 respectively). In addition, the value and distribution of TN map was quite consistent with the field measurements and growth status.

  15. 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.

  16. Optical design of wide swath hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Wang, Yueming; Yuan, Liyin; Wang, Jianyu

    2014-05-01

    This paper describes a design concept for wide swath hyperspectral imager. The challenge is to meet the requirement of good image quality and high precision registration from 400nm to 2500nm. A new type spherical prism imaging spectrometer is presented in the paper. The swath of system can reach 60 kilometer from a 600km sun-synchronous orbit with 30 meter ground sample distance (GSD). The optical system consists of a TMA objective and 2 30mm-slit spherical prism spectrometer operating both VNIR and SWIR. Key features of the design include (1) high signal to noise ratio for high efficiency of F-silica prism; (2) high precision band registration for same spectrometer operating from 400nm to 2500nm.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. Hyperspectral image segmentation using active contours

    NASA Astrophysics Data System (ADS)

    Lee, Cheolha P.; Snyder, Wesley E.

    2004-08-01

    Multispectral or hyperspectral image processing has been studied as a possible approach to automatic target recognition (ATR). Hundreds of spectral bands may provide high data redundancy, compensating the low contrast in medium wavelength infrared (MWIR) and long wavelength infrared (LWIR) images. Thus, the combination of spectral (image intensity) and spatial (geometric feature) information analysis could produce a substantial improvement. Active contours provide segments with continuous boundaries, while edge detectors based on local filtering often provide discontinuous boundaries. The segmentation by active contours depends on geometric feature of the object as well as image intensity. However, the application of active contours to multispectral images has been limited to the cases of simply textured images with low number of frames. This paper presents a supervised active contour model, which is applicable to vector-valued images with non-homogeneous regions and high number of frames. In the training stage, histogram models of target classes are estimated from sample vector-pixels. In the test stage, contours are evolved based on two different metrics: the histogram models of the corresponding segments and the histogram models estimated from sample target vector-pixels. The proposed segmentation method integrates segmentation and model-based pattern matching using supervised segmentation and multi-phase active contour model, while traditional methods apply pattern matching only after the segmentation. The proposed algorithm is implemented with both synthetic and real multispectral images, and shows desirable segmentation and classification results even in images with non-homogeneous regions.

  4. 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

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. Does virtual dimensionality work in hyperspectral images?

    NASA Astrophysics Data System (ADS)

    Bajorski, Peter

    2009-05-01

    The effective dimensionality (ED) of hyperspectral images is often viewed as the dimensionality of an affine subspace defined by linear combinations of spectra of materials present in the image. That affine subspace is expected to give an acceptable approximation to all pixels. At this point, there is no precise definition of ED. In an effort to assess ED, a notion of virtual dimensionality (VD) has been developed, and it is being used in many papers including those published in TGARS. The ever- spreading use of VD warrants its thorough investigation. In this paper, we investigate properties of VD, and we show that VD largely depends on the average value of all spectra rather than on ED. We show specific examples when VD would give entirely misleading results. We also explain fallacies associated with justifications for VD.

  11. 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

  12. 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.

  13. Analysis of hyper-spectral AVIRIS image data over a mixed-conifer forest in Maine

    NASA Technical Reports Server (NTRS)

    Lawrence, William T.; Shimabukuro, Yosio E.; Gao, Bo-Cai

    1993-01-01

    An introduction to some of the potential uses of hyperspectral data for ecosystem analysis is presented. The examples given are derived from a digital dataset acquired over a sub-boreal forest in central Maine in 1990 by the NASA-JPL Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument gathers data from 400 to 2500 nm in 224 channels at bandwidths of approximately 10 nm. As a preview to the uses of the hyperspectral data, several products from this dataset were extracted. They range from the traditional false color composite made from simulated Thematic Mapper bands and the well known normalized difference vegetation index to much more exotic products such as fractions of vegetation, soil and shade based on linear spectral mixing models and estimates of the leaf water content at the landscape level derived using spectrum-matching techniques. Our research and that of many others indicates that the hyperspectral datasets carry much important information which is only beginning to be understood. This analysis gives an initial indication of the utility of hyperspectral data. Much work still remains to be done in algorithm development and in understanding the physics behind the complex information signal carried in the hyperspectral datasets. This work must be carried out to provide the fullest science support for high spectral resolution data to be acquired by many of the instruments to be launched as part of the Earth Observing System program in the mid-1990's.

  14. 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

  15. Characterization of burns using hyperspectral imaging technique - a preliminary study.

    PubMed

    Calin, Mihaela Antonina; Parasca, Sorin Viorel; Savastru, Roxana; Manea, Dragos

    2015-02-01

    Surgical burn treatment depends on accurate estimation of burn depth. Many methods have been used to asses burns, but none has gained wide acceptance. Hyperspectral imaging technique has recently entered the medical research field with encouraging results. In this paper we present a preliminary study (case presentation) that aims to point out the value of this optical method in burn wound characterization and to set up future lines of investigation. A hyperspectral image of a leg and foot with partial thickness burns was obtained in the fifth postburn day. The image was analyzed using linear spectral unmixing model as a tool for mapping the investigated areas. The article gives details on the mathematical bases of the interpretation model and correlations with clinical examination pointing out the advantages of hyperspectral imaging technique. While the results were encouraging, further more extended and better founded studies are being prepared before recognizing hyperspectral imaging technique as an applicable method of burn wound assessment. PMID:24997530

  16. 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.

  17. Airborne surveillance of water basins with hyperspectral FLS-LiDAR

    NASA Astrophysics Data System (ADS)

    Babichenko, S.; Alekseyev, V.; Lapimaa, J.; Lisin, A.; Poryvkina, L.; Shchemelyov, S.; Sobolev, I.; Vint, L.

    2010-10-01

    The airborne FLS-Lidars are based on the method of Laser Induced Fluorescence (LIF) and aimed at the analytical remote sensing of water objects. Scanning the laser beam across the flight trajectory and recording the comprehensive LIF spectrum with hyperspectral detector per every laser pulse provide detail maps of spectral properties of the water basins. A multi-tier model for integrated environmental assessment is applied for further analysis of this information to combine the benefits of "big-picture" capability of remote sensing techniques and GIS solutions with localized on-theground environmental data gathering. In this concept far looking satellite and airborne systems provide the highest tier information. The airborne data acquisition with FLS-Lidar is considered as the middle tier characterized by vast amount of LIF data with high spatial (less than 10 m) and spectral (less than 5 nm in UV/VIS spectral ranges) resolution. The lower tier is anchored with the geographical locations of important findings detected at the middle tier. Taken water samples are analyzed with fastscreening technology of Spectral Fluorescence Signatures (SFS) giving more analytical qualitative and quantitative results. And the base tier includes detail laboratory analysis of characteristic samples selected at the lower tier. Precisely geo-referenced LIF data of hyperspectral FLS-Lidar anchored to and calibrated by the ground SFS data allows detection of pollution incidents and mapping of environmental trends over vast water systems like coastal zone, lakes and rivers.r

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Hyperspectral image compression using an online learning method

    NASA Astrophysics Data System (ADS)

    Ülkü, Ä.°rem; Töreyin, B. Uǧur

    2015-05-01

    A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes.

  3. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Paz, Abel; Plaza, Antonio

    2010-12-01

    Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets) searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over theWorld Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

  4. 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.

  5. 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.

  6. 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

  7. An evaluation of popular hyperspectral images classification approaches

    NASA Astrophysics Data System (ADS)

    Kuznetsov, Andrey; Myasnikov, Vladislav

    2015-12-01

    This work is devoted to the problem of the best hyperspectral images classification algorithm selection. The following algorithms are used for comparison: decision tree using full cross-validation; decision tree C 4.5; Bayesian classifier; maximum-likelihood method; MSE minimization classifier, including a special case - classification by conjugation; spectral angle classifier (for empirical mean and nearest neighbor), spectral mismatch classifier and support vector machine (SVM). There are used AVIRIS and SpecTIR hyperspectral images to conduct experiments.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. Autonomous, rapid classifiers for hyperspectral imagers

    NASA Astrophysics Data System (ADS)

    Gilmore, M. S.; Bornstein, B.; Castano, R.; Greenwood, J.

    2006-05-01

    Hyperspectral systems collect huge volumes of multidimensional data that require time consuming, expert analysis. The data analysis costs of global datasets restrict rapid classification to only a subset of an entire mission dataset, reducing mission science return. Data downlink restrictions from planetary missions also highlight the need for robust mineral detection algorithms. For example, both OMEGA and CRISM will map only approximately 5% of the Mars surface at full spatial and spectral resolution. While some targets are preselected for full resolution study, other high priority targets on Mars will be selected in response to observations made by the instruments in a multispectral survey mode. The challenge is to create mineral detection algorithms that can be utilized to analyze any and all image cubes (x, y, λ) for a selected system to help ensure that priority targets are not overlooked in these datasets. This goal is critical both for onboard, real time processing to direct target acquisition and for the mining of returned data. While an ultimate goal would be to accurately classify the composition of every pixel on a planet's surface, this is made difficult by the fact that most pixels are complex mixtures of n materials, which may or may not be represented in library (training) data. We instead focus on the identification of specific important mineral compositions within pixels in the data. For Mars, high priority targets include minerals associated with the presence of water. We have developed highly accurate artificial neural network (ANN) and Support Vector Machine (SVM) based detectors capable of identifying calcite (CaCO3) and jarosite (KFe3(SO4)2(OH)6) in the visible/NIR (350 to 2500 nm) spectra of both laboratory specimens and rocks in Mars analogue field environments. The detectors are trained using a generative model to create 1000s of linear mixtures of library end-member spectra in geologically realistic percentages. Here we will discuss

  14. 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...

  15. Prediction of senescent rangeland canopy structural attributes with airborne hyperspectral imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Canopy structural and chemical data are needed for senescent, mixed-grass prairie landscapes in autumn, yet models driven by image data are lacking for rangelands dominated by non-photosynthetically active vegetation (NPV). Here, we report how aerial hyperspectral imagery might be modeled to predic...

  16. 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 ...

  17. 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.

  18. 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

  19. 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.

  20. Application of high spatial resolution airborne hyperspectral remote sensing data in thematic information extraction

    NASA Astrophysics Data System (ADS)

    Xu, Hong-gen; Ma, Hong-chao; Li, De-ren; Song, Yan

    2006-10-01

    The airborne hyperspectral remote sensing data, such as PHI, OMIS, has the virtues of high spatial and spectral resolution. Hence, from the view of target classification we can consider that it can provide the ability of discriminating targets more detailedly than other data. So it's important to extract thematic information and update database using this kind of data. Whereas, the hyperspectral data has abundant bands and high between-band correlation, the traditional classification methods such as maximum likelihood classifier (MLC) and spectral angle mapper (SAM) have performed poorly in thematic information extraction. For this reason, we present a new method for thematic information extraction with hyperspectral remote sensing data. We perform classification by means of combining the self-organizing map (SOM) neural network which is considered as full-pixel technique with linear spectral mixture analysis (LSMA) which is considered as mixed-pixel technique. The SOM neural network is improved from some aspects to classify the pure data and find the mixed data. And then the mixed data are unmixed and classified by LSMA. The result of experiment shows that we can have the better performance in thematic information extraction with PHI by this means.

