Sample records for hyperspectral imaging sensors

  1. A sensor-data-based denoising framework for hyperspectral images.

    PubMed

    Deger, Ferdinand; Mansouri, Alamin; Pedersen, Marius; Hardeberg, Jon Y; Voisin, Yvon

    2015-02-01

    Many denoising approaches extend image processing to a hyperspectral cube structure, but do not take into account a sensor model nor the format of the recording. We propose a denoising framework for hyperspectral images that uses sensor data to convert an acquisition to a representation facilitating the noise-estimation, namely the photon-corrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values. The subsequent denoising is based on an extended variational denoising model, which is suited for a Poisson distributed noise. A spatially and spectrally adaptive total variation regularisation term accounts the structural proposition of a hyperspectral image cube. We evaluate the approach on a synthetic dataset that guarantees a noise-free ground truth, and the best results are achieved when the dark current is taken into account. PMID:25836066

  2. 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.

  3. 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.

  4. Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion

    NASA Astrophysics Data System (ADS)

    Khandelwal, A.; Rajan, K. S.

    2014-11-01

    In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-off between improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy of using vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensor simulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approach demonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuracies over mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metric shows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures.

  5. Methods for gas detection using stationary hyperspectral imaging sensors

    DOEpatents

    Conger, James L. (San Ramon, CA); Henderson, John R. (Castro Valley, CA)

    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.

  6. A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

    PubMed Central

    Tao, Dongxing; Jia, Guorui; Yuan, Yan; Zhao, Huijie

    2014-01-01

    Sensor simulators can be used in forecasting the imaging quality of a new hyperspectral imaging spectrometer, and generating simulated data for the development and validation of the data processing algorithms. This paper presents a novel digital sensor simulator for the pushbroom Offner hyperspectral imaging spectrometer, which is widely used in the hyperspectral remote sensing. Based on the imaging process, the sensor simulator consists of a spatial response module, a spectral response module, and a radiometric response module. In order to enhance the simulation accuracy, spatial interpolation-resampling, which is implemented before the spatial degradation, is developed to compromise the direction error and the extra aliasing effect. Instead of using the spectral response function (SRF), the dispersive imaging characteristics of the Offner convex grating optical system is accurately modeled by its configuration parameters. The non-uniformity characteristics, such as keystone and smile effects, are simulated in the corresponding modules. In this work, the spatial, spectral and radiometric calibration processes are simulated to provide the parameters of modulation transfer function (MTF), SRF and radiometric calibration parameters of the sensor simulator. Some uncertainty factors (the stability, band width of the monochromator for the spectral calibration, and the integrating sphere uncertainty for the radiometric calibration) are considered in the simulation of the calibration process. With the calibration parameters, several experiments were designed to validate the spatial, spectral and radiometric response of the sensor simulator, respectively. The experiment results indicate that the sensor simulator is valid. PMID:25615727

  7. A digital sensor simulator of the pushbroom Offner hyperspectral imaging spectrometer.

    PubMed

    Tao, Dongxing; Jia, Guorui; Yuan, Yan; Zhao, Huijie

    2014-01-01

    Sensor simulators can be used in forecasting the imaging quality of a new hyperspectral imaging spectrometer, and generating simulated data for the development and validation of the data processing algorithms. This paper presents a novel digital sensor simulator for the pushbroom Offner hyperspectral imaging spectrometer, which is widely used in the hyperspectral remote sensing. Based on the imaging process, the sensor simulator consists of a spatial response module, a spectral response module, and a radiometric response module. In order to enhance the simulation accuracy, spatial interpolation-resampling, which is implemented before the spatial degradation, is developed to compromise the direction error and the extra aliasing effect. Instead of using the spectral response function (SRF), the dispersive imaging characteristics of the Offner convex grating optical system is accurately modeled by its configuration parameters. The non-uniformity characteristics, such as keystone and smile effects, are simulated in the corresponding modules. In this work, the spatial, spectral and radiometric calibration processes are simulated to provide the parameters of modulation transfer function (MTF), SRF and radiometric calibration parameters of the sensor simulator. Some uncertainty factors (the stability, band width of the monochromator for the spectral calibration, and the integrating sphere uncertainty for the radiometric calibration) are considered in the simulation of the calibration process. With the calibration parameters, several experiments were designed to validate the spatial, spectral and radiometric response of the sensor simulator, respectively. The experiment results indicate that the sensor simulator is valid. PMID:25615727

  8. 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.

  9. 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.

  10. Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing.

    PubMed

    Skauli, Torbjørn

    2011-07-01

    Many types of hyperspectral image processing can benefit from knowledge of noise levels in the data, which can be derived from sensor physics. Surprisingly, such information is rarely provided or exploited. Usually, the image data are represented as radiance values, but this representation can lead to suboptimal results, for example in spectral difference metrics. Also, radiance data do not provide an appropriate baseline for calculation of image compression ratios. This paper defines two alternative representations of hyperspectral image data, aiming to make sensor noise accessible to image processing. A "corrected raw data" representation is proportional to the photoelectron count and can be processed like radiance data, while also offering simpler estimation of noise and somewhat more compact storage. A variance-stabilized representation is obtained by square-root transformation of the photodetector signal to make the noise signal-independent and constant across all bands while also reducing data volume by almost a factor 2. Then the data size is comparable to the fundamental information capacity of the sensor, giving a more appropriate measure of uncompressed data size. It is noted that the variance-stabilized representation has parallels in other fields of imaging. The alternative data representations provide an opportunity to reformulate hyperspectral processing algorithms to take actual sensor noise into account. PMID:21747455

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

    Microsoft Academic Search

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

    2004-01-01

    This paper presents a novel maximum a posteriori estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here, we focus on the use of high-resolution panchromatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed

  12. 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.

  13. Detection of surface mines using hyperspectral sensors

    Microsoft Academic Search

    Edwin M. Winter

    2004-01-01

    Hyperspectral imaging is an important technology for the detection of surface and buried land mines from an airborne platform. For this reason, hyperspectral was included in the two experiments that were executed by the Army RDECOM Night Vision and Electronic Sensors Directorate (NVESD) in Fall 2002 and in Spring 2003. The purpose of these experiments was to bring together a

  14. Mine detection experiments using hyperspectral sensors

    Microsoft Academic Search

    Edwin M. Winter; Miranda A. Miller; Christopher G. Simi; Anthony B. Hill; Timothy J. Williams; David Hampton; Mark Wood; Jerry Zadnick; Marc D. Sviland

    2004-01-01

    Hyperspectral imaging is an important technology for the detection of surface and buried land mines from an airborne platform. For this reason, hyperspectral was included with SAR sensors in the two deployments that were executed by the CECOM RDEC Night Vision and Electronic Systems Directorate (NVESD) in Fall 2002 and in Spring 2003. The purpose of these deployments was to

  15. Infrared hyperspectral imaging polarimeter using birefringent prisms

    E-print Network

    Dereniak, Eustace L.

    Infrared hyperspectral imaging polarimeter using birefringent prisms Julia Craven-Jones,* Michael W hyperspectral imaging polarimeter (IHIP) is introduced. The sensor includes a pair of sapphire Wollaston prisms and several high-order retarders to form an imaging Fourier transform spectropolarimeter. The Wollaston prisms

  16. 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.

  17. 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...

  18. 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.

  19. Fast compression implementation for hyperspectral sensor

    NASA Astrophysics Data System (ADS)

    Hihara, Hiroki; Yoshida, Jun; Ishida, Juro; Takada, Jun; Senda, Yuzo; Suzuki, Makoto; Seki, Taeko; Ichikawa, Satoshi; Ohgi, Nagamitsu

    2010-11-01

    Fast and small foot print lossless image compressors aiming at hyper-spectral sensor for the earth observation satellite have been developed. Since more than one hundred channels are required for hyper-spectral sensors on optical observation satellites, fast compression algorithm with small foot print implementation is essential for reducing encoder size and weight resulting in realizing light-weight and small-size sensor system. The image compression method should have low complexity in order to reduce size and weight of the sensor signal processing unit, power consumption and fabrication cost. Coding efficiency and compression speed enables enlargement of the capacity of signal compression channels, which resulted in reducing signal compression channels onboard by multiplexing sensor signal channels into reduced number of compression channels. The employed method is based on FELICS1, which is hierarchical predictive coding method with resolution scaling. To improve FELICS's performance of image decorrelation and entropy coding, we applied two-dimensional interpolation prediction and adaptive Golomb-Rice coding, which enables small footprint. It supports progressive decompression using resolution scaling, whilst still delivering superior performance as measured by speed and complexity. The small footprint circuitry is embedded into the hyper-spectral sensor data formatter. In consequence, lossless compression function has been added without additional size and weight.

  20. Airborne hyperspectral imaging in the visible-to-mid wave infrared spectral range by fusing three spectral sensors

    NASA Astrophysics Data System (ADS)

    Jakovels, Dainis; Filipovs, Jevgenijs; Erinš, Gatis; Taskovs, Juris

    2014-10-01

    Airborne hyperspectral imaging is widely used for remote sensing of environment. The choice of spectral region usually depends on the availability and cost of the sensor. Visible-to-near infrared (400-1100 nm) spectral range corresponds to spectral sensitivity of relatively cheap Si detectors therefore it is the most commonly used. The implementation of shortwave infrared (1100-3000 nm) requires more expensive solutions, but can provide valuable information about the composition of the substance. Mid wave infrared (3000-8000 nm) is rarely used for civilian applications, but it provides information on the thermal emission of materials. The fusion of different sensors allows spectral analysis of a wider spectral range combining and improving already existing algorithms for the analysis of chemical content and classification. Here we introduce our Airborne Surveillance and Environmental Monitoring System (ARSENAL) that was developed by fusing seven sensors. The first test results from the fusion of three hyperspectral imaging sensors in the visible-to-mid wave infrared (365-5000 nm) are demonstrated. Principal component analysis (PCA) is applied to test correlation between principal components (PCs) and common vegetation indices.

  1. Infrared upconversion hyperspectral imaging.

    PubMed

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

    2015-03-15

    In this Letter, hyperspectral imaging in the mid-IR spectral region is demonstrated based on nonlinear frequency upconversion and subsequent imaging using a standard Si-based CCD camera. A series of upconverted images are acquired with different phase match conditions for the nonlinear frequency conversion process. From this, a sequence of monochromatic images in the 3.2-3.4 ?m range is generated. The imaged object consists of a standard United States Air Force resolution target combined with a polystyrene film, resulting in the presence of both spatial and spectral information in the infrared image. PMID:25768151

  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. Inverse Problems in Hyperspectral Imaging

    NASA Astrophysics Data System (ADS)

    Berisha, Sebastian

    In hyperpsectral imaging, multiple images of the same scene are obtained over a contiguous range of wavelengths in the electromagnetic spectrum. Hyperspectral images represent observations of a scene at many different wavelengths and most importantly associate to each pixel in the imaged scene a full spectral vector or spectral signature. However, due to the presence of spectral mixtures (at different scales) in the scene and/or low spatial resolution of the hyperspectral sensor, the acquired spectral vectors of each pixel are actually a mixture of the spectra of the various materials present in the spatial coverage area of the corresponding pixel, and they also contain additional degradations caused by atmospheric blurring.We present a numerical approach for deblurring and sparse unmixing of space objects taken by ground based telescopes. A major challenge for deblurring hyperspectral images is that of estimating the overall blurring operator, taking into account the fact that the blurring operator point spread function (PSF) can be wavelength dependent and depend on the imaging system as well as the effects of atmospheric turbulence. We formulate the PSF estimation as a nonlinear least squares problem, which is solved using a variable projection Gauss-Newton method. Our analysis shows that the Jacobian can be potentially very ill-conditioned. To deal with this ill-conditioning, we use a combination of subset selection and regularization. We then incorporate the PSF estimation scheme with a preconditioned alternating direction method of multipliers to solve the deblurring and sparse unmixing problem. Experimental results illustrate the effectiveness of the resulting numerical schemes.

  4. 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.

  5. Rapid hyperspectral fluorescence lifetime imaging.

    PubMed

    De Beule, Pieter; Owen, Dylan M; Manning, Hugh B; Talbot, Clifford B; Requejo-Isidro, Jose; Dunsby, Christopher; McGinty, James; Benninger, Richard K P; Elson, Daniel S; Munro, Ian; John Lever, M; Anand, Praveen; Neil, Mark A A; French, Paul M W

    2007-05-01

    We report a rapid hyperspectral fluorescence lifetime imaging (FLIM) instrument that exploits high-speed FLIM technology in a line-scanning microscope. We demonstrate the acquisition of whole-field optically sectioned hyperspectral fluorescence lifetime image stacks (with 32 spectral bins) in less than 40 s and illustrate its application to unstained biological tissue. PMID:17366615

  6. 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 reflected sunlight.

  7. Remote Sensing of Selected Water-Quality Indicators with the Hyperspectral Imager for the Coastal Ocean (HICO) Sensor

    EPA Science Inventory

    The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring...

  8. Compressive hyperspectral sensor for LWIR gas detection

    NASA Astrophysics Data System (ADS)

    Russell, Thomas A.; McMackin, Lenore; Bridge, Bob; Baraniuk, Richard

    2012-06-01

    Focal plane arrays with associated electronics and cooling are a substantial portion of the cost, complexity, size, weight, and power requirements of Long-Wave IR (LWIR) imagers. Hyperspectral LWIR imagers add significant data volume burden as they collect a high-resolution spectrum at each pixel. We report here on a LWIR Hyperspectral Sensor that applies Compressive Sensing (CS) in order to achieve benefits in these areas. The sensor applies single-pixel detection technology demonstrated by Rice University. The single-pixel approach uses a Digital Micro-mirror Device (DMD) to reflect and multiplex the light from a random assortment of pixels onto the detector. This is repeated for a number of measurements much less than the total number of scene pixels. We have extended this architecture to hyperspectral LWIR sensing by inserting a Fabry-Perot spectrometer in the optical path. This compressive hyperspectral imager collects all three dimensions on a single detection element, greatly reducing the size, weight and power requirements of the system relative to traditional approaches, while also reducing data volume. The CS architecture also supports innovative adaptive approaches to sensing, as the DMD device allows control over the selection of spatial scene pixels to be multiplexed on the detector. We are applying this advantage to the detection of plume gases, by adaptively locating and concentrating target energy. A key challenge in this system is the diffraction loss produce by the DMD in the LWIR. We report the results of testing DMD operation in the LWIR, as well as system spatial and spectral performance.

  9. Chemical agent detection and identification with a hyperspectral imaging infrared sensor

    NASA Astrophysics Data System (ADS)

    Farley, Vincent; Vallières, Alexandre; Villemaire, André; Chamberland, Martin; Lagueux, Philippe; Giroux, Jean

    2007-10-01

    Standoff detection, identification and quantification of chemical agents are fundamental needs in several fields of applications. Additional required sensor characteristics include high sensitivity, low false alarms and high-speed (ideally real-time) operation, all in a compact and robust package. The thermal infrared portion of the electromagnetic spectrum has been utilized to implement such chemical sensors, either with spectrometers (with none or moderate imaging capability) or with imagers (with moderate spectral capability). Only with the recent emergence of high-speed, large format infrared imaging arrays, has it been possible to design chemical sensors offering uncompromising performance in the spectral, spatial, as well as the temporal domain. Telops has developed an innovative instrument that can not only provide an early warning for chemical agents and toxic chemicals, but also one that provides a "Chemical Map" in the field of view. To provide to best field imaging spectroscopy instrument, Telops has developed the FIRST, Field-portable Imaging Radiometric Spectrometer Technology, instrument. This instrument is based on a modular design that includes: a high- performance infrared FPA and data acquisition electronics, onboard data processing electronics, a high- performance Fourier transform modulator, dual integrated radiometric calibration targets and a visible boresight camera. These modules, assembled together in an environmentally robust structure, used in combination with Telops' proven radiometric and spectral calibration algorithms make this instrument a world-class passive standoff detection system for chemical imaging. This paper presents chemical detection and identification results obtained with the FIRST sensor.

  10. Chemical agent standoff detection and identification with a hyperspectral imaging infrared sensor

    NASA Astrophysics Data System (ADS)

    Lagueux, Philippe; Vallières, Alexandre; Villemaire, André; Chamberland, Martin; Farley, Vincent; Giroux, Jean

    2009-09-01

    Standoff detection, identification and quantification of chemical agents are fundamental needs in several fields of applications. Additional required sensor characteristics include high sensitivity, low false alarms and high-speed (ideally real-time) operation, all in a compact and robust package. The thermal infrared portion of the electromagnetic spectrum has been utilized to implement such chemical sensors, either with spectrometers (with none or moderate imaging capability) or with imagers (with moderate spectral capability). Only with the recent emergence of high-speed, large format infrared imaging arrays, has it been possible to design chemical sensors offering uncompromising performance in the spectral, spatial, as well as the temporal domain. Telops has developed an innovative instrument that can not only provide an early warning for chemical agents and toxic chemicals, but also one that provides a "Chemical Map" in the field of view. To provide to best field imaging spectroscopy instrument, Telops has developed the FIRST, Field-portable Imaging Radiometric Spectrometer Technology, instrument. This instrument is based on a modular design that includes: a high-performance infrared FPA and data acquisition electronics, onboard data processing electronics, a high-performance Fourier transform modulator, dual integrated radiometric calibration targets and a visible boresight camera. These modules, assembled together in an environmentally robust structure, used in combination with Telops' proven radiometric and spectral calibration algorithms make this instrument a world-class passive standoff detection system for chemical imaging. This paper presents chemical detection and identification results obtained with the FIRST sensor.

  11. Chemical agent detection and identification with a hyperspectral imaging infrared sensor

    NASA Astrophysics Data System (ADS)

    Farley, Vincent; Chamberland, Martin; Lagueux, Philippe; Vallières, Alexandre; Villemaire, André; Giroux, Jean

    2007-09-01

    Standoff detection, identification and quantification of chemical agents are fundamental needs in several fields of applications. Additional required sensor characteristics include high sensitivity, low false alarms and high-speed (ideally real-time) operation, all in a compact and robust package. The thermal infrared portion of the electromagnetic spectrum has been utilized to implement such chemical sensors, either with spectrometers (with none or moderate imaging capability) or with imagers (with moderate spectral capability). Only with the recent emergence of high-speed, large format infrared imaging arrays, has it been possible to design chemical sensors offering uncompromising performance in the spectral, spatial, as well as the temporal domain. Telops has developed an innovative instrument that can not only provide an early warning for chemical agents and toxic chemicals, but also one that provides a "Chemical Map" of the field of view. To provide to best field imaging spectroscopy instrument, Telops has developed the FIRST, Field-portable Imaging Radiometric Spectrometer Technology, instrument. This instrument is based on a modular design that includes: a high performance infrared FPA and data acquisition electronics, onboard data processing electronics, a high performance Fourier transform modulator, dual integrated radiometric calibration targets and a visible boresighted camera. These modules, assembled together in an environmentally robust structure, used in combination with Telops' proven radiometric and spectral calibration algorithms make this instrument a world-class passive standoff detection system for chemical imaging. This paper presents chemical detection and identification results obtained with the FIRST sensor.

  12. Continued development of a portable widefield hyperspectral imaging (HSI) sensor for standoff detection of explosive, chemical, and narcotic residues

    NASA Astrophysics Data System (ADS)

    Nelson, Matthew P.; Gardner, Charles W.; Klueva, Oksana; Tomas, David

    2014-05-01

    Passive, standoff detection of chemical, explosive and narcotic threats employing widefield, shortwave infrared (SWIR) hyperspectral imaging (HSI) continues to gain acceptance in defense and security fields. A robust and user-friendly portable platform with such capabilities increases the effectiveness of locating and identifying threats while reducing risks to personnel. In 2013 ChemImage Sensor Systems (CISS) introduced Aperio, a handheld sensor, using real-time SWIR HSI for wide area surveillance and standoff detection of explosives, chemical threats, and narcotics. That SWIR HSI system employed a liquid-crystal tunable filter for real-time automated detection and display of threats. In these proceedings, we report on a next generation device called VeroVision™, which incorporates an improved optical design that enhances detection performance at greater standoff distances with increased sensitivity and detection speed. A tripod mounted sensor head unit (SHU) with an optional motorized pan-tilt unit (PTU) is available for precision pointing and sensor stabilization. This option supports longer standoff range applications which are often seen at checkpoint vehicle inspection where speed and precision is necessary. Basic software has been extended to include advanced algorithms providing multi-target display functionality, automatic threshold determination, and an automated detection recipe capability for expanding the library as new threats emerge. In these proceedings, we report on the improvements associated with the next generation portable widefield SWIR HSI sensor, VeroVision™. Test data collected during development are presented in this report which supports the targeted applications for use of VeroVision™ for screening residue and bulk levels of explosive and drugs on vehicles and personnel at checkpoints as well as various applications for other secure areas. Additionally, we highlight a forensic application of the technology for assisting forensic investigators in locating bone remains in a cluttered background environment.

  13. Super-resolution reconstruction of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Elbakary, Mohamed; Alam, Mohammad S.

    2007-04-01

    Hyperspectral imagery is used for a wide variety of applications, including target detection, tacking, agricultural monitoring and natural resources exploration. The main reason for using hyperspectral imagery is that these images reveal spectral information about the scene that are not available in a single band. Unfortunately, many factors such as sensor noise and atmospheric scattering degrade the spatial quality of these images. Recently, many algorithms are introduced in the literature to improve the resolution of hyperspectral images [7]. In this paper, we propose a new method to produce high resolution bands from low resolution bands that are strongly correlated to the corresponding high resolution panchromatic image. The proposed method is based on using the local correlation instead of using the global correlation to improve the estimated interpolation in order to construct the high resolution image. The utilization of local correlation significantly improved the resolution of high resolution images when compared to the corresponding results obtained using the traditional algorithms. The local correlation is implemented by using predefined small windows across the low resolution image. In addition, numerous experiments are conducted to investigate the effect of the chosen window size in the image quality. Experiments results obtained using real life hyperspectral imagery is presented to verify the effectiveness of the proposed algorithm.

  14. COMPRESSIVE PUSHBROOM AND WHISKBROOM SENSING FOR HYPERSPECTRAL REMOTE-SENSING IMAGING

    E-print Network

    Fowler, James E.

    COMPRESSIVE PUSHBROOM AND WHISKBROOM SENSING FOR HYPERSPECTRAL REMOTE-SENSING IMAGING James E to the sensor. On the other hand, hyperspectral im- agery in remote-sensing applications is frequently acquired- ically, hyperspectral remote-sensing sensors are mounted on some type of airborne or satellite

  15. Parametric adaptive signal detection for hyperspectral imaging

    Microsoft Academic Search

    Hongbin Li; James H. Michels

    2006-01-01

    Hyperspectral imaging (HSI) sensors can provide very fine spectral resolution that allows remote identification of ground objects smaller than a full pixel in an HSI image. Traditional approaches to the so-called subpixel target signal detection problem are training inefficient due to the need for an estimate of a large-size covariance matrix of the background from target-free training pixels. This imposes

  16. 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

  17. 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.

  18. Hyperspectral Image Analysis for Skin Tumor Detection

    NASA Astrophysics Data System (ADS)

    Kong, Seong G.; Park, Lae-Jeong

    This chapter presents hyperspectral imaging of fluorescence for nonin-vasive detection of tumorous tissue on mouse skin. Hyperspectral imaging sensors collect two-dimensional (2D) image data of an object in a number of narrow, adjacent spectral bands. This high-resolution measurement of spectral information reveals a continuous emission spectrum for each image pixel useful for skin tumor detection. The hyperspectral image data used in this study are fluorescence intensities of a mouse sample consisting of 21 spectral bands in the visible spectrum of wavelengths ranging from 440 to 640 nm. Fluorescence signals are measured using a laser excitation source with the center wavelength of 337 nm. An acousto-optic tunable filter is used to capture individual spectral band images at a 10-nm resolution. All spectral band images are spatially registered with the reference band image at 490 nm to obtain exact pixel correspondences by compensating the offsets caused during the image capture procedure. The support vector machines with polynomial kernel functions provide decision boundaries with a maximum separation margin to classify malignant tumor and normal tissue from the observed fluorescence spectral signatures for skin tumor detection.

  19. Texture Based Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Kumar, B.; Dikshit, O.

    2014-11-01

    This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of hyperspectral imagery. The moment invariants of an image can derive shape characteristics, elongation, and orientation along its axis. In this investigation second order geometric moments within small window around each pixel are computed which are further used to compute texture features. The textural and spectral features of the image are combined to form a joint feature vector that is used for classification. The experiments are performed on different types of hyperspectral images using multi-class one-vs-one support vector machine (SVM) classifier to evaluate the robustness of the proposed methodology. The results demonstrate that integration of texture features produced statistically significantly better results than spectral classification.

  20. Snapshot hyperspectral imaging and practical applications

    NASA Astrophysics Data System (ADS)

    Wong, G.

    2009-07-01

    Traditional broadband imaging involves the digital representation of a remote scene within a reduced colour space. Hyperspectral imaging exploits the full spectral dimension, which better reflects the continuous nature of actual spectra. Conventional techniques are all time-delayed whereby spatial or spectral scanning is required for hypercube generation. An innovative and patented technique developed at Heriot-Watt University offers significant potential as a snapshot sensor, to enable benefits for the wider public beyond aerospace imaging. This student-authored paper seeks to promote awareness of this field within the photonic community and its potential advantages for real-time practical applications.

  1. Airborne hyperspectral data collection with the UMBC VNIR sensor

    Microsoft Academic Search

    J. X. Warner; J. M. Grossmann; D. A. Chu; K. F. Huemmrich; R. A. Warner

    2006-01-01

    The University of Maryland Baltimore County (UMBC) airborne Visible-Near Infrared (VNIR) hyperspectral sensor is a grating spectrometer that collects data in the 380 to 985 nm spectral range with spectral resolution as high as 1.15 nm. This imager is a push-broom type sensor utilizing a two dimensional charge coupled device (CCD, 480×640) camera to collect the spectral information along a

  2. Curvelet based hyperspectral image fusion

    NASA Astrophysics Data System (ADS)

    Wang, Sha; Feng, Hua-jun; Xu, Zhi-hai; Li, Qi; Chen, Yue-ting

    2013-08-01

    Hyperspectral imagery typically possesses high spectral resolution but low spatial resolution. One way to enhance the spatial resolution of a hyperspectral image is to fuse its spectral information and the spatial information of another high resolution image. In this paper, we propose a novel image fusion strategy for hyperspectral image and high spatial resolution panchromatic image, which is based on the curvelet transform. Firstly, determine a synthesized image with the specified RGB bands of the original hyperspectral images according to the optimal index factor (OIF) model. Then use the IHS transform to extract the intensity component of the synthesized image. After that, the histogram matching is performed between the intensity component and the panchromatic image. Thirdly, the curvelet transform is applied to decompose the two source images (the intensity component and the panchromatic image) in different scales and directions. Different fusion strategies are applied to coefficients in various scales and directions. Finally, the fused image is achieved by the inverse IHS transform. The experimental result shows that the proposed method has a superior performance. Comparing with the traditional methods such as the PCA transform, wavelet-based or pyramid-based methods and the multi-resolution fusion methods (shearlet or contourlet decomposition), the fused image achieves the highest entropy index and average gradient value. While providing a better human visual quality, a good correlation coefficient index indicates that the fused image keeps good spectral information. Both visual quality and objective evaluation criteria demonstrate that this method can well preserve the spatial quality and the spectral characteristics.

  3. Hyperspectral sensor HSC3000 for nano-satellite TAIKI

    NASA Astrophysics Data System (ADS)

    Satori, S.; Aoyanagi, Y.; Hara, U.; Mitsuhashi, R.; Takeuchi, Y.

    2008-12-01

    Hokkaido Satellite Project was kicked off at April in 2003 by the volunteer group that consists of students, researchers and engineers in order to demonstrate the space business models using nanosatellites of 15kg/50kg in Japan. The Hokkaido satellite named "TAIKI" is characterized by a hyperspectral sensor with a VNIR (visible and near infrared range) and a laser communication instrument for data downlink communication. At the beginning of 2008 we started to develop a space qualified hyperspectral sensor HSC3000 based on the optical design of HSC1700. Last year we developed the hyperspectral camera HSC-3000 BBM funded by New Energy Development Organization (NEDO) as the position of the breadboard model of HSC3000. HSC-3000 BBM is specified by the spectral range from 400nm to 1000nm, 81 spectral bands, image size of 640 x 480 pixels, radiometric resolution of 10 bits and data transfer rate of 200 f/s. By averaging outputs of several adjacent pixels to increase S/N, HSC3000 of the spaceborne is targeted at the specification of 30 m spatial resolution, 61 spectral bands, 10 nm spectral resolution and S/N300. Spin-off technology of the hyperspectral imager is also introduced. We have succeeded to develop a hyperspectral camera as the spin-off product named HSC1700 which installs both the hyperspectral sensor unit and a scanning mechanism inside. The HSC1700 is specified by the spectral range from 400nm to 800nm, 81 spectral bands, image size of 640 x 480 pixels, radiometric resolution of 8 bits and data transfer rate of 30 f/s.

  4. Calibration procedures and measurements for the COMPASS hyperspectral imager

    Microsoft Academic Search

    Jerome Zadnik; Daniel Guerin; Robert Moss; Alan Orbeta; Roberta Dixon; Christopher G. Simi; Susannah Dunbar; Anthony Hill

    2004-01-01

    The COMPact Airborne Spectral Sensor (COMPASS) hyperspectral imager (HSI) developed at the Army Night Vision and Electronic Sensors Directorate (NVESD) operates in the solar reflective region. The fundamental advance of the COMPASS instrument is the ability to capture 400nm to 2350nm on a single focal plane, eliminating boresighting and co-registration issues characteristic of dual FPA instruments for visible and SWIR

  5. The application of hyperspectral sensors to the detection of land mines

    Microsoft Academic Search

    Edwin M. Winter

    2005-01-01

    Hyperspectral imaging is an important technology for the detection of surface and buried land mines from an airborne platform. For this reason, hyperspectral was included in the three experiments that were executed by the Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) in Fall 2002, Spring 2003 and Summer 2004. The purpose of these experiments was to bring

  6. Sonification of hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Bernhardt, Mark; Cowell, Catherine; Oxford, William

    2007-04-01

    There are many reconnaissance tasks which involve an image analyst viewing data from hyperspectral imaging systems and attempting to interpret it. Hyperspectral image data is intrinsically hard to understand, even when armed with mathematical knowledge and a range of current processing algorithms. This research is motivated by the search for new ways to convey information about the spectral content of imagery to people. In order to develop and assess the novel algorithms proposed, we have developed a tool for transforming different aspects of spectral imagery into sounds that an analyst can hear. Trials have been conducted which show that the use of these sonic mappings can assist a user in tasks such as rejecting false alarms generated by automatic detection algorithms. This paper describes some of the techniques used and reports on the results of user trials.

  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. 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.

  9. 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.

  10. 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.

  11. Novel miniaturized hyperspectral sensor for UAV and space applications

    NASA Astrophysics Data System (ADS)

    Saari, Heikki; Aallos, Ville-Veikko; Akujärvi, Altti; Antila, Tapani; Holmlund, Christer; Kantojärvi, Uula; Mäkynen, Jussi; Ollila, Jyrki

    2009-09-01

    In many hyperspectral applications it is beneficial to produce 2D spatial images with a single exposure at a few selected wavelength bands instead of 1D spatial and all spectral band images like in push-broom instruments. VTT has developed a new concept based on the Piezo actuated Fabry-Perot Interferometer to enable recording of 2D spatial images at the selected wavelength bands simultaneously. The sensor size is compatible with light weight UAV platforms. In our spectrometer the multiple orders of the Fabry-Perot Interferometer are used at the same time matched to the sensitivities of a multispectral RGB-type image sensor channels. We have built prototypes of the new spectrograph fitting inside of a 40 mm x 40 mm x 20 mm envelope and with a mass less than 50 g. The operational wavelength range of built prototypes can be tuned in the range 400 - 1100 nm and the spectral resolution is in the range 5 - 10 nm @ FWHM. Presently the spatial resolution is 480 x 750 pixels but it can be increased simply by changing the image sensor. The hyperspectral imager records simultaneously a 2D image of the scenery at three narrow wavelength bands determined by the selected three orders of the Fabry-Perot Interferometer which depend on the air gap between the mirrors of the Fabry-Perot Cavity. The new sensor can be applied on UAV, aircraft, and other platforms requiring small volume, mass and power consumption. The new low cost hyperspectral imager can be used also in many industrial and medical applications.

  12. Hyperspectral Image Analysis Program

    NASA Technical Reports Server (NTRS)

    1985-01-01

    Information on imaging spectrometry is given in the forms of outlines, graphs, and charts. Topics covered include impacts on science users, program objectives, expert systems for imaging spectrometry, and imaging spectrometer data analysis methods.

  13. Quality Metrics Evaluation of Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Singh, A. K.; Kumar, H. V.; Kadambi, G. R.; Kishore, J. K.; Shuttleworth, J.; Manikandan, J.

    2014-11-01

    In this paper, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation. After classification the assessment of similarity between original image and classified image is achieved by measurements of image quality parameters. Experiments were carried out on four different types of hyperspectral images. Aerial and spaceborne hyperspectral images with different spectral and geometric resolutions were considered for quality metrics evaluation. Principal Component Analysis (PCA) has been applied to reduce the dimensionality of hyperspectral data. PCA was ultimately used for reducing the number of effective variables resulting in reduced complexity in processing. In case of ordinary images a human viewer plays an important role in quality evaluation. Hyperspectral data are generally processed by automatic algorithms and hence cannot be viewed directly by human viewers. Therefore evaluating quality of classified image becomes even more significant. An elaborate comparison is made between k-means clustering and segmentation for all the images by taking Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Maximum Squared Error, ratio of squared norms called L2RAT and Entropy. First four parameters are calculated by comparing the quality of original hyperspectral image and classified image. Entropy is a measure of uncertainty or randomness which is calculated for classified image. Proposed methodology can be used for assessing the performance of any hyperspectral image classification techniques.

  14. Hyperspectral imaging for nondestructive evaluation of tomatoes

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  15. HYPERSPECTRAL IMAGING FOR FOOD PROCESSING AUTOMATION

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging system could be used effectively for detecting feces (from duodenum, ceca, and colon) and ingesta on the surface of poultry carcasses, and potential application for real-time, on-line processing of poultry for automatic safety inspection. The hyperspectral imaging system inc...

  16. Improving Hyperspectral Image Classification Using Spatial Preprocessing

    Microsoft Academic Search

    Santiago Velasco-Forero; Vidya Manian

    2009-01-01

    Spatial smoothing over the original hyperspectral data based on wavelet and anisotropic partial differential equations is incorporated using composite kernel in graph-based classifiers. The kernels combine spectral-spatial relationships using the smoothed and original hyperspectral images. Experiments with different real hyperspectral scenarios are presented. Comparison with recent graph-based methods shows that the proposed scheme gives better classification with lower computational cost.

  17. SVM and MRF-Based Method for Accurate Classification of Hyperspectral Images

    Microsoft Academic Search

    Yuliya Tarabalka; Mathieu Fauvel; Jocelyn Chanussot; Jón Atli Benediktsson

    2010-01-01

    The high number of spectral bands acquired by hyperspectral sensors increases the capability to distinguish physical materials and objects, presenting new challenges to image analysis and classification. This letter presents a novel method for accurate spectral-spatial classification of hyperspectral images. The proposed technique consists of two steps. In the first step, a probabilistic support vector machine pixelwise classification of the

  18. 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.

  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. 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.

  1. Development of a handheld widefield hyperspectral imaging (HSI) sensor for standoff detection of explosive, chemical, and narcotic residues

    NASA Astrophysics Data System (ADS)

    Nelson, Matthew P.; Basta, Andrew; Patil, Raju; Klueva, Oksana; Treado, Patrick J.

    2013-05-01

    The utility of Hyper Spectral Imaging (HSI) passive chemical detection employing wide field, standoff imaging continues to be advanced in detection applications. With a drive for reduced SWaP (Size, Weight, and Power), increased speed of detection and sensitivity, developing a handheld platform that is robust and user-friendly increases the detection capabilities of the end user. In addition, easy to use handheld detectors could improve the effectiveness of locating and identifying threats while reducing risks to the individual. ChemImage Sensor Systems (CISS) has developed the HSI Aperio™ sensor for real time, wide area surveillance and standoff detection of explosives, chemical threats, and narcotics for use in both government and commercial contexts. Employing liquid crystal tunable filter technology, the HSI system has an intuitive user interface that produces automated detections and real-time display of threats with an end user created library of threat signatures that is easily updated allowing for new hazardous materials. Unlike existing detection technologies that often require close proximity for sensing and so endanger operators and costly equipment, the handheld sensor allows the individual operator to detect threats from a safe distance. Uses of the sensor include locating production facilities of illegal drugs or IEDs by identification of materials on surfaces such as walls, floors, doors, deposits on production tools and residue on individuals. In addition, the sensor can be used for longer-range standoff applications such as hasty checkpoint or vehicle inspection of residue materials on surfaces or bulk material identification. The CISS Aperio™ sensor has faster data collection, faster image processing, and increased detection capability compared to previous sensors.

  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. 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.

  4. Developing a new hyperspectral imaging interferometer for earth observation

    NASA Astrophysics Data System (ADS)

    Barducci, Alessandro; Castagnoli, Francesco; Castellini, Guido; Guzzi, Donatella; Lastri, Cinzia; Marcoionni, Paolo; Nardino, Vanni; Pippi, Ivan

    2012-11-01

    The Aerospace Leap-frog Imaging Stationary interferometer for Earth Observation (ALISEO) is a hyperspectral imaging interferometer for Earth remote sensing. The instrument belongs to the class of Sagnac stationary interferometers and acquires the image of the target superimposed to the pattern of autocorrelation functions of the electromagnetic field coming from each pixel. The ALISEO sensor together with the data processing algorithms that retrieve the at-sensor spectral radiance are discussed. A model describing the instrument OPD and interferogram center is also discussed, improving the procedures for phase retrieval and spectral estimation. Images acquired by ALISEO are shown, and examples of retrieved reflectance spectra are presented.

  5. Transformation From Hyperspectral Radiance Data to Data of Other Sensors Based on Spectral Superresolution

    Microsoft Academic Search

    Huijie Zhao; Guorui Jia; Na Li

    2010-01-01

    Hyperspectral radiance spectra are the sensor's response, through its spectral response functions (SRFs), to the at-sensor radiance field. As the SRFs vary with sensors, hyperspectral radiance data need to be transformed for cross-calibration with another sensor or data simulation of a future sensor. In fact, the hyperspectral radiance data are composed of average radiance in the sensor's passbands and bear

  6. 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.

  7. Adaptive hyperspectral imaging with a MEMS-based full-frame programmable spectral filter

    NASA Astrophysics Data System (ADS)

    Graff, David L.; Love, Steven P.

    2014-05-01

    Rapidly programmable spatial light modulation devices based on MEMS technology have opened an exciting new arena in spectral imaging: rapidly reprogrammable, high spectral resolution, multi-band spectral filters that enable hyperspectral processing directly in the optical hardware of an imaging sensor. Implemented as a multiplexing spectral selector, a digital micro-mirror device (DMD) can independently choose or reject dozens or hundreds of spectral bands and present them simultaneously to an imaging sensor, forming a complete 2D image. The result is a high-speed, highresolution, programmable spectral filter that gives the user complete control over the spectral content of the image formed at the sensor. This technology enables a wide variety of rapidly reprogrammable operational capabilities within the same sensor including broadband, color, false color, multispectral, hyperspectral and target specific, matched filter imaging. Of particular interest is the ability to implement target-specific hyperspectral matched filters directly into the optical train of the sensor, producing an image highlighting a target within a spectrally cluttered scene in real time without further processing. By performing the hyperspectral image processing at the sensor, such a system can operate with high performance, greatly reduced data volume, and at a fraction of the cost of traditional push broom hyperspectral instruments. Examples of color, false color and target-specific matched-filter images recorded with our visible-spectrum prototype will be displayed, and extensions to other spectral regions will be discussed.

  8. Analysis of Pre-Germinated Barley using Hyperspectral Image Analysis.

    E-print Network

    Analysis of Pre-Germinated Barley using Hyperspectral Image Analysis. TECHNICAL REPORT v1.0 Morten) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 5 Germination Time Classification entitled 'Analysis of Pre- Germinated Barley using Hyperspectral Image Analysis.' [1]. It is not self

  9. Hyperspectral Image Classification with Mahalanobis Relevance Vector Machines

    E-print Network

    Camps-Valls, Gustavo

    Hyperspectral Image Classification with Mahalanobis Relevance Vector Machines Gustavo Camps Machines (RVM) for remote sensing hyperspectral image classifi- cation. We also include the Mahalanobis of high dimensional labeled pixels. In addition, we introduce the Mahalanobis kernel distance

  10. 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, ?) 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

  11. Data system design for a hyperspectral imaging mission concept

    Microsoft Academic Search

    Christine M. Hartzell; L. C. Graham; T. S. Tao; H. R. Goldberg; J. Carpena-Nunez; D. M. Racek; C. E. Taylor; Charles D. Norton

    2009-01-01

    Global ecosystem observations are important for Earth-system studies. The National Research Council's report entitled Earth Science and Applications from Space is currently guiding NASA's Earth science missions. It calls for a global land and coastal area mapping mission. The mission, scheduled to launch in the 2013-2016 timeframe, includes a hyperspectral imager and a multi-spectral thermal-infrared sensor. These instruments will enable

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

    E-print Network

    Paris-Sud XI, Université de

    Unsupervised linear unmixing of hyperspectral image for crop yield estimation Bin Luo GIPSA are often used for estimating crop yield. This paper describes an unsu- pervised unmixing scheme of hyperspectral images on field in order to estimate the crop yield. From the hyperspectral images, the endmembers

  13. Non-negative structural sparse representation for high resolution hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Meng, Guiyu; Li, Guangyu; Dong, Weisheng; Shi, Guangming

    2014-11-01

    High resolution hyperspectral images have important applications in many areas, such as anomaly detection, target recognition and image classification. Due to the limitation of the sensors, it is challenging to obtain high spatial resolution hyperspectral images. Recently, the methods that reconstruct high spatial resolution hyperspectral images from the pair of low resolution hyperspectral images and high resolution RGB image of the same scene have shown promising results. In these methods, sparse non-negative matrix factorization (SNNMF) technique was proposed to exploit the spectral correlations among the RGB and spectral images. However, only the spectral correlations were exploited in these methods, ignoring the abundant spatial structural correlations of the hyperspectral images. In this paper, we propose a novel algorithm combining the structural sparse representation and non-negative matrix factorization technique to exploit the spectral-spatial structure correlations and nonlocal similarity of the hyperspectral images. Compared with SNNMF, our method makes use of both the spectral and spatial redundancies of hyperspectral images, leading to better reconstruction performance. The proposed optimization problem is efficiently solved by using the alternating direction method of multipliers (ADMM) technique. Experiments on a public database show that our approach performs better than other state-of-the-art methods on the visual effect and in the quantitative assessment.

  14. 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...

  15. 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

  16. Flight evaluation of hyperspectral and multipolarizable imaging spectropolarimeter at JAXA

    NASA Astrophysics Data System (ADS)

    Homma, Kohzo; Shingu, Hirokimi; Yamamoto, Hiromichi; Shibayama, Michio; Sugahara, Kazuo

    2005-01-01

    The demand for airborne remote sensing based on the Earth environment observation has been growing, motivated by the need to protect the Earth's environment. Attention has been focused on hyperspectral sensors as new type of Earth observation sensor for measuring the surface conditions from the air. The Japan Aerospace Exploration Agency (JAXA) has developed an LCTF hyper-spectral imaging spectropolarimeter with selectable plane of polarization which senses radiation in the 400-720 nm visible light wavelength band, and has constructed an airborne optical observation system based on the sensor. Flight evaluation of this sensor using JAXA's Beechcraft 65 research airplane has been continuing over the past few years, and this paper first outlines this flight evaluation. Next, we report on current aerial observations of water contamination in the rivers or lakes and the growth stages of crops are shown, with spectral images taken at various wavelengths and polarization angles presented as the analyzed results of flight experiment data. The flight experiments have confirmed that spectral images of targets with differing characteristics do indeed show different spectropolarimetric properties. Plans for future flight evaluations are also described. Finally it is concluded that the way has been paved for applying the visible light sensor to airborne remote sensing, aiming at the determination of surface conditions.

  17. Surface emissivity and temperature retrieval for a hyperspectral sensor

    SciTech Connect

    Borel, C.C.

    1998-12-01

    With the growing use of hyper-spectral imagers, e.g., AVIRIS in the visible and short-wave infrared there is hope of using such instruments in the mid-wave and thermal IR (TIR) some day. The author believes that this will enable him to get around using the present temperature-emissivity separation algorithms using methods which take advantage of the many channels available in hyper-spectral imagers. A simple fact used in coming up with a novel algorithm is that a typical surface emissivity spectrum are rather smooth compared to spectral features introduced by the atmosphere. Thus, a iterative solution technique can be devised which retrieves emissivity spectra based on spectral smoothness. To make the emissivities realistic, atmospheric parameters are varied using approximations, look-up tables derived from a radiative transfer code and spectral libraries. One such iterative algorithm solves the radiative transfer equation for the radiance at the sensor for the unknown emissivity and uses the blackbody temperature computed in an atmospheric window to get a guess for the unknown surface temperature. By varying the surface temperature over a small range a series of emissivity spectra are calculated. The one with the smoothest characteristic is chosen. The algorithm was tested on synthetic data using MODTRAN and the Salisbury emissivity database.

  18. Image quality measures to assess hyperspectral compression techniques

    NASA Astrophysics Data System (ADS)

    Lurie, Joan B.; Evans, Bruce W.; Ringer, Brian; Yeates, Mathew

    1994-12-01

    The term 'multispectral' is used to describe imagery with anywhere from three to about 20 bands of data. The images acquired by Landsat and similar earth sensing satellites including the French Spot platform are typical examples of multispectral data sets. Applications range from crop observation and yield estimation, to forestry, to sensing of the environment. The wave bands typically range from the visible to thermal infrared and are fractions of a micron wide. They may or may not be contiguous. Thus each pixel will have several spectral intensities associated with it but detailed spectra are not obtained. The term 'hyperspectral' is typically used for spectral data encompassing hundreds of samples of a spectrum. Hyperspectral, electro-optical sensors typically operate in the visible and near infrared bands. Their characteristic property is the ability to resolve a large number (typically hundreds) of contiguous spectral bands, thus producing a detailed profile of the electromagnetic spectrum. Like multispectral sensors, recently developed hyperspectral sensors are often also imaging sensors, measuring spectral over a two dimensional spatial array of picture elements of pixels. The resulting data is thus inherently three dimensional - an array of samples in which two dimensions correspond to spatial position and the third to wavelength. The data sets, commonly referred to as image cubes or datacubes (although technically they are often rectangular solids), are very rich in information but quickly become unwieldy in size, generating formidable torrents of data. Both spaceborne and airborne hyperspectral cameras exist and are in use today. The data is unique in its ability to provide high spatial and spectral resolution simultaneously, and shows great promise in both military and civilian applications. A data analysis system has been built at TRW under a series of Internal Research and Development projects. This development has been prompted by the business opportunities, by the series of instruments built here and by the availability of data from other instruments. The products of the processing system has been used to process data produced by TRW sensors and other instruments. Figure 1 provides an overview of the TRW hyperspectral collection, data handling and exploitation capability. The Analysis and Exploitation functions deal with the digitized image cubes. The analysis system was designed to handle various types of data but the emphasis was on the data acquired by the TRW instruments.

  19. The inpainting of hyperspectral images: a survey and adaptation to hyperspectral data

    NASA Astrophysics Data System (ADS)

    Chen, Alex

    2012-11-01

    In this work, we survey image reconstruction methods for hyperspectral imagery. First, a review of image interpolation methods, both linear and nonlinear, is given. Second, image inpainting methods, especially from the variational perspective, are analyzed with respect to their suitability for hyperspectral inpainting. The ability to connect edges through occlusions and the structure of the space in which the hyperspectral data lies are especially considered when propagating data into unknown regions. Finally, a general method for adapting image reconstruction methods to the hyperspectral case is presented.

  20. Automatic Denoising and Unmixing in Hyperspectral Image Processing

    NASA Astrophysics Data System (ADS)

    Peng, Honghong

    This thesis addresses two important aspects in hyperspectral image processing: automatic hyperspectral image denoising and unmixing. The first part of this thesis is devoted to a novel automatic optimized vector bilateral filter denoising algorithm, while the remainder concerns nonnegative matrix factorization with deterministic annealing for unsupervised unmixing in remote sensing hyperspectral images. The need for automatic hyperspectral image processing has been promoted by the development of potent hyperspectral systems, with hundreds of narrow contiguous bands, spanning the visible to the long wave infrared range of the electromagnetic spectrum. Due to the large volume of raw data generated by such sensors, automatic processing in the hyperspectral images processing chain is preferred to minimize human workload and achieve optimal result. Two of the mostly researched processing for such automatic effort are: hyperspectral image denoising, which is an important preprocessing step for almost all remote sensing tasks, and unsupervised unmixing, which decomposes the pixel spectra into a collection of endmember spectral signatures and their corresponding abundance fractions. Two new methodologies are introduced in this thesis to tackle the automatic processing problems described above. Vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios. Typical vector bilateral filtering usage does not employ parameters that have been determined to satisfy optimality criteria. This thesis also introduces an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means. The approach is based on posing the filtering problem as one of nonlinear estimation and minimizing the Stein's unbiased risk estimate (SURE) of this nonlinear estimator. Along the way, this thesis provides a plausibility argument with an analytical example as to why vector bilateral filtering outperforms band-wise 2D bilateral filtering in enhancing SNR. Experimental results show that the optimized vector bilateral filter provides improved denoising performance on multispectral images when compared to several other approaches. Non-negative matrix factorization (NMF) technique and its extensions were developed to find part based, linear representations of non-negative multivariate data. They have been shown to provide more interpretable results with realistic non-negative constrain in unsupervised learning applications such as hyperspectral imagery unmixing, image feature extraction, and data mining. This thesis extends the NMF method by incorporating deterministic annealing optimization procedure, which will help solve the non-convexity problem in NMF and provide a better choice of sparseness constrain. The approach is based on replacing the difficult non-convex optimization problem of NMF with an easier one by adding an auxiliary convex entropy constrain term and solving this first. Experiment results with hyperspectral unmixing application show that the proposed technique provides improved unmixing performance compared to other state-of-the-art methods.

  1. The enhanced MODIS airborne simulator hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Guerin, Daniel C.; Fisher, John; Graham, Edward R.

    2011-06-01

    The EMAS-HS or Enhanced MODIS Airborne Simulator is an upgrade to the solar reflected and thermal infrared channels of NASA's MODIS Airborne Simulator (MAS). In the solar reflected bands, the MAS scanner functionality will be augmented with the addition of this separate pushbroom hyperspectral instrument. As well as increasing the spectral resolution of MAS beyond 10 nm, this spectrometer is designed to maintain a stable calibration that can be transferred to the existing MAS sensor. The design emphasizes environmental control and on-board radiometric stability monitoring. The system is designed for high-altitude missions on the ER-2 and the Global Hawk platforms. System trades optimize performance in MODIS spectral bands that support land, cloud, aerosol, and atmospheric water studies. The primary science mission driving the development is high altitude cloud imaging, with secondary missions possible for ocean color. The sensor uses two Offner spectrometers to cover the 380-2400 nm spectral range. It features an all-reflective telescope with a 50° full field-of-view. A dichroic cold mirror will split the image from the telescope, with longer radiation transmitted to the SWIR spectrometer. The VNIR spectrometer uses a TE-cooled Si CCD detector that samples the spectrum at 2.5 nm intervals, while the SWIR spectrometer uses a Stirling-cooled hybrid HgCdTe detector to sample the spectrum at 10 nm per band. Both spectrometers will feature 1.05 mRad instantaneous fields-of-view registered to the MAS scanner IFOV's.

  2. Parallel Implementation of Hyperspectral Image Processing Algorithms

    E-print Network

    Plaza, Antonio J.

    Space Flight Center in Maryland, developed the concept of Beowulf cluster with the aim of creating a cost-effective parallel computing system from commodity components to satisfy specific computational hyperspectral imaging applications require analysis al- gorithms able to provide a response in (near) real-time

  3. 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...

  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. 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.

  6. Unsupervised Segmentation of Hyperspectral Images Using Modified Phase Correlation

    Microsoft Academic Search

    A. Erturkerturk; S. Erturkerturk

    2006-01-01

    This letter presents hyperspectral image segmentation based on the phase-correlation measure of subsampled hyperspectral data, which is referred to as modified phase correlation. The hyperspectral spectrum of each pixel is initially subsampled to gain robustness against noise and spatial variability, and phase correlation is applied to determine spectral similarity. Similar and dissimilar pixels are decided according to the peak value

  7. 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 prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.

  8. 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 profile. Smaller interesting regions can also easily be selected from the hyperspectral images for more detailed study at microscopic scale.

  9. 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 ?m (MWIR) and 1.5 to 1.8 ?m (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.

  10. 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.

  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. Morphology-based fusion method of hyperspectral image

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  13. Wideband Hyperspectral Imaging for Space Situational Awareness

    Microsoft Academic Search

    Ian S. Robinson; A. Klier

    2006-01-01

    Wideband hyperspectral imaging (WHSI) systems collect simultaneous spectral and spatial imagery across a broad spectrum that includes the visible\\/near infrared (VNIR), short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) regimes. These passive optical systems capture reflected sunlight and thermal emissions from targets enabling the characterization of surface material, thermal properties, propellants, and gaseous emissions when targets are sunlit

  14. Polarimetric Hyperspectral Imaging Systems and Applications

    NASA Technical Reports Server (NTRS)

    Cheng, Li-Jen; Mahoney, Colin; Reyes, George; Baw, Clayton La; Li, G. P.

    1996-01-01

    This paper reports activities in the development of AOTF Polarimetric Hyperspectral Imaging (PHI) Systems at JPL along with field observation results for illustrating the technology capabilities and advantages in remote sensing. In addition, the technology was also used to measure thickness distribution and structural imperfections of silicon-on-silicon wafers using white light interference phenomenon for demonstrating the potential in scientific and industrial applications.

  15. 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.

  16. Hyperspectral Image Analysis in Planetary Science and Astronomy

    NASA Astrophysics Data System (ADS)

    Merenyi, Erzsebet

    2014-01-01

    Hyperspectral images (spectral signatures acquired in hundreds of narrow, contiguous band passes on a regular spatial grid over a target area) have long been utilized in planetary astronomy for remote geochemical analyses. Typical hyperspectral imagery spans the visible to near-and-thermal-infrared wavelengths with 5-20 nm (?/?? > 100) resolution, sufficient to resolve the discriminating spectral features of (near-)surface compounds. Compared with broad-band, multi-spectral imagery, hyperspectral data brings a phase change in the complexity of spectral patterns and the cluster structure and richness of the data space, and consequently in the analysis challenges for tasks like clustering, classification, regression, and parameter inference. Many traditional favorite techniques do not meet these challenges if one’s aim is to fully exploit the rich, intricate information captured by the sensor, ensure discovery of surprising small anomalies, and more. In stellar astronomy, where Ångström resolution is typical, the data complexity can grow even higher. With the advent of 21st century observatories such as ALMA, high spatial and spectral resolution image cubes with thousands of bands are extending into new and wider wavelength domains, adding impetus to develop and deploy increasingly powerful and efficient knowledge extraction techniques. In this talk I will highlight applications of brain-like machine learning, specifically advanced forms of neural maps that mimic analogous behaviors in natural neural maps in brains (for example, preferential attention to rare signals, to enhance discovery of small clusters). I will present examples of information extraction from hyperspectral data in planetary astronomy, and point out advantages over more traditional techniques, for “precision” data mining, discovery of small anomalies in the face of highly irregular cluster structure, accurate inference of non-linearly entangled latent parameters, or non-linear dimension reduction. These works were done in close collaboration with colleagues in planetary science and astronomy, supported in part by the Applied Information Systems Research Program.

  17. Absolute Calibration Accuracy for Hyperspectral Imagers in the Solar Reflective

    NASA Technical Reports Server (NTRS)

    Thome, Kurtis

    2009-01-01

    The characterization and calibration of hyperspectral imagers is a challenging one that is expected to become even more challenging as needs increase for highly-accurate radiometric data from such systems. The preflight calibration of the Advanced Responsive Tactically Effective Military Imaging Spectrometer (ARTEMIS) is used as an example of the difficulties to calibrate hyperspectrally. Results from a preflight solar radiation-based calibration are presented with a discussion of the uncertainties in such a method including the NISI-traceable and SItraceable aspects. Expansion on the concept of solar-based calibration is given with descriptions of methods that view the solar disk directly, illuminate a solar diffuser that is part of the sensor's inflight calibration, and illuminate an external diffuser that is imaged by the sensor. The results of error analysis show that it is feasible to achieve preflight calibration using the sun as a source at the same level of uncertainty as those of lamp-based approaches. The error analysis is evaluated and verified through the solar-radiation-based calibration of several of laboratory grade radiometers. Application of these approaches to NASA's upcoming CLARREO mission are discussed including proposed methods for significantly reducing the uncertainties to allow CLARREO data to be used for climate data records.

  18. Hyperspectral imaging in earth road construction planning

    NASA Astrophysics Data System (ADS)

    Nie, Yixiang; Gomez, Richard B.; Kafatos, Menas; Yang, Ruixin

    2001-06-01

    Some types of clay, esp. montmorillonite, become slippery when getting wet. Clay movement is very harmful for various constructions and can also cause trouble for both wheeled and tracked vehicles in military operations at some rural areas when raining. We present a summary of a project using hyperspectral imaging in assisting earth roads construction planning and cross-country trafficability analysis. Spectral signature libraries are used to help identify materials and define those areas to be avoided, which have significant montmorillonite content. We perform a case study in this kind of application; some methods of data processing and analyzing are discussed. We also discussed the problems we met in this application. Hyperspectral sensing is a relatively new but mature technology; development of applications and corresponding analyzing procedures will be the major impetus of this technology.

  19. 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

  20. Graph based hyperspectral image segmentation with improved affinity matrix

    NASA Astrophysics Data System (ADS)

    Fan, Lei; Messinger, David W.

    2014-06-01

    Image segmentation and clustering is a method to extract a set of components whose members are similar in some way. Instead of focusing on the consistencies of local image characteristics such as borders and regions in a perceptual way, the spectral graph theoretic approach is based on the eigenvectors of an affinity matrix; therefore it captures perceptually important non-local properties of an image. A typical spectral graph segmentation algorithm, normalized cuts, incorporates both the dissimilarity between groups and similarity within groups by capturing global consistency making the segmentation process more balanced and stable. For spectral graph partitioning, we create a graph-image representation wherein each pixel is taken as a graph node, and two pixels are connected by an edge based on certain similarity criteria. In most cases, nearby pixels are likely to be in the same region, therefore each pixel is connected to its spatial neighbors in the normalized cut algorithm. However, this ignores the difference between distinct groups or the similarity within a group. A hyperspectral image contains high spatial correlation among pixels, but each pixel is better described by its high dimensional spectral feature vector which provides more information when characterizing the similarities among every pair of pixels. Also, to facilitate the fact that boundary usually resides in low density regions in spectral domain, a local density adaptive affinity matrix is presented in this paper. Results will be shown for airborne hyperspectral imagery collected with the HyMAP, AVIRIS, HYDICE sensors.

  1. ASSESSMENT OF HYPERSPECTRAL IMAGING SYSTEM FOR POULTRY SAFETY INSPECTION

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging system demonstrated potential to detect surface fecal and ingesta contaminants on poultry carcasses. Hyperspectral data were analyzed with four pre-processing methods considering two parameters: calibration and 20-nm spectral smoothing. A band-ratio image-processing algorit...

  2. 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...

  3. Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview

    Microsoft Academic Search

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

    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

  4. 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...

  5. 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...

  6. Joint Multiframe Blind Deconvolution and Spectral Unmixing of Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Zhang, P.; Pauca, V. P.; Plemmons, R. J.

    2013-09-01

    Our interest here is spectral imaging for space object identification based upon imaging using simultaneous measurements at different wavelengths. AMOS sensors can collect simultaneous images ranging from visible to LWIR. On the other hand, multiframe blind deconvolution (MFBD) has demonstrated success by acquiring near-simultaneous multiple images for reconstructing space objects, and another success has been shown through adding phase diversity (PD) by splitting the light beam in channels with different phase functions. So far, most MFBD and PD applications have been focused on monochromatic images, with a few MFBD studies on multispectral images, also called the wavelength diversity. In particular, B. Calef has shown that wavelength-diverse MFBD is a promising technique for combining data from multiple sensors to yield a higher-quality reconstructed image. Here, we present optimization algorithms to blindly deconvolve observed blurred and noisy hyperspectral images with phase diversity at each wavelength channel. We use the facts that at longer wavelengths, turbulence effects on the phase are less severe, while diffraction effects at shorter wavelengths are less severe. Moreover, because the blurring kernels of all wavelength channels essentially share the same optimal path difference (OPD) function, we have greatly reduced the number of parameters in the blurring kernel. We model the true hyperspectral object by a linear spectral unmixing model, which reduces the number of pixels to be recovered. Because the number of known parameters is far greater than the number of unknowns, the method enjoys an enhanced capability of successful reconstruction. We simultaneously reconstruct the true object, estimate the blurring kernels, and separate the object into spectrally homogeneous segments, each characterized by its support and spectral signature, an important step for analyzing the material compositions of space objects.

  7. Extending the fractional order Darwinian particle swarm optimization to segmentation of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Ghamisi, Pedram; Couceiro, Micael S.; Benediktsson, Jon Atli

    2012-11-01

    Hyperspectral sensors generate detailed information about the earth's surface and climate in numerous contiguous narrow spectral bands, being widely used in resource management, agriculture, environmental monitoring, and others. However, due to the high dimensionality of hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for hyperspectral imagery. In this paper a new multilevel thresholding method for segmentation of hyperspectral images into different homogenous regions is proposed. The new method is based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. The FODPSO is used to solve the so-called Otsu problem for each channel of the hyperspectral data as a grayscale image that indicates the spectral response to a particular frequency in the electromagnetic spectrum. In other words, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results successfully compare the FODPSO with the traditional PSO for multi-level segmentation of hyperspectral images. The FODPSO acts better than the other method in terms of both CPU time and fitness, thus being able to find the optimal set of thresholds with a larger between-class variance in less computational time.

  8. Methods for Improving Image Quality and Reducing Data Load of NIR Hyperspectral Images

    PubMed Central

    Firtha, Ferenc; Fekete, András; Kaszab, Tímea; Gillay, Bíborka; Nogula-Nagy, Médea; Kovács, Zoltán; Kantor, David B.

    2008-01-01

    Near Infrared Hyperspectral Imaging (NIRHSI) is an emerging technology platform that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Two important problems in NIRHSI are those of data load and unserviceable pixels in the NIR sensor. Hyperspectral imaging experiments generate large amounts of data (typically > 50 MB per image), which tend to overwhelm the memory capacity of conventional computer systems. This inhibits the utilisation of NIRHSI for routine online industrial application. In general, approximately 1% of pixels in NIR detectors are unserviceable or ‘dead’, containing no useful information. While this percentage of pixels is insignificant for single wavelength imaging, the problem is amplified in NIRHSI, where > 100 wavelength images are typically acquired. This paper describes an approach for reducing the data load of hyperspectral experiments by using sample-specific vector-to-scalar operators for real time feature extraction and a systematic procedure for compensating for ‘dead’ pixels in the NIR sensor. The feasibility of this approach was tested for prediction of moisture content in carrot tissue.

  9. Real-time data processor for the COMPASS hyperspectral sensor system

    Microsoft Academic Search

    William E. Schaff; Anthony Copeland; Mike Steffen; Rory O'Connor; Christopher Simi; Jerry Zadnik; Ed M. Winter; Glenn Healey

    2003-01-01

    The NVESD COMPASS instrument is an airborne dispersive hyperspectral imager that covers the VNIR through SWIR bands and incorporates a real-time data processing system. The processing system consists of a Data Processing Computer (DPC) and an Operator Display\\/Control Computer (ODC). The high-performance DPC executes real-time sensor calibration and multiple spectral detection algorithms on 13 G4-processors in a Race++ switched backplane.

  10. Real-time data processor for the COMPASS hyperspectral sensor system

    Microsoft Academic Search

    William E. Schaff; Anthony Copeland; Mike Steffen; Rory O'Connor; Christopher Simi; Jerry Zadnik; Ed M. Winter; Glenn Healey

    2004-01-01

    The NVESD COMPASS instrument is an airborne dispersive hyperspectral imager that covers the VNIR through SWIR bands and incorporates a real-time data processing system. The processing system consists of a Data Processing Computer (DPC) and an Operator Display\\/Control Computer (ODC). The high-performance DPC executes real-time sensor calibration and multiple spectral detection algorithms on 13 G4-processors in a Race++ switched backplane.

  11. A new architecture for hyperspectral image compression based on wavelets transformation and fractal composition

    NASA Astrophysics Data System (ADS)

    Hu, Xingtang; Zhang, Bing; Zhang, Xia; Hu, Fangchao; Wei, Zheng

    2006-03-01

    A fractal-based image compression algorithm under wavelet transformation for hyper-spectral remote sensing image was introduced in this paper (also named AWFC algorithm). With the development of the hyperspectral remote sensing we have to obtain more and more spectral bands and how to store and transmit the huge data measured by TB bits level becomes a disaster to the limited electrical bandwidth. It is important to compress the huge hyperspectral image data acquired by hyperspectral sensor such as MODIS, PHI, OMIS etc. Otherwise, conventional lossless compression algorithm couldn't reach satisfied compression ratio while other loss compression methods could get results of high compression ratio but no good image fidelity especially to the hyperspectral image data. As the third generation image compression algorithm-fractal image compression is superior than traditional compression methods with high compression ratio, good image fidelity and less time complexity. In order to keep the spectral dimension invariability, we have compared the results of two compression algorithms based on the outside storage file structure of BSQ and BIP separately. The HV and Quad-tree partitioning and the domain-range matching algorithms have also been improved to accelerate the encode/decode efficiency. The proposed method has been realized and obtained perfect experimental results. At last, the possible modifications algorithm and the limitations of the method are also analyzed and discussed in this paper.

  12. Potential roles of satellite hyperspectral IR sensors in monitoring greenhouse effects

    Microsoft Academic Search

    Hsiao-hua Burke; Bill Snow; Kris Farrar

    2005-01-01

    As we enter a new era of using satellite hyperspectral sensors for weather and other environmental applications, this paper discusses the applicability of using IR hyperspectral data for climate change monitoring; in particular, for quantifying the greenhouse effects. While broadband 1st order statistics quantify radiative forcings, the IR hyperspectral data provides a means of monitoring feedback processes. Radiative transfer modeling

  13. GPU Implementation of Target and Anomaly Detection Algorithms for Remotely Sensed Hyperspectral Image

    E-print Network

    Plaza, Antonio J.

    timely responses for swift decisions which depend upon high computing performance of algorithm analysis or satellite sensor. Hyperspectral imaging instruments such as the NASA Jet Propulsion Laboratory's Airborne 927 257000 (Ext. 51662); URL: http://www.umbc.edu/rssipl/people/aplaza Satellite Data Compression

  14. Commodity Cluster-Based Parallel Implementation of an Automatic Target Generation Process for Hyperspectral Image Analysis

    E-print Network

    Plaza, Antonio J.

    for swift decisions which depend upon high computing performance of algorithm analysis. A popular algo or satellite sensor. Hyperspectral imaging instruments such as the NASA Jet Propulsion Laboratory s Airbone as propagating fires) often require timely responses for swift decisions that depend upon high computing

  15. Snapshot hyperspectral imaging in ophthalmology

    Microsoft Academic Search

    William R. Johnson; Daniel W. Wilson; Wolfgang Fink; Mark Humayun; Greg Bearman

    2007-01-01

    Retinal imaging spectroscopy can provide functional maps using chromophore spectra. For example, oxygen saturation maps show ischemic areas from diabetes and venous occlusions. Obtaining retinal spatial-spectral data has been difficult due to saccades and long data acquisition times 5s . We present a snapshot imaging spectrometer with far-reaching applicability that acquires a complete spatial-spectral image cube in 3m sfrom 450

  16. Compact high-resolution VIS/NIR hyperspectral sensor

    NASA Astrophysics Data System (ADS)

    Hyvärinen, Timo; Herrala, Esko; Procino, Wes; Weatherbee, Oliver

    2011-06-01

    Current hyperspectral imagers are either bulky with good performance, or compact with only moderate performance. This paper presents a new hyperspectral technology which overcomes this drawback, and makes it possible to integrate extremely compact and high performance push-broom hyperspectral imagers for Unmanned Aerial Vehicles (UAV) and other demanding applications. Hyperspectral imagers in VIS/NIR, SWIR, MWIR and LWIR spectral ranges have been implemented. This paper presents the measured performance attributes for a VIS/NIR imager which covers 350 to 1000 nm with spectral resolution of 3 nm. The key innovation is a new imaging spectrograph design which employs both transmissive and reflective optics in order to achieve high light throughput and large spatial image size in an extremely compact format. High light throughput is created by numerical aperture of F/2.4 and high diffraction efficiency. Image distortions are negligible, keystone being <2 um and smile 0.13 nm across the full focal plane image size of 24 mm (spatially) x 6 m (spectrally). The spectrograph is integrated with an advanced camera which provides 1300 spatial pixels and image rate of 160 Hz. A higher resolution version with 2000 spatial pixels will produce up to 100 images/s. The camera achieves, with spectral binning, an outstanding signal-to-noise ratio of 800:1, orders of magnitude higher than any current compact VIS/NIR imager. The imager weighs only 1.4 kg, including fore optics, imaging spectrograph with shutter and camera, in a format optimized for installation in small payload compartments and gimbals. In addition to laboratory characterization, results from a flight test mission are presented.

  17. 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.

  18. Sparse Superpixel Unmixing for Hyperspectral Image Analysis

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

    Software was developed that automatically detects minerals that are present in each pixel of a hyperspectral image. An algorithm based on sparse spectral unmixing with Bayesian Positive Source Separation is used to produce mineral abundance maps from hyperspectral images. A superpixel segmentation strategy enables efficient unmixing in an interactive session. The algorithm computes statistically likely combinations of constituents based on a set of possible constituent minerals whose abundances are uncertain. A library of source spectra from laboratory experiments or previous remote observations is used. A superpixel segmentation strategy improves analysis time by orders of magnitude, permitting incorporation into an interactive user session (see figure). Mineralogical search strategies can be categorized as supervised or unsupervised. Supervised methods use a detection function, developed on previous data by hand or statistical techniques, to identify one or more specific target signals. Purely unsupervised results are not always physically meaningful, and may ignore subtle or localized mineralogy since they aim to minimize reconstruction error over the entire image. This algorithm offers advantages of both methods, providing meaningful physical interpretations and sensitivity to subtle or unexpected minerals.

  19. Novel miniaturized hyperspectral sensor for UAV and space applications

    Microsoft Academic Search

    Heikki Saari; Ville-Veikko Aallos; Altti Akujärvi; Tapani Antila; Christer Holmlund; Uula Kantojärvi; Jussi Mäkynen; Jyrki Ollila

    2009-01-01

    In many hyperspectral applications it is beneficial to produce 2D spatial images with a single exposure at a few selected wavelength bands instead of 1D spatial and all spectral band images like in push-broom instruments. VTT has developed a new concept based on the Piezo actuated Fabry-Perot Interferometer to enable recording of 2D spatial images at the selected wavelength bands

  20. 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.

  1. 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 JPL Publ. 96-4, vol. 1, pp. 105-113. Lockwood, Ronald B., Thomas W. Cooley, Richard M. Nadile, James A. Gardner, Peter S. Armstrong, Abraham M. Payton, Thom M. Davis, Stanley D. Straight, Thomas G. Chrien, Edward L. Gussin, and David Makowski (2006). Advanced Responsive Tactically-Effective Military Imaging Spectrometer (ARTEMIS) Design, in Proceedings of the 2006 IEEE International Geoscience and Remote Sensing Symposium, 31 July-4 August 2006, Denver, Colorado. Ramsey, Michael S., and Luke P. Flynn (2004). Strategies, insights, and the recent advances in volcanic monitoring and mapping with data from NASA’s Earth Observing System, Jour. of Volcanology and Geothermal Research, vol. 135, pp. 1-11. Young, Joseph (2009). EO-1 Weekly status report for September 24-30, 2009, Earth Science Mission Operations (ESMO) Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771.

  2. Radiometric calibration and noise estimation of acousto-optic tunable filter hyperspectral imaging systems.

    PubMed

    Katrašnik, Jaka; Pernuš, Franjo; Likar, Boštjan

    2013-05-20

    The accuracy of the radiometric response of acousto-optic tunable filter (AOTF) hyperspectral imaging systems is crucial for obtaining reliable measurements. It is therefore important to know the radiometric response and noise characteristics of the hyperspectral imaging system used. A radiometric model of an AOTF hyperspectral imaging system composed of an imaging sensor radiometric model (CCD, CMOS, and sCMOS) and an AOTF light transmission model is proposed. Using the radiometric model, a method for obtaining the fixed pattern noise (FPN) of the imaging system by displacing and imaging an illuminated reference target is developed. Methods for estimating the temporal noise of the imaging system, using the photon transfer method, and for correcting FPN are also presented. Noise estimation and image restoration methods were tested on an AOTF hyperspectral imaging system. The results indicate that the developed methods can accurately calculate temporal and FPN, and can effectively correct the acquired images. After correction, the signal-to-noise ratio of the acquired images was shown to increase by 26%. PMID:23736239

  3. 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.

  4. Detection of explosives by differential hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2014-02-01

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

  5. Feature reduction and morphological processing for hyperspectral image data.

    PubMed

    Casasent, David; Chen, Xue-Wen

    2004-01-10

    An automatic target detection system that uses hyperspectral (HS) imagery is proposed. HS images contain both spatial and spectral response information that provides detailed descriptions of an object. These new, to our knowledge, sensor data are useful in automatic target recognition applications. To provide discrimination information from the HS images and to select features that generalize well, we describe a new, to our knowledge, high-dimensional generalized discriminant feature-extraction algorithm and compare its performance with that of other feature-reduction methods for two HS target detection applications (mine and vehicle detection) by using a nearest-neighbor classifier. We also advance an approach to simultaneously optimize both spatial and spectral responses. PMID:14735942

  6. Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines

    PubMed Central

    Plaza, Javier; Plaza, Antonio J.; Barra, Cristina

    2009-01-01

    Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extremely high dimensionality of the data. In this paper, we explore the use of multi-channel morphological profiles for feature extraction prior to classification of remotely sensed hyperspectral data sets using support vector machines (SVMs). In order to introduce multi-channel morphological transformations, which rely on ordering of pixel vectors in multidimensional space, several vector ordering strategies are investigated. A reduced implementation which builds the multi-channel morphological profile based on the first components resulting from a dimensional reduction transformation applied to the input data is also proposed. Our experimental results, conducted using three representative hyperspectral data sets collected by NASA's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor and the German Digital Airborne Imaging Spectrometer (DAIS 7915), reveal that multi-channel morphological profiles can improve single-channel morphological profiles in the task of extracting relevant features for classification of hyperspectral data using small training sets. PMID:22389595

  7. Hyperspectral imaging for melanoma screening

    NASA Astrophysics Data System (ADS)

    Martin, Justin; Krueger, James; Gareau, Daniel

    2014-03-01

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

  8. 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 wavelength light-emitting-diode (LED) sources and white-light LED sources designed to produce consistently spatially stable light. White LEDs provide illumination for the measurement of reflectance spectra, while narrowband blue and UV LEDs are used to excite fluorescence. Each spectral type of LED can be turned on or off depending on the specific remote-sensing process being performed. Uniformity of illumination is achieved by using an array of LEDs and/or an integrating sphere or other diffusing surface. The image plane scanner uses a fore optic with a field of view large enough to provide an entire scan line on the image plane. It builds up a two-dimensional image in pushbroom fashion as the target is scanned across the image plane either by moving the object or moving the fore optic. For fluorescence detection, spectral filtering of a narrowband light illumination source is sometimes necessary to minimize the interference of the source spectrum wings with the fluorescence signal. Spectral filtering is achieved with optical interference filters and absorption glasses. This dual spectral imaging capability will enable the optimization of reflective, fluorescence, and fused datasets as well as a cost-effective design for multispectral imaging solutions. This system has been used in plant stress detection studies and in currency analysis.

  9. High-performance hyperspectral imaging using virtual slit optics

    NASA Astrophysics Data System (ADS)

    Behr, Bradford B.; Meade, Jeffrey T.; Hajian, Arsen R.; Cenko, Andrew T.

    2013-05-01

    The High Throughput Virtual Slit (or HTVS) is a new optical technology which can significantly increase the throughput and resolution of a dispersive spectrometer. The HTVS is able to preserve spectrometer étendue, mitigating photon losses normally associated with a slit. Originally implemented in multimode fiber-input spectrometers, HTVS has now been shown to be broadly applicable to a wide variety of spatially scanning hyperspectral imagers and standoff sensors, enhancing their performance and unlocking new application areas. In essence, the anamorphic elements of the HTVS optical system provide a means to decouple the spatial (iFOV) and spectral resolution of nearly any HSI system. In some scenarios, HTVS can be used to achieve better spectral resolution with the same input slit width. Alternatively, the slit can be widened (to increase the collected signal) while maintaining the same spectral resolution. This newfound flexibility in optimizing critical performance parameters not only improves the performance of HSI systems in existing remote sensing contexts, but also opens up numerous new application areas which were previously inaccessible to hyperspectral techniques. This method adds substantial value to existing HSI designs, particularly in applications involving targets with large spatial extent and requiring high spectral resolution (e.g. standoff Raman spectroscopy). We present recent experimental results from our prototype HTVS pushbroom imager and discuss case studies of standoff Raman detection of hazardous materials, passive detection of faint narrowband and monochromatic sources, and optimal disentangling of target spectral signatures from the solar spectrum under daytime illumination.

  10. Hyperspectral image reconstruction for diffuse optical tomography.

    PubMed

    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

  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. 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.