  1. 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.

  2. 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.

  3. Airborne imaging spectrometer development tasks

    NASA Astrophysics Data System (ADS)

    Bolten, John

    The tasks that must be completed to design and build an airborne imaging spectrometer are listed. The manpower and resources required to do these tasks must be estimated by the people responsible for that work. The tasks are broken down by instrument subsystem or discipline. The instrument performance can be assessed at various stages during the development. The initial assessment should be done with the preliminary computer model. The instrument calibration facilities should be designed, but no calibration facilities are needed. The intermediate assessment can be done when the front end has been assembled. The preliminary instrument calibration facility should be available at this stage. The final assessment can only be done when the instrument is complete and ready for flight. For this, the final instrument calibration facility and the flight qualification facilities must be ready. The final assessment is discussed in each discipline under the section on integration and test.

  4. A methodology for luminance map retrieval using airborne hyperspectral and photogrammetric data

    NASA Astrophysics Data System (ADS)

    Pipia, Luca; Alamús, Ramon; Tardà, Anna; Pérez, Fernando; Palà, Vicenç; Corbera, Jordi

    2014-10-01

    This paper puts forward a methodology developed at the Institut Cartogràfic i Geològic de Catalunya (ICGC) to quantify upwelling light flux using hyperspectral and photogrammetric airborne data. The work was carried out in the frame of a demonstrative study requested by the municipality of Sant Cugat del Vallès, in the vicinity of Barcelona (Spain), and aimed to envisage a new approach to assess artificial lighting policies and actions as alternative to field campaigns. Hyperspectral and high resolution multispectral/panchromatic data were acquired simultaneously over urban areas. In order to avoid moon light contributions, data were acquired during the first days of new moon phase. Hyperspectral data were radiometrically calibrated. Then, National Center for Environmental Prediction (NCEP) atmospheric profiles were employed to estimate the actual Column Water Vapor (CWV) to be passed to ModTran5.0 for the atmospheric transmissivity τ calculation. At-the-ground radiance was finally integrated using the photopic sensitivity curve to generate a luminance map (cdm-2) of the flown area by mosaicking the different flight tracks. In an attempt to improve the spatial resolution and enhance the dynamic range of the luminance map, a sensor-fusion strategy was finally looked into. DMC Photogrammetric data acquired simultaneously to hyperspectral information were converted into at-the-ground radiance and upscaled to CASI spatial resolution. High-resolution (HR) luminance maps with enhanced dynamic range were finally generated by linearly fitting up-scaled DMC mosaics to the CASI-based luminance information. In the end, a preliminary assessment of the methodology is carried out using non-simultaneous in-situ measurements.

  5. Meat quality evaluation by hyperspectral imaging technique: an overview.

    PubMed

    Elmasry, Gamal; Barbin, Douglas F; Sun, Da-Wen; Allen, Paul

    2012-01-01

    During the last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there has been a renewed interest in using hyperspectral imaging in quality evaluation of different food products. The main inducement for developing the hyperspectral imaging system is to integrate both spectroscopy and imaging techniques in one system to make direct identification of different components and their spatial distribution in the tested product. By combining spatial and spectral details together, hyperspectral imaging has proved to be a promising technology for objective meat quality evaluation. The literature presented in this paper clearly reveals that hyperspectral imaging approaches have a huge potential for gaining rapid information about the chemical structure and related physical properties of all types of meat. In addition to its ability for effectively quantifying and characterizing quality attributes of some important visual features of meat such as color, quality grade, marbling, maturity, and texture, it is able to measure multiple chemical constituents simultaneously without monotonous sample preparation. Although this technology has not yet been sufficiently exploited in meat process and quality assessment, its potential is promising. Developing a quality evaluation system based on hyperspectral imaging technology to assess the meat quality parameters and to ensure its authentication would bring economical benefits to the meat industry by increasing consumer confidence in the quality of the meat products. This paper provides a detailed overview of the recently developed approaches and latest research efforts exerted in hyperspectral imaging technology developed for evaluating the quality of different meat products and the possibility of its widespread

  6. 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.

  7. Hyperspectral image compression using bands combination wavelet transformation

    NASA Astrophysics Data System (ADS)

    Wang, Wenjie; Zhao, Zhongming; Zhu, Haiqing

    2009-10-01

    Hyperspectral imaging technology is the foreland of the remote sensing development in the 21st century and is one of the most important focuses of the remote sensing domain. Hyperspectral images can provide much more information than multispectral images do and can solve many problems which can't be solved by multispectral imaging technology. However this advantage is at the cost of massy quantity of data that brings difficulties of images' process, storage and transmission. Research on hyperspectral image compression method has important practical significance. This paper intends to do some improvement of the famous KLT-WT-2DSPECK (Karhunen-Loeve transform+ wavelet transformation+ two-dimensional set partitioning embedded block compression) algorithm and advances KLT + bands combination 2DWT + 2DSPECK algorithm. Experiment proves that this method is effective.

  8. 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.

  9. 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

  10. Hyperspectral fluorescence lifetime imaging for optical biopsy.

    PubMed

    Nie, Zhaojun; An, Ran; Hayward, Joseph E; Farrell, Thomas J; Fang, Qiyin

    2013-09-01

    A hyperspectral fluorescence lifetime imaging (FLIM) instrument is developed to study endogenous fluorophores in biological tissue as an optical biopsy tool. This instrument is able to spectrally, temporally, and spatially resolve fluorescence signal, thus providing multidimensional information to assist clinical tissue diagnosis. An acousto-optic tunable filter (AOTF) is used to realize rapid wavelength switch, and a photomultiplier tube and a high-speed digitizer are used to collect the time-resolved fluorescence decay at each wavelength in real time. The performance of this instrument has been characterized and validated on fluorescence tissue phantoms and fresh porcine skin specimens. This dual-arm AOTF design achieves high spectral throughput while allowing microsecond nonsequential, random wavelength switching, which is highly desirable for time-critical applications. In the results reported here, a motorized scanning stage is used to realize spatial scanning for two-dimensional images, while a rapid beam steering technique is feasible and being developed in an ongoing project. PMID:24002188

  11. Applying linear spectral unmixing to airborne hyperspectral imagery for mapping yield variability in grain sorghum and cotton fields

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study examined linear spectral unmixing techniques for mapping the variation in crop yield for precision agriculture. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery collected from a grain sorghum field and a cotton field. A pair...

  12. Detection of European Corn Borer Infestation in Iowa Corn Plots using Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing technology was used to distinguish corn infested with European corn borers, Ostrinia nubilalis, from corn that was not infested. In 2004 and 2005, eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery. Manual inocu...

  13. 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.

  14. Synthesis of Multispectral Bands from Hyperspectral Data: Validation Based on Images Acquired by AVIRIS, Hyperion, ALI, and ETM+

    NASA Technical Reports Server (NTRS)

    Blonksi, Slawomir; Gasser, Gerald; Russell, Jeffrey; Ryan, Robert; Terrie, Greg; Zanoni, Vicki

    2001-01-01

    Multispectral data requirements for Earth science applications are not always studied rigorously studied before a new remote sensing system is designed. A study of the spatial resolution, spectral bandpasses, and radiometric sensitivity requirements of real-world applications would focus the design onto providing maximum benefits to the end-user community. To support systematic studies of multispectral data requirements, the Applications Research Toolbox (ART) has been developed at NASA's Stennis Space Center. The ART software allows users to create and assess simulated datasets while varying a wide range of system parameters. The simulations are based on data acquired by existing multispectral and hyperspectral instruments. The produced datasets can be further evaluated for specific end-user applications. Spectral synthesis of multispectral images from hyperspectral data is a key part of the ART software. In this process, hyperspectral image cubes are transformed into multispectral imagery without changes in spatial sampling and resolution. The transformation algorithm takes into account spectral responses of both the synthesized, broad, multispectral bands and the utilized, narrow, hyperspectral bands. To validate the spectral synthesis algorithm, simulated multispectral images are compared with images collected near-coincidentally by the Landsat 7 ETM+ and the EO-1 ALI instruments. Hyperspectral images acquired with the airborne AVIRIS instrument and with the Hyperion instrument onboard the EO-1 satellite were used as input data to the presented simulations.

  15. Airborne Multi-Angle Hyper-Spectral Measurements of White Caps on the Open Ocean

    NASA Astrophysics Data System (ADS)

    Laveigne, J.; Cairns, B.; Diner, D. J.

    2004-05-01

    The influence of whitecaps on the atmospheric correction of ocean color measurements is highly dependent on the spectral variation of albedo that is assumed for the whitecaps. Field measurements of breaking waves in the surf zone indicate a strong spectral variation in whitecap reflectance with the reflectance at 1650 nm nm decreasing by 95% relative to that at 440 nm. The cause of this spectral variation is thought to be the strong absorption by water at longer wavelengths that attenuates light reflected from submerged bubbles. Measurements made during an ocean cruise suggest that the magnitude of this decrease is typically less in the open ocean where the wave breaking is less violent and bubbles are not injected as deep into the water. Nonetheless, even in the open ocean, when whitecaps are large and bright similar decreases in reflectance from 440 nm to 860 nm to those observed in the surf zone are seen. Unfortunately, although measurements in the vicinity of 1600 and 2200 nm are important for remote sensing of aerosols and the atmospheric correction of ocean color measurements, the longest wavelength used for the open ocean measurements was 860 nm. Information about typical reflectance decreases from 440 nm to these longer wavelengths is therefore missing. One approach to remedying this absence of information about the spectral variation of white cap albedo across the solar spectrum is to use an airborne imaging spectrometer. However, a significant difficulty in using airborne, or ship-borne, instrumentation to measure the spectral albedo of whitecaps is the contamination of data by sun glitter. It is usually much more difficult than anticipated to filter data to reject glitter, even for ship-borne measurements with a television camera that provides a visual reference. This means that most data that is reported is obtained under overcast conditions. One approach to alleviating the problems caused by sun glitter is to using multi-angle remote sensing. If

  16. 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.

  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. 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.

  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. 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...

  1. Detecting Citrus Canker using Hyperspectral Reflectance Imaging and PCA-based Image Classification Method

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A portable hyperspectral imaging system was developed to measure the reflectance images from citrus samples with normal and various common diseased skin conditions in the wavelength range between 400 nm and 900 nm. PCA was used to reduce the spectral dimension of the 3-D hyperspectral image data and...