  13. Underwater monitoring experiment using hyperspectral sensor, LiDAR and high resolution satellite imagery

    NASA Astrophysics Data System (ADS)

    Yang, Chan-Su; Kim, Sun-Hwa

    2014-10-01

    In general, hyper-spectral sensor, LiDAR and high spatial resolution satellite imagery for underwater monitoring are dependent on water clarity or water transparency that can be measured using a Secchi disk or satellite ocean color data. Optical properties in the sea waters of South Korea are influenced mainly by a strong tide and oceanic currents, diurnal, daily and seasonal variations of water transparency. The satellite-based Secchi depth (ZSD) analysis showed the applicability of hyper-spectral sensor, LiDAR and optical satellite, determined by the location connected with the local distribution of Case 1 and 2 waters. The southeast coastal areas of Jeju Island are selected as test sites for a combined underwater experiment, because those areas represent Case 1 water. Study area is a small port (<15m) in the southeast area of the island and linear underwater target used by sewage pipe is located in this area. Our experiments are as follows: 1. atmospheric and sun-glint correction methods to improve the underwater monitoring ability; 2. intercomparison of water depths obtained from three different sensors. Three sensors used here are the CASI-1500 (Wide-Array Airborne Hyperspectral VNIR Imager (0.38-1.05 microns), the Coastal Zone Mapping and Imaging Lidar (CZMIL) and Korean Multi-purpose Satellite-3 (KOMPSAT-3) with 2.8 meter multi-spectral resolution. The experimental results were affected by water clarity and surface condition, and the bathymetric results of three sensors show some differences caused by sensor-itself, bathymetric algorithm and tide level. It is shown that CASI-1500 was applicable for bathymetry and underwater target detection in this area, but KOMPSAT-3 should be improved for Case 1 water. Although this experiment was designed to compare underwater monitoring ability of LIDAR, CASI-1500, KOMPSAT-3 data, this paper was based on initial results and suggested only results about the bathymetry and underwater target detection.

  14. 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

  15. Stray light characterization of an InGaAs anamorphic hyperspectral imager.

    PubMed

    Lin, Mike; Swanson, Rand; Moon, Thomas; Smith, Casey; Kehoe, Michael; Brown, Steven W; Lykke, Keith R

    2010-08-01

    Compact hyperspectral sensors potentially have a wide range of applications, including machine vision, quality control, and surveillance from small Unmanned Aerial Vehicles (UAVs). With the development of Indium Gallium Arsenide (InGaAs) focal plane arrays, much of the Short Wave Infra-Red (SWIR) spectral regime can be accessed with a small hyperspectral imaging system, thereby substantially expanding hyperspectral sensing capabilities. To fully realize this potential, system performance must be well-understood. Here, stray light characterization of a recently-developed push-broom hyperspectral sensor sensitive in the 1 microm -1.7 microm spectral regime is described. The sensor utilizes anamorphic fore-optics that partially decouple image formation along the spatial and spectral axes of the instrument. This design benefits from a reduction in complexity over standard high-performance spectrometer optical designs while maintaining excellent aberration control and spatial and spectral distortion characteristics. The stray light performance characteristics of the anamorphic imaging spectrometer were measured using the spectral irradiance and radiance responsivity calibrations using uniform sources (SIRCUS) facility at the National Institute of Standards and Technology (NIST). A description of the measurements and results are presented. Additionally, a stray-light matrix was assembled for the instrument to improve the instrument's spectral accuracy. Transmittance of a silicon wafer was measured to validate this approach. PMID:20721136

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. Hyperspectral Remote Sensing-Sensors and Applications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multispectral remote sensors have been traditionally used to map and monitor anthropogenic and environmental changes in the biosphere. While these sensors have proven robust for many applications, they often lack the spectral resolution necessary to differentiate characteristics of the Earth’s surfa...

  1. GPU implementation of JPEG2000 for hyperspectral image compression

    NASA Astrophysics Data System (ADS)

    Ciznicki, Milosz; Kurowski, Krzysztof; Plaza, Antonio

    2011-11-01

    Hyperspectral image compression has received considerable interest in recent years due to the enormous data volumes collected by imaging spectrometers for Earth Observation. JPEG2000 is an important technique for data compression which has been successfully used in the context of hyperspectral image compression, either in lossless and lossy fashion. Due to the increasing spatial, spectral and temporal resolution of remotely sensed hyperspectral data sets, fast (onboard) compression of hyperspectral data is becoming a very important and challenging objective, with the potential to reduce the limitations in the downlink connection between the Earth Observation platform and the receiving ground stations on Earth. For this purpose, implementation of hyperspectral image compression algorithms on specialized hardware devices are currently being investigated. In this paper, we develop an implementation of the JPEG2000 compression standard in commodity graphics processing units (GPUs). These hardware accelerators are characterized by their low cost and weight, and can bridge the gap towards on-board processing of remotely sensed hyperspectral data. Specifically, we develop GPU implementations of the lossless and lossy modes of JPEG2000. For the lossy mode, we investigate the utility of the compressed hyperspectral images for different compression ratios, using a standard technique for hyperspectral data exploitation such as spectral unmixing. In all cases, we investigate the speedups that can be gained by using the GPU implementations with regards to the serial implementations. Our study reveals that GPUs represent a source of computational power that is both accessible and applicable to obtaining compression results in valid response times in information extraction applications from remotely sensed hyperspectral imagery.

  2. Exploiting spatiospectral correlation for impulse denoising in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Aggarwal, Hemant Kumar; Majumdar, Angshul

    2015-01-01

    This paper proposes a technique for reducing impulse noise from corrupted hyperspectral images. We exploit the spatiospectral correlation present in hyperspectral images to sparsify the datacube. Since impulse noise is sparse, denoising is framed as an ?1-norm regularized ?1-norm data fidelity minimization problem. We derive an efficient split Bregman-based algorithm to solve the same. Experiments on real datasets show that our proposed technique, when compared with state-of-the-art denoising algorithms, yields better results.

  3. 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 ...

  4. 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 8.0~12.5?m. 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.

  5. Best band selection of hyperspectral remote sensing image based on differential evolution algorithm

    NASA Astrophysics Data System (ADS)

    Cai, Z.; Li, Z.; Jiang, A.; Chen, X.

    2010-12-01

    The hyperspectral remote sensing makes use of spectrum resolution with the nano-scale collecting image data simultaneously in dozens or hundreds of narrow and adjacent spectral bands above the earth's surface. These hyperspectral remote sensors make it possible to derive a continuous spectrum line for each image pixel (or a special sort of material). It can acquire space information, radiated information and spectrum information of images synchronously, so that it has remarkable application value and extensive development prospect in many related fields. However, the hyperspectral remote sensing images' characteristics, such as hundreds of bands, high spectral resolution and large volumes of data, have induced many problems such as high ratio of redundant information, large-scale storage space query, long processing delay, the Hughes phenomenon and so on. The main approach to solve these problems is making dimensional reduction before the classification or visual interpretation with the hyperspectral image data. There are two main methods for dimensional reduction: feature abstraction and bands selection. Although the feature abstraction that can achieve the purpose of dimensional reduction, in the process of feature abstraction or non-linear changes in both linear transformation, it will cause the loss of the physical implication of the original image data and also make it hard to apply hyperspectral images to visual interpretation. In contrast, band selection method outperforms in terms of being more universal for application. The selected bands can not only be used as attributes (features) for classification but also synthesize RGB false color image for visual interpretation. Therefore, band selection of hyperspectral remote sensing images is an important dimensional reduction method. Here, we design a hyperspectral remote sensing image band selection algorithm based on differential evolution algorithm. Differential evolution is an evolutionary method based on the idea of recombining different individuals. Evolutionary approach imitates the natural evolution in order to optimize the parameters. Differential evolution method illustrates individuals with floating-point vectors, and processes simple operations to search the optimal solution by natural selection. We employ hyperspectral remote sensing images of 701 uranium deposit (EO1H1320332005197110PY) in the areas of Gansu, China. We compared our new method with the traditional bands selection ways such as genetic algorithm, exhaustive search and steepest rise methods. The experiment results show that the new algorithm can improve the efficiency and stability of the band selection algorithm.

  6. Unmixing hyperspectral images using Markov random fields

    SciTech Connect

    Eches, Olivier; Dobigeon, Nicolas; Tourneret, Jean-Yves [University of Toulouse, IRIT/INP-ENSEEIHT, 2 rue Camichel, 31071 Toulouse cedex 7 (France)

    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.

  7. 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, ?) 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 preliminary results of the application of these classifiers to hyperspectral data sets. Since each mineral detector is focused on only a single mineral or mineral subclass, entire hyperspectral data cubes can be rapidly analyzed. This makes such detectors ideal for use in exploratory data analysis, where rapid feedback enhances discovery potential.

  8. SPARSE SUPERPIXEL UNMIXING FOR EXPLORATORY ANALYSIS OF CRISM HYPERSPECTRAL IMAGES

    E-print Network

    Royer, Dana

    or facilitate data mining in large hyperspectral catalogs. In this work, sparse spectral unmixing with Bayesian Reconnaissance Imaging Spectrome- ter (CRISM) images. We demonstrate a novel "superpixel" segmentation strategy. Index Terms-- Sparse Unmixing, CRISM, Hyperspec- tral Images, Superpixels, Image Segmentation 1

  9. 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, the window would be a slit, the CCD would contain a one-dimensional array of pixels, and the objective lens would be moved along an axis perpendicular to the slit to spatially scan the image of the specimen in pushbroom fashion. The image built up by scanning in this case would be an ordinary (non-spectral) image. In another version, the optics of which are depicted in the lower part of the figure, the spatial window would be a slit, the CCD would contain a two-dimensional array of pixels, the slit image would be refocused onto the CCD by a relay-lens pair consisting of a collimating and a focusing lens, and a prism-gratingprism optical spectrometer would be placed between the collimating and focusing lenses. Consequently, the image on the CCD would be spatially resolved along the slit axis and spectrally resolved along the axis perpendicular to the slit. As in the first-mentioned version, the objective lens would be moved along an axis perpendicular to the slit to spatially scan the image of the specimen in pushbroom fashion.

  10. 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

  11. Non-destructive hyperspectral imaging of quarantined Mars Returned Samples

    Microsoft Academic Search

    Alexandre Simionovici; Michel Viso; Pierre Beck; Laurence Lemelle; Andrew Westphal; Laszlo Vincze; Tom Schoonjans; Francois Fihman; Pascale Chazalnoel; Tristan Ferroir; Vicente Armando Solé; R. Tucoulou

    2010-01-01

    Introduction: In preparation for the upcoming International Mars Sample Return mission (MSR), returning samples containing potential biohazards, we have implemented a hyperspec-tral method of in-situ analysis of grains performed in BSL4 quarantine conditions, by combining several non-destructive imaging diagnostics. This allows sample transportation on optimized experimental setups, while monitoring the sample quarantine conditions. Our hyperspectral methodology was tested during analyses

  12. Utilizing fluorescence hyperspectral imaging to differentiate corn inoculated with toxigenic and atoxigenic fungal strains

    NASA Astrophysics Data System (ADS)

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

    2012-05-01

    Naturally occurring Aspergillus flavus strains can be either toxigenic or atoxigenic, indicating their ability to produce aflatoxin or not, under specific conditions. Corn contaminated with toxigenic strains of A. flavus can result in great losses to the agricultural industry and pose threats to public health. Past research showed that fluorescence hyperspectral imaging could be a potential tool for rapid and non-invasive detection of aflatoxin contaminated corn. The objective of the current study was to assess, with the use of a hyperspectral sensor, the difference in fluorescence emission between corn kernels inoculated with toxigenic and atoxigenic inoculums of A. flavus. Corn ears were inoculated with AF13, a toxigenic strain of A. flavus, and AF38, an atoxigenic strain of A. flavus, at dough stage of development and harvested 8 weeks after inoculation. After harvest, single corn kernels were divided into three groups prior to imaging: control, adjacent, and glowing. Both sides of the kernel, germplasm and endosperm, were imaged separately using a fluorescence hyperspectral imaging system. It was found that the classification accuracies of the three manually designated groups were not promising. However, the separation of corn kernels based on different fungal inoculums yielded better results. The best result was achieved with the germplasm side of the corn kernels. Results are expected to enhance the potential of fluorescence hyperspectral imaging for the detection of aflatoxin contaminated corn.

  13. Derivative hyperspectral image analysis for land use classification

    NASA Astrophysics Data System (ADS)

    Tsai, Fu-An

    As hyperspectral remote sensing data become commonly available, researchers need an effective tool specifically designed for analyzing this new type of data. Derivative analysis has been proved a useful tool capable of detecting subtle information from hyperspectral data sets. However, there are no systematic procedures for effectively applying derivative analysis to remote sensing hyperspectral images yet. This research developed a systematic procedure for using derivative analysis to help improve supervised classification of hyperspectral images. The algorithm allows investigators to identify derivative features better separating target classes according to the Jeffries-Matusita distances between classes. These features can be added into the classification image in order to improve the classification result. A maximum likelihood classification for vegetation was used as an example in this research. It demonstrated the effectiveness of using derivatives to detect useful information that might be lost during feature reduction operations. Classification accuracies of classes that were poorly classified in a 10-band principal component image gradually improved as more appropriate derivative features were extracted from the original image and appended to the base image. With the data set used in this study, derivative analysis did not generally provide a better performance than principal component analysis, but it may be suitable for some data sets and applications. The procedure developed in this research can be used as a starting point for subsequently designing an advanced system to systematically analyze hyperspectral images for remote sensing applications.

  14. 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.

  15. D Hyperspectral Frame Imager Camera Data in Photogrammetric Mosaicking

    NASA Astrophysics Data System (ADS)

    Mäkeläinen, A.; Saari, H.; Hippi, I.; Sarkeala, J.; Soukkamäki, J.

    2013-08-01

    A new 2D hyperspectral frame camera system has been developed by VTT (Technical Research Center of Finland) and Rikola Ltd. It contains frame based and very light camera with RGB-NIR sensor and it is suitable for light weight and cost effective UAV planes. MosaicMill Ltd. has converted the camera data into proper format for photogrammetric processing, and camera's geometrical accuracy and stability are evaluated to guarantee required accuracies for end user applications. MosaicMill Ltd. has also applied its' EnsoMOSAIC technology to process hyperspectral data into orthomosaics. This article describes the main steps and results on applying hyperspectral sensor in orthomosaicking. The most promising results as well as challenges in agriculture and forestry are also described.

  16. Impacts of hyperspectral sensor spectral coverage, sampling and resolution on cross-comparison with broadband sensor for reflective solar bands

    NASA Astrophysics Data System (ADS)

    Wu, Aisheng; Xiong, Xiaoxiong; Wenny, Brian

    2013-09-01

    A new generation of hyperspectral imagers requires a much higher absolute accuracy for reflected solar radiation measurements to further improve climate monitoring capabilities. For example, the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission, a future satellite mission led and developed by NASA and partner organizations, is currently considered to consist of two hyperspectral imagers that cover the reflected solar (RS) and infrared radiation. The design of the CLARREO RS instrument operates from 320 to 2300 nm with 4 nm in spectral sampling and 8 nm in spectral resolution. In this study, the sensitivity of spectral coverage, sampling and resolution of the CLARREO RS type instrument is tested for their impacts on integrated radiances using the relative spectral responses (RSR) of existing broadband sensors. As a proxy, our hyperspectral data is based on MODTRAN simulations and SCIAMACHY observations and the RSR data is from those used in MODIS, VIIRS and AVHRR level 1B (L1B) products. The sensitivity is conducted for ocean, forest, desert, snow and cloud.

  17. 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 ?m 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.

  18. 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 ?m 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.

  19. 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.

  20. GPUs for parallel on-board hyperspectral image radiometric normalization

    NASA Astrophysics Data System (ADS)

    Wu, Yuanfeng; Zhang, Bing; Zhao, Haina; Gao, Jianwei; Ni, Li; Yang, Wei

    2013-05-01

    This paper proposed a GPU-based implementation of radiometric normalization algorithms, which is used as a representative case study of on-board data processing techniques for hyperspectral image. Three algorithms of radiometric normalization based on the column average and standard deviation of raw image statistical characteristics were implemented and applied to real hyperspectral images for evaluating their performance. These algorithms have been implemented using the compute device unified architecture (CUDA), and tested on the NVidia Tesla C2075 architecture. The airborne Pushbroom Hyperspectral Imager (PHI) was flown to acquire the spectrally contiguous images as experimental datasets. The results show that MN worked best among the three methods and the speedups achieved by the GPU implementation over their CPU counterparts are outstanding.

  1. 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.

  2. 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...

  3. 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...

  4. Effects of light pollution revealed during a nocturnal aerial survey by two hyperspectral imagers

    Microsoft Academic Search

    Alessandro Barducci; Paolo Marcoionni; Ivan Pippi; Marco Poggesi

    2003-01-01

    A remote-sensing campaign was performed in September 2001 at nighttime under clear-sky conditions before moonrise to assess the level of light pollution of urban and industrial origin. Two hyperspectral sensors, namely, the Multispectral Infrared and Visible Imaging Spectrometer and the Visible Infrared Scanner-200, which provide spectral coverage from the visible to the thermal infrared, were flown over the Tuscany coast

  5. 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.

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

    PubMed

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

    2013-07-01

    The potential of hyperspectral imaging technology was evaluated for discriminating three types of waxed apples. Three types of apples smeared with fruit wax, with industrial wax, and not waxed respectively were imaged by a hyperspectral imaging system with a spectral range of 308-1 024 nm. ENVI software processing platform was used for extracting hyperspectral image object of diffuse reflection spectral response characteristics. Eighty four of 126 apple samples were selected randomly as calibration set and the rest were prediction set. After different preprocess, the related mathematical models were established by using the partial least squares (PLS), the least squares support vector machine (LS-SVM) and BP neural network methods and so on. The results showed that the model of MSC-SPA-LSSVM was the best to discriminate three kinds of waxed apples with 100%, 100% and 92.86% correct prediction respectively. PMID:24059202

  7. 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.

  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. 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

  10. 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

  11. 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.

  12. An FPGA-based demonstration hyperspectral image compression system

    NASA Astrophysics Data System (ADS)

    Woolston, Tom L.; Bingham, Gail E.; Holt, Niel S.; Wada, Glen

    2008-04-01

    The Space Dynamics Laboratory (SDL) has developed an FPGA-based hyperspectral demonstration compression system. The system consists of two boards: the first board performs a decorrelation process using a 5/3 wavelet; the second board performs the JPEG 2000 image compression. The hardware and firmware design of this system is described here and data is presented that shows the results of compressed hyperspectral data cubes containing various types of image content. This paper presents the importance of bit rate control among the individual spectral bands. Some of the theory for basing bit rate control on JPEG 2000 compression, bit rate control based on the 5/3 wavelet, as well as advantages and disadvantages of each method are discussed. Concepts for developing hyperspectral image compression technology for systems that can be used for remote sensing in real applications are also presented.

  13. 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...

  14. Subspace-Based Striping Noise Reduction in Hyperspectral Images

    Microsoft Academic Search

    N. Acito; M. Diani; G. Corsini

    2011-01-01

    In this paper, a new algorithm for striping noise reduction in hyperspectral images is proposed. The new algorithm exploits the orthogonal subspace approach to estimate the striping component and to remove it from the image, preserving the useful signal. The algorithm does not introduce artifacts in the data and also takes into account the dependence on the signal intensity of

  15. 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...

  16. SLEX-NWFE feature extraction method for hyperspectral image classification

    Microsoft Academic Search

    Hsiao-Yun Huang; Bor-Chen Kuo; Hsiang-Chuan Liu; Yu-Lung Liu

    2007-01-01

    Each pixel of the hyperspectral image is composed of hundreds of individual bands. Usually, these pixels are considered as high dimensional vectors. NWFE is a very robust and superior feature extraction method in this aspect of view of image pixel. On the other hand, since adjacent bands in a pixel are usually highly correlated, each pixel can also be viewed

  17. Hyperspectral Imaging with Stimulated Raman Scattering by Chirped Femtosecond Lasers

    E-print Network

    Xie, Xiaoliang Sunney

    Hyperspectral Imaging with Stimulated Raman Scattering by Chirped Femtosecond Lasers Dan Fu, Gary imaging system using chirped femtosecond lasers to achieve rapid Raman spectra acquisition while retaining laser beams with an energy difference tuned to the vibrational frequency of the molecule of interest

  18. Refining the Concept of Combining Hyperspectral and Multi-Angle Sensors for Land Surface Applications

    NASA Astrophysics Data System (ADS)

    Simic, Anita

    Assessment of leaf and canopy chlorophyll content provides information on plant physiological status; it is related to nitrogen content and hence, photosynthesis process, net primary productivity and carbon budget. In this study, a method is developed for the retrieval of total chlorophyll content (Chlorophyll a+b) per unit leaf and per unit ground area based on improved vegetation structural parameters which are derived using multispectral multi-angle remote sensing data. Structural characteristics such as clumping and gaps within a canopy affect its solar radiation absorption and distribution and impact its reflected radiance acquired by a sensor. One of the main challenges for the remote sensing community is to accurately estimate vegetation structural parameters, which inevitably influence the retrieval of leaf chlorophyll content. Multi-angle optical measurements provide a means to characterize the anisotropy of surface reflectance, which has been shown to contain information on vegetation structural characteristics. Hyperspectral optical measurements, on the other hand, provide a fine spectral resolution at the red-edge, a narrow spectral range between the red and near infra-red spectra, which is particularly useful for retrieving chlorophyll content. This study explores a new refined measurement concept of combining multi-angle and hyperspectral remote sensing that employs hyperspectral signals only in the vertical (nadir) direction and multispectral measurements in two additional (off-nadir) directions within two spectral bands, red and near infra-red (NIR). The refinement has been proposed in order to reduce the redundancy of hyperspectral data at more than one angle and to better retrieve the three-dimensional vegetation structural information by choosing the two most useful angles of measurements. To illustrate that hyperspectral data acquired at multiple angles exhibit redundancy, a radiative transfer model was used to generate off-nadir hyperspectral reflectances. It has been successfully demonstrated that the off-nadir hyperspectral simulations could be closely reconstructed based on the nadir hyperspectral reflectance and off-nadir multi-spectral reflectance in the red and NIR bands. This is shown using the Compact High-resolution Imaging Spectrometer (CHRIS) and Compact Airborne Spectrographic Imager (CASI) data acquired over a forested area in the Sudbury region (Ontario, Canada). Through intensive validation using field data, it is demonstrated that the combination of reflectances at two angles, the hotspot and darkspot, through the Normalized Difference between Hotspot and Darkspot (NDHD) index has the strongest response to changes in vegetation clumping, an important structural component of canopy. Clumping index (O) and Leaf Area Index (LAI) maps are generated based on previous algorithms as well as empirical relationships developed in this study. To retrieve chlorophyll content, inversion of the 5-Scale model is performed by developing Look-Up Tables (LUTs) that are based on the improved structural characteristics developed using multi-angle data. The generated clumping index and LAI maps are used in the LUTs to estimate leaf reflectance. Inversion of the leaf reflectance model, PROSPECT, is further employed to estimate chlorophyll content per unit leaf area. The estimated leaf chlorophyll contents are in good agreement with field-measured values. The refined measurement concept of combining hyperspectral with multispectral multi-angle data provides the opportunity for simultaneous retrieval of vegetation structural and biochemical parameters.

  19. 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 collapsed the two main towers and other buildings in the WTC complex.

  20. High-Performance Computing in Remotely Sensed Hyperspectral Imaging: The Pixel Purity Index Algorithm as a Case Study

    Microsoft Academic Search

    Antonio Plaza; David Valencia; Javier Plaza

    2006-01-01

    The incorporation of last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area

  1. [Geometric distortion correction for hyperspectral image using a rotating scan reflector].

    PubMed

    Ke, Gang-yang; An, Ning; Tian, Yang-chao; Ma, Zhi-hong; Huang, Wen-jiang; Wang, Qiu-ping

    2012-08-01

    Offner imaging spectrometer is a kind of pushbroom imaging system. Hyperspectral images acquired by Offner imaging spectrometers require relative motion of sensor and scene that is translation or rotation. Via rotating scan with a reflector at the front of sensor's len, large objects can be entirely captured. But for the changes in object distances, geometric distortion occurs. A formula of space projection from an object point to an image point by one capture was derived. According to the projection relation and slit's motion curve, the object points' coordinates on a reference plan were obtained with rotation angle for a variable. A rotating scan device using a reflector was designed and installed on an Offner imaging spectrometer. Clear images were achieved from the processing of correction algorithm. PMID:23156786

  2. Dynamic scene generation, multimodal sensor design, and target tracking demonstration for hyperspectral/polarimetric performance-driven sensing

    NASA Astrophysics Data System (ADS)

    Presnar, Michael D.; Raisanen, Alan D.; Pogorzala, David R.; Kerekes, John P.; Rice, Andrew C.

    2010-04-01

    Simulation of moving vehicle tracking has been demonstrated using hyperspectral and polarimetric imagery (HSI/PI). Synthetic HSI/PI image cubes of an urban scene containing moving vehicle content were generated using the Rochester Institute of Technology's Digital Imaging and Remote Sensing Image Generation (DIRSIG) Megascene #1 model. Video streams of sensor-reaching radiance frames collected from a virtual orbiting aerial platform's imaging sensor were used to test adaptive sensor designs in a target tracking application. A hybrid division-of-focal-plane imaging sensor boasting an array of 2×2 superpixels containing both micromirrors and micropolarizers was designed for co-registered HSI/PI aerial remote sensing. Pixel-sized aluminum wire-grid linear polarizers were designed and simulated to measure transmittance, extinction ratio, and diattenuation responses in the presence of an electric field. Wire-grid spacings of 500 [nm] and 80 [nm] were designed for lithographic deposition and etching processes. Both micromirror-relayed panchromatic imagery and micropolarizer-collected PI were orthorectified and then processed by Numerica Corporation's feature-aided target tracker to perform multimodal adaptive performance-driven sensing of moving vehicle targets. Hyperspectral responses of selected target pixels were measured using micromirror-commanded slits to bolster track performance. Unified end-to-end track performance case studies were completed using both panchromatic and degree of linear polarization sensor modes.

  3. Fast, electrically tunable filters for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

  4. Hyperspectral image classification based on volumetric texture and dimensionality reduction

    NASA Astrophysics Data System (ADS)

    Su, Hongjun; Sheng, Yehua; Du, Peijun; Chen, Chen; Liu, Kui

    2014-11-01

    A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural features were extracted by volumetric gray-level co-occurrence matrices (VGLCM). The spectral features were extracted by minimum estimated abundance covariance (MEAC) and linear prediction (LP)-based band selection, and a semi-supervised k-means (SKM) clustering method with deleting the worst cluster (SKMd) bandclustering algorithms. Moreover, four feature combination schemes were designed for hyperspectral image classification by using spectral and textural features. It has been proven that the proposed method using VGLCM outperforms the gray-level co-occurrence matrices (GLCM) method, and the experimental results indicate that the combination of spectral information with volumetric textural features leads to an improved classification performance in hyperspectral imagery.

  5. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  7. Recognition of wheat preharvest sprouting based on hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Wu, Qiong; Zhu, Dazhou; Wang, Cheng; Ma, Zhihong; Wang, Jihua

    2012-11-01

    An imaging spectrometer was used to acquire hyperspectral images of 120 strains of wheat ears and seeds under four different watering treatments. Whether wheat preharvest sprouting occurred could be reflected by spectral characteristics. Therefore, it was possible to judge whether wheat ears sprouted according to changes of spectral curve at 675 nm. According to principal component analysis of mean spectral reflectivity values of wheat seeds, it was found that wheat seeds watered three times every day and wheat seeds watered once every day were significantly different from nonsprouting wheat seeds soaked all day and original dry seeds due to significant sprouting situations, suggesting that imaging spectra can differentiate different extent of wheat preharvest sprouting. Glume had an influence on the hyperspectral images of wheat ears, therefore the hyperspectral images of wheat ears could be used to measure sprouting only when serious sprouting occurred. At an early stage of sprouting, only the hyperspectral images of wheat seeds could be used to analyze the sprouting of wheat.

  8. Hyperspectral imaging from a light-weight (up to 75 kg) unmanned aerial vehicle platform

    NASA Astrophysics Data System (ADS)

    Mitchell, J.; Hruska, R.; Anderson, M.; Glenn, N. F.

    2011-12-01

    Since 2009 the Idaho National Lab (INL) has been developing advanced remote sensing capabilities that combine increasingly sophisticated miniaturized sensors with relatively affordable, light weight (under 75 kg) unmanned aerial vehicles (UAVs). UAV-based hyperspectral sensing capabilities have been routinely refined via flight tests conducted at INL's UAV Runway Research Park in southeastern Idaho, and at the Orchard Training Area in central Idaho. Idaho State University (ISU) Boise Center Aerospace Lab (BCAL) has provided field data collection and image processing support to target ground versus aerial data comparisons, assess spectral and geometric data accuracy and determine classification algorithms appropriate for vegetation management applications. We report instrumentation, sensor and image validation results, optimal flight parameters, and methods for improving the geometric accuracies of the datasets. We also assess the accuracy of narrowband vegetation indices and shrub cover estimates derived from the imagery. Preliminary results indicate that the UAV-based hyperspectral imaging system has potential to bridge the gap between costly in-situ data collections, coarse resolution satellite data collections, or infrequent and costly manned hyperspectral data collections. Furthermore, new areas of research may be possible with this UAV platform by providing an affordable, on-demand platform that can rapidly collect transect data and stay on station for hours.

  9. Hyperspectral confocal fluorescence imaging: exploring alternative multivariate curve resolution approaches.

    PubMed

    Haaland, David M; Jones, Howland D T; Van Benthem, Mark H; Sinclair, Michael B; Melgaard, David K; Stork, Christopher L; Pedroso, Maria C; Liu, Ping; Brasier, Allan R; Andrews, Nicholas L; Lidke, Diane S

    2009-03-01

    Hyperspectral confocal fluorescence microscopy, when combined with multivariate curve resolution (MCR), provides a powerful new tool for improved quantitative imaging of multi-fluorophore samples. Generally, fully non-negatively constrained models are used in the constrained alternating least squares MCR analyses of hyperspectral images since real emission components are expected to have non-negative pure emission spectra and concentrations. However, in this paper, we demonstrate four separate cases in which partially constrained models are preferred over the fully constrained MCR models. These partially constrained MCR models can sometimes be preferred when system artifacts are present in the data or where small perturbations of the major emission components are present due to environmental effects or small geometric changes in the fluorescing species. Here we demonstrate that in the cases of hyperspectral images obtained from multicomponent spherical beads, autofluorescence from fixed lung epithelial cells, fluorescence of quantum dots in aqueous solutions, and images of mercurochrome-stained endosperm portions of a wild-type corn seed, these alternative, partially constrained MCR analyses provide improved interpretability of the MCR solutions. Often the system artifacts or environmental effects are more readily described as first and/or second derivatives of the main emission components in these alternative MCR solutions since they indicate spectral shifts and/or spectral broadening or narrowing of the emission bands, respectively. Thus, this paper serves to demonstrate the need to test alternative partially constrained models when analyzing hyperspectral images with MCR methods. PMID:19281642

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

    PubMed Central

    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.

    2012-01-01

    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

  11. 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.

  12. 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.

  13. The enhanced MODIS airborne simulator hyperspectral imager

    Microsoft Academic Search

    Daniel C. Guerin; John Fisher; Edward R. Graham

    2011-01-01

    The EMAS-HS or Enhanced MODIS Airborne Simulator is an upgrade to the solar reflected and thermal infrared channels of NASA's MODIS Airborne Simulator (MAS). In the solar reflected bands, the MAS scanner functionality will be augmented with the addition of this separate pushbroom hyperspectral instrument. As well as increasing the spectral resolution of MAS beyond 10 nm, this spectrometer is

  14. Classification of hyperspectral remote sensing image based on genetic algorithm and SVM

    NASA Astrophysics Data System (ADS)

    Zhou, Mandi; Shu, Jiong; Chen, Zhigang

    2010-08-01

    Hyperspectral remote sensing data has been widely used in Terrain Classification for its high resolution. The classification of urban vegetation, identified as an indispensable and essential part of urban development system, is now facing a major challenge as different complex land-cover classes having similar spectral signatures. For a better accuracy in classification of urban vegetation, a classifier model was designed in this paper based on genetic algorithm (GA) and support vector machine (SVM) to address the multiclass problem, and tests were made with the classification of PHI hyperspectral remote sensing images acquired in 2003 which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. SVM, based on statistical learning theory and structural risk minimization, is now widely used in classification in many fields such as two-class classification, and also the multi-class classification later due to its superior performance. On the other hand as parameters are very important factors affecting SVM's ability in classification, therefore, how to choose the optimal parameters turned out to be one of the most urgent problems. In this paper, GA was used to acquire the optimal parameters with following 3 steps. Firstly, useful training samples were selected according to the features of hyperspectral images, to build the classifier model by applying radial basis function (RBF) kernel function and decision Directed Acyclic Graph (DAG) strategy. Secondly, GA was introduced to optimize the parameters of SVM classification model based on the gridsearch and Bayesian algorithm. Lastly, the proposed GA-SVM model was tested for results' accuracy comparison with the maximum likelihood estimation and neural network model. Experimental results showed that GA-SVM model performed better classified accuracy, indicating the coupling of GA and SVM model could improve classification accuracy of hyperspectral remote sensing images, especially in vegetation classification.

  15. Vegetable green coverage estimation from an airborne hyperspectral image

    Microsoft Academic Search

    Naoko KOSAKA; Sanae MIYAZAKI; Ushio INOUE

    2002-01-01

    We propose a new vegetation index using TIR (thermal infrared) to estimate green coverage of vegetable crops from an airborne hyperspectral image, which is affected by their background of plastic film covering the ground. The amount of water content is different from green coverage of vegetables, and it leads to difference of temperature correlated with TIR. We propose a new

  16. 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...

  17. 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...

  18. 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

  19. 3D Plant Modelling via Hyperspectral Imaging Australian National University

    E-print Network

    Zhou, Jun

    of drought tolerance and flowering behavior. Therefore, ro- bust and accurate measurement plant methods3D Plant Modelling via Hyperspectral Imaging Jie Liang Australian National University Australia jie Australia jun.zhou@griffith.edu.au Xavier Sirault CSIRO Australia xavier.sirault@csiro.au Abstract Plant

  20. Unmixing Prior to Supervised Classification of Remotely Sensed Hyperspectral Images

    Microsoft Academic Search

    Inmaculada Dopido; Maciel Zortea; Alberto Villa; Antonio Plaza; Paolo Gamba

    2011-01-01

    Supervised classification of hyperspectral images is a very challenging task due to the generally unfavorable ratio between the number of spectral bands and the number of train- ing samples available ap riori, which results in the Hughes phe- nomenon. For this purpose, several feature extraction methods have been investigated in order to reduce the dimensionality of the data to the

  1. Supervised classification algorithms for poultry hyperspectral image analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging system with optimum classifiers enables us to identify the type and source of various contaminants (duodenum, ceca, colon, and ingesta) to determine critical control point for science-based federal poultry safety inspection program. The classification accuracies varied from ...

  2. 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 (...

  3. 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...

  4. 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...

  5. HYPERSPECTRAL REFLECTANCE IMAGING FOR DETECTION OF BRUISES ON PICKLING CUCUMBERS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Mechanical injury often causes hidden internal damage to pickling cucumbers, which is difficult to detect in visual inspection. Bruised pickling cucumbers lower the quality of pickled products and can incur economic losses to the processor. A near-infrared hyperspectral imaging system was developed ...

  6. 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...

  7. Hyperspectral Imaging Applied to Medical Diagnoses and Food Safety

    Microsoft Academic Search

    Oscar Carrasco; Richard Gomez; Arun Chainani; William Roper

    This paper analyzes the feasibility and performance of HSI systems for medical diagnosis as well as for food safety. Illness prevention and early disease detection are key elements for maintaining good health. Health care practitioners worldwide rely on innovative electronic devices to accurately identify disease. Hyperspectral imaging (HSI) is an emerging technique that may provide a less invasive procedure than

  8. The use of hyperspectral imaging (HSI) in wound healing

    NASA Astrophysics Data System (ADS)

    La Fontaine, Javier; Lavery, Lawrence; Zuzak, Karel

    2014-03-01

    A hyperspectral imaging system (HsI), described previously, was utilized to evaluate and monitor wounds and their healing surgery and post-operatively. Briefly, the system consists of a DLP® based spectral light modulator providing active spectral illumination that is synchronized with a digital focal plan array for collecting spectroscopic images that are processed for mapping the percentage of oxyhemoglobin at each detector pixel non-invasively and at near video rates ~8 chemically encode images per second.

  9. A new method for quality assessment of hyperspectral images

    Microsoft Academic Search

    A. Garzelli; F. Nencini; L. Alparone; S. Baronti

    2007-01-01

    This work focuses on quality assessment of fusion of hyperspectral (HS) images with high-resolution panchromatic (Pan) data. A novel fidelity index suitable for HS images is defined from the theory of hypercomplex numbers (2n-ons). Both spectral and spatial distortion measurements are encapsulated in a unique score index. Some fusion methods capable to selectivity inject spatial-frequencies from the higher-resolution Pan image

  10. Characterisation methods for the hyperspectral sensor HySpex at DLR's calibration home base

    NASA Astrophysics Data System (ADS)

    Baumgartner, Andreas; Gege, Peter; Köhler, Claas; Lenhard, Karim; Schwarzmaier, Thomas

    2012-09-01

    The German Aerospace Center's (DLR) Remote Sensing Technology Institute (IMF) operates a laboratory for the characterisation of imaging spectrometers. Originally designed as Calibration Home Base (CHB) for the imaging spectrometer APEX, the laboratory can be used to characterise nearly every airborne hyperspectral system. Characterisation methods will be demonstrated exemplarily with HySpex, an airborne imaging spectrometer system from Norsk Elektro Optikks A/S (NEO). Consisting of two separate devices (VNIR-1600 and SWIR-320me) the setup covers the spectral range from 400 nm to 2500 nm. Both airborne sensors have been characterised at NEO. This includes measurement of spectral and spatial resolution and misregistration, polarisation sensitivity, signal to noise ratios and the radiometric response. The same parameters have been examined at the CHB and were used to validate the NEO measurements. Additionally, the line spread functions (LSF) in across and along track direction and the spectral response functions (SRF) for certain detector pixels were measured. The high degree of lab automation allows the determination of the SRFs and LSFs for a large amount of sampling points. Despite this, the measurement of these functions for every detector element would be too time-consuming as typical detectors have 105 elements. But with enough sampling points it is possible to interpolate the attributes of the remaining pixels. The knowledge of these properties for every detector element allows the quantification of spectral and spatial misregistration (smile and keystone) and a better calibration of airborne data. Further laboratory measurements are used to validate the models for the spectral and spatial properties of the imaging spectrometers. Compared to the future German spaceborne hyperspectral Imager EnMAP, the HySpex sensors have the same or higher spectral and spatial resolution. Therefore, airborne data will be used to prepare for and validate the spaceborne system's data.

  11. 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.

  12. A non-parametric approach to anomaly detection in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Veracini, Tiziana; Matteoli, Stefania; Diani, Marco; Corsini, Giovanni; de Ceglie, Sergio U.

    2010-10-01

    In the past few years, spectral analysis of data collected by hyperspectral sensors aimed at automatic anomaly detection has become an interesting area of research. In this paper, we are interested in an Anomaly Detection (AD) scheme for hyperspectral images in which spectral anomalies are defined with respect to a statistical model of the background Probability Density Function (PDF).The characterization of the PDF of hyperspectral imagery is not trivial. We approach the background PDF estimation through the Parzen Windowing PDF estimator (PW). PW is a flexible and valuable tool for accurately modeling unknown PDFs in a non-parametric fashion. Although such an approach is well known and has been widely employed, its use within an AD scheme has been not investigated yet. For practical purposes, the PW ability to estimate PDFs is strongly influenced by the choice of the bandwidth matrix, which controls the degree of smoothing of the resulting PDF approximation. Here, a Bayesian approach is employed to carry out the bandwidth selection. The resulting estimated background PDF is then used to detect spectral anomalies within a detection scheme based on the Neyman-Pearson approach. Real hyperspectral imagery is used for an experimental evaluation of the proposed strategy.

  13. "Multimodal Contrast" from the Multivariate Analysis of Hyperspectral CARS Images

    NASA Astrophysics Data System (ADS)

    Tabarangao, Joel T.

    The typical contrast mechanism employed in multimodal CARS microscopy involves the use of other nonlinear imaging modalities such as two-photon excitation fluorescence (TPEF) microscopy and second harmonic generation (SHG) microscopy to produce a molecule-specific pseudocolor image. In this work, I explore the use of unsupervised multivariate statistical analysis tools such as Principal Component Analysis (PCA) and Vertex Component Analysis (VCA) to provide better contrast using the hyperspectral CARS data alone. Using simulated CARS images, I investigate the effects of the quadratic dependence of CARS signal on concentration on the pixel clustering and classification and I find that a normalization step is necessary to improve pixel color assignment. Using an atherosclerotic rabbit aorta test image, I show that the VCA algorithm provides pseudocolor contrast that is comparable to multimodal imaging, thus showing that much of the information gleaned from a multimodal approach can be sufficiently extracted from the CARS hyperspectral stack itself.

  14. 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...

  15. 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...

  16. 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

  17. 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.

  18. Investigation of the potential use of hyperspectral imaging for stand-off detection of person-borne IEDs

    NASA Astrophysics Data System (ADS)

    Cooksey, Catherine; Allen, David

    2011-06-01

    Advances in hyperspectral sensors and algorithms may benefit the detection of person-borne improvised explosive devices (PB-IEDs). While portions of the electromagnetic spectrum, such as the x-ray and terahertz regions, have been investigated for this application, the spectral region of the ultraviolet (UV) through shortwave infrared (SWIR) (250 nm to 2500 nm) has received little attention. The purpose of this work was to investigate what, if any, potential there may be for exploiting the spectral region of the UV through SWIR for the detection of hidden objects under the clothing of individuals. The optical properties of both common fabrics and materials potentially used to contain threat objects were measured, and a simple example using a hyperspectral imager is provided to illustrate the combined effect. The approach, measurement methods, and results are described in this paper, and the potential for hyperspectral imaging is addressed.

  19. 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.

  20. Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor

    PubMed Central

    Kobryn, Halina T.; Wouters, Kristin; Beckley, Lynnath E.; Heege, Thomas

    2013-01-01

    Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km2 of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km2). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas. PMID:23922921

  1. Ningaloo reef: shallow marine habitats mapped using a hyperspectral sensor.

    PubMed

    Kobryn, Halina T; Wouters, Kristin; Beckley, Lynnath E; Heege, Thomas

    2013-01-01

    Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km(2) of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km(2)). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas. PMID:23922921

  2. 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.

  3. 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

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

    PubMed Central

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

    2014-01-01

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

  5. Identification of unknown waste sites using MIVIS hyperspectral images

    SciTech Connect

    Gomarasca, M.A.; Strobelt, S. [National Research Council, Milano (Italy)

    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.

  6. COMPARISON OF SEDIMENT TRANSPORT MODELING WITH HYPERSPECTRAL AIRBORNE DATA ANALYSIS FOR THE RETRIEVAL OF SEDIMENT CONCENTRATION

    Microsoft Academic Search

    Els Knaeps; Sindy Sterckx; Mark Bollen; Koen Trouw; Rik Houthuys

    2007-01-01

    On June 15th 2005 hyperspectral airborne data were collected from the Lower Sea Scheldt at dif- ferent stages during the tidal cycle with the AHS Advanced Hyperspectral Sensor (SenSytech Inc). Simultaneously with the airborne campaign a field survey took place. The goal was to collect ground truth data while the hyperspectral sensor was imaging the study area. This ground truth

  7. 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.

  8. Hyperspectral image classification based on NMF Features Selection Method

    NASA Astrophysics Data System (ADS)

    Abe, Bolanle T.; Jordaan, J. A.

    2013-12-01

    Hyperspectral instruments are capable of collecting hundreds of images corresponding to wavelength channels for the same area on the earth surface. Due to the huge number of features (bands) in hyperspectral imagery, land cover classification procedures are computationally expensive and pose a problem known as the curse of dimensionality. In addition, higher correlation among contiguous bands increases the redundancy within the bands. Hence, dimension reduction of hyperspectral data is very crucial so as to obtain good classification accuracy results. This paper presents a new feature selection technique. Non-negative Matrix Factorization (NMF) algorithm is proposed to obtain reduced relevant features in the input domain of each class label. This aimed to reduce classification error and dimensionality of classification challenges. Indiana pines of the Northwest Indiana dataset is used to evaluate the performance of the proposed method through experiments of features selection and classification. The Waikato Environment for Knowledge Analysis (WEKA) data mining framework is selected as a tool to implement the classification using Support Vector Machines and Neural Network. The selected features subsets are subjected to land cover classification to investigate the performance of the classifiers and how the features size affects classification accuracy. Results obtained shows that performances of the classifiers are significant. The study makes a positive contribution to the problems of hyperspectral imagery by exploring NMF, SVMs and NN to improve classification accuracy. The performances of the classifiers are valuable for decision maker to consider tradeoffs in method accuracy versus method complexity.

  9. 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.

  10. Thermal luminescence spectroscopy chemical imaging sensor.

    PubMed

    Carrieri, Arthur H; Buican, Tudor N; Roese, Erik S; Sutter, James; Samuels, Alan C

    2012-10-01

    The authors present a pseudo-active chemical imaging sensor model embodying irradiative transient heating, temperature nonequilibrium thermal luminescence spectroscopy, differential hyperspectral imaging, and artificial neural network technologies integrated together. We elaborate on various optimizations, simulations, and animations of the integrated sensor design and apply it to the terrestrial chemical contamination problem, where the interstitial contaminant compounds of detection interest (analytes) comprise liquid chemical warfare agents, their various derivative condensed phase compounds, and other material of a life-threatening nature. The sensor must measure and process a dynamic pattern of absorptive-emissive middle infrared molecular signature spectra of subject analytes to perform its chemical imaging and standoff detection functions successfully. PMID:23033092

  11. Near-infrared hyperspectral imaging of atherosclerotic tissue phantom

    NASA Astrophysics Data System (ADS)

    Ishii, K.; Nagao, R.; Kitayabu, A.; Awazu, K.

    2013-06-01

    A method to identify vulnerable plaques that are likely to cause acute coronary events has been required. The object of this study is identifying vulnerable plaques by hyperspectral imaging in near-infrared range (NIR-HSI) for an angioscopic application. In this study, NIR-HSI of atherosclerotic tissue phantoms was demonstrated under simulated angioscopic conditions. NIR-HSI system was constructed by a NIR super continuum light and a mercury-cadmium-telluride camera. Spectral absorbance values were obtained in the wavelength range from 1150 to 2400 nm at 10 nm intervals. The hyperspectral images were constructed with spectral angle mapper algorithm. As a result, detections of the lipid area in the atherosclerotic tissue phantom under angioscopic observation conditions were achieved especially in the wavelength around 1200 nm, which corresponds to the second overtone of CH stretching vibration mode.

  12. MIST Final Report: Multi-sensor Imaging Science and Technology

    SciTech Connect

    Lind, Michael A.; Medvick, Patricia A.; Foley, Michael G.; Foote, Harlan P.; Heasler, Patrick G.; Thompson, Sandra E.; Nuffer, Lisa L.; Mackey, Patrick S.; Barr, Jonathan L.; Renholds, Andrea S.

    2008-03-15

    The Multi-sensor Imaging Science and Technology (MIST) program was undertaken to advance exploitation tools for Long Wavelength Infra Red (LWIR) hyper-spectral imaging (HSI) analysis as applied to the discovery and quantification of nuclear proliferation signatures. The program focused on mitigating LWIR image background clutter to ease the analyst burden and enable a) faster more accurate analysis of large volumes of high clutter data, b) greater detection sensitivity of nuclear proliferation signatures (primarily released gasses) , and c) quantify confidence estimates of the signature materials detected. To this end the program investigated fundamental limits and logical modifications of the more traditional statistical discovery and analysis tools applied to hyperspectral imaging and other disciplines, developed and tested new software incorporating advanced mathematical tools and physics based analysis, and demonstrated the strength and weaknesses of the new codes on relevant hyperspectral data sets from various campaigns. This final report describes the content of the program and the outlines the significant results.

  13. Hyperspectral Image Classification Using Multi-Class SLEX Model

    Microsoft Academic Search

    Hsiao-Yun Huang; Hsiang-Chuan Liu; Bor-Chen Kuo; Tien-Yu Hsieh

    2006-01-01

    In this paper, a new discrimination scheme is proposed for classifying multi-group hyperspectral image. The smooth localized complex exponentials (SLEX) library and a modified Bottom-Up Generalized Local Discriminant Bases (MGLDB-BU) algorithm are adopted for extracting ideal features for discrimination. With the extracted features, a mechanism based on Chernoff information is employed for classification. The effectiveness of the proposed scheme as

  14. Cross-Calibration Approach Improves Accuracy for Hyperspectral Imaging

    Microsoft Academic Search

    G. Kopp; P. Pilewskie; D. Harber; J. Harder; B. McClintock; R. Lewis; E. Richard; T. Sparn; J. Young

    2007-01-01

    Hyperspectral imaging of the Earth's surface in the visible and near infrared from the CLARREO mission requires high levels of radiometric accuracy with stability maintained on-orbit. Traditional approaches rely on ground- based calibrations for accuracy and on-board sources for tracking post-launch changes in instrument sensitivity. The proposed on-orbit cross-calibration approach improves radiometric accuracy and stability by transferring the highly accurate

  15. Non-destructive hyperspectral imaging of quarantined Mars Returned Samples

    NASA Astrophysics Data System (ADS)

    Simionovici, Alexandre; Viso, Michel; Beck, Pierre; Lemelle, Laurence; Westphal, Andrew; Vincze, Laszlo; Schoonjans, Tom; Fihman, Francois; Chazalnoel, Pascale; Ferroir, Tristan; Solé, Vicente Armando; Tucoulou, R.

    Introduction: In preparation for the upcoming International Mars Sample Return mission (MSR), returning samples containing potential biohazards, we have implemented a hyperspec-tral method of in-situ analysis of grains performed in BSL4 quarantine conditions, by combining several non-destructive imaging diagnostics. This allows sample transportation on optimized experimental setups, while monitoring the sample quarantine conditions. Our hyperspectral methodology was tested during analyses of meteorites [1-2] and cometary and interstellar grains from the recent NASA Stardust mission [3-6]. Synchrotron Radiation protocols: X-ray analysis methods are widely accepted as the least destructive probes of fragile, unique samples. Diffraction, X-ray fluorescence and ab-sorption micro/nano-spectroscopies were performed on chondritic test samples using focused monochromatic beams at the ESRF synchrotron in Grenoble, France. 2D maps of grain com-position down to ppm concentrations and polycrystalline structure have simultaneously been acquired, followed by X-ray absorption performed on elements of Z 26. Ideally, absorption micro-tomography can later be performed in full-beam mode to record the 3D morphology of the grain followed by fluorescence-tomography in focus-beam mode which complements this picture with a 3D elemental image of the grain. Lab-based protocols: Raman and IR-based spectroscopies have been performed in reflection mode for mineralogical imaging of the grains in the laboratory using commercial microscopes. The spatial resolution varied in the 1-10 m range. Laser limited penetration of opaque samples permits only 2D imaging of the few nanometer-thick outer layers of the grains. Mineralogical maps are now routinely acquired using Raman spectroscopy at sub-micron scales through the 3 container walls of the Martian sample holder, followed by IR few-micrometer spot measurements recording C-based and potential aqueous alteration distributions. Sample Holder: A miniaturized sample-holder [7] has been designed and built to allow direct analyses of a set of extraterrestrial grains confined in a sealed triple container and remotely po-sitioned in front of the X-ray or laser beams of the various setups. The grains are held in several thin walls (10 m) ultrapure silica capillaries which are sufficiently resistant for manual/remote-controlled micro-manipulation but semitransparent for the characteristic X-rays, Raman and IR radiations. Miniaturized pressure/temperature sensors located in each container periodically monitor the integrity of the ensemble, ensuring BSL4 condi-tions. References: [1] B. Golosio, A. Simionovici, A. Somogyi, L. Lemelle, M. Chukalina, A. Brunetti, Jrnl. of App. Phys. 94, 145-157, 2003 [2] L. Lemelle, A. Simionovici, R. Truche, Ch. Rau, M. Chukalina, Ph. Gillet, Am. Min. 87 , 547-553, 2004 [3] Michael E. Zolensky et al., Science 314, 1735-1739, 2006 [4] G. J. Flynn et al., Science 314, 1731-1735, 2006 [5] Pierre Bleuet, Alexandre Simionovici, Laurence Lemelle, Tristan Ferroir, Peter Cloetens, Rémi Tu-coulou, Jean Susini, App. Phys. Lett. 92, 213111-1-3, 2008. [6] A. J. Westphal, et al., AIP Proceedings of the ICXOM Congress, (in print) 2010. [8] A. Simionovici and CNES, patent pending.

  16. 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. ...

  17. 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...

  18. 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, ...

  19. 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 ?rad. 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.

  20. Spatial and temporal point tracking in real hyperspectral images

    NASA Astrophysics Data System (ADS)

    Aminov, Benjamin; Nichtern, Ofir; Rotman, S. R.

    2008-10-01

    This paper addresses the problem of tracking a dim moving point target from a sequence of hyperspectral cubes. The resulting tracking algorithm is useful for many staring technologies such as the ones used in space surveillance and missile tracking applications. In these applications, the images consist of targets moving at sub-pixel velocity and noisy background consisting of evolving clutter and noise. The demand for a low false alarm rate (FAR) on one hand and a high probability of detection (PD) on the other makes the tracking a challenging task. The use of hyperspectral images should be superior to current technologies using broadband IR images due to the ability of exploiting simultaneously two target specific properties: the spectral target characteristics and the time dependent target behavior. The proposed solution consists of three stages: the first stage transforms the hyperspectral cubes into a two dimensional sequence, using known point target detection acquisition methods; the second stage involves a temporal separation of the 2D sequence into sub-sequences and the usage of a variance filter (VF) to detect the presence of targets from the temporal profile of each pixel in each group, while suppressing clutter specific influences. This stage creates a new sequence containing a target with a seemingly faster velocity; the third stage applies the Dynamic Programming Algorithm (DPA) that proves to be a very effective algorithm for the tracking of moving targets with low SNR at around pixel velocity. The system is tested on both synthetic and real data.

  1. Efficient hyperspectral image segmentation using geometric active contour formulation

    NASA Astrophysics Data System (ADS)

    Albalooshi, Fatema A.; Sidike, Paheding; Asari, Vijayan K.

    2014-10-01

    In this paper, we present a new formulation of geometric active contours that embeds the local hyperspectral image information for an accurate object region and boundary extraction. We exploit self-organizing map (SOM) unsupervised neural network to train our model. The segmentation process is achieved by the construction of a level set cost functional, in which, the dynamic variable is the best matching unit (BMU) coming from SOM map. In addition, we use Gaussian filtering to discipline the deviation of the level set functional from a signed distance function and this actually helps to get rid of the re-initialization step that is computationally expensive. By using the properties of the collective computational ability and energy convergence capability of the active control models (ACM) energy functional, our method optimizes the geometric ACM energy functional with lower computational time and smoother level set function. The proposed algorithm starts with feature extraction from raw hyperspectral images. In this step, the principal component analysis (PCA) transformation is employed, and this actually helps in reducing dimensionality and selecting best sets of the significant spectral bands. Then the modified geometric level set functional based ACM is applied on the optimal number of spectral bands determined by the PCA. By introducing local significant spectral band information, our proposed method is capable to force the level set functional to be close to a signed distance function, and therefore considerably remove the need of the expensive re-initialization procedure. To verify the effectiveness of the proposed technique, we use real-life hyperspectral images and test our algorithm in varying textural regions. This framework can be easily adapted to different applications for object segmentation in aerial hyperspectral imagery.

  2. 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%.

  3. Calibration of an AOTF hyperspectral imager with configurable spectral selectivity

    NASA Astrophysics Data System (ADS)

    Liu, Jifan; Ma, Yanhua; Zhang, Lei; Wang, Jianyu; Shu, Rong

    2011-08-01

    The hyperspectral imager is a kind of camera that can image objects in many narrow spectral bands, and acousto-optic tunable filters (AOTFs) can be used as the optical filtering elements in such instruments. AOTFs have many advantages such as solid-state, small size, good environmental adaptability, programmable, electronically control and so on, which are suitable for space exploration. For instance, AOTFs have been used in Mars and Venus detection. However, more advantages of AOTFs can be utilized in spectral detection, such as random wavelength access and configurable spectral resolution, and more flexible imagers can be expected with these characteristics. As a result, a new hyperspectral imager based on AOTF has been realized. It can not only take images in the spectral range of 460~1100nm with more than one hundred narrow bands, but also allow users to select any set of bands and configure the spectral resolution in a certain range just by computer commands. To do so, a multi-channel RF generation system is developed to drive the AOTF. When multi RF frequencies are applied to the AOTF simultaneously, not only the central wavelength, but also the bandwidth and the passband shape of the selected band, can be controlled by configuring the RF signals. Such capability enhances the flexibility of hyperspectral imaging, but the increased number of configurable variables complicates the course of calibration, so some specific calibration setups and methods are needed. In this paper, the laboratory calibration of the imager is introduced, and some results are presented and analyzed.

  4. Blind and fully constrained unmixing of hyperspectral images.

    PubMed

    Ammanouil, Rita; Ferrari, André; Richard, Cédric; Mary, David

    2014-12-01

    This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem and is solved with the alternating direction method of multipliers. The second one accounts for signal-dependent noise and is addressed with a reweighted least squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach. PMID:25312929

  5. Blind and Fully Constrained Unmixing of Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Ammanouil, Rita; Ferrari, Andre; Richard, Cedric; Mary, David

    2014-12-01

    This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem, and is solved with the Alternating Direction Method of Multipliers. The second one accounts for signal-dependent noise, and is addressed with a Reweighted Least Squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach.

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  7. 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.

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

    PubMed

    Li, Qingli; Liu, Zhi

    2009-04-01

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

  9. 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.

  10. 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...

  11. MEASUREMENT OF THE OPTICAL PROPERTIES OF APPLES USING HYPERSPECTRAL DIFFUSE REFLECTANCE IMAGING

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper reports on the development of a novel hyperspectral imaging technique for rapid determination of the absorption and scattering properties of turbid food materials over the visible and near-infrared region of 450-1,000 nm. A hyperspectral imaging system in line scanning mode was tested and...

  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. 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...

  14. Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review

    Microsoft Academic Search

    Yao-Ze Feng; Da-Wen Sun

    2012-01-01

    Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a

  15. DETECTION OF BRUISES ON PICKLING CUCUMBERS USING NEAR-INFRARED HYPERSPECTRAL IMAGING

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Near-infrared (NIR) hyperspectral imaging technique was investigated for the detection of bruises on pickling cucumbers caused by mechanical stress. An NIR hyperspectral imaging system was developed to acquire both spatial and spectral information from pickling cucumbers in the spectral region of 9...

  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. Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images

    E-print Network

    Plaza, Antonio J.

    Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images, correspond- ing exceeds the num- ber of pure spectral components, called endmembers in hyperspectral analysis terminology

  18. Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review

    Microsoft Academic Search

    Gamal Elmasry; Mohammed Kamruzzaman; Da-Wen Sun; Paul Allen

    2012-01-01

    The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system

  19. 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...

  20. 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.

  1. 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.

  2. Hyperspectral imaging of microalgae using two-photon excitation.

    SciTech Connect

    Sinclair, Michael B.; Melgaard, David Kennett; Reichardt, Thomas A. (Sandia National Laboratories, Livermore, CA); 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-photon hyperspectral imaging results on a variety of microalgae species and show how these results can be used to characterize algal ponds and raceways.

  3. Hyperspectral image compression using SPIHT based on DCT and DWT

    NASA Astrophysics Data System (ADS)

    Wei, Haiping; Zhao, Baojun; He, Peikun

    2007-11-01

    In this paper, an algorithm for hyperspectral image compression is presented. It carries DCT (Discrete Cosine Transform) on spectral bands to exploit the spectral correlation and then DWT (Discrete Wavelet Transform) on every eigen image to exploit the spatial correlation. After that, 3D-SPIHT (three-dimensional Set Partitioning in Hierarchical Trees) is performed for encoding. Experiments were done on the OMIS-I (Operational Modular Imaging Spectrometer) image and the performance of this algorithm was compared with that of 2D-SPIHT. The results show that the performance of 3D-SPIHT based on DCT and DWT is much better than that of 2D-SPIHT and the quality of the reconstructed images is satisfying.

  4. Hyperspectral band selection and classification of Hyperion image of Bhitarkanika mangrove ecosystem, eastern India

    NASA Astrophysics Data System (ADS)

    Ashokkumar, L.; Shanmugam, S.

    2014-10-01

    Tropical mangrove forests along the coast evolve dynamically due to constant changes in the natural ecosystem and ecological cycle. Remote sensing has paved the way for periodic monitoring and conservation of such floristic resources, compared to labour intensive in-situ observations. With the laboratory quality image spectra obtained from hyperspectral image data, species level discrimination in habitats and ecosystems is attainable. One of the essential steps before classification of hyperspectral image data is band selection. It is important to eliminate the redundant bands to mitigate the problems of Hughes effect that are likely to affect further image analysis and classification accuracy. This paper presents a methodology for the selection of appropriate hyperspectral bands from the EO-1 Hyperion image for the identification and mapping of mangrove species and coastal landcover types in the Bhitarkanika coastal forest region, eastern India. Band selection procedure follows class based elimination procedure and the separability of the classes are tested in the band selection process. Individual bands are de-correlated and redundant bands are removed from the bandwise correlation matrix. The percent contribution of class variance in each band is analysed from the factors of PCA component ranking. Spectral bands are selected from the wavelength groups and statistically tested. Further, the band selection procedure is compared with similar techniques (Band Index and Mutual information) for validation. The number of bands in the Hyperion image was reduced from 196 to 88 by the Factor-based ranking approach. Classification was performed by Support Vector Machine approach. It is observed that the proposed Factor-based ranking approach performed well in discriminating the mangrove species and other landcover units compared to the other statistical approaches. The predominant mangrove species Heritiera fomes, Excoecaria agallocha and Cynometra ramiflora are spectral identified and the health status of these species are assessed by the selected band. Further, the performance of this band selection approaches are evaluated in multi-sensor image fusion for better mapping of mangrove ecosystems, wherein spatial resolution is enhanced while retaining the optimal number of hyperspectral bands.

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

    NASA Technical Reports Server (NTRS)

    Blonski, Slawomir; Glasser, Gerald; Russell, Jeffrey; Ryan, Robert; Terrie, Greg; Zanoni, Vicki

    2003-01-01

    Spectral band synthesis is a key step in the process of creating a simulated multispectral image from hyperspectral data. In this step, narrow hyperspectral bands are combined into broader multispectral bands. Such an approach has been used quite often, but to the best of our knowledge accuracy of the band synthesis simulations has not been evaluated thus far. Therefore, the main goal of this paper is to provide validation of the spectral band synthesis algorithm used in the ART software. The next section contains a description of the algorithm and an example of its application. Using spectral responses of AVIRIS, Hyperion, ALI, and ETM+, the following section shows how the synthesized spectral bands compare with actual bands, and it presents an evaluation of the simulation accuracy based on results of MODTRAN modeling. In the final sections of the paper, simulated images are compared with data acquired by actual satellite sensors. First, a Landsat 7 ETM+ image is simulated using an AVIRIS hyperspectral data cube. Then, two datasets collected with the Hyperion instrument from the EO-1 satellite are used to simulate multispectral images from the ALI and ETM+ sensors.

  6. Compression of hyperspectral images with discriminant features enhanced

    NASA Astrophysics Data System (ADS)

    Lee, Chulhee; Choi, Euisun; Jeong, Taeuk; Lee, Sangwook; Lee, Jonghwa

    2010-10-01

    In this paper, we propose two compression methods for hyperspectral images with discriminant features enhanced. Generally, when hyperspectral images are compressed with conventional image compression algorithms, which mainly minimize mean squared errors, discriminant features of the original data may not be well preserved since they may not be necessarily large in energy. In this paper, we propose two compression methods that do preserve the discriminant information. In the first method, we enhanced the discriminant features and then compressed the enhanced data using conventional image compression algorithms such as 3D JPEG 2000. In the second method, we applied a feature extraction method and extracted the discriminantly dominant feature vectors. By examining the dominant feature vectors, we determined the discriminant usefulness of each spectral band. Based on these findings, we determined the bit allocation of each spectral band assuming 2D compression methods are used. Experiments show that the proposed methods effectively preserved the discriminant information and yielded improved classification accuracies compared to existing compression algorithms.

  7. 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 ?m and 9.4+/-1.8 ?m, 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.

  8. Quantum cascade lasers (QCL) for active hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Yang, Quankui; Fuchs, Frank; Wagner, Joachim

    2014-04-01

    There is an increasing demand for wavelength agile laser sources covering the mid-infrared (MIR, 3.5-12 µm) wavelength range, among others in active imaging. The MIR range comprises a particularly interesting part of the electromagnetic spectrum for active hyperspectral imaging applications, due to the fact that the characteristic `fingerprint' absorption spectra of many chemical compounds lie in that range. Conventional semiconductor diode laser technology runs out of steam at such long wavelengths. For many applications, MIR coherent light sources based on solid state lasers in combination with optical parametric oscillators are too complex and thus bulky and expensive. In contrast, quantum cascade lasers (QCLs) constitute a class of very compact and robust semiconductor-based lasers, which are able to cover the mentioned wavelength range using the same semiconductor material system. In this tutorial, a brief review will be given on the state-of-the-art of QCL technology. Special emphasis will be addressed on QCL variants with well-defined spectral properties and spectral tunability. As an example for the use of wavelength agile QCL for active hyperspectral imaging, stand-off detection of explosives based on imaging backscattering laser spectroscopy will be discussed.

  9. Adaptive classification of hyperspectral images using local consistency

    NASA Astrophysics Data System (ADS)

    Bian, Xiaoyong; Zhang, Xiaolong; Liu, Renfeng; Ma, Li; Fu, Xiaowei

    2014-11-01

    A spatial method of multistructure sampling based rotation-invariant uniform local binary pattern (named MsLBPriu2) for classification of hyperspectral images is proposed. This method exploits the local property (micro-/macrostructure) of local image patches encoded in the classifier by considering a local neighboring structure around each central pixel and can well suppress the difference of rotational textures for each multicluster class. The proposed method is simple yet efficient for extracting isotropic and anisotropic spatial features from local image patches via different extended sampling on circular regions and elliptical ones with four different rotational angles. Furthermore, the rotation-invariant characteristic of extracted isotropic features is achieved by the inclusion of a rotation-invariant uniform LBP operator. Moreover, the proposed method becomes more robust with respect to the within-class variation. Finally, different classifiers, support vector machine, K-nearest neighbor, and linear discriminant analysis, are compared to evaluate MsLBPriu2 and other feature sets/entropy-based query-by-bagging active learning. We demonstrate the performance of our approach on four different hyperspectral remote sensing images. Experimental results show that the new set of reduced spatial features has a better performance than a variety of state-of-the-art classification algorithms.

  10. A novel binary tree support vector machine for hyperspectral remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Du, Peijun; Tan, Kun; Xing, Xiaoshi

    2012-06-01

    According to the principle of support vector machine (SVM) and the inter-class separability rule of hyperspectral data, a novel binary tree SVM classifier based on separability measure among different classes is proposed for hyperspectral image classification. J-M distance is used to measure the separability in order to generate the binary tree automatically. By experiments using airborne operational modular imaging spectrometer II (OMIS II) data, satellite EO-1 Hyperion hyperspectral data and airborne AVIRIS data, the classification accuracy of different multi-class SVMs is obtained and compared. Experimental results indicate that the proposed adaptive binary tree classifier outperforms other existing multi-class SVM strategies. Use of the adaptive binary tree SVM classifier is a novel approach to improve the accuracy of hyperspectral image classification and expand the possibilities for interpretation and application of hyperspectral remote sensing image.

  11. 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.

  12. Characterization of Chromobacterium violaceum pigment through a hyperspectral imaging system

    PubMed Central

    2014-01-01

    In this paper, a comprehensive spatio-spectral and temporal analysis for Chromobacterium violaceum colonies is reported. A hyperspectral imaging (HSI) system is used to recover the spectral signatures of pigment production in a non-homogeneous media with high spectral resolution and high sensitivity in vivo, without destructing the sample. This non-contact sensing technique opens avenues to study the temporal growing of a specific section in the bacterial colony. Further, from a 580 [nm] and 764 [nm] spatio-spectral time series, a wild-type and mutant Chromobacterium violaceum strains are characterized. Such study provides quantitative information about kinetic parameters of pigment production and bacterial growing. PMID:24417877

  13. Parallel random selection and projection for hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Du, Qian; Li, Xiaochao

    2014-10-01

    In this paper, we investigate the use of random selection (RS) and random projection (RP) for hyperspectral image analysis, which are data-independent and computationally more efficient than other widely used dimensionality reduction methods. Both anomaly detection and target detection are considered. Due to the random nature, multiple runs of RS or RP are conducted followed by decision fusion to ensure a stable output. Parallel implementations using graphics processing unit (GPU) and clusters are also investigated. The experimental results demonstrated that both RS and RP are capable of providing better target detection performance after decision fusion, while the overall computing time can be greatly decreased with parallel implementations.

  14. GPU implementation issues for fast unmixing of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Legendre, Maxime; Capriotti, Luca; Schmidt, Frédéric; Moussaoui, Saïd; Schmidt, Albrecht

    2013-04-01

    Space missions usually use hyperspectral imaging techniques to analyse the composition of planetary surfaces. Missions such as ESA's Mars Express and Venus Express generate extensive datasets whose processing demands so far have exceeded the resources available to many researchers. To overcome this limitation, the challenge is to develop numerical methods allowing to exploit the potential of modern calculation tools. The processing of a hyperspectral image consists of the identification of the observed surface components and eventually the assessment of their fractional abundances inside each pixel area. In this latter case, the problem is referred to as spectral unmixing. This work focuses on a supervised unmixing approach where the relevant component spectra are supposed to be part of an available spectral library. Therefore, the question addressed here is reduced to the estimation of the fractional abundances, or abundance maps. It requires the solution of a large-scale optimization problem subject to linear constraints; positivity of the abundances and their partial/full additivity (sum less/equal to one). Conventional approaches to such a problem usually suffer from a high computational overhead. Recently, an interior-point optimization using a primal-dual approach has been proven an efficient method to solve this spectral unmixing problem at reduced computational cost. This is achieved with a parallel implementation based on Graphics Processing Units (GPUs). Several issues are discussed such as the data organization in memory and the strategy used to compute efficiently one global quantity from a large dataset in a parallel fashion. Every step of the algorithm is optimized to be GPU-efficient. Finally, the main steps of the global system for the processing of a large number of hyperspectral images are discussed. The advantage of using a GPU is demonstrated by unmixing a large dataset consisting of 1300 hyperspectral images from Mars Express' OMEGA instrument and uses a spectral library of 41 possible ground components. The parallel implementation proposed requires three days of calculation to unmix this dataset, instead of twenty days for the optimized CPU implementation previously in use.

  15. 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

  16. Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Padma, S.; Sanjeevi, S.

    2014-10-01

    This paper proposes a novel hyperspectral matching technique by integrating the Jeffries-Matusita measure (JM) and the Spectral Angle Mapper (SAM) algorithm. The deterministic Spectral Angle Mapper and stochastic Jeffries-Matusita measure are orthogonally projected using the sine and tangent functions to increase their spectral ability. The developed JM-SAM algorithm is implemented in effectively discriminating the landcover classes and cover types in the hyperspectral images acquired by PROBA/CHRIS and EO-1 Hyperion sensors. The reference spectra for different land-cover classes were derived from each of these images. The performance of the proposed measure is compared with the performance of the individual SAM and JM approaches. From the values of the relative spectral discriminatory probability (RSDPB) and relative discriminatory entropy value (RSDE), it is inferred that the hybrid JM-SAM approach results in a high spectral discriminability than the SAM and JM measures. Besides, the use of the improved JM-SAM algorithm for supervised classification of the images results in 92.9% and 91.47% accuracy compared to 73.13%, 79.41%, and 85.69% of minimum-distance, SAM and JM measures. It is also inferred that the increased spectral discriminability of JM-SAM measure is contributed by the JM distance. Further, it is seen that the proposed JM-SAM measure is compatible with varying spectral resolutions of PROBA/CHRIS (62 bands) and Hyperion (242 bands).

  17. Latest advancements in fluorescence hyperspectral lidar imaging of the cultural heritage

    NASA Astrophysics Data System (ADS)

    Raimondi, V.; Conti, C.; Lognoli, D.; Palombi, L.

    2013-11-01

    Fluorescence lidar imaging can be regarded as an effective tool for early diagnostics and documentation of the outdoor cultural heritage, with the aim of a correct planning of conservation and restoration of monuments. In this paper we present the latest advancements on fluorescence hyperspectral lidar imaging recently achieved at IFAC-CNR in terms of instrumentation and novel applications. In particular, the paper focuses on the upgrading of some key technical features, such as: the scan speed of the sensor, spatial resolution at the surface and field of view of the instrument. The upgrading of these technical characteristics has also made it possible to successfully extend the applicability of the technique to the diagnostics on wall paintings, which requires an improved spatial resolution. Finally, we outline the potential of a new concept of fluorescence lidar imaging system, based on the integration of hyperspectral and fluorescence lifetime spectroscopy, which enhances the capabilities of the technique for the characterization of the materials to be investigated in cultural heritage assets.

  18. 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.

  19. 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.

  20. Identifying volcanic endmembers in hyperspectral images using spectral unmixing

    NASA Astrophysics Data System (ADS)

    Piscini, Alessandro; Carboni, Elisa; Del Frate, Fabio; Grainger, Roy Gordon

    2014-10-01

    Spectral unmixing technique is used in remote sensed data analysis for the determination of certain basis spectra called 'endmembers'. Once those spectra are found, the image cube can be 'unmixed' into fractional abundance of each material in each pixel. In the present work infrared spectra recorded by Infrared Atmospheric Sounding Interferometer (IASI) were used to characterize the emission from Grimsvotn volcanic eruption on 2011. In particular, a methodology based on spectral unmixing theory was used in order to extract the spectral signature of volcanic cloud constituents, such as ash and sulphur dioxide (SO2) and maps of their abundances in a IASI image were obtained. Taking the advantage of IASI broad spectral coverage the broadband signature in the Thermal Infrared (TIR) radiance spectra in the 1000-1410 cm-1 range associated with the presence of aerosols was obtained. Volcanic ash and SO2 spectral signatures were extracted, as well as those related to the simultaneous presence of ash, SO2 and cloud. The study proved that spectral unmixing, applied to Hyperspectral images, is able to identify volcanic aerosols and other species like SO2 despite a strong presence of meteorological clouds. Moreover, the analysis of hyperspectral datasets permitted to generate abundance maps for each endmember extracted. In particular, maps obtained for the test case of 2011 May, 23th put in evidence the separation between clouds of ejected SO2 and volcanic ash. The former dispersed at Northern latitudes, whilst the latter was situated at southern latitudes, South of Iceland.

  1. 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.

  2. A fast fully constrained geometric unmixing of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Zhou, Xin; Li, Xiao-run; Cui, Jian-tao; Zhao, Liao-ying; Zheng, Jun-peng

    2014-11-01

    A great challenge in hyperspectral image analysis is decomposing a mixed pixel into a collection of endmembers and their corresponding abundance fractions. This paper presents an improved implementation of Barycentric Coordinate approach to unmix hyperspectral images, integrating with the Most-Negative Remove Projection method to meet the abundance sum-to-one constraint (ASC) and abundance non-negativity constraint (ANC). The original barycentric coordinate approach interprets the endmember unmixing problem as a simplex volume ratio problem, which is solved by calculate the determinants of two augmented matrix. One consists of all the members and the other consist of the to-be-unmixed pixel and all the endmembers except for the one corresponding to the specific abundance that is to be estimated. In this paper, we first modified the algorithm of Barycentric Coordinate approach by bringing in the Matrix Determinant Lemma to simplify the unmixing process, which makes the calculation only contains linear matrix and vector operations. So, the matrix determinant calculation of every pixel, as the original algorithm did, is avoided. By the end of this step, the estimated abundance meet the ASC constraint. Then, the Most-Negative Remove Projection method is used to make the abundance fractions meet the full constraints. This algorithm is demonstrated both on synthetic and real images. The resulting algorithm yields the abundance maps that are similar to those obtained by FCLS, while the runtime is outperformed as its computational simplicity.

  3. 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, 8?m to 12 ?m) 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.

  4. Lightning Imaging Sensor Data

    NSDL National Science Digital Library

    Lightning and Atmospheric Electricity Research at the Global Hydrology and Climate Center provides these "GIF images showing a graphical representation of the Lightning Imaging Sensor orbit data for each day." The lightning distribution images are available by clicking on highlighted spots on a global map. Data are released one month at a time and currently include December 1997 to the present.

  5. 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 with respect to the target or by using a scan mirror. The SETA optical concept is mostly inherited from the SIMBIO-SYS/VIHI (Visible Infrared Hyperspectral Imager) imaging spectrometer aboard Bepi Colombo mission but also from other space flying imaging spectrometers, such as VIRTIS (on Rosetta and Venus Express, VIR on DAWN).

  6. Development of a Hyperspectral Imaging System for Online Quality Inspection of Pickling Cucumbers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper reports on the development of a hyperspectral imaging prototype for evaluation of external and internal quality of pickling cucumbers. The prototype consisted of a two-lane round belt conveyor, two illumination sources (one for reflectance and one for transmittance), and a hyperspectral i...

  7. Design and operation of SUCHI: the space ultra-compact hyperspectral imager for a small satellite

    NASA Astrophysics Data System (ADS)

    Crites, S. T.; Lucey, P. G.; Wright, R.; Chan, J.; Garbeil, H.; Horton, K. A.; Imai, A.; Pilger, E. J.; Wood, M.; Yoneshige, Lance

    2014-06-01

    The primary payload on the University of Hawaii-built `HiakaSat' micro-satellite will be the Space Ultra Compact Hyperspectral Imager (SUCHI). SUCHI is a low-mass (<9kg), low-volume (10x10x36 cm3) long wave infrared hyperspectral imager designed and built at the University of Hawaii. SUCHI is based on a variable-gap Fabry-Perot interferometer employed as a Fourier transform spectrometer with images collected by a commercial 320x256 microbolometer array. The microbolometer camera and vacuum-sensitive electronics are contained within a sealed vessel at 1 atm. SUCHI will collect spectral radiance data from 8 to 14 microns and demonstrate the potential of this instrument for geological studies from orbit (e.g. mapping of major rock-forming minerals) and volcanic hazard observation and assessment (e.g. quantification of volcanic sulfur dioxide pollution and lava flow cooling rates). The sensor has been integrated with the satellite which will launch on the Office of Responsive Space ORS-4 mission scheduled for 2014. The primary mission will last 6 months, with extended operations anticipated for approximately 2 years. A follow-on mission has been proposed to perform imaging of Earth's surface in the 3-5 micron range with a field of view of 5 km with 5.25 m sampling (from a 350 km orbit). The 19-kg proposed instrument will be a prototype sensor for a constellation of small satellites for Earth imaging. The integrated satellite properties will be incorporated into the Hawaii Space Flight Laboratory's constellation maintenance software environment COSMOS (Comprehensive Openarchitecture Space Mission Operations System) to ease future implementation of the instrument as part of a constellation.