  2. 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.

  3. Resolving Mixed Algal Species in Hyperspectral Images

    PubMed Central

    Mehrubeoglu, Mehrube; Teng, Ming Y.; Zimba, Paul V.

    2014-01-01

    We investigated a lab-based hyperspectral imaging system's response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system's performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert's law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements. PMID:24451451

  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. Random projection and SVD methods in hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Zhang, Jiani

    Hyperspectral imaging provides researchers with abundant information with which to study the characteristics of objects in a scene. Processing the massive hyperspectral imagery datasets in a way that efficiently provides useful information becomes an important issue. In this thesis, we consider methods which reduce the dimension of hyperspectral data while retaining as much useful information as possible. Traditional deterministic methods for low-rank approximation are not always adaptable to process huge datasets in an effective way, and therefore probabilistic methods are useful in dimension reduction of hyperspectral images. In this thesis, we begin by generally introducing the background and motivations of this work. Next, we summarize the preliminary knowledge and the applications of SVD and PCA. After these descriptions, we present a probabilistic method, randomized Singular Value Decomposition (rSVD), for the purposes of dimension reduction, compression, reconstruction, and classification of hyperspectral data. We discuss some variations of this method. These variations offer the opportunity to obtain a more accurate reconstruction of the matrix whose singular values decay gradually, to process matrices without target rank, and to obtain the rSVD with only one single pass over the original data. Moreover, we compare the method with Compressive-Projection Principle Component Analysis (CPPCA). From the numerical results, we can see that rSVD has better performance in compression and reconstruction than truncated SVD and CPPCA. We also apply rSVD to classification methods for the hyperspectral data provided by the National Geospatial-Intelligence Agency (NGA).

  6. 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.

  7. 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.

  8. 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.

  9. Iterative compressive sampling for hyperspectral images via source separation

    NASA Astrophysics Data System (ADS)

    Kamdem Kuiteing, S.; Barni, Mauro

    2014-03-01

    Compressive Sensing (CS) is receiving increasing attention as a way to lower storage and compression requirements for on-board acquisition of remote-sensing images. In the case of multi- and hyperspectral images, however, exploiting the spectral correlation poses severe computational problems. Yet, exploiting such a correlation would provide significantly better performance in terms of reconstruction quality. In this paper, we build on a recently proposed 2D CS scheme based on blind source separation to develop a computationally simple, yet accurate, prediction-based scheme for acquisition and iterative reconstruction of hyperspectral images in a CS setting. Preliminary experiments carried out on different hyperspectral images show that our approach yields a dramatic reduction of computational time while ensuring reconstruction performance similar to those of much more complicated 3D reconstruction schemes.

  10. Hyperspectral imaging techniques for the characterization of Haematococcus pluvialis (Chlorophyceae).

    PubMed

    Nogami, Satoru; Ohnuki, Shinsuke; Ohya, Yoshikazu

    2014-10-01

    A hyperspectral imaging camera was combined with a bright-field microscope to investigate the intracellular distribution of pigments in cells of the green microalga Haematococcus pluvialis, a synonym for H. lacustris (Chlorophyceae). We applied multivariate curve resolution to the hyperspectral image data to estimate the pigment contents in culture and revealed that the predicted values were consistent with actual measurements obtained from extracted pigments. Because it was possible to estimate pigment contents in every pixel, the intracellular distribution of the pigments was investigated during various life-cycle stages. Astaxanthin was localized specifically at the eyespot of zoospores in early culture stages. Then, it became widely distributed in cells, but subsequently localized differently than the chl. Integrated with our recently developed image-processing program "HaematoCalMorph," the hyperspectral imaging system was useful for monitoring intracellular distributions of pigments during culture as well as for studying cellular responses under various conditions. PMID:26988647

  11. 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.

  12. 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.

  13. Investigating coral hyperspectral properties across coral species and coral state using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Mehrubeoglu, Mehrube; Smith, Dustin K.; Smith, Shane W.; Strychar, Kevin B.; McLauchlan, Lifford

    2013-09-01

    Coral reefs are one of the most diverse and threatened ecosystems in the world. Corals worldwide are at risk, and in many instances, dying due to factors that affect their environment resulting in deteriorating environmental conditions. Because corals respond quickly to the quality of the environment that surrounds them, corals have been identified as bioindicators of water quality and marine environmental health. The hyperspectral imaging system is proposed as a noninvasive tool to monitor different species of corals as well as coral state over time. This in turn can be used as a quick and non-invasive method to monitor environmental health that can later be extended to climate conditions. In this project, a laboratory-based hyperspectral imaging system is used to collect spectral and spatial information of corals. In the work presented here, MATLAB and ENVI software tools are used to view and process spatial information and coral spectral signatures to identify differences among the coral data. The results support the hypothesis that hyperspectral properties of corals vary among different coral species, and coral state over time, and hyperspectral imaging can be a used as a tool to document changes in coral species and state.

  14. HRIS technology development results and their implementation in future hyperspectral imagers

    NASA Astrophysics Data System (ADS)

    Harnisch, Bernd; Fabbricotti, Marino; Meynart, Roland; Kunkel, Bernd P.; Posselt, Winfried; Schmidt, Elke; Davancens, Robert; Donnadieu, Olivier; Saint-Pe, Olivier; Charlton, Dave E.; Sankus, Liz; Basile, Giuseppe; Calamei, L.; Schweizer, Juergen; Juranek, Hans J.; Sand, Rolf; Schwarzer, Horst H.; Suemnich, Karl-Heinz; Slater, Philip N.

    1997-12-01

    The recent developments within the ESA funded HRIS (high resolution imaging spectrometer) technology program -- aiming at an airborne demonstrator model -- yielded rather successful subsystem developments. HRIS is designed as a true pushbroom hyperspectral imager with comparatively high spatial and spectral resolution, covering the spectral range from 450 to 2350 nm. The main breadboard units, with a space-near design, are essentially: a TMA (three mirro anastigmat, Carl Zeiss) front optics, a dual path spectrometer optics (Officine Galileo) with a novel in-field spectral separation unit, a 2-D SWIR CMT detector array with a dedicated CMOS readout multiplexer (GEC Marconi IR, MATRA MSF for testing), the signal processing electronics (DSS), some calibration elements (DLR + DSS), and the extensive testing of all units. The paper presents the essential results per unit, with possible exception of the front optics (which may not be completed at the conference paper presentation yet), including derived further development efforts. Also, the remaining steps towards an airborne test mission are outlined, together with a brief description of the envisaged high-altitude aircraft. We hope that this paper may also stir some potential users of later airborne HRIS test missions over dedicated target areas. Positive responses would support ESA to pursue the program. The technology units development under the HRIS contract have turned out useful for follow-on instrument developments such as the ESA Explorer mission candidate PRISM (processes research by an imaging space mission). This leads to the conclusion that the achieved development results are a sound basis for future airborne and spaceborne hyperspectral imager developments in Europe. A brief survey of the current PRISM baseline concept is added to the paper.

  15. Atmospheric correction of hyperspectral images based on approximate solution of transmittance equation

    NASA Astrophysics Data System (ADS)

    Belov, A. M.; Myasnikov, V. V.

    2015-02-01

    The paper presents a method of atmospheric correction of remote sensing hyperspectral images. The method based on approximate solution of MODTRAN transmittance equation using simultaneous analysis of remote sensing hyperspectral image and "ideal" hyperspectral image which is free from atmospheric distortions. Experimental results show that proposed method is applicable to perform atmospheric correction.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. Overall design technology of hyperspectral imaging system based on AOTF

    NASA Astrophysics Data System (ADS)

    Wang, Jun; Ding, Na; Zheng, Yawei; Zhao, Yujie; Gao, Fei; Li, Junna; Wang, Jilong; Gao, Meng; Wu, Jun

    2014-11-01

    An acousto-optic tunable filter (AOTF) is an acousto-optic modulator. In this paper, the characteristics and overall design method of AOTF hyperspectral imaging system are proposed, which operates in visible or near infrared waveband (0.4-1.0um) and middle wave or long wave (3-5um and 8-12um). Compared with conventional dispersion element, the AOTF hyperspectral imaging system has a larger clear aperture because of the special characteristic of beam separation mode. In particularly, if the non-collinear design mode is used, the AOTF will have a larger diffraction aperture angle and is more suitable for the application in spectral imaging domain. The AOTF hyperspectral imaging spectrometer that operates in visible/near infrared waveband was developed by the non-collinear TeO2 crystal (8mm×8mm). All lights that are through TeO2 crystal in whole field of view (FOV angle is 5 degree) forms an imagines onto the staring focal plane array by Bragg diffraction. The diffraction wavelength of AOTF can be adjusted by the radio frequency signal. The three-dimensional data cube is composed of two-dimension of object space and wavelength in this way, and the graph and spectral are synthesized and implemented. The AOTF hyperspectral imaging spectrometer operating in visible/near infrared waveband is analyzed, and the detailed analysis data is also presented. The AOTF hyperspectral imaging test is studied and developed, and the analysis of data and the next developing advice is given. We also analyze the method about selection of material and technological design in middle wave/long wave infrared waveband of AOTF hyperspectral imaging system.

  2. 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. ...

  3. Improved hyperspectral imaging system for fecal detection on poultry carcasses

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Agricultural Research Service (ARS) has developed imaging technology to detect fecal contaminants on poultry carcasses. The hyperspectral imaging system operates from about 400 to 1000 nm, but only a few wavelengths are used in a real-time multispectral system. Recently, the upgraded system, inc...

  4. 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.

  5. 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...

  6. Textural Analysis of Hyperspectral Images for Improving Detection Accuracy

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Detection of fecal contamination is crucial for food safety to protect consumers from food pathogens. Previous studies demonstrated a hyperspectral imaging system has a potential for poultry fecal contaminant detection by measuring reflectance intensity. The simple image ratio with optimal thresho...

  7. 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...

  8. Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    August, Yitzhak; Vachman, Chaim; Stern, Adrian

    2013-05-01

    Compressive hyperspectral imaging is based on the fact that hyperspectral data is highly redundant. However, there is no symmetry between the compressibility of the spatial and spectral domains, and that should be taken into account for optimal compressive hyperspectral imaging system design. Here we present a study of the influence of the ratio between the compression in the spatial and spectral domains on the performance of a 3D separable compressive hyperspectral imaging method we recently developed.

  9. Detection of Extreme Climate Event Impacts to Terrestrial Productivity From Airborne Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Desai, A. R.; DuBois, S.; Singh, A.; Serbin, S.; Goulden, M.; Baldocchi, D. D.; Oechel, W. C.; Kruger, E. L.; Townsend, P. A.