  8. A 868MHz-based wireless sensor network for ground truthing of soil moisture for a hyperspectral remote sensing campaign - design and preliminary results

    NASA Astrophysics Data System (ADS)

    Näthe, Paul; Becker, Rolf

    2014-05-01

    Soil moisture and plant available water are important environmental parameters that affect plant growth and crop yield. Hence, they are significant parameters for vegetation monitoring and precision agriculture. However, validation through ground-based soil moisture measurements is necessary for accessing soil moisture, plant canopy temperature, soil temperature and soil roughness with airborne hyperspectral imaging systems in a corresponding hyperspectral imaging campaign as a part of the INTERREG IV A-Project SMART INSPECTORS. At this point, commercially available sensors for matric potential, plant available water and volumetric water content are utilized for automated measurements with smart sensor nodes which are developed on the basis of open-source 868MHz radio modules, featuring a full-scale microcontroller unit that allows an autarkic operation of the sensor nodes on batteries in the field. The generated data from each of these sensor nodes is transferred wirelessly with an open-source protocol to a central node, the so-called "gateway". This gateway collects, interprets and buffers the sensor readings and, eventually, pushes the data-time series onto a server-based database. The entire data processing chain from the sensor reading to the final storage of data-time series on a server is realized with open-source hardware and software in such a way that the recorded data can be accessed from anywhere through the internet. It will be presented how this open-source based wireless sensor network is developed and specified for the application of ground truthing. In addition, the system's perspectives and potentials with respect to usability and applicability for vegetation monitoring and precision agriculture shall be pointed out. Regarding the corresponding hyperspectral imaging campaign, results from ground measurements will be discussed in terms of their contributing aspects to the remote sensing system. Finally, the significance of the wireless sensor network for the application of ground truthing shall be determined.

  9. Image reconstruction and subsurface detection by the application of Tikhonov regularization to inverse problems in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Jiminez-Rodriguez, Luis O.; Rodriguez-Diaz, Eladio; Velez-Reyes, Miguel; DiMarzio, Charles A.

    2003-05-01

    Hyperspectral Remote Sensing has the potential to be used as an effective coral monitoring system from space. The problems to be addressed in hyperspectral imagery of coastal waters are related to the medium, clutter, and the object to be detected. In coastal waters the variability due to the interaction between the coast and the sea can bring significant disparity in the optical properties of the water column and the sea bottom. In terms of the medium, there is high scattering and absorption. Related to clutter we have the ocean floor, dissolved salt and gases, and dissolved organic matter. The object to be detected, in this case the coral reefs, has a weak signal, with temporal and spatial variation. In real scenarios the absorption and backscattering coefficients have spatial variation due to different sources of variability (river discharge, different depths of shallow waters, water currents) and temporal fluctuations. The retrieval of information about an object beneath some medium with high scattering and absorption properties requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. This paper presents the development of algorithms for retrieving information and its application to the recognition and classification of coral reefs under water with particles that provide high absorption and scattering. The data was gathered using a high resolution imaging spectrometer (hyperspectral) sensor. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo, ?, of the ocean floor using a priori information. The a priori information is in the form of measured spectral signatures of objects of interest, such as sand, corals, and sea grass.

  10. Multiple Classifiers and Graph Cut Methods for Spectral Spatial Classification of Hyperspectral Image

    NASA Astrophysics Data System (ADS)

    Bhushan, D. B.; Nidamanuri, R. R.

    2014-11-01

    Hyperspectral image contains fine spectral and spatial resolutions for generating accurate land use and land cover maps. Supervised classification is the one of method used to exploit the information from the hyperspectral image. The traditional supervised classification methods could not be able to overcome the limitations of the hyperspectral image. The multiple classifier system (MCS) has the potential to increase the classification accuracy and reliability of the hyperspectral image. However, the MCS extracts only the spectral information from the hyperspectral image and neglects the spatial contextual information. Incorporating spatial contextual information along with spectral information is necessary to obtain smooth classification maps. Our objective of this paper is to design a methodology to fully exploit the spectral and spatial information from the hyperspectral image for land cover classification using MCS and Graph cut (GC) method. The problem is modelled as the energy minimization problem and solved using ?-expansion based graph cut method. Experiments are conducted with two hyperspectral images and the result shows that the proposed MCS based graph cut method produces good quality classification map.

  11. 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.

  12. Hyperspectral imaging applied to medical diagnoses and food safety

    NASA Astrophysics Data System (ADS)

    Carrasco, Oscar; Gomez, Richard B.; Chainani, Arun; Roper, William E.

    2003-08-01

    This paper analyzes the feasibility and performance of HSI systems for medical diagnosis as well as for food safety. Illness prevention and early disease detection are key elements for maintaining good health. Health care practitioners worldwide rely on innovative electronic devices to accurately identify disease. Hyperspectral imaging (HSI) is an emerging technique that may provide a less invasive procedure than conventional diagnostic imaging. By analyzing reflected and fluorescent light applied to the human body, a HSI system serves as a diagnostic tool as well as a method for evaluating the effectiveness of applied therapies. The safe supply and production of food is also of paramount importance to public health illness prevention. Although this paper will focus on imaging and spectroscopy in food inspection procedures -- the detection of contaminated food sources -- to ensure food quality, HSI also shows promise in detecting pesticide levels in food production (agriculture.)

  13. Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique

    NASA Astrophysics Data System (ADS)

    Guan, Yana; Li, Qingli; Wang, Yiting; Liu, Hongying; Zhu, Ziqiang

    2012-05-01

    White blood cells (WBC) are comparatively significant components in the human blood system, and they have a pathological relationship with some blood-related diseases. To analyze the disease information accurately, the most essential work is to segment WBCs. We propose a new method for pathological WBC segmentation based on a hyperspectral imaging system. This imaging system is used to capture WBC images, which is characterized by acquiring 1-D spectral information and 2-D spatial information for each pixel. A spectral information divergence algorithm is presented to segment pathological WBCs into four parts. In order to evaluate the performance of the new approach, K-means and spectral angle mapper-based segmental methods are tested in contrast on six groups of blood smears. Experimental results show that the presented method can segment pathological WBCs more accurately, regardless of their irregular shapes, sizes, and gray-values.

  14. Hyperspectral image segmentation of the common bile duct

    NASA Astrophysics Data System (ADS)

    Samarov, Daniel; Wehner, Eleanor; Schwarz, Roderich; Zuzak, Karel; Livingston, Edward

    2013-03-01

    Over the course of the last several years hyperspectral imaging (HSI) has seen increased usage in biomedicine. Within the medical field in particular HSI has been recognized as having the potential to make an immediate impact by reducing the risks and complications associated with laparotomies (surgical procedures involving large incisions into the abdominal wall) and related procedures. There are several ongoing studies focused on such applications. Hyperspectral images were acquired during pancreatoduodenectomies (commonly referred to as Whipple procedures), a surgical procedure done to remove cancerous tumors involving the pancreas and gallbladder. As a result of the complexity of the local anatomy, identifying where the common bile duct (CBD) is can be difficult, resulting in comparatively high incidents of injury to the CBD and associated complications. It is here that HSI has the potential to help reduce the risk of such events from happening. Because the bile contained within the CBD exhibits a unique spectral signature, we are able to utilize HSI segmentation algorithms to help in identifying where the CBD is. In the work presented here we discuss approaches to this segmentation problem and present the results.

  15. Monitoring biofilm attachment on medical devices surfaces using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Le, Hanh N. D.; Hitchins, Victoria M.; Ilev, Ilko K.; Kim, Do-Hyun

    2014-02-01

    Microbial biofilm is a colony of single bacteria cells (planktonic) that attached to surfaces, attract other microorganisms to attach and grow, and together they build an extracellular matrix composed of polysaccharides, protein, and DNA. Eventually, some cells will detach and spread to other surface. Biofilm on medical devices can cause severe infection to all age ranges from infant to adult. Therefore, it is important to detect biofilm in a fast and efficient manner. Hyperspectral imaging was utilized for distinguishing wide area of biofilm coverage on various materials and on different textures of stainless steeltest coupons. Not only is the coverage of biofilm important, but also the shear stress of biofilm on the attached surfaces is significant. This study investigates the effects of shear stress on the adhesion of biofilms on common medical device surfaces such as glass, polycarbonate, polytetrafluoroethylene, and stainless steel with different textures. Biofilm was grown using Ps. aeruginosa and growth was monitored after 24 and 48 hours at 37° C. The coupons covered with biofilm were tilted at 45 degrees and 90 degrees for 30 seconds to induce shear stress and Hyperspectral images were taken. We hypothesize that stronger attachment on rough surface would be able to withstand greater shear stress compared to smooth surface.

  16. Hyperspectral image reconstruction for diffuse optical tomography

    E-print Network

    Fantini, Sergio

    imaging," Med. Phys. 30, 235­247 (2002). 7. A. Li, G. Boverman, Y. Zhang, D. Brooks, E. L. Miller, M. E Feb 2011; revised 10 Mar 2011; accepted 10 Mar 2011; published 25 Mar 2011 (C) 2011 OSA 1 April 2011

  17. Development of co-boresighted Vis-NIR-SWIR hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

    Wong, Kwok-Keung

    2014-05-01

    Hyperspectral Imaging is used in many applications to identify or analyze materials in a scene based on the materials' spectral signatures. Unique features in the spectral signatures can span beyond the spectral range of the hyperspectral imager. Additionally, lighting conditions and other factors can adversely affect the quality of data. Expanding the spectral range of hyperspectral imaging systems can therefore improve the accuracy of object/material recognition/analysis by allowing the system to "see" more of the spectral signatures as well as expand the number of objects/materials in a scene that can be identified/analyzed. This is particularly important in applications where erroneous identification or analysis can result in substantial risk or cost. More and more users are using two (or more) hyperspectral imagers to obtain different spectral ranges for their applications. Very few are effectively combining the data from the different hyperspectral imagers because it would require the hyperspectral imagers to be operated under tightly controlled conditions and the process of pixel coregistration is a very tedious and problematic post-processing step. In addition, this post-processing step prevents the use of the combined data in real-time applications. This paper describes a co-boresighted Vis-NIR and SWIR hyperspectral imaging system which Headwall Photonics is currently developing. It integrates two hyperspectral imagers, each optimized for its respective spectral range, into a single system with real-time pixel co-registration resulting in a system capable of producing wide-spectrum hyperspectral images with high spectral resolution. Aside from enabling real-time wide spectrum applications, such a system significantly simplifies the data acquisition and analysis for the user.

  18. Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

    NASA Astrophysics Data System (ADS)

    Zemliachenko, Alexander N.; Kozhemiakin, Ruslan A.; Uss, Mikhail L.; Abramov, Sergey K.; Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Vozel, Benoît; Chehdi, Kacem

    2014-01-01

    A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and "difference" images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15-20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.

  19. Near-infrared hyperspectral imaging for quality analysis of agricultural and food products

    NASA Astrophysics Data System (ADS)

    Singh, C. B.; Jayas, D. S.; Paliwal, J.; White, N. D. G.

    2010-04-01

    Agricultural and food processing industries are always looking to implement real-time quality monitoring techniques as a part of good manufacturing practices (GMPs) to ensure high-quality and safety of their products. Near-infrared (NIR) hyperspectral imaging is gaining popularity as a powerful non-destructive tool for quality analysis of several agricultural and food products. This technique has the ability to analyse spectral data in a spatially resolved manner (i.e., each pixel in the image has its own spectrum) by applying both conventional image processing and chemometric tools used in spectral analyses. Hyperspectral imaging technique has demonstrated potential in detecting defects and contaminants in meats, fruits, cereals, and processed food products. This paper discusses the methodology of hyperspectral imaging in terms of hardware, software, calibration, data acquisition and compression, and development of prediction and classification algorithms and it presents a thorough review of the current applications of hyperspectral imaging in the analyses of agricultural and food products.

  20. Improving 3D Wavelet-Based Compression of Hyperspectral Images

    NASA Technical Reports Server (NTRS)

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

    2009-01-01

    Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a manner similar to that of a baseline hyperspectral- image-compression method. The mean values are encoded in the compressed bit stream and added back to the data at the appropriate decompression step. The overhead incurred by encoding the mean values only a few bits per spectral band is negligible with respect to the huge size of a typical hyperspectral data set. The other method is denoted modified decomposition. This method is so named because it involves a modified version of a commonly used multiresolution wavelet decomposition, known in the art as the 3D Mallat decomposition, in which (a) the first of multiple stages of a 3D wavelet transform is applied to the entire dataset and (b) subsequent stages are applied only to the horizontally-, vertically-, and spectrally-low-pass subband from the preceding stage. In the modified decomposition, in stages after the first, not only is the spatially-low-pass, spectrally-low-pass subband further decomposed, but also spatially-low-pass, spectrally-high-pass subbands are further decomposed spatially. Either method can be used alone to improve the quality of a reconstructed image (see figure). Alternatively, the two methods can be combined by first performing modified decomposition, then subtracting the mean values from spatial planes of spatially-low-pass subbands.

  1. Hyperspectral imaging for dermal hemoglobin spectroscopy

    NASA Astrophysics Data System (ADS)

    Dwyer, Peter J.; DiMarzio, Charles A.

    1999-10-01

    It has been shown previously that images collected at selected wavelengths in a sufficiently narrow bandwidth can be used to produce maps of the oxygen saturation of hemoglobin in the dermis. A four-wavelength algorithm has been developed based on a two-layer model of the skin, in which the blood is contained in the lower layer (dermis), while the upper layer attenuates some of the reflection and adds a clutter term. In the present work, the algorithm is compared analytically to simpler algorithms using three wavelengths and based on a single-layer model. It is shown through Monte-Carlo models that, for typical skin, the single-layer model is adequate to analyze data from fiber-optical reflectance spectroscopy, but the two-layer model produces better results for imaging systems. Although the model does not address the full complexity of reflectance of a two-layer skin, it has proven to be sufficient to recover the oxygen saturation, and perhaps other medically relevant information. The algorithm is demonstrated on a suction blister, where the epidermis is removed to reveal the underlying dermis. Applications for this imaging modality exist in dermatology, in surgery, and in developing treatment plans for various diseases.

  2. Estimating foliar water content of winter wheat with hyperspectral image

    NASA Astrophysics Data System (ADS)

    Zhang, Xia; Jiao, Quanjun; Wu, Di; Zhang, Bing; Gao, Lianru

    2007-11-01

    Estimates of vegetation water content are of great interest for assessing vegetation water status in agriculture and forestry, and have been used for drought assessment. This study focuses on the retrieval of foliar water content with hyperspectral data at canopy level. The hyperspectral image used in this study was acquired by the airborne operative modular imaging spectrometer (OMIS) at Demonstration Site for Precision Agriculture in Xiaotangshan area, Beijing, on April 26th, 2001. 40 image spectra were extracted to correspond to the quasi-synchronous meansurements of foliar water content (FWC). The image spectra of winter wheat were utilized to validate the sensitivity of the existing and novel water indices and parameters of three water absorption features in NIR and SWIR regions. Correlation analysis showed that, NDWI(860,1241) and NDWI(860,1200) both had significant linear relationships with FWC (R2 were 0.4124 and 0.4042 respectively). Red edge position (REP) could reflect indirectly the variations of wheat FWC to some extent. Significant linear relationships were also found between WI(820,1600) and FWC, and between WI(900,1200) and FWC, while no relationship was shown between the traditional WI(900,970) and FWC. The derived depth of water absorption centered around 2078nm, namely AD2078, had the highest linear correlation with FWC (R2 is 0.4551) , much higher than those parameters derived from the two water absorption around 1175 and 1409. In the end, AD2078 was applied to OMIS image to map the foliar water content. The value range of the inverted foliar water content ranged from 69.39 to 78.35%, which was quite close to that of the field measurements (70.72-78.12%). The distribution of the FWC map was quite consistent with growth status of winter wheat.

  3. Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging.

    PubMed

    Chaudhari, Abhijit J; Darvas, Felix; Bading, James R; Moats, Rex A; Conti, Peter S; Smith, Desmond J; Cherry, Simon R; Leahy, Richard M

    2005-12-01

    For bioluminescence imaging studies in small animals, it is important to be able to accurately localize the three-dimensional (3D) distribution of the underlying bioluminescent source. The spectrum of light produced by the source that escapes the subject varies with the depth of the emission source because of the wavelength-dependence of the optical properties of tissue. Consequently, multispectral or hyperspectral data acquisition should help in the 3D localization of deep sources. In this paper, we describe a framework for fully 3D bioluminescence tomographic image acquisition and reconstruction that exploits spectral information. We describe regularized tomographic reconstruction techniques that use semi-infinite slab or FEM-based diffusion approximations of photon transport through turbid media. Singular value decomposition analysis was used for data dimensionality reduction and to illustrate the advantage of using hyperspectral rather than achromatic data. Simulation studies in an atlas-mouse geometry indicated that sub-millimeter resolution may be attainable given accurate knowledge of the optical properties of the animal. A fixed arrangement of mirrors and a single CCD camera were used for simultaneous acquisition of multispectral imaging data over most of the surface of the animal. Phantom studies conducted using this system demonstrated our ability to accurately localize deep point-like sources and show that a resolution of 1.5 to 2.2 mm for depths up to 6 mm can be achieved. We also include an in vivo study of a mouse with a brain tumour expressing firefly luciferase. Co-registration of the reconstructed 3D bioluminescent image with magnetic resonance images indicated good anatomical localization of the tumour. PMID:16306643

  4. Apple ripeness detection using hyperspectral laser scatter imaging

    NASA Astrophysics Data System (ADS)

    Van Beers, Robbe; Aernouts, Ben; De Baerdemaeker, Josse; Saeys, Wouter

    2013-05-01

    A hyperspectral laser scatter imaging (HLSI) system based on a supercontinuum laser in combination with a monochromator has been developed for contactless and non-destructive measuring the ripeness of Braeburn apples. Reflectance images were obtained by a CCD camera at 91 different wavelengths ranging from 550 nm to 1000 nm and transformed into reflectance profiles. A linear function was fitted to the logarithm (log10) of the extracted profiles, resulting in an intercept and a slope. These two parameters were then correlated with apple ripeness parameters such as firmness and soluble solids content (SSC) measured by the reference, destructive methods. Preliminary results showed the potential of slope and intercept to be used as a ripeness indicator. Moreover, during fruit ripening, the new HLSI measurement technique clearly showed the degradation of chlorophyll over time.

  5. 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.

  6. Towards a colony counting system using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Masschelein, B.; Robles-Kelly, A.; Blanch, C.; Tack, N.; Simpson-Young, B.; Lambrechts, A.

    2012-03-01

    Colony counting is a procedure used in microbiology laboratories for food quality monitoring, environmental management, etc. Its purpose is to detect the level of contamination due to the presence and growth of bacteria, yeasts and molds in a given product. Current automated counters require a tedious training and setup procedure per product and bacteria type and do not cope well with diversity. This contrasts with the setting at microbiology laboratories, where a wide variety of food and bacteria types have to be screened on a daily basis. To overcome the limitations of current systems, we propose the use of hyperspectral imaging technology and examine the spectral variations induced by factors such as illumination, bacteria type, food source and age and type of the agar. To this end, we perform experiments making use of two alternative hyperspectral processing pipelines and compare our classification results to those yielded by color imagery. Our results show that colony counting may be automated through the automatic recovery of the illuminant power spectrum and reflectance. This is consistent with the notion that the recovery of the illuminant should minimize the variations in the spectra due to reflections, shadows and other photometric artifacts. We also illustrate how, with the reflectance at hand, the colonies can be counted making use of classical segmentation and classification algorithms.

  7. Pigment identification in pictorial layers by HyperSpectral Imaging

    NASA Astrophysics Data System (ADS)

    Capobianco, Giuseppe; Bonifazi, Giuseppe; Prestileo, Fernanda; Serranti, Silvia

    2014-05-01

    The use of Hyper-Spectral Imaging (HSI) as a diagnostic tool in the field of cultural heritage is of great interest presenting high potentialities. This analysis, in fact, is non-destructive, non-invasive and portable. Furthermore, the possibility to couple hyperspectral data with chemometric techniques allows getting qualitative and/or quantitative information on the nature and physical-chemical characteristics of the investigated materials. A study was carried out to explore the possibilities offered by this approach to identify pigments in paintings. More in detail, six pigments have been selected and they have been then mixed with four different binders and applied to a wood support. The resulting reference samples were acquired by HSI in the SWIR wavelength range (1000-2500 nm). Data were processed adopting a chemometric approach based on the PLS Toolbox (Eigenvector Research, Inc.) running inside Matlab® (The Mathworks, Inc.). The aim of the study was to verify, according to the information acquired in the investigated wavelength region, the correlation existing between collected spectral signatures and sample characteristics related to the different selected pigments and binders. Results were very good showing as correlations exist. New scenarios can thus be envisaged for analysis, characterization, conservation and restoration of paintings, considering that the developed approach allows to obtain, just "in one shot", information, not only on the type of pigment, but also on the utilized binder and support.

  8. 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.

  9. Multi- and hyperspectral UAV imaging system for forest and agriculture applications

    NASA Astrophysics Data System (ADS)

    Mäkynen, Jussi; Saari, Heikki; Holmlund, Christer; Mannila, Rami; Antila, Tapani

    2012-06-01

    VTT Technical Research Centre of Finland has developed a Fabry-Perot Interferometer (FPI) based hyperspectral imager compatible with light weight UAV (Unmanned Aerial Vehicle) platforms (SPIE Proc. 74741, 8186B2). The FPI based hyperspectral imager was used in a UAV imaging campaign for forest and agriculture tests during the summer 2011 (SPIE Proc. 81743). During these tests high spatial resolution Color-Infrared (CIR) images and hyperspectral images were recorded on separate flights. The spectral bands of the CIR camera were 500 - 580 nm for the green band, 580 - 700 nm for the red band and 700 - 1000 nm for the near infrared band. For the summer 2012 flight campaign a new hyperspectral imager is currently being developed. A custom made CIR camera will also be used. The system which includes both the high spatial resolution Color-Infrared camera and a light weight hyperspectral imager can provide all necessary data with just one UAV flight over the target area. The new UAV imaging system contains a 4 Megapixel CIR camera which is used for the generation of the digital surface models and CIR mosaics. The hyperspectral data can be recorded in the wavelength range 500 - 900 nm at a resolution of 10 - 30 nm at FWHM. The resolution can be selected from approximate values of 10, 15, 20 or 30 nm at FWHM.

  10. ICER-3D: A Progressive Wavelet-Based Compressor for Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Kiely, A.; Klimesh, M.; Xie, H.; Aranki, N.

    2005-01-01

    ICER-3D is a progressive, wavelet-based compressor for hyperspectral images. ICER-3D is derived from the ICER image compressor. ICER-3D can provide lossless and lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The three-dimensional wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of hyperspectral data sets, while facilitating elimination of spectral ringing artifacts. Correlation is further exploited by a context modeler that effectively exploits spectral dependencies in the wavelet-transformed hyperspectral data. Performance results illustrating the benefits of these features are presented.

  11. 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 ...

  12. Rapidly updated hyperspectral sounding and imaging data for severe storm prediction

    NASA Astrophysics Data System (ADS)

    Bingham, Gail; Jensen, Scott; Elwell, John; Cardon, Joel; Crain, David; Huang, Hung-Lung (Allen); Smith, William L.; Revercomb, Hank E.; Huppi, Ronald J.

    2013-09-01

    Several studies have shown that a geostationary hyperspectral imager/sounder can provide the most significant value increase in short term, regional numerical prediction weather models over a range of other options. In 1998, the Geostationary Imaging Fourier Transform Spectrometer (GIFTS) proposal was selected by NASA as the New Millennium Earth Observation 3 program over several other geostationary instrument development proposals. After the EO3 GIFTS flight demonstration program was changed to an Engineering Development Unit (EDU) due to funding limitations by one of the partners, the EDU was subjected to flight-like thermal vacuum calibration and testing and successfully validated the breakthrough technologies needed to make a successful observatory. After several government stops and starts, only EUMETSAT's Meteosat Third Generation (MTG-S) sounder is in operational development. Recently, a commercial partnership has been formed to fill the significant data gap. AsiaSat has partnered with GeoMetWatch (GMW)1 to fund the development and launch of the Sounding and Tracking Observatory for Regional Meteorology (STORMTM) sensor, a derivative of the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) EDU that was designed, built, and tested by Utah State University (USU). STORMTM combines advanced technologies to observe surface thermal properties, atmospheric weather, and chemistry variables in four dimensions to provide high vertical resolution temperature and moisture sounding information, with the fourth dimension (time) provided by the geosynchronous satellite platform ability to measure a location as often as desired. STORMTM will enhance the polar orbiting imaging and sounding measurements by providing: (1) a direct measure of moisture flux and altitude-resolved water vapor and cloud tracer winds throughout the troposphere, (2) an observation of the time varying atmospheric thermodynamics associated with storm system development, and (3) the transport of tropospheric pollutant gases. The AsiaSat/GMW partnership will host the first STORMTM sensor on their AsiaSat 9 telecommunications satellite at 122 E over the Asia Pacific area. GMW's business plan is to sell the unique STORM data and data products to countries and companies in the satellite coverage area. GMW plans to place 6 STORMTM sensors on geostationary telecommunications satellites to provide global hyperspectral sounding and imaging data. Utah State University's Advanced Weather Systems Laboratory (AWS) will build the sensors for GMW.

  13. An adaptive band selection method for dimension reduction of hyper-spectral remote sensing image

    NASA Astrophysics Data System (ADS)

    Yu, Zhijie; Yu, Hui; Wang, Chen-sheng

    2014-11-01

    Hyper-spectral remote sensing data can be acquired by imaging the same area with multiple wavelengths, and it normally consists of hundreds of band-images. Hyper-spectral images can not only provide spatial information but also high resolution spectral information, and it has been widely used in environment monitoring, mineral investigation and military reconnaissance. However, because of the corresponding large data volume, it is very difficult to transmit and store Hyper-spectral images. Hyper-spectral image dimensional reduction technique is desired to resolve this problem. Because of the High relation and high redundancy of the hyper-spectral bands, it is very feasible that applying the dimensional reduction method to compress the data volume. This paper proposed a novel band selection-based dimension reduction method which can adaptively select the bands which contain more information and details. The proposed method is based on the principal component analysis (PCA), and then computes the index corresponding to every band. The indexes obtained are then ranked in order of magnitude from large to small. Based on the threshold, system can adaptively and reasonably select the bands. The proposed method can overcome the shortcomings induced by transform-based dimension reduction method and prevent the original spectral information from being lost. The performance of the proposed method has been validated by implementing several experiments. The experimental results show that the proposed algorithm can reduce the dimensions of hyper-spectral image with little information loss by adaptively selecting the band images.

  14. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review.

    PubMed

    Elmasry, Gamal; Kamruzzaman, Mohammed; Sun, Da-Wen; Allen, Paul

    2012-01-01

    The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research. PMID:22823348

  15. Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

    Microsoft Academic Search

    Yuliya Tarabalka; Trym Vegard Haavardsholm; Ingebjørg Kåsen; Torbjørn Skauli

    2009-01-01

    Hyperspectral imaging, which records a detailed spectrum of light arriving in each pixel, has many potential uses in remote\\u000a sensing as well as other application areas. Practical applications will typically require real-time processing of large data\\u000a volumes recorded by a hyperspectral imager. This paper investigates the use of graphics processing units (GPU) for such real-time\\u000a processing. In particular, the paper

  16. Full field imaging based instantaneous hyperspectral absolute refractive index measurement

    SciTech Connect

    Baba, Justin S [ORNL; Boudreaux, Philip R [ORNL

    2012-01-01

    Multispectral refractometers typically measure refractive index (RI) at discrete monochromatic wavelengths via a serial process. We report on the demonstration of a white light full field imaging based refractometer capable of instantaneous multispectral measurement of absolute RI of clear liquid/gel samples across the entire visible light spectrum. The broad optical bandwidth refractometer is capable of hyperspectral measurement of RI in the range 1.30 1.70 between 400nm 700nm with a maximum error of 0.0036 units (0.24% of actual) at 414nm for a = 1.50 sample. We present system design and calibration method details as well as results from a system validation sample.

  17. Engineering model for the MightySat II.1 hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Otten, Leonard John, III; Meigs, Andrew D.; Jones, Bernard A.; Prinzing, Philip; Fronterhouse, Don S.; Sellar, R. Glenn; Rafert, Bruce; Hodge, C.

    1997-12-01

    The MightySat II.1 satellite carries as one of its primary payloads a Fourier transform hyperspectral imager, the first such sensor to be flown in space. Over the last year the sensor has passed its preliminary design and an engineering model of the sensor has been constructed. The model has started to be qualified. To date the sensor has met its weight, volume and power design goals. An unusually high random vibration qualification level has forced the redesign of two mirror mounting techniques. Custom, space qualified, VME electronic camera interface and control cards to handel 20 Mbytes/sec of imagery data has been designed, fabricated, and coupled to a set of four C-40 processors to provide 160 MIPS of onboard processing. Mission operations are now being developed that will demonstrate a 30 m GSD by using the on orbit three axis maneuvering capability of the satellite. The payload is on schedule for a delivery in early 1999 for integration on the bus.

  18. 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.

  19. On combining spectral and spatial information of hyperspectral image for camouflaged target detecting

    NASA Astrophysics Data System (ADS)

    Hua, Wenshen; Liu, Xun; Yang, Jia

    2013-12-01

    Detecting enemy's targets and being undetectable play increasingly important roles in modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. As supervised classification requires prior knowledge which cannot be acquired easily, unsupervised classification usually is adopted to process hyperspectral images to detect camouflaged target. But one of its drawbacks—low detecting accuracy confines its application for camouflaged target detecting. Most research on the processing of hyperspectral image tends to focus exclusively on spectral domain and ignores spatial domain. However current hyperspectral image provides high spatial resolution which contains useful information for camouflaged target detecting. A new method combining spectral and spatial information is proposed to increase the detecting accuracy using unsupervised classification. The method has two steps. In the first step, a traditional unsupervised classifier (i.e. K-MEANS, ISODATA) is adopted to classify the hyperspectral image to acquire basic classifications or clusters. During the second step, a 3×3 model and spectral angle mapping are utilized to test the spatial character of the hyperspectral image. The spatial character is defined as spatial homogeneity and calculated by spectral angle mapping. Theory analysis and experiment shows the method is reasonable and efficient. Camouflaged targets are extracted from the background and different camouflaged targets are also recognized. And the proposed algorithm outperforms K-MEANS in terms of detecting accuracy, robustness and edge's distinction. This paper demonstrates the new method is meaningful to camouflaged targets detecting.

  20. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for contaminant screening of leafy greens

    NASA Astrophysics Data System (ADS)

    Everard, Colm D.; Kim, Moon S.; Lee, Hoyoung

    2014-05-01

    The production of contaminant free fresh fruit and vegetables is needed to reduce foodborne illnesses and related costs. Leafy greens grown in the field can be susceptible to fecal matter contamination from uncontrolled livestock and wild animals entering the field. Pathogenic bacteria can be transferred via fecal matter and several outbreaks of E.coli O157:H7 have been associated with the consumption of leafy greens. This study examines the use of hyperspectral fluorescence imaging coupled with multivariate image analysis to detect fecal contamination on Spinach leaves (Spinacia oleracea). Hyperspectral fluorescence images from 464 to 800 nm were captured; ultraviolet excitation was supplied by two LED-based line light sources at 370 nm. Key wavelengths and algorithms useful for a contaminant screening optical imaging device were identified and developed, respectively. A non-invasive screening device has the potential to reduce the harmful consequences of foodborne illnesses.

  1. Mosaicing of Hyperspectral Images: The Application of a Spectrograph Imaging Device

    PubMed Central

    Moroni, Monica; Dacquino, Carlo; Cenedese, Antonio

    2012-01-01

    Hyperspectral monitoring of large areas (more than 10 km2) can be achieved via the use of a system employing spectrometers and CMOS cameras. A robust and efficient algorithm for automatically combining multiple, overlapping images of a scene to form a single composition (i.e., for the estimation of the point-to-point mapping between views), which uses only the information contained within the images themselves is described here. The algorithm, together with the 2D fast Fourier transform, provides an estimate of the displacement between pairs of images by accounting for rotations and changes of scale. The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system. This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification. The performances of the algorithm were evaluated using sample images and image sequences acquired during a proximal sensing field campaign conducted in San Teodoro (Olbia-Tempio—Sardinia). The hyperspectral cube closely corresponds to the mosaic. Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements. PMID:23112597

  2. The development of Chinese hyperspectral remote sensing technology

    NASA Astrophysics Data System (ADS)

    Wang, Jianyu; Shu, Rong; Xue, Yongqi

    2005-01-01

    Imaging spectrometer is characteristic of high spectral resolution, high spatial resolution, multitudinous bands and mass data. The ground object information of hyperspectral image can be used on target identification. On the base of theory on hyperspectral remote sensing imaging technology, the paper mainly introduces three spectrometers designed by Shanghai Institute of Technical Physics, CAS. There are CMODIS, which is the main payload of SZ-3 spaceship, OMIS and PHI, which are airborne sensors. Some applications about those sensors are discussed in the end.

  3. Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor

    USGS Publications Warehouse

    Pengra, B.W.; Johnston, C.A.; Loveland, T.R.

    2007-01-01

    Mapping tools are needed to document the location and extent of Phragmites australis, a tall grass that invades coastal marshes throughout North America, displacing native plant species and degrading wetland habitat. Mapping Phragmites is particularly challenging in the freshwater Great Lakes coastal wetlands due to dynamic lake levels and vegetation diversity. We tested the applicability of Hyperion hyperspectral satellite imagery for mapping Phragmites in wetlands of the west coast of Green Bay in Wisconsin, U.S.A. A reference spectrum created using Hyperion data from several pure Phragmites stands within the image was used with a Spectral Correlation Mapper (SCM) algorithm to create a raster map with values ranging from 0 to 1, where 0 represented the greatest similarity between the reference spectrum and the image spectrum and 1 the least similarity. The final two-class thematic classification predicted monodominant Phragmites covering 3.4% of the study area. Most of this was concentrated in long linear features parallel to the Green Bay shoreline, particularly in areas that had been under water only six years earlier when lake levels were 66??cm higher. An error matrix using spring 2005 field validation points (n = 129) showed good overall accuracy-81.4%. The small size and linear arrangement of Phragmites stands was less than optimal relative to the sensor resolution, and Hyperion's 30??m resolution captured few if any pure pixels. Contemporary Phragmites maps prepared with Hyperion imagery would provide wetland managers with a tool that they currently lack, which could aid attempts to stem the spread of this invasive species. ?? 2006 Elsevier Inc. All rights reserved.

  4. Comparison of broadband and hyperspectral thermal infrared imaging of buried threat objects

    NASA Astrophysics Data System (ADS)

    McFee, John E.; Achal, Steve B.; Diaz, Alejandra U.; Faust, Anthony A.

    2013-06-01

    Previous research by many groups has shown that broad-band thermal infrared (TIR) imagers can detect buried explosive threat devices, such as unexploded ordnance (UXO), landmines and improvised explosive devices (IEDs). Broad-band detection measures the apparent temperature - an average over the wave band of the product of the true soil surface temperature and the emissivity. Broad-band detection suffers from inconsistent performance (low signal, high clutter rates), due in part to diurnal variations, environmental and meteorological conditions, and soil surface effects. It has been suggested that hyperspectral TIR imaging might have improved performance since it can, in principle, allow extraction of the wavelength-dependent emissivity and the true soil surface temperature. This would allow the surface disturbance effects to be separated from the soil column (bulk) effects. A significant, and as yet unanswered, question is whether hyperspectral TIR images provide better detection capability (higher probability of detection and/or lower false alarm rate) than do broad-band thermal images. TIR hyperspectral image data of threat objects, buried and surface-laid in bare soil, were obtained in arid, desert-like conditions over full diurnal cycles for several days. Regions of interest containing threat objects and backgrounds were extracted throughout the time period. Simulated broad-band images were derived from the hyperspectral images. The diurnal variation of the images was studied. Hyperspectral was found to provide some advantage over broad-band imaging in detection of buried threat objects for the limited data set studied.

  5. Hyperspectral Imaging and Association Phenomenology of Pedestrians in a Cluttered Urban Environment

    NASA Astrophysics Data System (ADS)

    Herweg, Jared A.

    Remote hyperspectral imaging (HSI) has shown promise in several applications such as object detection and tracking. Typically research has focused on large objects, such as vehicles, for tracking due to the spatial resolution of current operational HSI systems. This research seeks to extend the utility of applying HSI to human pedestrian detection using the reflective solar spectral range between 400 - 2500 nm. A phenomenological investigation of a novel scheme to differentiate between pedestrians is studied. By applying the basics of detection theory, this research focuses on being able to differentiate between pedestrians, as well as background materials. Specifically, this research explores the likelihood of detecting and differentiating pedestrians based on four defined subregions comprised of the exposed hair, skin, and the fabrics used for shirts and trousers. The scope of this work encompassed detecting a pedestrian of interest outdoors among other pedestrians in an urban environment consisting of a mixture of asphalt, concrete, grass, and trees. Two unique datasets were created during the course of this effort. One dataset was a collection of fully ground-truthed hyperspectral images of pedestrians in an urban environment. A second dataset was a synthetic rendering of the real-world ground truthed pedestrian scene developed using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Subregion separability analysis results, using spectral reflectance data, provided strong evidence that combining the observable spectral features of detectable subregions is a viable means of distinguishing between pedestrians. Further analysis using real-world HSI data demonstrated that the detection and classification of the pedestrian subregions when changes in illumination, location, and background occur within the field of view of a hyperspectral sensor is achievable with a greater than 60% accuracy. In addition to the direct detection and association analysis using the full spectral range, trade-offs in using spectral subsets of the reflectance spectrum were explored for their utility in detecting and classifying each of the pedestrian subregions. The results suggested that the clothing worn on a pedestrian's torso is the dominant feature for classification and either using a full spectral range (400 - 2500 nm) with 152 spectral bands or the visible to near-infrared spectral range (400 - 1000 nm) with 39 bands provides similar capability to the full spectral range for distinguishing among pedestrians with similar skin and clothing types.

  6. Differentiation of bacterial colonies and temporal growth patterns using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Mehrübeoglu, Mehrube; Buck, Gregory W.; Livingston, Daniel W.

    2014-09-01

    Detection and identification of bacteria are important for health and safety. Hyperspectral imaging offers the potential to capture unique spectral patterns and spatial information from bacteria which can then be used to detect and differentiate bacterial species. Here, hyperspectral imaging has been used to characterize different bacterial colonies and investigate their growth over time. Six bacterial species (Pseudomonas fluorescens, Escherichia coli, Serratia marcescens, Salmonella enterica, Staphylococcus aureus, Enterobacter aerogenes) were grown on tryptic soy agar plates. Hyperspectral data were acquired immediately after, 24 hours after, and 96 hours after incubation. Spectral signatures from bacterial colonies demonstrated repeatable measurements for five out of six species. Spatial variations as well as changes in spectral signatures were observed across temporal measurements within and among species at multiple wavelengths due to strengthening or weakening reflectance signals from growing bacterial colonies based on their pigmentation. Between-class differences and within-class similarities were the most prominent in hyperspectral data collected 96 hours after incubation.

  7. Hyperspectral Image Classification Using Dictionary-Based Sparse Representation

    Microsoft Academic Search

    Yi Chen; Nasser M. Nasrabadi; Trac D. Tran

    2011-01-01

    A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse representation of an unknown pixel is expressed as a sparse vector whose nonzero entries correspond

  8. Wavelet compression techniques for hyperspectral data

    NASA Technical Reports Server (NTRS)

    Evans, Bruce; Ringer, Brian; Yeates, Mathew

    1994-01-01

    Hyperspectral sensors are electro-optic sensors which typically operate in visible and near infrared bands. Their characteristic property is the ability to resolve a relatively large number (i.e., tens to hundreds) of contiguous spectral bands to produce a detailed profile of the electromagnetic spectrum. In contrast, multispectral sensors measure relatively few non-contiguous spectral bands. Like multispectral sensors, hyperspectral sensors are often also imaging sensors, measuring spectra over an array of spatial resolution cells. The data produced may thus be viewed as a three dimensional array of samples in which two dimensions correspond to spatial position and the third to wavelength. Because they multiply the already large storage/transmission bandwidth requirements of conventional digital images, hyperspectral sensors generate formidable torrents of data. Their fine spectral resolution typically results in high redundancy in the spectral dimension, so that hyperspectral data sets are excellent candidates for compression. Although there have been a number of studies of compression algorithms for multispectral data, we are not aware of any published results for hyperspectral data. Three algorithms for hyperspectral data compression are compared. They were selected as representatives of three major approaches for extending conventional lossy image compression techniques to hyperspectral data. The simplest approach treats the data as an ensemble of images and compresses each image independently, ignoring the correlation between spectral bands. The second approach transforms the data to decorrelate the spectral bands, and then compresses the transformed data as a set of independent images. The third approach directly generalizes two-dimensional transform coding by applying a three-dimensional transform as part of the usual transform-quantize-entropy code procedure. The algorithms studied all use the discrete wavelet transform. In the first two cases, a wavelet transform coder was used for the two-dimensional compression. The third case used a three dimensional extension of this same algorithm.

  9. Development of real-time line-scan hyperspectral imaging system for online agricultural and food product inspection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper reports a recent development of a line-scan hyperspectral imaging system for real-time multispectral imaging applications in agricultural and food industries. The hyperspectral imaging system consisted of a spectrograph, an EMCCD camera, and application software. The real-time multispectr...

  10. Development and integration of Raman imaging capabilities to Sandia National Laboratories hyperspectral fluorescence imaging instrument.

    SciTech Connect

    Timlin, Jerilyn Ann; Nieman, Linda T.

    2005-11-01

    Raman spectroscopic imaging is a powerful technique for visualizing chemical differences within a variety of samples based on the interaction of a substance's molecular vibrations with laser light. While Raman imaging can provide a unique view of samples such as residual stress within silicon devices, chemical degradation, material aging, and sample heterogeneity, the Raman scattering process is often weak and thus requires very sensitive collection optics and detectors. Many commercial instruments (including ones owned here at Sandia National Laboratories) generate Raman images by raster scanning a point focused laser beam across a sample--a process which can expose a sample to extreme levels of laser light and requires lengthy acquisition times. Our previous research efforts have led to the development of a state-of-the-art two-dimensional hyperspectral imager for fluorescence imaging applications such as microarray scanning. This report details the design, integration, and characterization of a line-scan Raman imaging module added to this efficient hyperspectral fluorescence microscope. The original hyperspectral fluorescence instrument serves as the framework for excitation and sample manipulation for the Raman imaging system, while a more appropriate axial transmissive Raman imaging spectrometer and detector are utilized for collection of the Raman scatter. The result is a unique and flexible dual-modality fluorescence and Raman imaging system capable of high-speed imaging at high spatial and spectral resolutions. Care was taken throughout the design and integration process not to hinder any of the fluorescence imaging capabilities. For example, an operator can switch between the fluorescence and Raman modalities without need for extensive optical realignment. The instrument performance has been characterized and sample data is presented.

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

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  12. Near infrared hyperspectral imaging for forensic analysis of document forgery.

    PubMed

    Silva, Carolina S; Pimentel, Maria Fernanda; Honorato, Ricardo S; Pasquini, Celio; Prats-Montalbán, José M; Ferrer, Alberto

    2014-10-21

    Hyperspectral images in the near infrared range (HSI-NIR) were evaluated as a nondestructive method to detect fraud in documents. Three different types of typical forgeries were simulated by (a) obliterating text, (b) adding text and (c) approaching the crossing lines problem. The simulated samples were imaged in the range of 928-2524 nm with spectral and spatial resolutions of 6.3 nm and 10 ?m, respectively. After data pre-processing, different chemometric techniques were evaluated for each type of forgery. Principal component analysis (PCA) was performed to elucidate the first two types of adulteration, (a) and (b). Moreover, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used in an attempt to improve the results of the type (a) obliteration and type (b) adding text problems. Finally, MCR-ALS and Partial Least Squares-Discriminant Analysis (PLS-DA), employed as a variable selection tool, were used to study the type (c) forgeries, i.e. crossing lines problem. Type (a) forgeries (obliterating text) were successfully identified in 43% of the samples using both the chemometric methods (PCA and MCR-ALS). Type (b) forgeries (adding text) were successfully identified in 82% of the samples using both the methods (PCA and MCR-ALS). Finally, type (c) forgeries (crossing lines) were successfully identified in 85% of the samples. The results demonstrate the potential of HSI-NIR associated with chemometric tools to support document forgery identification. PMID:25118338

  13. Hyperspectral-imaging-based techniques applied to wheat kernels characterization

    NASA Astrophysics Data System (ADS)

    Serranti, Silvia; Cesare, Daniela; Bonifazi, Giuseppe

    2012-05-01

    Single kernels of durum wheat have been analyzed by hyperspectral imaging (HSI). Such an approach is based on the utilization of an integrated hardware and software architecture able to digitally capture and handle spectra as an image sequence, as they results along a pre-defined alignment on a surface sample properly energized. The study was addressed to investigate the possibility to apply HSI techniques for classification of different types of wheat kernels: vitreous, yellow berry and fusarium-damaged. Reflectance spectra of selected wheat kernels of the three typologies have been acquired by a laboratory device equipped with an HSI system working in near infrared field (1000-1700 nm). The hypercubes were analyzed applying principal component analysis (PCA) to reduce the high dimensionality of data and for selecting some effective wavelengths. Partial least squares discriminant analysis (PLS-DA) was applied for classification of the three wheat typologies. The study demonstrated that good classification results were obtained not only considering the entire investigated wavelength range, but also selecting only four optimal wavelengths (1104, 1384, 1454 and 1650 nm) out of 121. The developed procedures based on HSI can be utilized for quality control purposes or for the definition of innovative sorting logics of wheat.

  14. Programmable hyperspectral image mapper with on-array processing

    NASA Technical Reports Server (NTRS)

    Cutts, James A. (inventor)

    1995-01-01

    A hyperspectral imager includes a focal plane having an array of spaced image recording pixels receiving light from a scene moving relative to the focal plane in a longitudinal direction, the recording pixels being transportable at a controllable rate in the focal plane in the longitudinal direction, an electronic shutter for adjusting an exposure time of the focal plane, whereby recording pixels in an active area of the focal plane are removed therefrom and stored upon expiration of the exposure time, an electronic spectral filter for selecting a spectral band of light received by the focal plane from the scene during each exposure time and an electronic controller connected to the focal plane, to the electronic shutter and to the electronic spectral filter for controlling (1) the controllable rate at which the recording is transported in the longitudinal direction, (2) the exposure time, and (3) the spectral band so as to record a selected portion of the scene through M spectral bands with a respective exposure time t(sub q) for each respective spectral band q.

  15. Construction and hyperspectral imaging of quantum dot lysate arrays.

    PubMed

    Rosenblatt, Kevin P; Huebschman, Michael L; Garner, Harold R

    2012-01-01

    The emerging field of proteomic molecular profiling will be driven by new technologies that can measure dozens to hundreds of proteins from a small sample input from a patient's biopsy. Lysate arrays, or reverse-phase protein microarrays, provide a platform for complex mixtures of proteins extracted from cells and tissues to be directly immobilized onto a solid support (such as a biochip with protein binding capacity) in diminutive volumes (picoliter-to-nanoliter). The proteins are spotted using precision robotics and then quantitatively assayed using primary antibodies; important posttranslational modifications, such as phosphorylations that are important for protein activation, may also be assayed to provide an estimate of the regulation of cellular signaling. Until recently, chromogenic signals and fluorescence (using organic fluorophores) detection were two strategies relied upon for signal detection. Emerging regents such as quantum dots (Qdot® nanocrystals; QD) are now employed for improved performance. QD embody a more versatile detection system because the robust signals may be time averaged and the narrow spectral emissions enable many protein targets to be quantified within the same lysate spot. Previously, we found that commercially available pegylated, streptavidin-conjugated QD were effective detection agents, with low-background affinities to spurious components within heterogeneous protein mixtures. Hyperspectral imaging allows the simultaneous detection of the different colored QD reagents within a single lysate spot. Here, we described the construction and imaging of QD lysate arrays. This technology is an emerging, enabling tool within the exciting, clinically oriented field of clinical tissue proteomics. PMID:22081354

  16. Characterization and modeling of point spread function in push-broom hyperspectral imaging systems for spectral and spatial resolution enhancement

    NASA Astrophysics Data System (ADS)

    Jemec, Jurij; Bürmen, Miran; Kosec, Matjaž; Pernuš, Franjo; Likar, Boštjan

    2014-03-01

    Push-broom hyperspectral imaging system ideally disperses the spectral and spatial information in two orthogonal directions preferably aligned with the columns and rows of the imaging sensor. Due to the imperfections in the camera lens and in particular the optical components of spectrograph, wavelength dependent spectral and spatial distortions along with spatial and spectral blur are introduced in the recorded image. In this study, we propose and evaluate a novel method for characterization and resolution enhancement of push-broom hyperspectral imaging systems. First, the spatially and spectrally dependent response function is characterized by measuring the response of the system to spectral and spatial reference objects. The relevant variability of the response function in the imaging plane is captured by a global parametric model. Finally, the response function estimate is used to remove distortions and enhance the spectral and spatial resolution of the system. The resolution enhancements were assessed by observing the change in full width at half-maximum of spectral response function and rise width of the spatial response function. The results of validation show that the proposed method affectively removes geometric distortions and significantly enhances the spectral and spatial resolution of the recorded images.

  17. Investigation of NIR hyperspectral imaging for discriminating melamine in milk powder

    NASA Astrophysics Data System (ADS)

    Fu, Xiaping; Kim, Moon S.; Chao, Kuanglin; Qin, Jianwei; Lim, Jongguk; Lee, Hoyoung; Ying, Yibin

    2013-05-01

    Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized issue for which rapid and accurate identification methods are needed by the food industry. In this study, the feasibility and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for detecting melamine in milk powder. Hyperspectral NIR images (144 bands spanning from 990 to 1700 nm) were acquired for Petri dishes containing samples of milk powder mixed with melamine at various concentrations (0.02% to 1%). Spectral bands that showed the most significant differences between pure milk and pure melamine were selected, and two-band difference analysis was applied to the spectrum of each pixel in the sample images to identify melamine particles in milk powders. The resultant images effectively allowed visualization of melamine particle distributions in the samples. The study demonstrated that NIR hyperspectral imaging techniques can qualitatively and quantitatively identify melamine adulteration in milk powders.

  18. Methods for correcting morphological-based deficiencies in hyperspectral images of round objects

    Technology Transfer Automated Retrieval System (TEKTRAN)

    NIR images of curved surfaces contain undesirable artifacts that are a consequence of the morphology, or shape of the sample. A software correction was developed to remove the variation in pixel intensity in hyperspectral images of spherical samples generated on a linescan type imaging system. The c...

  19. Chapter 5 Hyperspectral Imaging: A New Technique for the Non-Invasive Study of Artworks

    Microsoft Academic Search

    Maria Kubik

    2007-01-01

    Hyperspectral imaging generates an accurate digital record for art conservation. This may be used for monitoring change or damage to paintings, as digital documentation resists deterioration better than photographs. Also, digital imaging can assist in the restoration of artwork. It has already been applied to assessing damage from laser cleaning, where computer-aided comparison of before and after cleaning the images

  20. Special Issue: Skin Cancer Imaging Reconstruction of hyperspectral cutaneous data from an artificial

    E-print Network

    Paris-Sud XI, Université de

    Special Issue: Skin Cancer Imaging Reconstruction of hyperspectral cutaneous data from in "Computerized Medical Imaging and Graphics 35 (2011) 85-88" #12;Special Issue: Skin Cancer Imaging reconstruction; Neural networks; Spectral reflectance; Skin cancer hal-00586919,version1-19Apr2011 #12;Special

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

    PubMed

    Lee, Hoonsoo; Kim, Moon S; Jeong, Danhee; Delwiche, Stephen R; Chao, Kuanglin; Cho, Byoung-Kwan

    2014-01-01

    The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000-1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments. PMID:25310472

  2. Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

    PubMed Central

    Lee, Hoonsoo; Kim, Moon S.; Jeong, Danhee; Delwiche, Stephen R.; Chao, Kuanglin; Cho, Byoung-Kwan

    2014-01-01

    The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments. PMID:25310472

  3. Demonstration of the Wide-Field Imaging Interferometer Testbed Using a Calibrated Hyperspectral Image Projector

    NASA Technical Reports Server (NTRS)

    Bolcar, Matthew R.; Leisawitz, David; Maher, Steve; Rinehart, Stephen

    2012-01-01

    The Wide-field Imaging Interferometer testbed (WIIT) at NASA's Goddard Space Flight Center uses a dual-Michelson interferometric technique. The WIIT combines stellar interferometry with Fourier-transform interferometry to produce high-resolution spatial-spectral data over a large field-of-view. This combined technique could be employed on future NASA missions such as the Space Infrared Interferometric Telescope (SPIRIT) and the Sub-millimeter Probe of the Evolution of Cosmic Structure (SPECS). While both SPIRIT and SPECS would operate at far-infrared wavelengths, the WIIT demonstrates the dual-interferometry technique at visible wavelengths. The WIIT will produce hyperspectral image data, so a true hyperspectral object is necessary. A calibrated hyperspectral image projector (CHIP) has been constructed to provide such an object. The CHIP uses Digital Light Processing (DLP) technology to produce customized, spectrally-diverse scenes. CHIP scenes will have approximately 1.6-micron spatial resolution and the capability of . producing arbitrary spectra in the band between 380 nm and 1.6 microns, with approximately 5-nm spectral resolution. Each pixel in the scene can take on a unique spectrum. Spectral calibration is achieved with an onboard fiber-coupled spectrometer. In this paper we describe the operation of the CHIP. Results from the WIIT observations of CHIP scenes will also be presented.

  4. Demonstration of the wide-field imaging interferometer testbed using a calibrated hyperspectral image projector

    NASA Astrophysics Data System (ADS)

    Bolcar, Matthew R.; Leisawitz, David; Maher, Stephen; Rinehart, Stephen

    2012-07-01

    The Wide-field Imaging Interferometer testbed (WIIT) at NASA’s Goddard Space Flight Center uses a dual-Michelson interferometric technique. The WIIT combines stellar interferometry with Fourier-transform interferometry to produce high-resolution spatial-spectral data over a large field-of-view. This combined technique could be employed on future NASA missions such as the Space Infrared Interferometric Telescope (SPIRIT) and the Sub-millimeter Probe of the Evolution of Cosmic Structure (SPECS). While both SPIRIT and SPECS would operate at far-infrared wavelengths, the WIIT demonstrates the dual-interferometry technique at visible wavelengths. The WIIT will produce hyperspectral image data, so a true hyperspectral object is necessary. A calibrated hyperspectral image projector (CHIP) has been constructed to provide such an object. The CHIP uses Digital Light Processing (DLP) technology to produce customized, spectrally-diverse scenes. CHIP scenes will have approximately 1.6-micron spatial resolution and the capability of producing arbitrary spectra in the band between 380 nm and 1.6 microns, with approximately 5-nm spectral resolution. Each pixel in the scene can take on a unique spectrum. Spectral calibration is achieved with an onboard fiber-coupled spectrometer. In this paper we describe the operation of the CHIP. Results from the WIIT observations of CHIP scenes will also be presented.

  5. Wavefront image sensor chip

    PubMed Central

    Cui, Xiquan; Ren, Jian; Tearney, Guillermo J.; Yang, Changhuei

    2010-01-01

    We report the implementation of an image sensor chip, termed wavefront image sensor chip (WIS), that can measure both intensity/amplitude and phase front variations of a light wave separately and quantitatively. By monitoring the tightly confined transmitted light spots through a circular aperture grid in a high Fresnel number regime, we can measure both intensity and phase front variations with a high sampling density (11 µm) and high sensitivity (the sensitivity of normalized phase gradient measurement is 0.1 mrad under the typical working condition). By using WIS in a standard microscope, we can collect both bright-field (transmitted light intensity) and normalized phase gradient images. Our experiments further demonstrate that the normalized phase gradient images of polystyrene microspheres, unstained and stained starfish embryos, and strongly birefringent potato starch granules are improved versions of their corresponding differential interference contrast (DIC) microscope images in that they are artifact-free and quantitative. Besides phase microscopy, WIS can benefit machine recognition, object ranging, and texture assessment for a variety of applications. PMID:20721059

  6. Auxiliary Sensors For "Pushbroom" Imaging

    NASA Technical Reports Server (NTRS)

    Jones, Kenneth L.

    1992-01-01

    Proposed subsystem of small auxiliary imaging sensors yields additional data on motion of linear imaging sensor scanned across scene perpendicular to its length. Sensors yield data on components of motion to which inertial, radar, and air-speed detectors insensitive. Additional data on motion enhances geometric fidelity.

  7. 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.

  8. Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system.

    PubMed

    Wu, Di; Shi, Hui; Wang, Songjing; He, Yong; Bao, Yidan; Liu, Kangsheng

    2012-05-13

    Because the shape of prawn is not round, spectroscopy instruments cannot measure the spectra of the whole prawn without containing background information. In this study, an online hyperspectral imaging system in the spectral region of 380-1100 nm was developed to determine the moisture content of prawns at different dehydrated levels. Hyperspectral images of prawns were acquired at different dehydration periods. The spectra of prawns then were extracted from hyperspectral images based on 'Manual Prawn Mask' and 'Automatic Prawn Mask', respectively. Spectral data were analyzed using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM) to establish the calibration models, respectively. Successive projections algorithm (SPA) was first applied for the optimal wavelength selection in the hyperspectral image analysis. Out of 482 wavelengths, only twelve wavelengths (428, 445, 544, 569, 629, 672, 697, 760, 827, 917, 958, and 999 nm) were selected by SPA as the optimum wavelengths for moisture prediction. Based on these optimum wavelengths, a multiple linear regression (MLR) calibration model was established and used to obtain the moisture distribution of each prawn. The overall results of this study revealed the potentiality of hyperspectral imaging as an objective and non-destructive method to obtain the content and distribution of moisture of prawns whose shapes are not round. PMID:22541014

  9. Multivariate curve resolution for the analysis of remotely sensed thermal infrared hyperspectral images.

    SciTech Connect

    Haaland, David Michael; Stork, Christopher Lyle; Keenan, Michael Robert

    2004-07-01

    While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.

  10. 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.

  11. Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

    As an emerging technology, hyperspectral imaging (HSI) combines both the chemical specificity of spectroscopy and the spatial resolution of imaging, which may provide a non-invasive tool for cancer detection and diagnosis. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. In this paper, we introduce a tensor-based computation and modeling framework for the analysis of hyperspectral images to detect head and neck cancer. The proposed classification method can distinguish between malignant tissue and healthy tissue with an average sensitivity of 96.97% and an average specificity of 91.42% in tumor-bearing mice. The hyperspectral imaging and classification technology has been demonstrated in animal models and can have many potential applications in cancer research and management.

  12. Non-destructive quality evaluation of pepper (Capsicum annuum L.) seeds using LED-induced hyperspectral reflectance imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this study, we develop a viability evaluation method for pepper (Capsicum annuum L.) seed based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumin...

  13. A hyperspectral imager for high radiometric accuracy Earth climate studies

    NASA Astrophysics Data System (ADS)

    Espejo, Joey; Drake, Ginger; Heuerman, Karl; Kopp, Greg; Lieber, Alex; Smith, Paul; Vermeer, Bill

    2011-10-01

    We demonstrate a visible and near-infrared prototype pushbroom hyperspectral imager for Earth climate studies that is capable of using direct solar viewing for on-orbit cross calibration and degradation tracking. Direct calibration to solar spectral irradiances allow the Earth-viewing instrument to achieve required climate-driven absolute radiometric accuracies of <0.2% (1?). A solar calibration requires viewing scenes having radiances 105 higher than typical Earth scenes. To facilitate this calibration, the instrument features an attenuation system that uses an optimized combination of different precision aperture sizes, neutral density filters, and variable integration timing for Earth and solar viewing. The optical system consists of a three-mirror anastigmat telescope and an Offner spectrometer. The as-built system has a 12.2° cross track field of view with 3 arcmin spatial resolution and covers a 350-1050 nm spectral range with 10 nm resolution. A polarization compensated configuration using the Offner in an out of plane alignment is demonstrated as a viable approach to minimizing polarization sensitivity. The mechanical design takes advantage of relaxed tolerances in the optical design by using rigid, non-adjustable diamond-turned tabs for optical mount locating surfaces. We show that this approach achieves the required optical performance. A prototype spaceflight unit is also demonstrated to prove the applicability of these solar cross calibration methods to on-orbit environments. This unit is evaluated for optical performance prior to and after GEVS shake, thermal vacuum, and lifecycle tests.

  14. Rapid calibrated high-resolution hyperspectral imaging using tunable laser source

    NASA Astrophysics Data System (ADS)

    Nguyen, Lam K.; Margalith, Eli

    2009-05-01

    We present a novel hyperspectral imaging technique based on tunable laser technology. By replacing the broadband source and tunable filters of a typical NIR imaging instrument, several advantages are realized, including: high spectral resolution, highly variable field-of-views, fast scan-rates, high signal-to-noise ratio, and the ability to use optical fiber for efficient and flexible sample illumination. With this technique, high-resolution, calibrated hyperspectral images over the NIR range can be acquired in seconds. The performance of system features will be demonstrated on two example applications: detecting melamine contamination in wheat gluten and separating bovine protein from wheat protein in cattle feed.

  15. Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

    PubMed

    Williams, Paul; Geladi, Paul; Fox, Glen; Manley, Marena

    2009-10-27

    The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images, PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses. PMID:19808104

  16. Destriping of hyperspectral image data: an evaluation of different algorithms using EO-1 Hyperion data

    NASA Astrophysics Data System (ADS)

    Scheffler, Daniel; Karrasch, Pierre

    2014-01-01

    Data from the Earth Observing-1 Hyperion instrument were used. Apart from atmospheric influences or topographic effects, the data represent a good choice in order to show different steps of the preprocessing process targeting sensor-internal sources of errors. These include diffuse sensor noise, striping, smile-effect, keystone effect, and spatial misalignments between the detector arrays. For this research paper, the authors focus on the striping effect by comparing and evaluating different algorithms, methods, and configurations to correct striping errors. The correction of striping effects becomes necessary due to imprecise calibration of the detector array. This inaccuracy affects, especially, the first 12 visual and near-infrared bands and also a large number of bands in the short-wave infrared array. Altogether six destriping techniques were tested on the basis of a Hyperion dataset covering a test site in Central Europe. For the final evaluation, various analyses across all Hyperion channels were performed. The results show that some correction methods have almost no effect on the striping in the images. Other methods may eliminate the striping, but analyses show that these algorithms also alter pixel values in adjacent areas, which originally had not been disturbed by the striping effect. Being the first comprehensive comparison study of different destriping algorithms, this paper gives valuable recommendations on how to reach reliable results in further analyses of hyperspectral data.

  17. Toward hyperspectral face recognition

    NASA Astrophysics Data System (ADS)

    Robila, Stefan A.

    2008-02-01

    Face recognition continues to meet significant challenges in reaching accurate results and still remains one of the activities where humans outperform technology. An attractive approach in improving face identification is provided by the fusion of multiple imaging sources such as visible and infrared images. Hyperspectral data, i.e. images collected over hundreds of narrow contiguous light spectrum intervals constitute a natural choice for expanding face recognition image fusion, especially since it may provide information beyond the normal visible range, thus exceeding the normal human sensing. In this paper we investigate the efficiency of hyperspectral face recognition through an in house experiment that collected data in over 120 bands within the visible and near infrared range. The imagery was produced using an off the shelf sensor in both indoors and outdoors with the subjects being photographed from various angles. Further processing included spectra collection and feature extraction. Human matching performance based on spectral properties is discussed.

  18. Biomedical Applications of the Information-efficient Spectral Imaging Sensor (ISIS)

    SciTech Connect

    Gentry, S.M.; Levenson, R.

    1999-01-21

    The Information-efficient Spectral Imaging Sensor (ISIS) approach to spectral imaging seeks to bridge the gap between tuned multispectral and fixed hyperspectral imaging sensors. By allowing the definition of completely general spectral filter functions, truly optimal measurements can be made for a given task. These optimal measurements significantly improve signal-to-noise ratio (SNR) and speed, minimize data volume and data rate, while preserving classification accuracy. The following paper investigates the application of the ISIS sensing approach in two sample biomedical applications: prostate and colon cancer screening. It is shown that in these applications, two to three optimal measurements are sufficient to capture the majority of classification information for critical sample constituents. In the prostate cancer example, the optimal measurements allow 8% relative improvement in classification accuracy of critical cell constituents over a red, green, blue (RGB) sensor. In the colon cancer example, use of optimal measurements boost the classification accuracy of critical cell constituents by 28% relative to the RGB sensor. In both cases, optimal measurements match the performance achieved by the entire hyperspectral data set. The paper concludes that an ISIS style spectral imager can acquire these optimal spectral images directly, allowing improved classification accuracy over an RGB sensor. Compared to a hyperspectral sensor, the ISIS approach can achieve similar classification accuracy using a significantly lower number of spectral samples, thus minimizing overall sample classification time and cost.

  19. FPGA-based On-board Multi\\/Hyperspectral Image Compression System

    Microsoft Academic Search

    Guoxia Yu; Tanya Vladimirova; Martin N. Sweeting

    2009-01-01

    Image compression is an important requirement of imaging payloads on board Earth Observation satellites. This paper presents a new on-board real-time compression system, capable of lossless and lossy image compression. A cost-effective lossless image compression scheme, based on the CCSDS recommendation, is proposed and tested with multi\\/hyperspectral images. An efficient hardware implementation is achieved using FPGA-based acceleration. The hardware accelerator

  20. Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat.

    PubMed

    Xiong, Zhenjie; Sun, Da-Wen; Xie, Anguo; Han, Zhong; Wang, Lu

    2015-05-15

    In this study, the potential of hyperspectral imaging (HSI) for predicting hydroxyproline content in chicken meat was investigated. Spectral data contained in the hyperspectral images (400-1000 nm) of chicken meat was extracted, and a partial least square regression (PLSR) model was then developed for predicting hydroxyproline content. The model yielded acceptable results with regression coefficient in prediction (Rp) of 0.874 and root mean error squares in prediction (RMESP) of 0.046. Based on the eight optimal wavelengths selected by regression coefficients (RC) from the PLSR model, a new RC-PLSR model was built and good results were shown with high Rp of 0.854 and low RMSEP of 0.049. Finally, distribution maps of hydroxyproline were created by transferring the RC-PLSR model to each pixel in the hyperspectral images. The results demonstrated that HSI has the capability for rapid and non-destructive determination of hydroxyproline content in chicken meat. PMID:25577100

  1. Different Optimal Band Selection of Hyperspectral Images Using a Continuous Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Talebi Nahr, S.; Pahlavani, P.; Hasanlou, M.

    2014-10-01

    In the most applications in remote sensing, there is no need to use all of available data, such as using all of bands in hyperspectral images. In this paper, a new band selection method was proposed to deal with the large number of hyperspectral images bands. We proposed a Continuous Genetic Algorithm (CGA) to achieve the best subset of hyperspectral images bands, without decreasing Overall Accuracy (OA) index in classification. In the proposed CGA, a multi-class SVM was used as a classifier. Comparing results achieved by the CGA with those achieved by the Binary GA (BGA) shows better performances in the proposed CGA method. At the end, 56 bands were selected as the best bands for classification with OA of 78.5 %.

  2. Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

    Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.

  3. A CCD CAMERA-BASED HYPERSPECTRAL IMAGING SYSTEM FOR STATIONARY AND AIRBORNE APPLICATIONS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper describes a charge coupled device (CCD) camera-based hyperspectral imaging system designed for both stationary and airborne remote sensing applications. The system consists of a high performance digital CCD camera, an imaging spectrograph, an optional focal plane scanner, and a PC comput...

  4. Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning

    Microsoft Academic Search

    Jun Li; José M. Bioucas-Dias; Antonio Plaza

    2010-01-01

    This paper presents a new semisupervised segmentation algorithm, suited to high-dimensional data, of which remotely sensed hyperspectral image data sets are an example. The algorithm implements two main steps: 1) semisupervised learning of the posterior class distributions followed by 2) segmentation, which infers an image of class labels from a posterior distribution built on the learned class distributions and on

  5. Hyperspectral imaging of cuttlefish camouflage indicates good color match in the eyes

    E-print Network

    Hanlon, Roger T.

    Hyperspectral imaging of cuttlefish camouflage indicates good color match in the eyes of fish domains, respectively. Cuttlefish can dynamically camouflage themselves on any natural substrate and to the substrate when viewed directly by humans or with RGB images. Live camouflaged cuttlefish on nat- ural

  6. Recent developments and future directions in parallel processing of remotely sensed hyperspectral images

    Microsoft Academic Search

    Antonio J. Plaza

    2009-01-01

    Remotely sensed hyperspectral imaging is a technique that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. Computationally effective processing of these image cubes can be greatly beneficial in many application domains, including environmental modeling, risk\\/hazard prevention and response, or defense\\/security. With the aim of providing an overview of recent

  7. Evaluation of Hyperspectral Imaging and Predictive Modeling to Determine Fertility and Development of Broiler Hatching Eggs

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging system and a predictive modeling technique was evaluated for determining fertility and early embryo development of broiler chicken hatching eggs. Twenty-four broiler-strain eggs were collected (12 fertile, 12 infertile) for each of 8 replicate trials (n=192) and imaged on Da...

  8. 3D optical sectioning with a new hyperspectral confocal fluorescence imaging system.

    SciTech Connect

    Nieman, Linda T.; Sinclair, Michael B.; Davidson, George S.; Van Benthem, Mark Hilary; Haaland, David Michael; Timlin, Jerilyn Ann; Sasaki, Darryl Yoshio; Bachand, George David; Jones, Howland D. T.

    2007-02-01

    A novel hyperspectral fluorescence microscope for high-resolution 3D optical sectioning of cells and other structures has been designed, constructed, and used to investigate a number of different problems. We have significantly extended new multivariate curve resolution (MCR) data analysis methods to deconvolve the hyperspectral image data and to rapidly extract quantitative 3D concentration distribution maps of all emitting species. The imaging system has many advantages over current confocal imaging systems including simultaneous monitoring of numerous highly overlapped fluorophores, immunity to autofluorescence or impurity fluorescence, enhanced sensitivity, and dramatically improved accuracy, reliability, and dynamic range. Efficient data compression in the spectral dimension has allowed personal computers to perform quantitative analysis of hyperspectral images of large size without loss of image quality. We have also developed and tested software to perform analysis of time resolved hyperspectral images using trilinear multivariate analysis methods. The new imaging system is an enabling technology for numerous applications including (1) 3D composition mapping analysis of multicomponent processes occurring during host-pathogen interactions, (2) monitoring microfluidic processes, (3) imaging of molecular motors and (4) understanding photosynthetic processes in wild type and mutant Synechocystis cyanobacteria.

  9. Line-scan hyperspectral imaging for real-time on-line poultry fecal detection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The preliminary results demonstrated that high speed line-scan hyperspectral imaging system has a potential for real-time online fecal detection during poultry processing. To improve detection accuracy, fully calibrated images both spatially and spectrally were acquired for further processing. In ad...

  10. ASSESSING THE MATURITY OF APPLES BY INTEGRATING HYPERSPECTRAL REFLECTANCE AND FLUORESCENCE IMAGING TECHNIQUES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Fluorescence and reflectance are two different forms of light interaction with the matter, and they can be complementary in measuring fruit maturity and quality. In this research, a hyperspectral imaging system was used to acquire both reflectance and fluorescence images from 'Golden Delicious' appl...

  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. Excitation-resolved hyperspectral fluorescence lifetime imaging using a UV-extended supercontinuum source.

    PubMed

    Owen, Dylan M; Auksorius, Egidijus; Manning, Hugh B; Talbot, Clifford B; de Beule, Pieter A A; Dunsby, Christopher; Neil, Mark A A; French, Paul M W

    2007-12-01

    We present a time-gated, optically sectioned, hyperspectral fluorescence lifetime imaging (FLIM) microscope incorporating a tunable supercontinuum excitation source extending into the UV. The system is capable of resolving the excitation spectrum, emission spectrum, and fluorescence decays in an optically sectioned image. PMID:18059949

  13. Detection of melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Melamine (2,4,6-triamino-1,3,5-triazine) contamination of food has become an urgent and broadly recognized topic as a result of several food safety scares in the past five years. Hyperspectral imaging techniques that combine the advantages of spectroscopy and imaging have been widely applied for a v...

  14. Monitoring Phosphorus Content in a Tropical Estuary Lagoon using an Hyperspectral Sensor and its Application to Water Quality Modeling

    E-print Network

    Gilbes, Fernando

    1 Monitoring Phosphorus Content in a Tropical Estuary Lagoon using an Hyperspectral Sensor and its Application to Water Quality Modeling Project Number: 2005PR20B Start: 03/01/2004 End: 12 Remote Sensing, Total Phosphorus Contamination, Non-Point Source Pollution Problem and Research

  15. A novel adaptive compression method for hyperspectral images by using EDT and particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Ghamisi, Pedram; Kumar, Lalit

    2012-01-01

    Hyperspectral sensors generate useful information about climate and the earth surface in numerous contiguous narrow spectral bands, and are widely used in resource management, agriculture, environmental monitoring, etc. Compression of the hyperspectral data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as hyperspectral data. Due to high redundancy in neighboring spectral bands and the tendency to achieve a higher compression ratio, using adaptive coding methods for hyperspectral data seems suitable for this purpose. This paper introduces two new compression methods. One of these methods is adaptive and powerful for the compression of hyperspectral data, which is based on separating the bands with different specifications by the histogram and Binary Particle Swarm Optimization (BPSO) and compressing each one a different manner. The new proposed methods improve the compression ratio of the JPEG standards and save storage space the transmission. The proposed methods are applied on different test cases, and the results are evaluated and compared with some other compression methods, such as lossless JPEG and JPEG2000.

  16. Hyperspectral image compression using entropy-constrained predictive trellis coded quantization.

    PubMed

    Abousleman, G P; Marcellin, M W; Hunt, B R

    1997-01-01

    A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the mean squared error (MSE) performance of an eight-state ECPTCQ system exceeds that of entropy-constrained differential pulse code modulation (ECDPCM) by up to 1.0 dB. In addition, a hyperspectral image compression system is developed, which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.125 b/pixel/band retains an average peak signal-to-noise ratio (PSNR) of greater than 43 dB over the spectral bands. PMID:18282949

  17. Monitoring of biofilm formation on different material surfaces of medical devices using hyperspectral imaging method

    NASA Astrophysics Data System (ADS)

    Kim, Do-Hyun; Kim, Moon S.; Hwang, Jeeseong

    2012-03-01

    Contamination of the inner surface of indwelling (implanted) medical devices by microbial biofilm is a serious problem. Some microbial bacteria such as Escherichia coli form biofilms that lead to potentially lifethreatening infections. Other types of medical devices such as bronchoscopes and duodenoscopes account for the highest number of reported endoscopic infections where microbial biofilm is one of the major causes for these infections. We applied a hyperspectral imaging method to detect biofilm contamination on the surface of several common materials used for medical devices. Such materials include stainless steel, titanium, and stainless-steeltitanium alloy. Potential uses of hyperspectral imaging technique to monitor biofilm attachment to different material surfaces are discussed.

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

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

    Sunglint, also known as the specular reflection of light from water surfaces, is a component of sensor-received radiance that represents a confounding factor on the characterization of water bodies by remote sensing. In airborne remote sensing images, the effect of sunglint can be minimized by optimizing the flight paths, directing the sensor towards or away from the Sun, and by keeping solar zenith angles between 30° and 60°. However, these guidelines cannot always be applied, often due to the irregular spatial pattern of lakes, estuaries and coastlines. The present study assessed the impact of sunglint on the relationship between the optically active substances (OAS) concentration, in optically complex waters, and the spectral information provided by an airborne high spatial resolution hyperspectral sensor (SpecTIR). The Ibitinga reservoir, located in southeastern Brazil (state of São Paulo), was selected as the study area because of its meandering shape. As a result, there is demanding constant changes in data acquisition geometry to achieve complete coverage, therefore not allowing sunglint conditions to be minimized during image acquisition. Field data collection was carried out on October 23 and 24, 2011. During these two days, 15 water stations along the reservoir were sampled, concurrently with the SpecTIR image acquisition in 357 bands (398-2455 nm) and at 3 m spatial resolution. Chlorophyll, pheophytin, total suspended solids, organic and inorganic suspended solids and colored dissolved matter were determined in laboratory. The images were corrected for the atmospheric effects using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm and then geometrically corrected. In order to evaluate the sunglint effects on the OAS characterization, the images were corrected for such effects using the deglint algorithm from Goodman et al. (2008). The SpecTIR 662-nm band reflectance was selected to be correlated to the OAS due to its sensibility to the absorption and scattering by the water constituents. One of the sunglint effects was to increase the reflectance values of the images by a factor of ~3, masking the correlations between the OAS and the spectral reflectance. After using the deglint algorithm, the relationships were improved. The correlations coefficients (r) between the reflectance at 662 nm and the OAS improved from -0.16 to -0.38 for pheophytin; -0.12 to -0.38 for chlorophyll; and from +0.52 to +0.66 for inorganic suspended solids. Despite the low correlation coefficients, results suggested that sunglint effects were capable of affecting the relationships between the spectral reflectance and the water concentration of OAS. They emphasize the importance of removing such effects on high spatial resolution hyperspectral data collected by airborne sensors at several flight lines over meandering water bodies where variations in Sun-viewing geometry are generally observed.

  19. Hyperspectral image classification with improved local-region filters

    NASA Astrophysics Data System (ADS)

    Ran, Qiong; Li, Wei; Du, Qian; Xiong, Mingming

    2014-01-01

    Two improved local-region filters, adaptive weighted filter (AWF) and collaborative representation filter (CoRF), are proposed for feature extraction and classification in hyperspectral imagery. The local-region filters generate spatial-spectral features of a hyperspectral pixel by incorporating its surrounding pixels. The work of this paper is an extension of our previously introduced local average filter (LAF). Unlike LAF, which gives the surrounding pixels the same weight, AWF and CoRF explore the internal similarity in the local region with an adaptive weight. More specifically, AWF is set up considering the spatial distance to the central pixel, and CoRF is constructed with spectral similarities adopting the idea of collaborative representation. The two improved local-region filters adaptively extract spectral-spatial features from neighboring pixels and are proven to be effective in many aspects, such as edge information preservation and classification performance, with experiments on two real hyperspectral datasets.