    2015-12-01

    Changes in drought frequency and intensity are likely to be some of the largest climate anomalies to influence the net productivity of ecosystems, especially in already water-limited regions. However, the physiological mechanisms that drive this response are limited by primarily infrequent and small-scale leaf-level measurements. Here, we integrated eddy covariance flux tower estimates of gross primary productivity (GPP) across an elevation-gradient in California with airborne imagery from the NASA HyspIRI Preparatory campaign to evaluate the potential of hyperspectral imagery to detect responses of GPP to prolonged drought. We observed a number of spectral features in the visible, infrared, and shortwave infrared regions that yielded stronger linkages than traditional broadband indices with space and time variation in GPP across a range of ecosystems in California experiencing water stress in the past three years. Further, partial least squares regression (PLSR) modeling offers the ability to generate predictive models of GPP from narrowband hyperspectral remote sensing that directly links plant chemistry and spectral properties to productivity, and could serve as a significant advance over broadband remote sensing of GPP anomalies.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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

  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. Combining hyperspectral imaging and Raman spectroscopy for remote chemical sensing

    NASA Astrophysics Data System (ADS)

    Ingram, John M.; Lo, Edsanter

    2008-04-01

    The Photonics Research Center at the United States Military Academy is conducting research to demonstrate the feasibility of combining hyperspectral imaging and Raman spectroscopy for remote chemical detection over a broad area of interest. One limitation of future trace detection systems is their ability to analyze large areas of view. Hyperspectral imaging provides a balance between fast spectral analysis and scanning area. Integration of a hyperspectral system capable of remote chemical detection will greatly enhance our soldiers' ability to see the battlefield to make threat related decisions. It can also queue the trace detection systems onto the correct interrogation area saving time and reconnaissance/surveillance resources. This research develops both the sensor design and the detection/discrimination algorithms. The one meter remote detection without background radiation is a simple proof of concept.

  4. 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.

  5. 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

  6. Hyperspectral imaging and quantitative analysis for prostate cancer detection

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

    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.

  7. 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%.

  8. Hyperspectral imaging and quantitative analysis for prostate cancer detection.

    PubMed

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

    2012-07-01

    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

  9. Characterization and reduction of stochastic and periodic anomalies in a hyperspectral imaging sensor system

    NASA Astrophysics Data System (ADS)

    Shetler, Bruce V.; Kieffer, Hugh H.

    1996-11-01

    HYDICE, the HYperspectral Digital Imagery Collection Experiment, is an airborne hyperspectral imaging sensor operating in a pushbroom mode. HYDICE collects data simultaneously in 210 wavelength bands from 0.4 to 2.5 micrometers using a prism as the dispersing element. While the overall quality of HYDICE data is excellent, certain data stream anomalies have been identified, among which are a periodic offset in DN level related to the operation of the system cryocooler and a quasi-random variation in the spectral alignment between the dispersed image and the focal plane. In this paper we report on an investigation into the above two effects and the development of algorithms and software to correct or minimize their impact in a production data processing system. We find the periodic variation to have unexpected time and band-dependent characteristics which argues against the possibility of correction in post- processing, but to be relatively insensitive to signal and consequently of low impact on the operation of the system. We investigate spectral jitter through an algorithm which performs a least squares fit to several atmospheric spectral features to characterize both the time-dependent jitter motion and systematic spectral mis-registration. This method is also implemented to correct the anomalies in the production data stream. A comprehensive set of hyperspectral sensor calibration and correction algorithm is also presented.

  10. Hyperspectral and Multispectral Imaging Technique for Food Quality and Safety Evaluation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this chapter, recently developed ARS line-scan hyperspectral-based sensing technologies to address agro-food safety concerns are presented including a case study using the laboratory-based hyperspectral imaging platforms. An online line-scan imaging system capable of both hyperspectral and multi...

  11. Detection of cracks on tomatoes using hyperspectral near-infrared reflectance imaging system

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detection of cuticle cracks on tomatoes. A hyperspectral near-infrared reflectance imaging system in the region of 1000-1700 nm was used to obtain hyperspectral reflectance ima...

  12. Hyperspectral imaging of an inter-coastal waterway

    NASA Astrophysics Data System (ADS)

    Bowles, Jeffrey H.; Maness, Shelia J.; Chen, Wei; Davis, Curtiss O.; Donato, Tim F.; Gillis, David B.; Korwan, Daniel; Lamela, Gia; Montes, Marcos J.; Rhea, W. Joseph; Snyder, William A.

    2005-10-01

    This paper demonstrates the characterization of the water properties, bathymetry, and bottom type of the Indian River Lagoon (IRL) on the eastern coast of Florida using hyperspectral imagery. Images of this region were collected from an aircraft in July 2004 using the Portable Hyperspectral Imager for Low Light Spectroscopy (PHILLS). PHILLS is a Visible Near InfraRed (VNIR) spectrometer that was operated at an altitude of 3000 m providing 4 m resolution with 128 bands from 400 to 1000 nm. The IRL is a well studied water body that receives fresh water drainage from the Florida Everglades and also tidal driven flushing of ocean water through several outlets in the barrier islands. Ground truth measurements of the bathymetry of IRL were acquired from recent sonar and LIDAR bathymetry maps as well as water quality studies concurrent to the hyperspectral data collections. From these measurements, bottom types are known to include sea grass, various algae, and a gray mud with water depths less than 6 m over most of the lagoon. Suspended sediments are significant (~35 mg/m3) with chlorophyll levels less than 10 mg/m3 while the absorption due to Colored Dissolved Organic Matter (CDOM) is less than 1 m-1 at 440 nm. Hyperspectral data were atmospherically corrected using an NRL software package called Tafkaa and then subjected to a Look-Up Table (LUT) approach which matches hyperspectral data to calculated spectra with known values for bathymetry, suspended sediments, chlorophyll, CDOM, and bottom type.

  13. Hyperspectral image analysis for plant stress detection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Abiotic and disease-induced stress significantly reduces plant productivity. Automated on-the-go mapping of plant stress allows timely intervention and mitigating of the problem before critical thresholds are exceeded, thereby, maximizing productivity. A hyperspectral camera analyzed the spectral ...

  14. 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.

  15. Algorithm for mapping cutaneous tissue oxygen concentration using hyperspectral imaging

    PubMed Central

    Miclos, Sorin; Parasca, Sorin Viorel; Calin, Mihaela Antonina; Savastru, Dan; Manea, Dragos

    2015-01-01

    The measurement of tissue oxygenation plays an important role in the diagnosis and therapeutic assessment of a large variety of diseases. Many different methods have been developed and are currently applied in clinical practice for the measurement of tissue oxygenation. Unfortunately, each of these methods has its own limitations. In this paper we proposed the use of hyperspectral imaging as new method for the assessment of the tissue oxygenation level. To extract this information from hyperspectral images a new algorithm for mapping cutaneous tissue oxygen concentration was developed. This algorithm takes into account and solves some problems related to setting and calculation of some parameters derived from hyperspectral images. The algorithm was tested with good results on synthetic images and then validated on the fingers of a hand with different blood irrigation states. The results obtained have proved the ability of hyperspectral imaging together with the developed algorithm to map the oxy- and deoxyhemoglobin distribution on the analyzed fingers. These are only preliminary results and other studies should be done before this approach to be used in the clinical setting for the diagnosis and monitoring of various diseases. PMID:26417511

  16. Developing digital tissue phantoms for hyperspectral imaging of ischemic wounds.

    PubMed

    Xu, Ronald X; Allen, David W; Huang, Jiwei; Gnyawali, Surya; Melvin, James; Elgharably, Haytham; Gordillo, Gayle; Huang, Kun; Bergdall, Valerie; Litorja, Maritoni; Rice, Joseph P; Hwang, Jeeseong; Sen, Chandan K

    2012-06-01

    Hyperspectral imaging has the potential to achieve high spatial resolution and high functional sensitivity for non-invasive assessment of tissue oxygenation. However, clinical acceptance of hyperspectral imaging in ischemic wound assessment is hampered by its poor reproducibility, low accuracy, and misinterpreted biology. These limitations are partially caused by the lack of a traceable calibration standard. We proposed a digital tissue phantom (DTP) platform for quantitative calibration and performance evaluation of spectral wound imaging devices. The technical feasibility of such a DTP platform was demonstrated by both in vitro and in vivo experiments. The in vitro DTPs were developed based on a liquid blood phantom model. The in vivo DTPs were developed based on a porcine ischemic skin flap model. The DTPs were projected by a Hyperspectral Image Projector (HIP) with high fidelity. A wide-gap 2nd derivative oxygenation algorithm was developed to reconstruct tissue functional parameters from hyperspectral measurements. In this study, we have demonstrated not only the technical feasibility of using DTPs for quantitative calibration, evaluation, and optimization of spectral imaging devices but also its potential for ischemic wound assessment in clinical practice. PMID:22741088

  17. 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.

  18. 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.

  19. 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.

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

    PubMed Central

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

    2011-01-01

    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

  1. 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.

  2. 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.

  3. 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.

  4. AOTF hyperspectral microscope imaging for foodborne pathogenic bacteria detection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral microscope imaging (HMI) method, which provides both spatial and spectral information, can be effective for foodborne pathogen detection. The acousto-optic tunable filter (AOTF)-based HMI method can be used to characterize spectral properties of biofilms formed by Salmonella enteritidi...

  5. 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...

  6. 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.

  7. Hyperspectral Reflectance Imaging for Detecting a Foodborne Pathogen: Campylobacter

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper is concerned with the development of a hyperspectral reflectance imaging technique for detecting and identifying one of the most common foodborne pathogens, Campylobacter. Direct plating using agars is an effective tool for laboratory tests and analyses of microorganisms. The morphology (...

  8. Classification of fecal contamination on leafy greens by hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral fluorescence imaging system was developed and used to obtain several two-waveband spectral ratios on leafy green vegetables, represented by romaine lettuce and baby spinach in this study. The ratios were analyzed to determine the proper one for detecting bovine fecal contamination on...

  9. 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

  10. Classification of Fecal Contamination on Leafy Greens by Hyperspectral Imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral fluorescence imaging system was developed and used to obtain several two-waveband spectral ratios on leafy green vegetables, represented by romaine lettuce and baby spinach in this study. The ratios were analyzed to determine the proper one for detecting bovine fecal contamination on...

  11. Hyperspectral Imaging for Detecting Pathogens Grown on Agar Plates

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper is concerned with the development of a hyperspectral imaging technique for detecting and identifying one of the most common foodborne pathogens, Campylobacter. Direct plating using agars is an effective tool for laboratory tests and analyses of microorganisms. The morphology (size, growth...