  20. Improved sequential search algorithms for classification in hyperspectral remote sensing images

    NASA Astrophysics Data System (ADS)

    Nakariyakul, Songyot

    2014-11-01

    Two new sequential search algorithms for feature selection in hyperspectral remote sensing images are proposed. Since many wavebands in hyperspectral images are redundant and irrelevant, the use of feature selection to improve classification results is highly needed. First, we present a new generalized steepest ascent (GSA) feature selection technique that improves upon the prior steepest ascent algorithm by selecting a better starting search point and performing a more thorough search. It is guaranteed to provide solutions that equal or exceed those of the classical sequential forward floating selection algorithm. However, when the number of available wavebands is large, the computational load required for the GSA algorithm becomes excessive. We thus propose a modification of the improved floating forward selection algorithm which is more computationally efficient. Experimental results for two hyperspectral data sets show that our proposed algorithms yield better classification results than other suboptimal search algorithms.

  1. Estimation of skin optical parameters for real-time hyperspectral imaging applications

    NASA Astrophysics Data System (ADS)

    Bjorgan, Asgeir; Milanic, Matija; Randeberg, Lise Lyngsnes

    2014-06-01

    Hyperspectral imaging combines high spectral and spatial resolution in one modality. This imaging technique is a promising tool for objective medical diagnostics. However, to be attractive in a clinical setting, the technique needs to be fast and accurate. Hyperspectral imaging can be used to analyze tissue properties using spectroscopic methods, and is thus useful as a general purpose diagnostic tool. We combine an analytic diffusion model for photon transport with real-time analysis of the hyperspectral images. This is achieved by parallelizing the inverse photon transport model on a graphics processing unit to yield optical parameters from diffuse reflectance spectra. The validity of this approach was verified by Monte Carlo simulations. Hyperspectral images of human skin in the wavelength range 400-1000 nm, with a spectral resolution of 3.6 nm and 1600 pixels across the field of view (Hyspex VNIR-1600), were used to develop the presented approach. The implemented algorithm was found to output optical properties at a speed of 3.5 ms per line of image data. The presented method is thus capable of meeting the defined real-time requirement, which was 30 ms per line of data.The algorithm is a proof of principle, which will be further developed.

  2. Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

    PubMed Central

    Zhang, Xiaolei; Liu, Fei; He, Yong; Li, Xiaoli

    2012-01-01

    Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. PMID:23235456

  3. Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds.

    PubMed

    Zhang, Xiaolei; Liu, Fei; He, Yong; Li, Xiaoli

    2012-01-01

    Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380-1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. PMID:23235456

  4. Investigating oyster shell thickness and strength using three imaging modalities: hyperspectral imaging, thermal imaging and digital photography

    NASA Astrophysics Data System (ADS)

    Mehrübeoglu, Mehrube; Smith, Dustin K.; Smith, Shane W.; Smee, Delbert L.; Simionescu, Petru-Aurelian

    2013-09-01

    A comparative study of three imaging technologies has been conducted to nondestructively assess the thickness and strength of oyster shells grown in various environmental conditions. Oyster shell thickness and strength are expected to be dependent on the harshness of the oyster's environment as well as other factors. Oysters have been grown in environments with and without predators, and within and out of tidal zones. Hyperspectral imaging has been used to detect possible differences in hyperspectral properties among oyster shells from each of the four environments. Thermal Imaging has been utilized to identify hot spots in the shells based on the principles of heat capacitance, indicating density or thickness of the shells. Finally, a visible-range digital photographic camera has been used to obtain digital images. The three technologies are compared to evaluate the effectiveness of each technology in identifying oyster shell thickness and strength. Although oyster shell thickness and strength are related, they may not be exactly correlated. The local thickness of the oyster shells have been measured with a micro caliper, and shells broken with a crush tester to establish a baseline and ground truth for average shell thickness and shell strength, respectively. The preliminary results from the three methods demonstrate that thermal imaging correlates the best with the invasive strength test results and weight measurements. Using hyperspectral data and principal component analysis, classification of the four oyster shell groups were achieved. Visible-range images mainly provided size, shape, color and texture information.

  5. Comparison of Spectral and Image Morphological Analysis for Egg Early Hatching Property Detection Based on Hyperspectral Imaging

    PubMed Central

    Zhang, Wei; Pan, Leiqing; Tu, Kang; Zhang, Qiang; Liu, Ming

    2014-01-01

    The use of non-destructive methods to detect egg hatching properties could increase efficiency in commercial hatcheries by saving space, reducing costs, and ensuring hatching quality. For this purpose, a hyperspectral imaging system was built to detect embryo development and vitality using spectral and morphological information of hatching eggs. A total of 150 green shell eggs were used, and hyperspectral images were collected for every egg on day 0, 1, 2, 3 and 4 of incubation. After imaging, two analysis methods were developed to extract egg hatching characteristic. Firstly, hyperspectral images of samples were evaluated using Principal Component Analysis (PCA) and only one optimal band with 822 nm was selected for extracting spectral characteristics of hatching egg. Secondly, an image segmentation algorithm was applied to isolate the image morphologic characteristics of hatching egg. To investigate the applicability of spectral and image morphological analysis for detecting egg early hatching properties, Learning Vector Quantization neural network (LVQNN) was employed. The experimental results demonstrated that model using image morphological characteristics could achieve better accuracy and generalization than using spectral characteristic parameters, and the discrimination accuracy for eggs with embryo development were 97% at day 3, 100% at day 4. In addition, the recognition results for eggs with weak embryo development reached 81% at day 3, and 92% at day 4. This study suggested that image morphological analysis was a novel application of hyperspectral imaging technology to detect egg early hatching properties. PMID:24551130

  6. Comparison of spectral and image morphological analysis for egg early hatching property detection based on hyperspectral imaging.

    PubMed

    Zhang, Wei; Pan, Leiqing; Tu, Kang; Zhang, Qiang; Liu, Ming

    2014-01-01

    The use of non-destructive methods to detect egg hatching properties could increase efficiency in commercial hatcheries by saving space, reducing costs, and ensuring hatching quality. For this purpose, a hyperspectral imaging system was built to detect embryo development and vitality using spectral and morphological information of hatching eggs. A total of 150 green shell eggs were used, and hyperspectral images were collected for every egg on day 0, 1, 2, 3 and 4 of incubation. After imaging, two analysis methods were developed to extract egg hatching characteristic. Firstly, hyperspectral images of samples were evaluated using Principal Component Analysis (PCA) and only one optimal band with 822 nm was selected for extracting spectral characteristics of hatching egg. Secondly, an image segmentation algorithm was applied to isolate the image morphologic characteristics of hatching egg. To investigate the applicability of spectral and image morphological analysis for detecting egg early hatching properties, Learning Vector Quantization neural network (LVQNN) was employed. The experimental results demonstrated that model using image morphological characteristics could achieve better accuracy and generalization than using spectral characteristic parameters, and the discrimination accuracy for eggs with embryo development were 97% at day 3, 100% at day 4. In addition, the recognition results for eggs with weak embryo development reached 81% at day 3, and 92% at day 4. This study suggested that image morphological analysis was a novel application of hyperspectral imaging technology to detect egg early hatching properties. PMID:24551130

  7. Monitoring, analysis and classification of vegetation and soil data collected by a small and lightweight hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

    Mönnig, Carsten

    2014-05-01

    The increasing precision of modern farming systems requires a near-real-time monitoring of agricultural crops in order to estimate soil condition, plant health and potential crop yield. For large sized agricultural plots, satellite imagery or aerial surveys can be used at considerable costs and possible time delays of days or even weeks. However, for small to medium sized plots, these monitoring approaches are cost-prohibitive and difficult to assess. Therefore, we propose within the INTERREG IV A-Project SMART INSPECTORS (Smart Aerial Test Rigs with Infrared Spectrometers and Radar), a cost effective, comparably simple approach to support farmers with a small and lightweight hyperspectral imaging system to collect remotely sensed data in spectral bands in between 400 to 1700nm. SMART INSPECTORS includes the whole remote sensing processing chain of small scale remote sensing from sensor construction, data processing and ground truthing for analysis of the results. The sensors are mounted on a remotely controlled (RC) Octocopter, a fixed wing RC airplane as well as on a two-seated Autogyro for larger plots. The high resolution images up to 5cm on the ground include spectra of visible light, near and thermal infrared as well as hyperspectral imagery. The data will be analyzed using remote sensing software and a Geographic Information System (GIS). The soil condition analysis includes soil humidity, temperature and roughness. Furthermore, a radar sensor is envisaged for the detection of geomorphologic, drainage and soil-plant roughness investigation. Plant health control includes drought stress, vegetation health, pest control, growth condition and canopy temperature. Different vegetation and soil indices will help to determine and understand soil conditions and plant traits. Additional investigation might include crop yield estimation of certain crops like apples, strawberries, pasture land, etc. The quality of remotely sensed vegetation data will be tested with ground truthing tools like a spectrometer, visual inspection and ground control panel. The soil condition will also be monitored with a wireless sensor network installed on the examined plots of interest. Provided with this data, a farmer can respond immediately to potential threats with high local precision. In this presentation, preliminary results of hyperspectral images of distinctive vegetation cover and soil on different pasture test plots are shown. After an evaluation period, the whole processing chain will offer farmers a unique, near real- time, low cost solution for small to mid-sized agricultural plots in order to easily assess crop and soil quality and the estimation of harvest. SMART INSPECTORS remotely sensed data will form the basis for an input in a decision support system which aims to detect crop related issues in order to react quickly and efficiently, saving fertilizer, water or pesticides.

  8. Image Sensors Enhance Camera Technologies

    NASA Technical Reports Server (NTRS)

    2010-01-01

    In the 1990s, a Jet Propulsion Laboratory team led by Eric Fossum researched ways of improving complementary metal-oxide semiconductor (CMOS) image sensors in order to miniaturize cameras on spacecraft while maintaining scientific image quality. Fossum s team founded a company to commercialize the resulting CMOS active pixel sensor. Now called the Aptina Imaging Corporation, based in San Jose, California, the company has shipped over 1 billion sensors for use in applications such as digital cameras, camera phones, Web cameras, and automotive cameras. Today, one of every three cell phone cameras on the planet feature Aptina s sensor technology.

  9. Sensitivity of soil property prediction obtained from Hyperspectral VNIR-SWIR imagery to atmospheric effects and degradation in image spatial resolutions

    NASA Astrophysics Data System (ADS)

    Oltra-Carrió, Rosa; Gomez, Cécile; Bacha, Sinan; Lagacherie, Philippe; Briottet, Xavier

    2014-05-01

    Visible and near-infrared (VNIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-2500 nm) hyperspectral satellite imaging is one of the most promising tools for soil property mapping because i) it is derived from a lab technique that has proven to be a good alternative to costly physical and chemical laboratory soil analysis for estimating a large range of soil properties; ii) it can benefit from the increasing number of methodologies developed for VNIR-SWIR hyperspectral airborne imaging; and iii) it provides a synoptic view of the area under study. Despite the great potential of VNIR-SWIR hyperspectral airborne data for soil property mapping, the transposition to satellite data must be evaluated. The objective of this study was to test the sensitivity of soil property prediction results to atmospheric effects and to degradation in image spatial resolutions. This may offer a first analysis of the potential of future hyperspectral satellite sensors (HYPXIM, PRIMSA, ENMAP and HyspIRI) for Soil applications. This study employed VNIR-SWIR AISA-DUAL hyperspectral airborne data acquired in the Mediterranean region over a large area (300 km²) with an initial spatial resolution of 5 m. These airborne data were simulated at the top of atmosphere, aggregated at 7 spatial resolutions (5, 10, 15, 20, 30, 60 and 90m) and then atmospherically corrected, to fit with future hyperspectral satellite sensors. The predicted soil property maps were obtained using the partial least squares regression (PLSR) method, and the studied soil property was the clay content. The large area of the studied region allows us to analyze different pedological patterns in terms of soil composition and spatial structures. Our results showed that (i) PLSR models had robust performances from images at 5 to 30m and were inaccurate from images at 60 and 90m; (ii) when a correct compensation of the atmosphere effects was done, no differences were detected between the clay maps retrieved from airborne imagery and the ones from spaceborne imagery; (iii) the spatial aggregation of the images meant a loss of the variance of the clay prediction from 15 m of spatial resolution and a loss of information on soil spatial structures from 30 m of spatial resolution.

  10. Hyperspectral and differential CARS microscopy for quantitative chemical imaging in human adipocytes.

    PubMed

    Di Napoli, Claudia; Pope, Iestyn; Masia, Francesco; Watson, Peter; Langbein, Wolfgang; Borri, Paola

    2014-05-01

    In this work, we demonstrate the applicability of coherent anti-Stokes Raman scattering (CARS) micro-spectroscopy for quantitative chemical imaging of saturated and unsaturated lipids in human stem-cell derived adipocytes. We compare dual-frequency/differential CARS (D-CARS), which enables rapid imaging and simple data analysis, with broadband hyperspectral CARS microscopy analyzed using an unsupervised phase-retrieval and factorization method recently developed by us for quantitative chemical image analysis. Measurements were taken in the vibrational fingerprint region (1200-2000/cm) and in the CH stretch region (2600-3300/cm) using a home-built CARS set-up which enables hyperspectral imaging with 10/cm resolution via spectral focussing from a single broadband 5 fs Ti:Sa laser source. Through a ratiometric analysis, both D-CARS and phase-retrieved hyperspectral CARS determine the concentration of unsaturated lipids with comparable accuracy in the fingerprint region, while in the CH stretch region D-CARS provides only a qualitative contrast owing to its non-linear behavior. When analyzing hyperspectral CARS images using the blind factorization into susceptibilities and concentrations of chemical components recently demonstrated by us, we are able to determine vol:vol concentrations of different lipid components and spatially resolve inhomogeneities in lipid composition with superior accuracy compared to state-of-the art ratiometric methods. PMID:24877002

  11. Automatic identification and quantitative morphometry of unstained spinal nerve using molecular hyperspectral imaging technology.

    PubMed

    Li, Qingli; Chen, Zenggan; He, Xiaofu; Wang, Yiting; Liu, Hongying; Xu, Qintong

    2012-12-01

    Quantitative observation of nerve fiber sections is often complemented by morphological analysis in both research and clinical condition. However, existing manual or semi-automated methods are tedious and labour intensive, fully automated morphometry methods are complicated as the information of color or gray images captured by traditional microscopy is limited. Moreover, most of the methods are time-consuming as the nerve sections need to be stained with some reagents before observation. To overcome these shortcomings, a molecular hyperspectral imaging system is developed and used to observe the spinal nerve sections. The molecular hyperspectral images contain both the structural and biochemical information of spinal nerve sections which is very useful for automatic identification and quantitative morphological analysis of nerve fibers. This characteristic makes it possible for researchers to observe the unstained spinal nerve and live cells in their native environment. To evaluate the performance of the new method, the molecular hyperspectral images were captured and the improved spectral angle mapper algorithm was proposed and used to segment the myelin contours. Then the morphological parameters such as myelin thickness and myelin area were calculated and evaluated. With these morphological parameters, the three dimension surface view images were drawn to help the investigators observe spinal nerve at different angles. The experiment results show that the hyperspectral based method has the potential to identify the spinal nerve more accurate than the traditional method as the new method contains both the spectral and spatial information of nerve sections. PMID:23059447

  12. Experimental comparison of support vector machines with random forests for hyperspectral image land cover classification

    NASA Astrophysics Data System (ADS)

    Abe, B. T.; Olugbara, O. O.; Marwala, T.

    2014-06-01

    The performances of regular support vector machines and random forests are experimentally compared for hyperspectral imaging land cover classification. Special characteristics of hyperspectral imaging dataset present diverse processing problems to be resolved under robust mathematical formalisms such as image classification. As a result, pixel purity index algorithm is used to obtain endmember spectral responses from Indiana pine hyperspectral image dataset. The generalized reduced gradient optimization algorithm is thereafter executed on the research data to estimate fractional abundances in the hyperspectral image and thereby obtain the numeric values for land cover classification. The Waikato environment for knowledge analysis (WEKA) data mining framework is selected as a tool to carry out the classification process by using support vector machines and random forests classifiers. Results show that performance of support vector machines is comparable to that of random forests. This study makes a positive contribution to the problem of land cover classification by exploring generalized reduced gradient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker to consider tradeoffs in method accuracy versus method complexity.

  13. Temporal analysis of emissivity values in urban areas from airborne hyperspectral thermal sensors

    NASA Astrophysics Data System (ADS)

    Lazzarini, Michele; Mitraka, Zina; Berger, Michael; Ghedira, Hosni

    2013-04-01

    Land Surface Temperature (LST) is one of the key parameters in the physics of land surface processes on regional and global scales, combining the results of surface - atmosphere interactions related to energy fluxes such as sensible and latent heat, and also of hydrological cycle. Accurate LST mapping has the potential to support applications in various areas and especially when referring to the urban environment, as for example the urban heat island monitoring. Spatiotemporal distributions of LST are estimated with the use of satellite remote sensing sensors. To accurately estimate LST, measured radiance should be corrected for spectral emissivity. Emissivity provides a measure of the inherent efficiency of the surface to convert heat energy into radiant energy. Surface emissivity varies largely in space and time due to the strong heterogeneity of land surface characteristics, such as the topography, the vegetation cover and the soil and its physical properties. The emissivity dependence on the surface physical condition emerges the study of temporal effects on emissivity. It is important to quantify the temporal variations of emissivity and their extend and the effect on the estimation of LST. The present work aims at contributing to a better understanding of the temporal variation of emissivity in urban environments. Thermal data acquired with the Airborne Hyperspectral Scanner (AHS) over Madrid, Spain and Athens, Greece during two ESA Campaigns (namely Desirex and Thermopolis) was used. A total of 30 and 28 images were acquired respectively for each city, covering a time period of six summer days, both day and night. Emissivity and LST were also estimated in the framework of the campaigns for seven spectral AHS bands between 7.5 - 12.0 ?m using the Temperature Emissivity Separation Algorithm. Field emissivity and LST measurements are also available from the campaign. A statistical analysis was performed to assess the variations of emissivity in time, to identify the behavior of the different surface types found in urban environments and to quantify the differences arising between day and night. Representative areas corresponding to different surface types were selected for the analysis by considering homogenous areas to avoid mixed pixels. Some temporal variations of emissivity values were observed in all cases and they were also depending on the surface type and the specific thermal bands.

  14. Programmable matched filter and Hadamard transform hyperspectral imagers based on micro-mirror arrays

    SciTech Connect

    Love, Steven P [Los Alamos National Laboratory

    2008-01-01

    Hyperspectral imaging (HSI), in which each pixel contains a high-resolution spectrum, is a powerful technique that can remotely detect, identify, and quantify a multitude of materials and chemicals. The advent of addressable micro-mirror arrays (MMAs) makes possible a new class of programmable hyperspectral imagers that can perform key spectral processing functions directly in the optical hardware, thus alleviating some of HSI's high computational overhead, as well as offering improved signal-to-noise in certain important regimes (e.g. when using uncooled infrared detectors). We have built and demonstrated a prototype UV-Visible micro-mirror hyperspectral imager that is capable not only of matched-filter imaging, but also of full hyperspectral imagery via the Hadamard transform technique. With this instrument, one can upload a chemical-specific spectral matched filter directly to the MMA, producing an image showing the location of that chemical without further processing. Target chemicals are changeable nearly instantaneously simply by uploading new matched-filter patterns to the MMA. Alternatively, the MMA can implement Hadamard mask functions, yielding a full-spectrum hyperspectral image upon inverting the transform. In either case, the instrument can produce the 2D spatial image either by an internal scan, using the MMA itself, or with a traditional external push-broom scan. The various modes of operation are selectable simply by varying the software driving the MMA. Here the design and performance of the prototype is discussed, along with experimental results confirming the signal-to-noise improvement produced by the Hadamard technique in the noisy-detector regime.

  15. PORTABLE HYPERSPECTRAL TUNABLE IMAGING SYSTEM (PHYTIS) - FOR PRECISION AGRICULTURE

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral remote sensing system can provide a contiguous spectrum of a scene made up of dozens to hundreds of narrow wavebands across the visible and near-infrared portions of the spectrum. This emerging technology provides spatial and spectral information that can be acquired simultaneously. ...

  16. Hyperspectral image analysis for water stress detection of apple trees

    Microsoft Academic Search

    Yunseop Kim; David M. Glenn; Johnny Park; Henry K. Ngugi; Brian L. Lehman

    2011-01-01

    Plant stress significantly reduces plant productivity. Automated on-the-go mapping of plant stress would allow for a timely intervention and mitigation of the problem before critical thresholds were exceeded, thereby maximizing productivity. The spectral signature of plant leaves was analyzed by a hyperspectral camera to identify the onset and intensity of plant water stress. Five different levels of water treatment were

  17. Characteristic extraction and matching algorithms of ballistic missile in near-space by hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Lu, Li; Sheng, Wen; Liu, Shihua; Zhang, Xianzhi

    2014-10-01

    The ballistic missile hyperspectral data of imaging spectrometer from the near-space platform are generated by numerical method. The characteristic of the ballistic missile hyperspectral data is extracted and matched based on two different kinds of algorithms, which called transverse counting and quantization coding, respectively. The simulation results show that two algorithms extract the characteristic of ballistic missile adequately and accurately. The algorithm based on the transverse counting has the low complexity and can be implemented easily compared to the algorithm based on the quantization coding does. The transverse counting algorithm also shows the good immunity to the disturbance signals and speed up the matching and recognition of subsequent targets.

  18. DETECTION OF MECHANICAL INJURY ON PICKLING CUCUMBERS USING NEAR-INFRARED HYPERSPECTRAL IMAGING

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Automated detection of defects on freshly harvested pickling cucumbers will help the pickle industry provide high quality pickle products and reduce potential economic losses. Research was conducted on using a hyperspectral imaging system for detecting defects on pickling cucumbers caused by mechani...

  19. Spectral Angle Mapper Classification of Fluorescence Hyperspectral Image for Aflatoxin Contaminated Corn

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Aflatoxin contamination in corn is a serious problem for both producers and consumers. The present study applied the Spectral Angle Mapper classification technique to classify single corn kernels into contaminated and healthy groups. Fluorescence hyperspectral images were used in the classification....

  20. Emission filter design to detect poultry skin tumors using Fluorescene hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The secure production of disease-free meat is crucial in the mass production environment. The fluorescence spectra of poultry have been gaining the practical use because the fluorescence response is very sensitive in detecting a particular biological element. A hyperspectral image contains spectral ...

  1. Time-lens Based Hyperspectral Stimulated Raman Scattering Imaging and Quantitative Spectral Analysis

    PubMed Central

    Wang, Ke; Zhang, Delong; Charan, Kriti; Slipchenko, Mikhail N.; Wang, Ping; Xu, Chris; Cheng, Ji-Xin

    2014-01-01

    We demonstrate a hyperspectral stimulated Raman scattering (SRS) microscope through spectral-transformed excitation. The 1064-nm Stokes pulse was from a synchronized time-lens source, generated through time-domain phase modulation of a continuous wave (CW) laser. The tunable pump pulse was from linear spectral filtering of a femtosecond laser output with an intra-pulse spectral scanning pulse shaper. By electronically modulating the time-lens source at 2.29 MHz, hyperspectral stimulated Raman loss (SRL) images were obtained on a laser-scanning microscope. Using this microscope, DMSO in aqueous solution with a concentration down to 28 mM could be detected at 2 ?s time constant. Hyper-spectral SRL images of prostate cancer cells were obtained. Multivariate curve resolution analysis was further applied to decompose the SRL images into concentration maps of CH2 and CH3 bonds. This method offers exciting potential in label-free imaging of live cells using fingerprint Raman bands. Hyperspectral SRS microscopy using a synchronized time-lens source allows mapping of different cellular contents. PMID:23840041

  2. Rapid Identification of Salmonella Serotypes with Stereo and Hyperspectral Microscope Imaging Methods

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The hyperspectral microscope imaging (HMI) method can reduce detection time within 8 hours including incubation process. The early and rapid detection with this method in conjunction with the high throughput capabilities makes HMI method a prime candidate for implementation for the food industry. Th...

  3. Recent Developments and Future Directions in Parallel Processing of Remotely Sensed Hyperspectral Images

    E-print Network

    Plaza, Antonio J.

    for the same area on the surface of the Earth. Computationally effective processing of these im- age cubes can hyperspectral image processing chain: 1) commodity Beowulf-type clusters, 2) heterogeneous net- works] and graphic processing units (GPUs) [12] has helped in bridging the gap towards real-time analysis of remotely

  4. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Nondestructive sensing is critical to assuring postharvest quality of apple fruit and increasing consumer acceptance and satisfaction. The objective of this research was to use a hyperspectral scattering technique to acquire spectral scattering images from apple fruit and to develop a data analysis ...

  5. Detection of microbial biofilms on food processing surfaces: Hyperspectral fluorescence imaging study

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We used a portable hyperspectral fluorescence imaging system to evaluate biofilm formations on four types of food processing surface materials including stainless steel, polypropylene used for cutting boards, and household counter top materials such as formica and granite. The objective of this inve...

  6. Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification

    Microsoft Academic Search

    Xiuping Jia; John A. Richards

    1999-01-01

    A segmented, and possibly multistage, principal components transformation (PCT) is proposed for efficient hyperspectral remote-sensing image classification and display. The scheme requires, initially, partitioning the complete set of bands into several highly correlated subgroups. After separate transformation of each subgroup, the single-band separabilities are used as a guide to carry out feature selection. The selected features can then be transformed

  7. [An algorithm of spectral minimum shannon entropy on extracting endmember of hyperspectral image].

    PubMed

    Yang, Ke-ming; Liu, Shi-wen; Wang, Lin-wei; Yang, Jie; Sun, Yang-yang; He, Dan-dan

    2014-08-01

    It's significant to study the algorithm of endmember extraction, which is the key for pixel unmixing,in the fields of feature identification, abundance inversion, quantitative remote sensing and so on. Based on the theory of shannon entropy and Gaussian distribution function, a new algorithm, named spectral minimum shannon entropy (SMSE) method for extracting end-members of hyperspectral images, is proposed in the present paper after analyzing the characteristics of spectra of the hyperspectral images. This algorithm was applied to extract the endmembers of an AVRIRS hyperspectral image, it was found that these extracted endmember spectra have higher precision by matching with the spectral library of United States Geological Survey (USGS). At the same time, it was also found that the SMSE algorithm has better efficiency and accuracy for extracting endmember spectra through comparing and analyzing comprehensively the results of endmember extraction of the experimental data by using the methods of SMSE, pixel purity index (PPI), sequential maximum angle convex cone (SMACC) and so on. In addition, the SMACC and SMSE are used to extract the endmembers in a Hyperion hyperspectral image, and it is concluded that the results of the SMSE is better than the SMACC's. Thus, the SMSE algorithm can be thought to have a certain degree of universal applicability. PMID:25474967

  8. [An algorithm of spectral minimum shannon entropy on extracting endmember of hyperspectral image].

    PubMed

    Yang, Ke-ming; Liu, Shi-wen; Wang, Lin-wei; Yang, Jie; Sun, Yang-yang; He, Dan-dan

    2014-08-01

    It's significant to study the algorithm of endmember extraction, which is the key for pixel unmixing,in the fields of feature identification, abundance inversion, quantitative remote sensing and so on. Based on the theory of shannon entropy and Gaussian distribution function, a new algorithm, named spectral minimum shannon entropy (SMSE) method for extracting end-members of hyperspectral images, is proposed in the present paper after analyzing the characteristics of spectra of the hyperspectral images. This algorithm was applied to extract the endmembers of an AVRIRS hyperspectral image, it was found that these extracted endmember spectra have higher precision by matching with the spectral library of United States Geological Survey (USGS). At the same time, it was also found that the SMSE algorithm has better efficiency and accuracy for extracting endmember spectra through comparing and analyzing comprehensively the results of endmember extraction of the experimental data by using the methods of SMSE, pixel purity index (PPI), sequential maximum angle convex cone (SMACC) and so on. In addition, the SMACC and SMSE are used to extract the endmembers in a Hyperion hyperspectral image, and it is concluded that the results of the SMSE is better than the SMACC's. Thus, the SMSE algorithm can be thought to have a certain degree of universal applicability. PMID:25508746

  9. Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in

    E-print Network

    Paris-Sud XI, Université de

    Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen Melgueil, F-34130 Mauguio, France. Abstract Nitrogen is the most important crop limiting factor, thus plant nitrogen sta- tus during plant cycle is a key parameter for crop monitoring. Many new techniques, based

  10. Rice Seed Cultivar Identification Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

    PubMed Central

    Kong, Wenwen; Zhang, Chu; Liu, Fei; Nie, Pengcheng; He, Yong

    2013-01-01

    A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique. PMID:23857260

  11. Hyperspectral imaging using a color camera and its application for pathogen detection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper reports the results of a feasibility study for the development of a hyperspectral image recovery (reconstruction) technique using a RGB color camera and regression analysis in order to detect and classify colonies of foodborne pathogens. The target bacterial pathogens were the six represe...

  12. Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging

    Microsoft Academic Search

    Bart M. Nicolaï; Elmi Lötze; Ann Peirs; Nico Scheerlinck; Karen I. Theron

    2006-01-01

    A hyperspectral NIR imaging system was developed to identify bitter pit lesions on apples. A discriminant PLS calibration model was constructed to discriminate between pixels of unaffected apple skin and bitter pit lesions. The calibration model was successfully validated on a different apple. The system was able to identify bitter pit lesions, even when not visible to the naked eye

  13. Determination of Wheat Kernel Black Point Damage using Hyper-Spectral Imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A feasibility study was conducted on the use of hyperspectral imaging to differentiate sound wheat kernels from those with a damage condition called black point or black tip. Individual kernels of hard red spring wheat were loaded in indented slots on a blackened machined aluminum plate. Damage cond...

  14. INTEGRATION OF HYPERSPECTRAL REFLECTANCE AND LASER-INDUCED FLUORESCENCE IMAGING FOR ASSESSING APPLE MATURITY

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Fluorescence and reflectance are two different forms of light interaction with matter, and they can be complementary in measuring fruit quality and condition. The objective of this research was to develop an integrated hyperspectral reflectance and fluorescence imaging system for measuring apple mat...

  15. Hyperspectral imaging for detection of black tip damage in wheat kernels

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A feasibility study was conducted on the use of hyperspectral imaging to differentiate sound wheat kernels from those with the fungal condition called black point or black tip. Individual kernels of hard red spring wheat were loaded in indented slots on a blackened machined aluminum plate. Damage co...

  16. Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, the hyperspectral imaging (HSI) technique is investigated for the determination of moisture content in cooked chicken breast over the VIS/NIR (400–1000 nm) spectral ranges. Moisture measurements we...

  17. Hyperspectral Imaging in Diabetic Foot Wound Care Dmitry Yudovsky, M.S.,1

    E-print Network

    Pilon, Laurent

    1099 Hyperspectral Imaging in Diabetic Foot Wound Care Dmitry Yudovsky, M.S.,1 Aksone Nouvong, D mellitus are preceded by a foot ulcer.5 In fact, in 2006, diabetic foot ulcers were responsible for more cost, the associated financial cost of diabetic foot ulceration and amputations to the U.S. healthcare

  18. Hyperspectral near-infrared reflectance imaging for detection of defect tomatoes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Cuticle cracks on tomatoes are potential sites of pathogenic infection that may cause deleterious consequences both to consumer health and to fresh and fresh-cut produce markets. The feasibility of a hyperspectral near-infrared imaging technique in the spectral range of 1000 nm to 1700 nm was inves...

  19. Automation of waste recycling using hyperspectral image analysis Artzai Picon1

    E-print Network

    Whelan, Paul F.

    Automation of waste recycling using hyperspectral image analysis Artzai Picon1 Ovidiu Ghita2 Pedro. In this paper we present a novel methodology to automate the recycling process of non-ferrous metal Waste from that the proposed solution can be used to replace the manual procedure that is currently used in WEEE recycling

  20. A line-scan hyperspectral system for high-throughput Raman chemical imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A line-scan hyperspectral system was developed to enable Raman chemical imaging for large sample areas. A custom-designed 785 nm line-laser, based on a scanning mirror, serves as an excitation source. A 45° dichroic beamsplitter reflects the laser light to form a 24 cm × 1 mm excitation line normall...

  1. DETECTION OF SKIN TUMORS ON CHICKEN CARCASSES USING HYPERSPECTRAL FLUORESCENCE IMAGING

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper presents a method for detecting skin tumors on chicken carcasses using hyperspectral fluorescence imaging data, which provide both spectral and spatial information. Since these two different kinds of information are complementary to one another, it is necessary to exploit them in a syner...

  2. Rapid identification of salmonella serotypes with stereo and hyperspectral microscope imaging Methods

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The hyperspectral microscope imaging (HMI) method can reduce detection time within 8 hours including incubation process. The early and rapid detection with this method in conjunction with the high throughput capabilities makes HMI method a prime candidate for implementation for the food industry. Th...

  3. Evaluation of Internal Defect and Surface Color of Whole Pickles Using Hyperspectral Imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging operated under simultaneous reflectance (400-675 nm) and transmittance (675-1000 nm) modes was studied for non-destructive and non-contact sensing of surface color and bloater damage in whole pickles. Good and defective pickles were collected from a commercial pickle processing...

  4. VISIBLE/NEAR-INFRARED HYPERSPECTRAL TRANSMITTANCE IMAGING FOR DETECTION OF INTERNAL MECHANICAL INJURY IN PICKLING CUCUMBERS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Internal product quality is an important aspect in the quality control and assurance of pickled products. A rapid and non-destructive method for internal defect detection would be of value to the pickling cucumber industry. A hyperspectral transmittance imaging technique was developed to detect inte...

  5. Portable hyperspectral fluorescence imaging system for detection of biofilms on stainless steel surfaces

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A rapid nondestructive technology is needed to detect bacterial contamination on the surfaces of food processing equipment to reduce public health risks. A portable hyperspectral fluorescence imaging system was used to evaluate potential detection of microbial biofilm on stainless steel typically u...

  6. Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant

    PubMed Central

    Yu, Ke-Qiang; Zhao, Yan-Ru; Li, Xiao-Li; Shao, Yong-Ni; Liu, Fei; He, Yong

    2014-01-01

    Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP?=?0.876) and root mean square error (RMSE) for prediction (RMSEP?=?0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant. PMID:25549353

  7. Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis

    E-print Network

    Kersting, Kristian

    Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image. The method was tested for drought stress, applied to potted barley plants in a controlled rain-out shelter research in the understanding of plant adaptation under drought to improve management practices

  8. Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI

    E-print Network

    Plaza, Antonio J.

    missions. To address the need for cost-effective parallel hyperspectral imaging algorithms, this paper and objects in the air, land and water on the basis of the unique reflectance patterns that result from the interaction of solar energy with the molecular structure of the material [1]. Most applications

  9. Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer

    NASA Astrophysics Data System (ADS)

    Goto, Atsushi; Nishikawa, Jun; Kiyotoki, Shu; Nakamura, Munetaka; Nishimura, Junichi; Okamoto, Takeshi; Ogihara, Hiroyuki; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao

    2015-01-01

    Hyperspectral imaging (HSI) is a new technology that obtains spectroscopic information and renders it in image form. This study examined the difference in the spectral reflectance (SR) of gastric tumors and normal mucosa recorded with a hyperspectral camera equipped with HSI technology and attempted to determine the specific wavelength that is useful for the diagnosis of gastric cancer. A total of 104 gastric tumors removed by endoscopic submucosal dissection from 96 patients at Yamaguchi University Hospital were recorded using a hyperspectral camera. We determined the optimal wavelength and the cut-off value for differentiating tumors from normal mucosa to establish a diagnostic algorithm. We also attempted to highlight tumors by image processing using the hyperspectral camera's analysis software. A wavelength of 770 nm and a cut-off value of 1/4 the corrected SR were selected as the respective optimal wavelength and cut-off values. The rates of sensitivity, specificity, and accuracy of the algorithm's diagnostic capability were 71%, 98%, and 85%, respectively. It was possible to enhance tumors by image processing at the 770-nm wavelength. HSI can be used to measure the SR in gastric tumors and to differentiate between tumorous and normal mucosa.

  10. REFLECTANCE CALIBRATION OF FOCAL PLANE ARRAY HYPERSPECTRAL IMAGING SYSTEM FOR AGRICULTURAL AND FOOD SAFETY APPLICATIONS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A method to calibrate a pushbroom hyperspectral imaging system for "near-field" applications in agricultural and food safety has been demonstrated. The method consists of a modified geometric control point correction applied to a focal plane array to remove smile and keystone distortion from the sy...

  11. Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging.

    PubMed

    Chen, Shanshan; Zhang, Fangfang; Ning, Jifeng; Liu, Xu; Zhang, Zhenwen; Yang, Shuqin

    2015-04-01

    The aim of this study was to demonstrate the capability of hyperspectral imaging in predicting anthocyanin content changes in wine grapes during ripening. One hundred twenty groups of Cabernet Sauvignon grapes were collected periodically after veraison. The hyperspectral images were recorded by a hyperspectral imaging system with a spectral range from 900 to 1700 nm. The anthocyanin content was measured by the pH differential method. A quantitative model was developed using partial least squares regression (PLSR) or support vector regression (SVR) for calculating the anthocyanin content. The best model was obtained using SVR, yielding a coefficient of validation (P-R(2)) of 0.9414 and a root mean square error of prediction (RMSEP) of 0.0046, higher than the PLSR model, which had a P-R(2) of 0.8407 and a RMSEP of 0.0129. Therefore, hyperspectral imaging can be a fast and non-destructive method for predicting the anthocyanin content of wine grapes during ripening. PMID:25442621

  12. ONLINE HYPERSPECTRAL LINE-SCAN FLUORESCENCE IMAGING FOR SAFETY INSPECTION OF APPLES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A recently developed fast hyperspectral line-scan imaging system integrated with a commercial apple-sorting machine was evaluated for rapid detection of animal feces matter on apples online. Golden Delicious apples obtained from a local orchard were artificially contaminated with a thin smear of co...

  13. Assessment of bacterial biofilm on stainless steel by hyperspectral fluorescence imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral fluorescence imaging techniques were investigated for detection of two genera of microbial biofilms on stainless steel material which is commonly used to manufacture food processing equipment. Stainless steel coupons were deposited in nonpathogenic E. coli O157:H7 and Salmonella cultu...

  14. Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images

    Microsoft Academic Search

    Yakoub Bazi; Farid Melgani

    2006-01-01

    Recent remote sensing literature has shown that support vector machine (SVM) methods generally outperform traditional statistical and neural methods in classification problems involving hyperspectral images. However, there are still open issues that, if suitably addressed, could allow further improvement of their performances in terms of classification accuracy. Two especially critical issues are: 1) the determination of the most appropriate feature

  15. PORTABLE HYPERSPECTRAL FLUORESCENCE IMAGING SYSTEM FOR DETECTION OF BIOFILM ON STAINLESS STEEL COUPON

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Portable hyperspectral fluorescence imaging, as a rapid nondestructive method, was used to investigate detection of bacterial contamination on the surfaces of food processing equipment. In this study, stainless steel plates typically used to manufacture food processing equipment was utilized to gro...

  16. Validation and experiment plans for a space based hyperspectral imaging system

    Microsoft Academic Search

    Thomas Cooley; Kirtland AFB

    2000-01-01

    The Air Force Research Laboratory Space Vehicles Directorate (AFRL\\/VS) 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) camera titled Warfighter-1 (WF-1). The program is an advanced technology demonstration utilizing a commercially based space capability to provide unique functionality in remote sensing

  17. 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.