  12. Evaluation of physiological status of potato tubers using hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Visible and near-infrared hyperspectral reflectance imaging was evaluated as a rapid technique to predict the glucose and sucrose percentages in two common fresh use and chipping potato cultivars. Tubers were sampled in the 2009 season and held in multiple storage temperatures in attempt to develop ...

  13. 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.

  14. Hyperspectral Imaging Technologies for Nondestructive Agro-Food Evaluation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Over the past decade, researchers at the Agricultural Research Service (ARS), United States Department of Agriculture (USDA), have developed several versions of line-scan-based hyperspectral imaging systems capable of both visible to near-infrared reflectance and fluorescence methods. These line-s...

  15. 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

  16. [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

  17. 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.

  18. 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.

  19. 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

  20. Sophisticated Vegetation Classification Based on Feature Band Set Using Hyperspectral Image.

    PubMed

    Shang, Kun; Zhang, Xia; Sun Yan-li; Zhang, Li-fu; Wang, Shu-dong; Zhuang, Zhi

    2015-06-01

    There are two major problems of sophisticated vegetation classification (SVC) using hyperspectral image. Classification results using only spectral information can hardly meet the application requirements with the needed vegetation type becoming more sophisticated. And applications of classification image are also limited due to salt and pepper noise. Therefore the SVC strategy based on construction and optimization of vegetation feature band set (FBS) is proposed. Besides spectral and texture features of original image, 30 spectral indices which are sensitive to biological parameters of vegetation are added into FBS in order to improve the separability between different kinds of vegetation. And to achieve the same goal a spectral-dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed. A spatial-dimension optimization algorithm of FBS based on neighborhood pixels' spectral angle distance (NPSAD) is proposed so that detailed information can be kept during the image smoothing process. The results of SVC experiments based on airborne hyperspectral image show that the proposed method can significantly improve the accuracy of SVC so that some widespread application prospects like identification of crop species, monitoring of invasive species and precision agriculture are expectable. PMID:26601388

  1. 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...

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 sp...

  3. 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.

  4. 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.

  5. [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

  6. 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.

  7. Hyperspectral imaging for non-contact analysis of forensic traces.

    PubMed

    Edelman, G J; Gaston, E; van Leeuwen, T G; Cullen, P J; Aalders, M C G

    2012-11-30

    Hyperspectral imaging (HSI) integrates conventional imaging and spectroscopy, to obtain both spatial and spectral information from a specimen. This technique enables investigators to analyze the chemical composition of traces and simultaneously visualize their spatial distribution. HSI offers significant potential for the detection, visualization, identification and age estimation of forensic traces. The rapid, non-destructive and non-contact features of HSI mark its suitability as an analytical tool for forensic science. This paper provides an overview of the principles, instrumentation and analytical techniques involved in hyperspectral imaging. We describe recent advances in HSI technology motivating forensic science applications, e.g. the development of portable and fast image acquisition systems. Reported forensic science applications are reviewed. Challenges are addressed, such as the analysis of traces on backgrounds encountered in casework, concluded by a summary of possible future applications. PMID:23088824

  8. 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.

  9. 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.

  10. Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV

    NASA Astrophysics Data System (ADS)

    Pölönen, I.; Saari, H.; Kaivosoja, J.; Honkavaara, E.; Pesonen, L.

    2013-10-01

    Hyperspectral imaging based precise fertilization is challenge in the northern Europe, because of the cloud conditions. In this paper we will introduce schemes for the biomass and nitrogen content estimations from hyperspectral images. In this research we used the Fabry-Perot interferometer based hypespectral imager that enables hyperspectral imaging from lightweight UAVs. During the summers 2011 and 2012 imaging and flight campaigns were carried out on the Finnish test field. Estimation mehtod uses features from linear and non-linear unmixing and vegetation indices. The results showed that the concept of small hyperspectral imager, UAV and data analysis is ready to operational use.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Manifold alignment for classification of multitemporal hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Yang, Hsiu-Han

    Analyzing remotely sensed images to obtain land cover classification maps is an effective approach for acquiring information over landscapes that can be accomplished over extended areas with limited ground surveys. Further, with advances in remote sensing technology, spaceborne hyperspectral sensors provide the capability to acquire a set of images that have both high spectral and temporal resolution. These images are suitable for monitoring and analyzing environmental changes with subtle spectral characteristics. However, inherent characteristics of multitemporal hyperspectral images, including high dimensionality, nonlinearity, and nonstationarity phenomena over time and across large areas, pose several challenges for classification. This research addresses the issues of classification tasks in the presence of spectral shifts within multitemporal hyperspectral images by leveraging the concept of the data manifold. Although manifold learning has been applied successfully in single image hyperspectral data classification to address high dimensionality and nonlinear spectral responses, research related to manifold learning for multitemporal classification studies is limited. The proposed approaches utilize spectral signatures and spatial proximity to construct similar "local" geometries of temporal images. By aligning these underlying manifolds optimally, the impacts of nonstationary effects are mitigated and classification is accomplished in a representative temporal data manifold. "Global" manifolds learned from temporal hyperspectral images have a major advantage in faithful representation of the data in an image, such as retaining relationships between different classes. Local manifolds are favored in discriminating difficult classes and for computation efficiency. A new hybrid global-local manifold alignment method that combines the advantages of global and local manifolds for effective multitemporal image classification is also proposed. Results illustrate the

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. Recent developments in hyperspectral imaging for assessment of food quality and safety.

    PubMed

    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

  5. 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

  6. 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.

  7. [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

  8. Land surface emissivity retrieval from airborne hyperspectral scanner thermal infrared data over urban surfaces

    NASA Astrophysics Data System (ADS)

    Gao, C. X.; Qian, Y. G.; Wang, N.; Ma, L. L.; Jiang, X. G.

    2015-12-01

    Land surface emissivity (LSE) is a key parameter for characterizing the land surface, and is vital for a wide variety of surface-atmosphere studies. This paper retrieved LSEs of land surfaces over the city of Madrid, Spain from airborne hyperspectral scanner (AHS) thermal infrared data using temperature emissivity separation (TES) method. Six different kinds of urban surfaces: asphalt, bare soil, granite, pavement, shrub and grass pavement, were selected to evaluate the performance of the TES method in urban areas. The results demonstrate that the TES method can be successfully applied to retrieve LSEs in urban area. The six urban surfaces have similar curve shape of emissivity spectra, with the lowest emissivity in band 73, and highest in band 78; the LSE for bare soil varies significantly with spectra, approximately from 0.90 in band 72 to 0.98 in band 78, whereas the LSE for grass has the smallest spectral variation, approximately from 0.965 in band 72 to 0.974 in band 78, and the shrub presents higher LSE than other surfaces in bands 72, 73, 75-77, but a little lower in bands 78 and 79. Furthermore, it is worth noting that band 73 is suitable for discriminating different urban surfaces because large LSE differences exist in this channel for different urban surfaces.

  9. 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

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

    PubMed

    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

  11. Correction of axial optical aberrations in hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

    Špiclin, Žiga; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

    In hyper-spectral imaging systems with a wide spectral range, axial optical aberrations may lead to a significant blurring of image intensities in certain parts of the spectral range. Axial optical aberrations arise from the indexof- refraction variations that is dependent on the wavelength of incident light. To correct axial optical aberrations the point-spread function (PSF) of the image acquisition system needs to be identified. We proposed a multiframe joint blur identification and image restoration method that maximizes the likelihood of local image energy distributions between spectral images. Gaussian mixture model based density estimate provides a link between corresponding spatial information shared among spectral images so as to find and restore the image edges via a PSF update. Model of the PSF was assumed to be a linear combination of Gaussian functions, therefore the blur identification process had to find only the corresponding scalar weights of each Gaussian function. Using the identified PSF, image restoration was performed by the iterative Richardson-Lucy algorithm. Experiments were conducted on four different biological samples using a hyper-spectral imaging system based on acousto-optic tunable filter in the visible spectral range (0.55 - 1.0 μm). By running the proposed method, the quality of raw spectral images was substantially improved. Image quality improvements were quantified by a measure of contrast and demonstrate the potential of the proposed method for the correction of axial optical aberrations.

  12. 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.

  13. 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.

  14. Preprocessing and compression of Hyperspectral images captured onboard UAVs

    NASA Astrophysics Data System (ADS)

    Herrero, Rolando; Cadirola, Martin; Ingle, Vinay K.

    2015-10-01

    Advancements in image sensors and signal processing have led to the successful development of lightweight hyperspectral imaging systems that are critical to the deployment of Photometry and Remote Sensing (PaRS) capabilities in unmanned aerial vehicles (UAVs). In general, hyperspectral data cubes include a few dozens of spectral bands that are extremely useful for remote sensing applications that range from detection of land vegetation to monitoring of atmospheric products derived from the processing of lower level radiance images. Because these data cubes are captured in the challenging environment of UAVs, where resources are limited, source encoding by means of compression is a fundamental mechanism that considerably improves the overall system performance and reliability. In this paper, we focus on the hyperspectral images captured by a state-of-the-art commercial hyperspectral camera by showing the results of applying ultraspectral data compression to the obtained data set. Specifically the compression scheme that we introduce integrates two stages; (1) preprocessing and (2) compression itself. The outcomes of this procedure are linear prediction coefficients and an error signal that, when encoded, results in a compressed version of the original image. Second, preprocessing and compression algorithms are optimized and have their time complexity analyzed to guarantee their successful deployment using low power ARM based embedded processors in the context of UAVs. Lastly, we compare the proposed architecture against other well known schemes and show how the compression scheme presented in this paper outperforms all of them by providing substantial improvement and delivering both lower compression rates and lower distortion.

  15. 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

  16. 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.

  17. Hyperspectral imaging for detecting pathogens grown on agar plates

    NASA Astrophysics Data System (ADS)

    Yoon, Seung Chul; Lawrence, Kurt C.; Siragusa, Gregory R.; Line, John E.; Park, Bosoon; Windham, William R.

    2007-09-01

    This paper is concerned with the development of a hyperspectral imaging technique for detecting and identifying one of the most common foodborne pathogens, Campylobacter. Direct plating using agars is an effective tool for laboratory tests and analyses of microorganisms. The morphology (size, growth pattern, color, etc.) of colonies grown on agar plates has been widely used to tentatively differentiate organisms. However, it is sometimes difficult to differentiate target organisms like Campylobacters from other contaminants grown together on the same agar plates. A hyperspectral imaging system operating at the visible and near infrared (VNIR) spectral region from 400 nm to 900 nm was set up to measure spectral signatures of 17 different Campylobacter and non-Campylobacter subspecies. Protocols for culturing, imaging samples and for calibrating measured data were developed. The VNIR spectral library of all 17 organisms commonly encountered in poultry was established from calibrated hyperspectral images. A classification algorithm was developed to locate and identify Campylobacters, non-Campylobacter contaminants, and background agars with 99.29% accuracy. This research has a potential to be expanded to detect other pathogens grown on agar media.

  18. 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.

  19. 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.

  20. Airborne lidar imaging of salmon.

    PubMed

    Churnside, James H; Wilson, James J

    2004-02-20

    Lidar images of adult salmon are presented. The lidar system is built around a pulsed green laser and a gated intensified CCD camera. The camera gating is timed to collect light scattered from the turbid water below the fish to produce shadows in the images. Image processing increases the estimated contrast-to-noise ratio from 3.4 in the original image to 16.4 by means of a matched filter. PMID:15008549

  1. 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.

  2. 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.

  3. Hyperspectral imaging of plasmon resonances in metallic nanoparticles.

    PubMed

    Zopf, David; Jatschka, Jacqueline; Dathe, André; Jahr, Norbert; Fritzsche, Wolfgang; Stranik, Ondrej

    2016-07-15

    The spectroscopy of metal nanoparticles shows great potential for label-free sensing. In this article we present a hyper-spectral imaging system combined with a microfluidic system, which allows full spectroscopic characterization of many individual nanoparticles simultaneously (>50 particles). With such a system we were able overcome several limitations that are present in LSPR sensing with nanoparticle ensemble. We experimentally quantified (incorporating atomic force microscopy as well) the correlation between geometry, position of plasmon resonance (λPeak) and sensitivity of the particles (Sb=1.63λPeak-812.47[nm/RIU]). We were able to follow the adsorption of protein layers and determined their spatial inhomogeneity with the help of the hyperspectral imaging. PMID:26974477

  4. 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. PMID:23683098

  5. [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

  6. 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.

  7. Visible hyperspectral imaging for standoff detection of explosives on surfaces

    NASA Astrophysics Data System (ADS)

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

    2010-10-01

    There is an ever-increasing need to be able to detect the presence of explosives, preferably from standoff distances of tens of meters. 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.

  8. 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.

  9. Hyperspectral cathodoluminescence imaging of modern and fossil carbonate shells

    NASA Astrophysics Data System (ADS)

    England, Jennifer; Cusack, Maggie; Paterson, Niall W.; Edwards, Paul; Lee, Martin R.; Martin, Robert

    2006-09-01

    Optical cathodoluminescence (CL) is commonly used to identify diagenetically altered carbonate fossils, yet such an interpretation is problematic as present-day carbonate shells may also luminesce. Hyperspectral CL imaging combines CL microscopy and CL spectroscopy to quantitatively analyze luminescence emission. Cold optical CL and hyperspectral CL imaging were carried out on four modern biominerals, a Rhynchonelliform brachiopod, a Craniid brachiopod, a bivalve, and the eggshell of the domestic fowl. A fossil Craniid brachiopod was analyzed to compare luminescence emission with that from the modern Craniid brachiopod. The beam conditions used for optical CL vary between studies, which hinders the direct comparison of CL analyses. This study assesses the effect of beam current and beam diameter on the intensity of luminescence emission. By characterizing the effect of beam conditions on different CaCO3 biominerals, comparisons can be made between CL studies. Hyperspectral CL imaging can be carried out in combination with WDS element analysis. By comparing hyperspectral CL images with element maps the causes of luminescence can to some extent be determined. The intensity of luminescence emitted from the modern biominerals differs under the same beam conditions. All four modern shells emit blue luminescence. In N. anomala, there is a correlation between Mn2+ concentration and luminescence intensity in the 620- to 630-nm wavelength band, which is apparent in the inner region of the shell. The fossil Craniid also emits blue luminescence, and texture within the shell wall is apparent; however, the luminescence emission between 620 and 630 nm that is evident in N. anomala is absent.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. A short wave infrared hyperspectral imager for landmine detection

    NASA Astrophysics Data System (ADS)

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

    2005-06-01

    DRDC Suffield and Itres Research have jointly investigated the use of visible and infrared hyperspectral imaging for landmine detection since 1988. There has been considerable success detecting surface-laid landmines by classification of their visible/near infrared (VNIR - 400 to 1000 nm wavelength) spectral signatures, but it has not been possible to find VNIR spectral characteristics that would generically distinguish anthropogenic objects from natural features such as rocks, vegetation, soil, etc. Preliminary studies in 1998 suggested that it might be possible to develop such a generic classifier in the short wave infrared (SWIR) and that detection performance might improve. Because of a lack of available SWIR hyperspectral imagers with adequate performance for mine detection, a prototype pushbroom SWIR hyperspectral imager was developed and completed in summer 2002. The now commercially available instrument, sasi, has 160 bands over a spectral range of 850 to 2450 nm, signal to noise ratio of 400:1 with f/1.8 fore-optics, and 600 pixels over a 37.7° field of view. A number of mission flights have been carried out and excellent imagery obtained. In October 2003, Itres and DRDC Suffield personnel obtained field SWIR hyperspectral imagery in the DRDC Suffield Mine Pen of numerous surface-laid mines, one buried mine, other surface-laid human-made items, background materials and people from a horizontally scanning personnel-lift at an altitude of roughly 5 m. Preliminary indications are that a simple generic classification decision boundary should be able to distinguish surface-laid landmines from many human-made artifacts and natural materials. The buried mine was not detected, but the mine had been buried for several years and hence there would be no residual surface disturbance. Furthermore, the small sample size and limited observation time make it difficult to generalize about SWIR performance for buried mines. The instrument is described and the preliminary

  18. 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%.

  19. 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.

  20. 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...

  1. Visible to SWIR hyperspectral imaging for produce safety and quality evaluation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging techniques, combining the advantages of spectroscopy and imaging, have found wider use in food quality and safety evaluation applications during the past decade. In light of the prevalent use of hyperspectral imaging techniques in the visible to near-infrared (VNIR: 400 -1000 n...

  2. PARTIAL LEAST SQUARES REGRESSION OF HYPERSPECTRAL IMAGES FOR CONTAMINATION DETECTION ON POULTRY CARCASSES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Abstract The U.S. Department of Agriculture has developed multispectral and hyperspectral imaging systems to detect faecal contaminants. Until recently, the hyperspectral imaging system has been used as a research tool to detect a few optimum wavelengths for use in a multispectral imaging system. ...

  3. Partial Least Squares Regression of Hyperspectral Images for Contaminant Detection on Poultry Carcasses

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The U.S. Department of Agriculture has developed multispectral and hyperspectral imaging systems to detect faecal contaminants. Until recently, the hyperspectral imaging system has been used as a research tool to detect a few optimum wavelengths for use in a multispectral imaging system. However, ...

  4. 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).

  5. 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%.

  6. 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

  7. 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.

  8. AOTF hyperspectral microscopic imaging for foodborne pathogenic bacteria detection

    NASA Astrophysics Data System (ADS)

    Park, Bosoon; Lee, Sangdae; Yoon, Seung-Chul; Sundaram, Jaya; Windham, William R.; Hinton, Arthur, Jr.; Lawrence, Kurt C.

    2011-06-01

    Hyperspectral microscope imaging (HMI) method which provides both spatial and spectral information can be effective for foodborne pathogen detection. The AOTF-based hyperspectral microscope imaging method can be used to characterize spectral properties of biofilm formed by Salmonella enteritidis as well as Escherichia coli. The intensity of spectral imagery and the pattern of spectral distribution varied with system parameters (integration time and gain) of HMI system. The preliminary results demonstrated determination of optimum parameter values of HMI system and the integration time must be no more than 250 ms for quality image acquisition from biofilm formed by S. enteritidis. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 498, 522, 550 and 594 nm were distinctive for biofilm; whereas, the intensity of spectral images at 546 nm was distinctive for E. coli. For more accurate comparison of intensity from spectral images, a calibration protocol, using neutral density filters and multiple exposures, need to be developed to standardize image acquisition. For the identification or classification of unknown food pathogen samples, ground truth regions-of-interest pixels need to be selected for "spectrally pure fingerprints" for the Salmonella and E. coli species.

  9. 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.

  10. 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.

  11. Fusion of hyperspectral images and lidar-based dems for coastal mapping

    NASA Astrophysics Data System (ADS)

    Elaksher, Ahmed F.

    2008-07-01

    Coastal mapping is essential for a variety of applications such as coastal resource management, coastal environmental protection, and coastal development and planning. Various mapping techniques, like ground and aerial surveying, have been utilized in mapping coastal areas. Recently, multispectral and hyperspectral satellite images and elevation data from active sensors have also been used in coastal mapping. Integrating these datasets can provide more reliable coastal information. This paper presents a novel technique for coastal mapping from an airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral image and a light detection and ranging (LIDAR)-based digital elevation model (DEM). The DEM was used to detect and create a vector layer for building polygons. Subsequently, building pixels were removed from the AVIRIS image and the image was classified with a supervised classifier to discriminate road and water pixels. Two vector layers for the road network and the shoreline segments were vectorized from road pixels and water-body border pixels using several image-processing algorithms. The geometric accuracy and completeness of the results were evaluated. The average positional accuracies for the building, road network, and shoreline layers were 2.3, 5.7, and 7.2 m, respectively. The detection rates of the three layers were 93.2%, 91.3%, and 95.2%, respectively. Results confirmed that utilizing laser ranging data to detect and remove buildings from optical images before the classification process enhances the outcomes of this process. Consequently, integrating laser and optical data provides high-quality and more reliable coastal geospatial information.

  12. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

    NASA Technical Reports Server (NTRS)

    Macenka, Steven A.; Chrisp, Michael P.

    1988-01-01

    The development of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has been completed at JPL. This paper outlines the functional requirements of the spectrometer optics subsystem, and describes the spectrometer optical design. The optical subsystem performance is shown in terms of spectral modulation transfer functions, radial energy distributions, and system transmission at selected wavelengths for the four spectrometers. An outline of the spectrometer alignment is included.

  13. 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.

  14. Characterizing pigments with hyperspectral imaging variable false-color composites

    NASA Astrophysics Data System (ADS)

    Hayem-Ghez, Anita; Ravaud, Elisabeth; Boust, Clotilde; Bastian, Gilles; Menu, Michel; Brodie-Linder, Nancy

    2015-11-01

    Hyperspectral imaging has been used for pigment characterization on paintings for the last 10 years. It is a noninvasive technique, which mixes the power of spectrophotometry and that of imaging technologies. We have access to a visible and near-infrared hyperspectral camera, ranging from 400 to 1000 nm in 80-160 spectral bands. In order to treat the large amount of data that this imaging technique generates, one can use statistical tools such as principal component analysis (PCA). To conduct the characterization of pigments, researchers mostly use PCA, convex geometry algorithms and the comparison of resulting clusters to database spectra with a specific tolerance (like the Spectral Angle Mapper tool on the dedicated software ENVI). Our approach originates from false-color photography and aims at providing a simple tool to identify pigments thanks to imaging spectroscopy. It can be considered as a quick first analysis to see the principal pigments of a painting, before using a more complete multivariate statistical tool. We study pigment spectra, for each kind of hue (blue, green, red and yellow) to identify the wavelength maximizing spectral differences. The case of red pigments is most interesting because our methodology can discriminate the red pigments very well—even red lakes, which are always difficult to identify. As for the yellow and blue categories, it represents a good progress of IRFC photography for pigment discrimination. We apply our methodology to study the pigments on a painting by Eustache Le Sueur, a French painter of the seventeenth century. We compare the results to other noninvasive analysis like X-ray fluorescence and optical microscopy. Finally, we draw conclusions about the advantages and limits of the variable false-color image method using hyperspectral imaging.

  15. 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.

  16. 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

  17. 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.

  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. 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.

  20. Superpixel-Augmented Endmember Detection for Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Thompson, David R.; Castano, Rebecca; Gilmore, Martha

    2011-01-01

    Superpixels are homogeneous image regions comprised of several contiguous pixels. They are produced by shattering the image into contiguous, homogeneous regions that each cover between 20 and 100 image pixels. The segmentation aims for a many-to-one mapping from superpixels to image features; each image feature could contain several superpixels, but each superpixel occupies no more than one image feature. This conservative segmentation is relatively easy to automate in a robust fashion. Superpixel processing is related to the more general idea of improving hyperspectral analysis through spatial constraints, which can recognize subtle features at or below the level of noise by exploiting the fact that their spectral signatures are found in neighboring pixels. Recent work has explored spatial constraints for endmember extraction, showing significant advantages over techniques that ignore pixels relative positions. Methods such as AMEE (automated morphological endmember extraction) express spatial influence using fixed isometric relationships a local square window or Euclidean distance in pixel coordinates. In other words, two pixels covariances are based on their spatial proximity, but are independent of their absolute location in the scene. These isometric spatial constraints are most appropriate when spectral variation is smooth and constant over the image. Superpixels are simple to implement, efficient to compute, and are empirically effective. They can be used as a preprocessing step with any desired endmember extraction technique. Superpixels also have a solid theoretical basis in the hyperspectral linear mixing model, making them a principled approach for improving endmember extraction. Unlike existing approaches, superpixels can accommodate non-isometric covariance between image pixels (characteristic of discrete image features separated by step discontinuities). These kinds of image features are common in natural scenes. Analysts can substitute superpixels

  1. 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.

  2. 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.

  3. 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.

  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. 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.

  6. Multichannel tunable imager architecture for hyperspectral imaging in relevant spectral domains.

    PubMed

    Goenka, Chhavi; Semeter, Joshua; Noto, John; Baumgardner, Jeffrey; Riccobono, Juanita; Migliozzi, Mike; Dahlgren, Hanna; Marshall, Robert; Kapali, Sudha; Hirsch, Michael; Hampton, Donald; Akbari, Hassanali

    2016-04-20

    In this paper, we present a technique for dimensionality reduction in hyperspectral imaging during the data collection process. A four-channel hyperspectral imager using liquid crystal Fabry-Perot etalons has been built and used to verify this method for four applications: auroral imaging, plant study, landscape classification, and anomaly detection. This imager is capable of making measurements simultaneously in four wavelength ranges while being tunable within those ranges, and thus can be used to measure narrow contiguous bands in four spectral domains. In this paper, we describe the design, concept of operation, and deployment of this instrument. The results from preliminary testing of this instrument are discussed and are promising and demonstrate this instrument as a good candidate for hyperspectral imaging. PMID:27140081

  7. 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

  8. 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.

  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. 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 ...

  11. Highly Protable Airborne Multispectral Imaging System

    NASA Technical Reports Server (NTRS)

    Lehnemann, Robert; Mcnamee, Todd

    2001-01-01

    A portable instrumentation system is described that includes and airborne and a ground-based subsytem. It can acquire multispectral image data over swaths of terrain ranging in width from about 1.5 to 1 km. The system was developed especially for use in coastal environments and is well suited for performing remote sensing and general environmental monitoring. It includes a small,munpilotaed, remotely controlled airplance that carries a forward-looking camera for navigation, three downward-looking monochrome video cameras for imaging terrain in three spectral bands, a video transmitter, and a Global Positioning System (GPS) reciever.

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. Measurement of the Absorption and Scattering Properties of Turbid Liquid Foods Using Hyperspectral Imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper reports on the development of a hyperspectral imaging technique for rapid determination of the absorption and scattering properties of turbid liquid foods over the visible and near-infrared region of 530-900 nm. A hyperspectral imaging system in line scanning mode was first tested and val...

  17. On-Line Hyperspectral Transmittance Imaging for Internal Defect Detection of Pickling Cucumbers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging technique under transmittance mode was investigated for detection of internal defect in pickling cucumbers such as carpel suture separation or hollow cucumbers caused by mechanical stress. A prototype of on-line hyperspectral transmittance imaging system was developed for real...

  18. First results of ground-based LWIR hyperspectral imaging remote gas detection

    NASA Astrophysics Data System (ADS)

    Zheng, Wei-jian; Lei, Zheng-gang; Yu, Chun-chao; Wang, Hai-yang; Fu, Yan-peng; Liao, Ning-fang; Su, Jun-hong

    2014-11-01

    The new progress of ground-based long-wave infrared remote sensing is presented. The LWIR hyperspectral imaging by using the windowing spatial and temporal modulation Fourier spectroscopy, and the results of outdoor ether gas detection, verify the features of LWIR hyperspectral imaging remote sensing and technical approach. It provides a new technical means for ground-based gas remote sensing.

  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. 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.

  1. 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 ...

  2. Determination of germination quality of cucumber (Cucumis sativus) seed by LED-induced hyperspectral reflectance imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Purpose: We developed a viability evaluation method for cucumber (Cucumis sativus) seed using hyperspectral reflectance imaging. Methods: Reflectance spectra of cucumber seeds in the 400 to 1000 nm range were collected from hyperspectral reflectance images obtained using blue, green, and red LED ill...

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. Lossless compression of hyperspectral images using conventional recursive least-squares predictor with adaptive prediction bands

    NASA Astrophysics Data System (ADS)

    Gao, Fang; Guo, Shuxu

    2016-01-01

    An efficient lossless compression scheme for hyperspectral images using conventional recursive least-squares (CRLS) predictor with adaptive prediction bands is proposed. The proposed scheme first calculates the preliminary estimates to form the input vector of the CRLS predictor. Then the number of bands used in prediction is adaptively selected by an exhaustive search for the number that minimizes the prediction residual. Finally, after prediction, the prediction residuals are sent to an adaptive arithmetic coder. Experiments on the newer airborne visible/infrared imaging spectrometer (AVIRIS) images in the consultative committee for space data systems (CCSDS) test set show that the proposed scheme yields an average compression performance of 3.29 (bits/pixel), 5.57 (bits/pixel), and 2.44 (bits/pixel) on the 16-bit calibrated images, the 16-bit uncalibrated images, and the 12-bit uncalibrated images, respectively. Experimental results demonstrate that the proposed scheme obtains compression results very close to clustered differential pulse code modulation-with-adaptive-prediction-length, which achieves best lossless compression performance for AVIRIS images in the CCSDS test set, and outperforms other current state-of-the-art schemes with relatively low computation complexity.

  8. 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.

  9. 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.

  10. [Impact analysis of atmospheric state for target detection in hyperspectral radiance image].

    PubMed

    Zhang, Bing; Sha, Jian-jun; Wang, Xiang-wei; Gao, Lian-ru

    2012-08-01

    Target detection based on hyperspectral radiance images can improve data processing efficiency to meet the requirements of real-time processing. However, the spectral radiance acquired by the remote sensor will be affected by the atmosphere. In the present paper, hyperspectral imaging process is simulated to analyze the effects of the changes in atmospheric state on target detection in hyperspectral radiance image. The results show that hyperspectral radiance image can be directly used for target detection, different atmospheric states have little impacts on the RXD detection, whereas the MF detection is dependent on the accuracy of the input spectrum, and good results can only be obtained by the MF detector when the atmospheric states are similar between the radiance spectrum of the target to be detected and the simulated hyperspectral image. PMID:23156749

  11. 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.

  12. Improved MCA-TV algorithm for interference hyperspectral image decomposition

    NASA Astrophysics Data System (ADS)

    Wen, Jia; Zhao, Junsuo; Cailing, Wang

    2015-12-01

    The technology of interference hyperspectral imaging, which can get the spectral and spatial information of the observed targets, is a very powerful technology in the field of remote sensing. Due to the special imaging principle, there are many position-fixed interference fringes in each frame of the interference hyperspectral image (IHI) data. This characteristic will affect the result of compressed sensing theory and traditional compression algorithms used on IHI data. According to this characteristic of the IHI data, morphological component analysis (MCA) is adopted to separate the interference fringes layers and the background layers of the LSMIS (Large Spatially Modulated Interference Spectral Image) data, and an improved MCA and Total Variation (TV) combined algorithm is proposed in this paper. An update mode of the threshold in traditional MCA is proposed, and the traditional TV algorithm is also improved according to the unidirectional characteristic of the interference fringes in IHI data. The experimental results prove that the proposed improved MCA-TV (IMT) algorithm can get better results than the traditional MCA, and also can meet the convergence conditions much faster than the traditional MCA.

  13. Accurate reconstruction of hyperspectral images from compressive sensing measurements

    NASA Astrophysics Data System (ADS)

    Greer, John B.; Flake, J. C.

    2013-05-01

    The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.

  14. 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

  15. An algorithm for simultaneous inversion of aerosol properties and surface reflectance from airborne GeoTASO hyperspectral data

    NASA Astrophysics Data System (ADS)

    Hou, W.; Wang, J.; Xu, X.; Ding, S.; Han, D.; Leitch, J. W.; Delker, T.; Chen, G.

    2014-12-01

    This paper presents an inversion method to retrieve aerosol properties from the hyperspectral data collected by airborne GeoTASO (Geostationary Trance gas and Aerosol Sensor Optimization). Mounted on the NASA HU-25C aircraft, GeoTASO measures radiation in 1000 spectral bands from 415 nm to 696 nm, and is a prototype for the TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument. It flew over Houston during September 2013 and gathered several days' of airborne hyperspectral remote sensing data for our research. Our inversion method, which is based on the optimization theory and different from the traditional lookup table (LUT) retrieval technique, can simultaneously retrieve parameters of atmospheric aerosols such as the aerosol optical depth and other aerosol parameters, as well as the surface reflectance albedo. To provide constraints of hyperspectral surface reflectance in the inversion, we first conduct principal component analysis (PCA) using 46 reflectance spectra of various plants and vegetation to identify the most influential components. With the first six principal components and the corresponding calculated weight vector, the spectra could be reconstructed with an accuracy of 1%. UNL-VRTM (UNified Linearized Radiative Transfer Model) is employed for forward model calculation, and its outputs include not only the Stokes 4-vector elements, but also their sensitivities (Jacobians) with respect to the aerosol properties parameters and the principal components of surface spectral reflectance. The inversion is carried out with optimization algorithm L-BFGS-B (Large scale BFGS Bound constrained), and is conducted iteratively until the modeled spectral radiance fits with GeoTASO measurements. Finally, the retrieval results of aerosol optical depth and other aerosol parameters are compared against those retrieved by AEROENT and/or in situ measurements during the aircraft campaign.

  16. 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.

  17. AESMIR: A New NASA Airborne Microwave Imager

    NASA Technical Reports Server (NTRS)

    Kim, Edward J.; Hood, Robbie; Hildebrand, Peter H. (Technical Monitor)

    2001-01-01

    The Airborne Earth Science Microwave Imaging Radiometer (AESMIR) is a versatile new airborne imaging radiometer under development by NASA. The AESMIR design is unique in that it will perform dual-polarized imaging at all AMSR frequency bands (6.9 through 89 GHz) using only one sensor head/scanner package, providing an efficient solution for AMSR-type science applications (snow, soil moisture/land parameters, precip, ocean winds, SST, water vapor, sea ice, etc.). The microwave radiometers themselves will incorporate state-of-the-art receivers, with particular attention given to instrument calibration for the best possible accuracy and sensitivity. The single-package design of AESMIR makes it compatible with high-altitude aircraft platforms such as the NASA ER-2s and the Proteus. The arbitrary 2-axis gimbal can perform conical and cross-track scanning, as well as fixed-beam staring. This compatibility with high-altitude platforms coupled with the flexible scanning configuration, opens up previously unavailable science opportunities for convection/precip/cloud science and co-flying with complementary instruments, as well as providing wider swath coverage for all science applications. By designing AESMIR to be compatible with these high-altitude platforms, we are also compatible with the NASA P-3, the NASA DC-8, and ground-based deployments. Thus AESMIR can provide low-, mid-, and high altitude microwave imaging.

  18. Material characterization using a hyperspectral infrared imaging spectrometer

    SciTech Connect

    Aimonetti, W D; Bixler, J V; Roberts, R S

    1998-10-30

    Fourier transform spectroscopy has found application in many areas including chemometrics, biomedical and biochemical studies, and atmospheric chemistry. This paper describes an investigation into the application of the LLNL Hyperspectral Infrared Imaging Spectrometer (HIRIS) to the non-destructive evaluation of man-made and natural materials. We begin by describing the HIRIS system and the objects studied in the investigation. Next, we describe the technique used to collect the hyperspec- tral imagery, and discuss the processing required to transform the data into usable form. We then describe a technique to analyze the data, and provide some preliminary results.

  19. Application of novel hyperspectral imaging technologies in combat casualty care

    NASA Astrophysics Data System (ADS)

    Cancio, Leopoldo C.

    2010-02-01

    Novel hyperspectral imaging (HSI) methods may play several important roles in Combat Casualty Care: (1) HSI of the skin may provide spatial data on hemoglobin saturation of oxygen, as a "window" into perfusion during shock. (2) HSI or similar technology could be incorporated into closed-loop, feedback-controlled resuscitation systems. (3) HSI may provide information about tissue viability and/or wound infection. (4) HSI in the near-infrared range may provide information on the tissue water content--greatly affected, e.g., by fluid resuscitation. Thus, further refinements in the speed and size of HSI systems are sought to make these capabilities available on the battlefield.

  20. 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.

  1. 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.

  2. Advanced hyperspectral video imaging system using Amici prism.

    PubMed

    Feng, Jiao; Fang, Xiaojing; Cao, Xun; Ma, Chenguang; Dai, Qionghai; Zhu, Hongbo; Wang, Yongjin

    2014-08-11

    In this paper, we propose an advanced hyperspectral video imaging system (AHVIS), which consists of an objective lens, an occlusion mask, a relay lens, an Amici prism and two cameras. An RGB camera is used for spatial reading and a gray scale camera is used for measuring the scene with spectral information. The objective lens collects more light energy from the observed scene and images the scene on an occlusion mask, which subsamples the image of the observed scene. Then, the subsampled image is sent to the gray scale camera through the relay lens and the Amici prism. The Amici prism that is used to realize spectral dispersion along the optical path reduces optical distortions and offers direct view of the scene. The main advantages of the proposed system are improved light throughput and less optical distortion. Furthermore, the presented configuration is more compact, robust and practicable. PMID:25321019

  3. 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.

  4. 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.

  5. Hyperspectral imaging of UVR effects on fungal spectrum

    NASA Astrophysics Data System (ADS)

    Hruska, Zuzana; Yao, Haibo; DiCrispino, Kevin; Brabham, Kori; Lewis, David; Beach, Jim; Brown, Robert L.; Cleveland, Thomas E.

    2005-08-01

    The present report evaluated ultraviolet radiation (UVR) effects on the spectral signature of mycotoxin producing fungus Aspergillus flavus (A. flavus). Ultraviolet radiation has long been used to reduce microbe contamination and to inactivate mold spores. In view of the known effects of UVR on microorganisms, and because certain spectral bands in the signature of some fungi may be in the UV range, it is important to know the maximum acceptable limit of UVR exposure that does not significantly alter the fungal spectral signature and affect detection accuracy. A visible-near-infrared (VNIR) hyperspectral imaging system using focal plane pushbroom scanning for high spatial and spectral resolution imaging was utilized to detect any changes. A. flavus cultures were grown for 5 days and imaged after intermittent or continuous UVR treatment. The intermittent group was treated at 1-minute intervals for 10 minutes, and VNIR images were taken after each UVR treatment. The continuous group was irradiated for 10 minutes and imaged before and after treatment. A control sample group did not undergo UVR treatment, but was also imaged at 1-minute intervals for 10 minutes in the same manner as the intermittent group. Before and after UVR treatment, mean fungal sample reflectance was obtained through spatial subset of the image along with standard deviation and pre- and post-treatment reflectance was compared for each sample. Results show significant difference between the reflectances of treated and control A. flavus cultures after 10 min of UV radiation. Aditionally, the results demonstrate that even lethal doses of UVR do not immediately affect the spectral signature of A. flavus cultures suggesting that the excitation UV light source used in the present experiment may be safe to use with the UV hyperspectral imaging system when exposure time falls below 10 min.

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. Hyperspectral pixel classification from coded-aperture compressive imaging

    NASA Astrophysics Data System (ADS)

    Ramirez, Ana; Arce, Gonzalo R.; Sadler, Brian M.

    2012-06-01

    This paper describes a new approach and its associated theoretical performance guarantees for supervised hyperspectral image classification from compressive measurements obtained by a Coded Aperture Snapshot Spectral Imaging System (CASSI). In one snapshot, the two-dimensional focal plane array (FPA) in the CASSI system captures the coded and spectrally dispersed source field of a three-dimensional data cube. Multiple snapshots are used to construct a set of compressive spectral measurements. The proposed approach is based on the concept that each pixel in the hyper-spectral image lies in a low-dimensional subspace obtained from the training samples, and thus it can be represented as a sparse linear combination of vectors in the given subspace. The sparse vector representing the test pixel is then recovered from the set of compressive spectral measurements and it is used to determine the class label of the test pixel. The theoretical performance bounds of the classifier exploit the distance preservation condition satisfied by the multiple shot CASSI system and depend on the number of measurements collected, code aperture pattern, and similarity between spectral signatures in the dictionary. Simulation experiments illustrate the performance of the proposed classification approach.

  11. 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

  12. 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.

  13. 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

  14. AN ECOLOGICAL ASSESSMENT OF OPPORTUNISTIC PLANT SPECIES IN GREAT LAKES COASTAL WETLANDS USING AIRBORNE HYPERSPECTRAL DATE

    EPA Science Inventory

    Airbome hyperspectral data were used to detect dense patches of Phragmites australis, a native opportunist plant species, at the Pointe Mouillee coastal wetland complex (Wayne and Monroe Counties, Michigan). This study provides initial results from one of thirteen coastal wetland...

  15. 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.

  16. Detection of a buoyant coastal wastewater discharge using airborne hyperspectral and infrared imagery

    NASA Astrophysics Data System (ADS)

    Marmorino, George O.; Smith, Geoffrey B.; Miller, W. D.; Bowles, Jeffrey H.

    2010-01-01

    Municipal wastewater discharged into the ocean through a submerged pipe, or outfall, can rise buoyantly to the sea surface, resulting in a near-field mixing zone and, in the presence of an ambient ocean current, an extended surface plume. In this paper, data from a CASI (Compact Airborne Spectrographic Imager) and an airborne infrared (IR) camera are shown to detect a municipal wastewater discharge off the southeast coast of Florida, U.S.A., through its elevated levels of chromophoric dissolved organic matter plus detrital material (CDOM) and cooler sea surface temperatures. CDOM levels within a ~15-m-diameter surface 'boil' are found to be about twice those in the ambient shelf water, and surface temperatures near the boil are lower by ~0.4°C, comparable to the vertical temperature difference across the ambient water column. The CASI and IR imagery show a nearly identically shaped buoyant plume, consistent with a fully surfacing discharge, but the IR data more accurately delineate the area of most rapid dilution as compared with previous in-situ measurements. The imagery also allows identification of ambient oceanographic processes that affect dispersion and transport in the far field. This includes an alongshore front, which limits offshore dispersion of the discharge, and shoreward-propagating nonlinear internal waves, which may be responsible for an enhanced onshore transport of the discharge.

  17. 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.

  18. Methods for gas detection using stationary hyperspectral imaging sensors

    DOEpatents

    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.

  19. Hyperspectral data collections with the new wedge imaging spectrometer

    SciTech Connect

    Jeter, J.W.; Hartshorne, R.; Thunen, J.G.

    1996-11-01

    The Wedge Imaging Spectrometer (WIS) applies a unique technology to hyperspectral imaging systems, allowing flexibility and high performance in a very compact package. This innovation is based on the use of a linear spectral wedge filter mated directly to an area detector array, avoiding the use of bulky and complex optics required for imaging spectrometers based on gratings or prism concepts. The technology was realized in an earlier flight demonstration system as previously reported. Second generation VNIR and SWIR instruments have now been developed, each with two filters whose spectral bandwidths are optimized for specific spectral features. The SWIR instrument can be extended to operate in the 3-5 PM mid-wave spectral region. The new instrument is currently completing its integration and test phase. Preliminary results indicate excellent performance potential for a wide range of applications. 2 figs., 1 tab.

  20. 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 system