  18. MultiSpec—a tool for multispectral hyperspectral image data analysis

    NASA Astrophysics Data System (ADS)

    Biehl, Larry; Landgrebe, David

    2002-12-01

    MultiSpec is a multispectral image data analysis software application. It is intended to provide a fast, easy-to-use means for analysis of multispectral image data, such as that from the Landsat, SPOT, MODIS or IKONOS series of Earth observational satellites, hyperspectral data such as that from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and EO-1 Hyperion satellite system or the data that will be produced by the next generation of Earth observational sensors. The primary purpose for the system was to make new, otherwise complex analysis tools available to the general Earth science community. It has also found use in displaying and analyzing many other types of non-space related digital imagery, such as medical image data and in K-12 and university level educational activities. MultiSpec has been implemented for both the Apple Macintosh ® and Microsoft Windows ® operating systems (OS). The effort was first begun on the Macintosh OS in 1988. The GLOBE ( http://www.globe.gov) program supported the development of a subset of MultiSpec for the Windows OS in 1995. Since then most (but not all) of the features in the Macintosh OS version have been ported to the Windows OS version. Although copyrighted, MultiSpec with its documentation is distributed without charge. The Macintosh and Windows versions and documentation on its use are available from the World Wide Web at URL: http://dynamo.ecn.purdue.edu/˜biehl/MultiSpec/ MultiSpec is copyrighted (1991-2001) by Purdue Research Foundation, West Lafayette, Indiana 47907.

  19. Characterization of post-consumer polyolefin wastes by hyperspectral imaging for quality control in recycling processes.

    PubMed

    Serranti, Silvia; Gargiulo, Aldo; Bonifazi, Giuseppe

    2011-11-01

    In this paper new analytical inspection strategies, based on hyperspectral imaging (HSI) in the VIS-NIR and NIR wavelength ranges (400-1000 and 1000-1700 nm, respectively), have been investigated and set up in order to define quality control logics that could be applied at industrial plant level for polyolefins recycling. The research was developed inside the European FP7 Project W2Plastics "Magnetic Sorting and Ultrasound Sensor Technologies for Production of High Purity Secondary Polyolefins from Waste". The main aim of the project is the separation of pure polyethylene and polypropylene adopting an innovative process, the magnetic density separation (MDS). Spectra of plastic particles and contaminants resulting from post-consumer complex wastes and of virgin polyolefins have been acquired by HSI and by Raman spectroscopy. The classification results obtained applying principal component analysis (PCA) on HSI data have been compared with those obtained by Raman spectroscopy, in order to validate the proposed innovative methodology. Results showed that HSI sensing techniques allow to identify both polyolefins and contaminants. Results also demonstrated that HSI has a great potentiality as a tool for quality control of feed (identification of contaminants in the plastic waste) and of the two different pure polypropylene and polyethylene flow streams resulting from the MDS-based recycling process. PMID:21745732

  20. Development of Research Infrastructure in Nevada for the Exploitation of Hyperspectral Image Data to Address Proliferation and Detection of Chemical and Biological Materials.

    SciTech Connect

    James V. Taranik

    2007-12-31

    This research was to exploit hyperspectral reflectance imaging technology for the detection and mapping variability (clutter) of the natural background against which gases in the atmosphere are imaged. The natural background consists of landscape surface cover composed of consolidated rocks, unconsolidated rock weathering products, soils, coatings on rock materials, vegetation, water, materials constructed by humans, and mixtures of the above. Human made gases in the atmosphere may indicate industrial processes important to detecting non-nuclear chemical and biological proliferation. Our research was to exploit the Visible and Near-Infrared (NIR) and the Short-wave Infrared (SWIR) portions of the electromagnetic spectrum to determine the properties of solid materials on the earth’s surface that could influence the detection of gases in the Long-Wave Infrared (LWIR). We used some new experimental hyperspectral imaging technologies to collect data over the Non-Proliferation Test and Evaluation Center (NPTEC) located on the Nevada Test Site (NTS). The SpecTIR HyperSpecTIR (HST) and Specim Dual hyperspectral sensors were used to understand the variability in the imaged background (clutter), that detected, measured, identified and mapped with operational commercial hyperspectral techniques. The HST sensors were determined to be more experimental than operational because of problems with radiometric and atmospheric data correction. However the SpecTIR Dual system, developed by Specim in Finland, eventually was found to provide cost-effective hyperspectral image data collection and it was possible to correct the Dual system’s data for specific areas. Batch processing of long flightlines was still complex, and if comparison to laboratory spectra was desired, the Dual system data still had to be processed using the empirical line method. This research determined that 5-meter spatial resolution was adequate for mapping natural background variations. Furthermore, this research determined that spectral resolution of 10um was adequate, but a signal to noise above 300:1 was desirable for hyperspectral sensors with this spectral resolution. Finally, we acquired a hyperspectral thermal dataset (SEBASS) at 3m spatial resolution over our study area in Beatty, Nevada that can be co-registered with the hyperspectral reflectance, LIDAR and digital Orthophoto data sets. This data set will enable us to quantify how measurements in the reflected infrared can be used to make inferences about the response of materials in the thermal infrared, the topic of our follow-on NA-22 investigation ending in 2008. These data provide the basis for our investigations proposed for the NA-22 2008 Broad Area Announcement. Beginning in June 2008, SpecTIR Corporation and Aerospace Corporation plan to fly the SpecTIR Dual and SEBASS in a stabilized mount in a twin Otter aircraft. This research provides the foundation for using reflected and emitted hyperspectral measurements together for mapping geologic and soil materials in arid to semi-arid regions.

  1. Millimeter-wave imaging sensor

    NASA Technical Reports Server (NTRS)

    Wilson, W. J.; Howard, R. J.; Ibbott, A. C.; Parks, G. S.; Ricketts, W. B.

    1986-01-01

    A scanning 3-mm radiometer system has been built and used on a helicopter to produce moderate-resolution (0.5 deg) images of the ground. This millimeter-wave sensor can be used for a variety of remote-sensing applications and produces images through clouds, smoke, and dust when visual and IR sensors are not usable. The system is described and imaging results are presented.

  2. Update on the imaging sensor for GIFTS

    NASA Astrophysics Data System (ADS)

    Stobie, James A.; Tobin, Stephen P.; Norton, Peter W.; Hutchins, Mark A.; Wong, Kwok-Keung; Huppi, Ronald J.; Huppi, Ray

    2004-11-01

    Remote temperature sounding from the vantage point of Earth Orbit improves our weather forecasting, monitoring and analysis capability. Recent advances in the infrared hyperspectral sensor technology promise to improve the spatial and temperature resolution, while offering relatively quick re-look times to witness atmospheric dynamics. One approach takes advantage of a two-dimensional, imaging Fourier transform spectrometer to obtain a data cube with the field of view along one plane and multiple IR spectra (one for every FPA pixel) along the orthogonal axis. Only the pixel pitch in the imaging focal plane and the optics used to collect the data limit the spatial resolution. The maximum optical path difference in the Michelson FTS defines the spectral resolution and dictates the number of path-length interferogram samples (FPA frames required per cube). This paper discusses the unique challenges placed on the focal plane by the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) approach and how advanced focal plane technology is applied to satisfy these challenges. The instrument requires a midwave spectral band from 4.4 to 6.1m to capture the C02 and H20 absorption bands, and an optional VLWIR spectral band to cover from 8.85-14.6m. The paper presents performance data of Liquid Phase Epitaxy (LPE) fabricated HgCdTe detectors and design details of the advanced readout integrated circuit necessary to meet the demanding requirements of the imaging sensor for the GIFTS instrument. Point defects are removed by using a unique super-pixel approach to improve operability for the VLWIR focal plane. Finally, early focal plane performance measurements are reported, including Noise Equivalent Input, responsivity uniformity, output offset stability and 1/f noise knee.

  3. Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3-D wavelet coding.

    PubMed

    Christophe, Emmanuel; Mailhes, Corinne; Duhamel, Pierre

    2008-12-01

    Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties. PMID:19004706

  4. Mixed-spectrum generation mechanism analysis of dispersive hyperspectral imaging for improving environmental monitoring of coastal waters

    NASA Astrophysics Data System (ADS)

    Xie, Feng; Xiao, Gonghai; Qi, Hongxing; Shu, Rong; Wang, Jianyu; Xue, Yongqi

    2010-11-01

    At present, most part of coast zone in China belong to Case II waters with a large volume of shallow waters. Through theories and experiences of ocean water color remote sensing has a prominent improvement, there still exist many problems mainly as follows: (a) there is not a special sensor for heat pollution of coast water remote sensing up to now; (b) though many scholars have developed many water quality parameter retrieval models in the open ocean, there still exists a large gap from practical applications in turbid coastal waters. It is much more difficult due to the presence of high concentrations of suspended sediments and dissolved organic material, which overwhelm the spectral signal of sea water. Hyperspectral remote sensing allows a sensor on a moving platform to gather emitted radiation from the Earth's surface, which opens a way to reach a better analysis and understanding of coast water. Operative Modular Imaging Spectrometer (OMIS) is a type of representative imaging spectrometer developed by the Chinese Academy of Sciences. OMIS collects reflective and radiation light from ground by RC telescope with the scanning mirror cross track and flight of plane along track. In this paper, we explore the use of OMIS as the airborne sensor for the heat pollution monitoring in coast water, on the basis of an analysis on the mixed-spectrum arising from the image correcting process for geometric distortion. An airborne experiment was conducted in the winter of 2009 on the coast of the East Sea in China.

  5. Mapping oil spills on sea water using spectral mixture analysis of hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Plaza, Javier; Pérez, Rosa; Plaza, Antonio; Martínez, Pablo; Valencia, David

    2005-11-01

    During the last years, several terrestrial ecosystems have suffered from large spill oil events threatening coastal habitats and species. Some recent examples include the 2002 Prestige tanker oil spill in Galicia, Northern Spain, as well as repeated oil spill leaks evidenced in the Santa Barbara coastline in California, and the Patuxent river (Chesapeake watershed) in Maryland. Both spaceborne and airborne hyperspectral sensors allow detailed identification of materials, and very accurate (sub-pixel) estimates of their fractional abundance covers. In the event of an oil spill, the information produced by remotely sensed hyperspectral instruments can be used to design an effective environmental oil spill protection and response plan, which could help to reduce the environmental consequences of the spill and cleanup efforts, as well as to protect human life. In this paper, we discuss a novel automated hyperspectral target detection technique for determining the level of oil contamination of polluted areas in the shoreline. The method is based on the simultaneous use of spatial and spectral information by extended mathematical morphology operations. Both simulated and real hyperspectral data, collected over polluted areas, are used in this work to illustrate the effectiveness of the proposed approach.

  6. CMOS foveal image sensor chip

    NASA Technical Reports Server (NTRS)

    Bandera, Cesar (Inventor); Scott, Peter (Inventor); Sridhar, Ramalingam (Inventor); Xia, Shu (Inventor)

    2002-01-01

    A foveal image sensor integrated circuit comprising a plurality of CMOS active pixel sensors arranged both within and about a central fovea region of the chip. The pixels in the central fovea region have a smaller size than the pixels arranged in peripheral rings about the central region. A new photocharge normalization scheme and associated circuitry normalizes the output signals from the different size pixels in the array. The pixels are assembled into a multi-resolution rectilinear foveal image sensor chip using a novel access scheme to reduce the number of analog RAM cells needed. Localized spatial resolution declines monotonically with offset from the imager's optical axis, analogous to biological foveal vision.

  7. Is hyperspectral imaging a possible new approach for fire reconstruction?

    NASA Astrophysics Data System (ADS)

    Debret, M.; van Exem, A.; Copard, Y.; Butz, C.; Vannière, B.; Sabatier, P.; Desmet, M.; Grosjean, M.; Arnaud, F.; Reyss, J.

    2013-12-01

    Lacustrine records hold considerable archives providing access to paleoenvironmental reconstructions such as paleofire. However, to reach this goal, it is necessary to develop fast, non-destructive and high resolution methods. In this study we develop a new fire reconstitution proxy by studying a lacustrine core sampled in an area concerned by fire events (Esterel massif, SE France). In this aim, we seek for charcoals deposited and preserved in lake sediments by coupling complementary methods: classical charcoals counting, spectrophotometry and hyperspectral analyses in the VIS-NIR range. Charcoal counting is destructive, time consuming and provides data at low resolution. Spectrophotometry is used classically to quantify color is non-destructive, very fast, and provides data at an intermediate resolution (2 mm). Hyperspectral data have the same advantage than spectrophotometry but with higher spatial resolution (43 um pixel size) and higher spectral resolution (3 nm). Our main finding is based on the identification of a new proxy for fire signal obtained at very high resolution with hyperspectral investigations.

  8. Characterization of BxPC3-transplanted mice by hyperspectral autofluorescence imaging and Raman spectroscopy

    NASA Astrophysics Data System (ADS)

    Sawa, Masanori; Andriana, Bibin Bintang; Sato, Hidetoshi

    2014-02-01

    Live subcutaneous tumor grown in nude mouse is studied in situ with hyperspectral autofluorescence imaging and Raman spectroscopy. The purpose of the study is to develop methods for characterization of biochemical changing and of histological type of tumor without labeling. The results show that there are site depending variation in the fluorescence and Raman spectra. At the spot in which calcification is in process, Raman spectra showed a strong and specific band at 957 cm-1 due to PO4 species. The autofluosescence image can prove the histological changes based on the NADH and FAD which are major fluorophores in biological tissues. The hyperspectral image is analyzed with principal component analysis and the reconstructed images successfully depicts a different between necrotic and viable part within living subcutaneous tumor.

  9. Hyperspectral remote sensing image classification based on combined SVM and LDA

    NASA Astrophysics Data System (ADS)

    Zhang, Chunsen; Zheng, Yiwei

    2014-11-01

    This paper presents a novel method for hyperspectral image classification based on the minimum noise fraction (MNF) and an approach combining support vector machine (SVM) and linear discriminant analysis (LDA). A new SVM/LDA algorithm is used for the classification. First, we use MNF method to reduce the dimension and extract features of the image, and then use the SVM/LDA algorithm to transform the extracted features. Next, we train the result of transformation, optimize the parameters through cross-validation and grid search method, then get a optimal hyperspectral image classifier. Finally, we use this classifier to complete classification. In order to verify the proposed method, the AVIRIS Indian Pines image was used. The experimental results show that the proposed method can solve the contradiction between the small amount of samples and high dimension, improve classification accuracy compared to the classical SVM method.

  10. Hyperspectral laser-induced flourescence imaging for assessing internal quality of kiwi fruit

    NASA Astrophysics Data System (ADS)

    Liu, Muhua; Liao, Yifeng; Zhou, Xiaomei

    2008-03-01

    This paper describes an experimental study on non-destructive methods for predicting quality of kiwifruits using fluorescence imaging. The method is based on hyperspectral laser-induced fluorescence imaging in the region between 700 and 1110 nm, and estimates the kiwifruits quality in terms of internal sugar content and firmness. A station for acquiring hyperspectral laser-induced fluorescence imaging has been designed and carefully choosing each component. The fluorescence imaging acquired by the station has been pre-processed by selecting regions of interest (ROIs) of 50 100 × pixels. A line regressing prediction method estimates the quality of kiwifruit samples. The results obtained in classification show that the station and prediction model enables the correct discrimination of kiwifruits internal sugar content and firmness with a percentage of r= 98.5%, SEP=0.4 and r=99.9%, SEP=0.62.

  11. Design of hyper-spectral and full-polarization imager based on AOTF and LCVR

    NASA Astrophysics Data System (ADS)

    Li, Feifei; Xu, Yingyu; Ma, Yanhua

    2014-11-01

    With the development of spectral imaging technology and polarization imaging technology, capturing the spectral profile and polarization signatures simultaneously will provide a wealth of evidence which helps to recognize the objects. Thus it has become a new trend in the area of remote sensing technology. In this paper, the existing polarization spectral imaging technologies are introduced and compared a new designing scheme to realize the miniaturized hyper-spectral and full-polarization imager are proposed, which is based on the combination of Acousto-Optic Tunable Filter (AOTF) and Liquid Crystal Variable Retarder (LCVR). The designing scheme is mainly composed of three modules: the spectral splitting module based on AOTF, the polarization control module based on LCVR and the image acquisition module based on Charge Coupled Device (CCD). The use of AOTF assists in achieving a hyper-spectral resolution higher than 5nm, as well as the abundant spectral information. While the LCVR enables us to gain multiple sets of polarization images of the target, after that, the polarization state of the target can be extracted according to Stokes vector and Mueller matrix. This designing scheme ensures a wide spectral range from 400nm to 2400nm by means of electronic tuning, and also achieves the hyper-spectral and full-polarization images of the target in rapid succession without mechanical moving parts. Besides, the development, testing, calibration and test scheme of the system are also introduced in the rest of the paper.

  12. Active, optical range imaging sensors

    Microsoft Academic Search

    Paul J. Besl

    1988-01-01

    Active, optical range imaging sensors collect three-dimensional coordinate data from object surfaces and can be useful in a wide variety of automation appli- cations, including shape acquisition, bin picking, assem- bly, inspection, gaging, robot navigation, medical diagno- sis, and cartography. They are unique imaging devices in that the image data points explicitly represent scene sur- face geometry in a sampled

  13. Parallel implementation of linear and nonlinear spectral unmixing of remotely sensed hyperspectral images

    NASA Astrophysics Data System (ADS)

    Plaza, Antonio; Plaza, Javier

    2011-11-01

    Hyperspectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. It addresses the (possibly) mixed nature of pixels collected by instruments for Earth observation, which are due to several phenomena including limited spatial resolution, presence of mixing effects at different scales, etc. Spectral unmixing involves the separation of a mixed pixel spectrum into its pure component spectra (called endmembers) and the estimation of the proportion (abundance) of endmember in the pixel. Two models have been widely used in the literature in order to address the mixture problem in hyperspectral data. The linear model assumes that the endmember substances are sitting side-by-side within the field of view of the imaging instrument. On the other hand, the nonlinear mixture model assumes nonlinear interactions between endmember substances. Both techniques can be computationally expensive, in particular, for high-dimensional hyperspectral data sets. In this paper, we develop and compare parallel implementations of linear and nonlinear unmixing techniques for remotely sensed hyperspectral data. For the linear model, we adopt a parallel unsupervised processing chain made up of two steps: i) identification of pure spectral materials or endmembers, and ii) estimation of the abundance of each endmember in each pixel of the scene. For the nonlinear model, we adopt a supervised procedure based on the training of a parallel multi-layer perceptron neural network using intelligently selected training samples also derived in parallel fashion. The compared techniques are experimentally validated using hyperspectral data collected at different altitudes over a so-called Dehesa (semi-arid environment) in Extremadura, Spain, and evaluated in terms of computational performance using high performance computing systems such as commodity Beowulf clusters.

  14. Assessment of the hyperspectral sensor CASI-2 for macroalgal discrimination on the Ría de Vigo coast (NW Spain) using field spectroscopy and modelled spectral libraries

    NASA Astrophysics Data System (ADS)

    Casal, G.; Kutser, T.; Domínguez-Gómez, J. A.; Sánchez-Carnero, N.; Freire, J.

    2013-03-01

    Hyperspectral remote sensing from aircraft and satellite sensors is an important tool for mapping shallow benthic habitats in areas where high spectral and spatial resolutions are needed e.g. spatially heterogeneous coastal environments. However, the acquisition of hyperspectral images sometimes involves a high economic investment and there is no prior guarantee of their utility. Physics-based methods like bio-optical models are an alternative to assess sensors without purchasing the images. In this study we used a simple bio-optical model that simulates reflectance spectra for waters with variable depth and benthic macroalgal cover. The modelled spectral library was used to assess the capability of CASI-2 sensor to recognise different benthic macroalgal species. Reflectance spectra of 17 macroalgal species were used in the model simulations. Uni- and multi-variate statistical analyses were used to evaluate the spectral differences between several species. The results show that only a few species seem to be clearly differentiable: Codium tomentosum, Laminaria saccharina and Corallina officinalis. Therefore, further bio-optical modelling was applied at the level of taxonomic groups rather than species level. Emerged green, brown and red macroalgae as well as sand can be differentiated from each other when using the CASI-2 sensor. The bio-optical simulations showed that the three macroalgal groups can be separated from each other in waters shallower than 4 m when CASI-2 is used. Green and brown macroalgae can be differentiated from deep water if the water depth does not exceed 6 m whereas red macroalgae can be optically separated from deep water when the water depth is less than 5 m. Sea-beds covered with the three macroalgal groups are separable from sandy sea-beds in waters shallower than 10 m.

  15. A Tool for Rapid Non-Destructive Characterization of Planetary Materials: Hyperspectral Imaging in the Visible/Near-Infrared

    NASA Astrophysics Data System (ADS)

    Cannon, K. M.; Mustard, J. F.; Milliken, R. E.; Pieters, C. M.; Hiroi, T.; Wilson, J. H.

    2014-09-01

    Reflectance spectroscopy is an invaluable tool for meteorite analysis, and new developments in hyperspectral imaging allow for rapid, non-destructive, high spatial and spectral resolution mapping of meteorite samples.

  16. Single-nanoparticle detection and spectroscopy in cells using a hyperspectral darkfield imaging technique

    NASA Astrophysics Data System (ADS)

    Fairbairn, Natasha; Fernandes, Rute; Carter, Rachel; Elliot, Timothy J.; Kanaras, Antonios G.; Muskens, Otto L.

    2013-02-01

    We discuss a hyperspectral darkfield microscopy technique capable of imaging single nanoparticles at biologically compatible (<0.5 W/cm2) illumination conditions. The microscope was tested on an array of lithographically produced gold nanorod antennas and on colloidal gold nanorods deposited on a glass substrate. As a test for in-vitro imaging capabilities, we studied the uptake of gold nanorods by DC2.4 dendritic cells.

  17. Focal-Plane Processing Architectures for Real-Time Hyperspectral Image Processing

    Microsoft Academic Search

    Sek M. Chai; Antonio Gentile; Wilfredo E. Lugo-Beauchamp; Javier Fonseca; D. Scott Wills

    2000-01-01

    Real-time image processing requires high computational and IyO throughputs obtained by use of opto- electronic system solutions. A novel architecture that uses focal-plane optoelectronic-area IyO with a fine-grain, low-memory, single-instruction-multiple-data ~SIMD! processor array is presented as an efficient computational solution for real-time hyperspectral image processing. The architecture is eval- uated by use of realistic workloads to determine data throughputs, processing

  18. Passive Standoff Detection of RDX Residues on Metal Surfaces via Infrared Hyperspectral Imaging

    SciTech Connect

    Blake, Thomas A.; Kelly, James F.; Gallagher, Neal B.; Gassman, Paul L.; Johnson, Timothy J.

    2009-09-01

    Hyperspectral images of galvanized steel plates, each containing a stain of RDX, were recorded using a commercial longwave infrared imaging spectrometer. Demonstrations of passive RDX chemical detection at areal dosages between 16 and 90 µg / cm2 were carried out over practical stand-off ranges between 14 and 50 m. Efforts to develop better chemical anomaly and target detection through chemometric analyses are described.

  19. Static induction transistor image sensors

    Microsoft Academic Search

    JUN-ICHI NISHIZAWA; TAKASHIGE TAMAMUSHI; TADAHIRO OHMI

    1979-01-01

    New image sensors, based on the operational principle of static induction transistor (SIT), are described in this paper. Two operational modes of SIT image sensors are described here. One is the electron-accumulation mode in which electrons are stored in the floating-cell region and another is the electron-depletion mode in which electrons are removed from the floating-cell region in response to

  20. Fungal Damage Detection in Wheat Using Short-Wave Near-Infrared Hyperspectral and Digital Colour Imaging

    Microsoft Academic Search

    C. B. Singh; D. S. Jayas; J. Paliwal; N. D. G. White

    2012-01-01

    Healthy and fungal-damaged wheat kernels infected by the species of storage fungi, namely Penicillium spp., Aspergillus glaucus, and A. niger, were scanned using a short-wave near-infrared hyperspectral imaging system in the 700–1100 nm wavelength range and an area scan colour camera. A multivariate image analysis was used to reduce the dimensionality of the hyperspectral data and to select the significant

  1. FUNGAL DAMAGE DETECTION IN WHEAT USING SHORT-WAVE NEAR-INFRARED HYPERSPECTRAL AND DIGITAL COLOUR IMAGING

    Microsoft Academic Search

    C. B. Singh; D. S. Jayas; J. Paliwal; N. D. G. White

    2010-01-01

    Healthy and fungal-damaged wheat kernels infected by the species of storage fungi namely Penicillium spp., Aspergillus glaucus, and A. niger were scanned using short-wave near-infrared hyperspectral imaging system in the 700–1100 nm wavelength range and an area scan colour camera. Multivariate image (MVI) analysis was used to reduce the dimensionality of the hyperspectral data and to select the significant wavelength

  2. Correction for reflected sky radiance in low-altitude coastal hyperspectral images.

    PubMed

    Kim, Minsu; Park, Joong Yong; Kopilevich, Yuri; Tuell, Grady; Philpot, William

    2013-11-10

    Low-altitude coastal hyperspectral imagery is sensitive to reflections of sky radiance at the water surface. Even in the absence of sun glint, and for a calm water surface, the wide range of viewing angles may result in pronounced, low-frequency variations of the reflected sky radiance across the scan line depending on the solar position. The variation in reflected sky radiance can be obscured by strong high-spatial-frequency sun glint and at high altitude by path radiance. However, at low altitudes, the low-spatial-frequency sky radiance effect is frequently significant and is not removed effectively by the typical corrections for sun glint. The reflected sky radiance from the water surface observed by a low-altitude sensor can be modeled in the first approximation as the sum of multiple-scattered Rayleigh path radiance and the single-scattered direct-solar-beam radiance by the aerosol in the lower atmosphere. The path radiance from zenith to the half field of view (FOV) of a typical airborne spectroradiometer has relatively minimal variation and its reflected radiance to detector array results in a flat base. Therefore the along-track variation is mostly contributed by the forward single-scattered solar-beam radiance. The scattered solar-beam radiances arrive at the water surface with different incident angles. Thus the reflected radiance received at the detector array corresponds to a certain scattering angle, and its variation is most effectively parameterized using the downward scattering angle (DSA) of the solar beam. Computation of the DSA must account for the roll, pitch, and heading of the platform and the viewing geometry of the sensor along with the solar ephemeris. Once the DSA image is calculated, the near-infrared (NIR) radiance from selected water scan lines are compared, and a relationship between DSA and NIR radiance is derived. We then apply the relationship to the entire DSA image to create an NIR reference image. Using the NIR reference image and an atmospheric spectral reflectance look-up table, the low spatial frequency variation of the water surface-reflected atmospheric contribution is removed. PMID:24216732

  3. Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics

    NASA Astrophysics Data System (ADS)

    Yi, Weisong; Zhang, Jian; Jiang, Houmin; Zhang, Niya

    2014-09-01

    Gastric cancer is one of the leading causes of cancer death in the world due to its high morbidity and mortality. Hyperspectral imaging (HSI) is an emerging, non-destructive, cutting edge analytical technology that combines conventional imaging and spectroscopy in one single system. The manuscript has investigated the application of near-infrared hyperspectral imaging (900-1700 nm) (NIR-HSI) for gastric cancer detection with algorithms. Major spectral differences were observed in three regions (950-1050, 1150-1250, and 1400-1500 nm). By inspecting cancerous mean spectrum three major absorption bands were observed around 975, 1215 and 1450 nm. Furthermore, the cancer target detection results are consistent and conformed with histopathological examination results. These results suggest that NIR-HSI is a simple, feasible and sensitive optical diagnostic technology for gastric cancer target detection with chemometrics.

  4. Research on classification of hyperspectral remote sensing image based on improved NPA in SVM

    NASA Astrophysics Data System (ADS)

    Shen, Zhaoqing; Shu, Ning; Tao, Jianbin; Sun, Jie; Lai, Zulong

    2008-12-01

    SVM (Support Vector Machine) is a new kind of machine learning method , it can solve classification and regression problems very successfully and accomplish classification with small sample incident perfectly. In this paper, the NPA is proposed to compute the optimization problem to achieve the classification for hyperspectral remote sensing (RS) image by "1 VS m" strategy and radial basis kernel function. Besides, a new method, the dual-binary tree + SVM algorithm is proposed, to solve the mutil-class, high-dimensional(HD) problems of hyperspectral RS image. In the end, the test is carried on the OMIS image. The comparative results of this algorithm with other methods are given, which shows that our algorithm is very competitive, particularly for the small samples and non-equilibrium surface features. Both the accuracy and speed of classification are improved greatly.

  5. [Investigation of the hyperspectral image characteristics of wheat leaves under different stress].

    PubMed

    Zhang, Dong-Yan; Zhang, Jing-Cheng; Zhu, Da-Zhou; Wang, Ji-Hua; Luo, Ju-Hua; Zhao, Jin-Ling; Huang, Wen-Jiang

    2011-04-01

    The diagnosis of growing status and vigor of crops under various stresses is an important step in precision agriculture. Hyperspectral imaging technology has the advantage of providing both spectral and spatial information simultaneously, and has become a research hot spot. In the present study, auto-development of the pushbroom imaging spectrometer (PIS) was utilized to collect hyperspectral images of wheat leaves which suffer from shortage of nutrient, pest and disease stress. The hyperspectral cube was processed by the method of pixel average step by step to highlight the spectral characteristics, which facilitate the analysis based on the differences of leaves reflectance. The results showed that the hyperspectra of leaves from different layers can display nutrient differences, and recognize intuitively different stress extent by imaging figures. With the 2 nanometer spectral resolution and millimeter level spatial resolution of PIS, the number of disease spot can be qualitatively calculated when crop is infected with diseases, and, the area of plant disease could also be quantitatively analyzed; when crop suffered from pest and insect, the spectral information of leaves with single aphid and aphids can be detected by PIS, which provides a new means to quantitatively detect the aphid destroying of wheat leaf. The present study demonstrated that hyperspecral imaging has a great potential in quantitative and qualitative analysis of crop growth. PMID:21714269

  6. [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

  7. Iterative retrieval of surface emissivity and temperature for a hyperspectral sensor

    SciTech Connect

    Borel, C.C.

    1997-11-01

    The central problem of temperature-emissivity separation is that we obtain N spectral measurements of radiance and need to find N + 1 unknowns (N emissivities and one temperature). To solve this problem in the presence of the atmosphere we need to find even more unknowns: N spectral transmissions {tau}{sub atmo}({lambda}) up-welling path radiances L{sub path}{up_arrow}({lambda}) and N down-welling path radiances L{sub path}{down_arrow}({lambda}). Fortunately there are radiative transfer codes such as MODTRAN 3 and FASCODE available to get good estimates of {tau}{sub atmo}({lambda}), L{sub path}{up_arrow}({lambda}) and L{sub path}{down_arrow}({lambda}) in the order of a few percent. With the growing use of hyperspectral imagers, e.g. AVIRIS in the visible and short-wave infrared there is hope of using such instruments in the mid-wave and thermal IR (TIR) some day. We believe that this will enable us to get around using the present temperature - emissivity separation (TES) algorithms using methods which take advantage of the many channels available in hyperspectral imagers. The first idea we had is to take advantage of the simple fact that a typical surface emissivity spectrum is rather smooth compared to spectral features introduced by the atmosphere. Thus iterative solution techniques can be devised which retrieve emissivity spectra {epsilon} based on spectral smoothness. To make the emissivities realistic, atmospheric parameters are varied using approximations, look-up tables derived from a radiative transfer code and spectral libraries. By varying the surface temperature over a small range a series of emissivity spectra are calculated. The one with the smoothest characteristic is chosen. The algorithm was tested on synthetic data using MODTRAN and the Salisbury emissivity database.

  8. Lossy and lossless compression of MERIS hyperspectral images with exogenous quasi-optimal spectral transforms

    NASA Astrophysics Data System (ADS)

    Akam Bita, Isidore Paul; Barret, Michel; Dalla Vedova, Florio; Gutzwiller, Jean-Louis

    2010-07-01

    Our research focuses on reducing complexity of hyperspectral image codecs based on transform and/or subband coding, so they can be on-board a satellite. It is well-known that the Karhunen Loeve transform (KLT) can be sub-optimal for non Gaussian data. However, it is generally recommended as the best calculable coding transform in practice. Now, for a compression scheme compatible with both the JPEG2000 Part2 standard and the CCSDS recommendations for onboard satellite image compression, the concept and computation of optimal spectral transforms (OST), at high bit-rates, were carried out, under low restrictive hypotheses. These linear transforms are optimal for reducing spectral redundancies of multi- or hyper-spectral images, when the spatial redundancies are reduced with a fixed 2-D discrete wavelet transform. The problem of OST is their heavy computational cost. In this paper we present the performances in coding of a quasi-optimal spectral transform, called exogenous OrthOST, obtained by learning an orthogonal OST on a sample of hyperspectral images from the spectrometer MERIS. Moreover, we compute an integer variant of OrthOST for lossless compression. The performances are compared to the ones of the KLT in both lossy and lossless compressions. We observe good performances of the exogenous OrthOST.

  9. Hyperspectral optical imaging of human iris in vivo: characteristics of reflectance spectra

    NASA Astrophysics Data System (ADS)

    Medina, José M.; Pereira, Luís M.; Correia, Hélder T.; Nascimento, Sérgio M. C.

    2011-07-01

    We report a hyperspectral imaging system to measure the reflectance spectra of real human irises with high spatial resolution. A set of ocular prosthesis was used as the control condition. Reflectance data were decorrelated by the principal-component analysis. The main conclusion is that spectral complexity of the human iris is considerable: between 9 and 11 principal components are necessary to account for 99% of the cumulative variance in human irises. Correcting image misalignments associated with spontaneous ocular movements did not influence this result. The data also suggests a correlation between the first principal component and different levels of melanin present in the irises. It was also found that although the spectral characteristics of the first five principal components were not affected by the radial and angular position of the selected iridal areas, they affect the higher-order ones, suggesting a possible influence of the iris texture. The results show that hyperspectral imaging in the iris, together with adequate spectroscopic analyses provide more information than conventional colorimetric methods, making hyperspectral imaging suitable for the characterization of melanin and the noninvasive diagnosis of ocular diseases and iris color.

  10. New microscopic pushbroom hyperspectral imaging system for application in diabetic retinopathy research.

    PubMed

    Li, Qingli; Xue, Yongqi; Xiao, Gonghai; Zhang, Jingfa

    2007-01-01

    To aid ophthalmologists in determining the pathogenesis of diabetic retinopathy and in evaluating the effects of medication, a microscopic pushbroom hyperspectral imaging system is developed. 40 healthy Wistar rats of half gender are selected in this study. They are divided into three groups (six rats failed to be models). 10 normal rats as the normal control group, 12 diabetic rats without any treatment as the model control group, and another 12 diabetic rats treated with LCVS1001 as the LCVS1001 group. The microscopic hyperspectral image of each retina section is collected and processed. Some typical spectrum curves between 400 and 800 nm of the outer nuclear layer are extracted, and images at various wavelengths are analyzed. The results show that a small trough appears near 522.2 nm in the typical spectrum curve of the model control group, and the transmittance of it is higher than that of the normal control group. In addition, the spectrum of the LCVS1001 group changes gradually to the normal spectrum after treatment with LCVS1001. Our findings indicate that LCVS1001 has some therapeutic effect on the diabetic retinopathy of rats, and the microscopic pushbroom hyperspectral imaging system can be used to study the pathogenesis of diabetic retinopathy. PMID:18163827

  11. A combined 3D and hyperspectral method for surface imaging of wounds

    NASA Astrophysics Data System (ADS)

    Paluchowski, Lukasz A.; Denstedt, Martin; Røren, Thomas; Pukstad, Brita; Randeberg, Lise Lyngsnes

    2013-03-01

    Information about the size and depth of a wound and how it is developing is an important prognostic tool in wound diagnostics. In this study a two-camera vision system has been developed to collect optical properties, shape and volume of chronic skin ulcers as tool for diagnostic assistance. This system combines the functionality of 2D imaging spectroscopy and 3D stereo-photogrammetry. A high resolution hyperspectral camera and a monochromatic video frame camera were mounted on the same scanning system. Stereo images were acquired to obtain information about the wound surface geometry. A Digital Surface Model (DSM) of the wound surface was reconstructed by applying stereophotogrammetric methods. The hyperspectral image was co-registered to the monochromatic frame image and the wound border was extracted by applying spectroscopic analysis (e.g. tissue oxygenation, pigmentation, classification). The resulting DSM of the undamaged surroundings of the wound was used to reconstruct the top surface above the wound and thus the wound volume. The analyses can, if desired, be limited to a certain depth of interest like the wound bed or wound border. Simultaneous analysis of the hyperspectral data and the surface model gives a promising, new, non-invasive tool for characterization of chronic wounds. Future work will concentrate on implementation of real time analysis and improvement of the accuracy of the system.

  12. Visualization of latent blood stains using visible reflectance hyperspectral imaging and chemometrics.

    PubMed

    Edelman, Gerda J; van Leeuwen, Ton G; Aalders, Maurice C

    2015-01-01

    The detection of latent traces is an important aspect of crime scene investigation. Blood stains on black backgrounds can be visualized using chemiluminescence, which is invasive and requires a darkened room, or near-infrared photography, for which investigators need to change filters manually to optimize contrast. We demonstrated the performance of visible reflectance hyperspectral imaging (400-720 nm) for this purpose. Several processing methods were evaluated: single wavelength bands, ratio images, principal component analysis (PCA), and "SIMPLe-to-use Interactive Self-modeling Mixture Analysis" (SIMPLISMA). Using these methods, we were able to enhance the contrast between blood stains and 12 different fabrics. On black cotton, blood dilutions were visible with a minimal concentration of 25% of whole blood. The hyperspectral camera system used in this study is portable and wireless, which makes it suitable for crime scene use. The described technique is noncontact and nondestructive, so all traces are preserved for further analysis. PMID:25382735

  13. Hierarchical Clustering of Hyperspectral Images Using Rank-Two Nonnegative Matrix Factorization

    NASA Astrophysics Data System (ADS)

    Gillis, Nicolas; Kuang, Da; Park, Haesun

    2015-04-01

    In this paper, we design a hierarchical clustering algorithm for high-resolution hyperspectral images. At the core of the algorithm, a new rank-two nonnegative matrix factorizations (NMF) algorithm is used to split the clusters, which is motivated by convex geometry concepts. The method starts with a single cluster containing all pixels, and, at each step, (i) selects a cluster in such a way that the error at the next step is minimized, and (ii) splits the selected cluster into two disjoint clusters using rank-two NMF in such a way that the clusters are well balanced and stable. The proposed method can also be used as an endmember extraction algorithm in the presence of pure pixels. The effectiveness of this approach is illustrated on several synthetic and real-world hyperspectral images, and shown to outperform standard clustering techniques such as k-means, spherical k-means and standard NMF.

  14. An average enumeration method of hyperspectral imaging data for quantitative evaluation of medical device surface contamination.

    PubMed

    Le, Hanh N D; Kim, Moon S; Hwang, Jeeseong; Yang, Yi; Thainual, Paweena U; Kang, Jin U; Kim, Do-Hyun

    2014-10-01

    We propose a quantification method called Mapped Average Principal component analysis Score (MAPS) to enumerate the contamination coverage on common medical device surfaces. The method was adapted from conventional Principal Component Analysis (PCA) on non-overlapped regions of a full frame hyperspectral image to resolve the percentage of contamination from the substrate. The concept was proven by using a controlled contamination sample with artificial test soil and color simulating organic mixture, and was further validated using a bacterial system including biofilm on stainless steel surface. We also validate the results of MAPS with other statistical spectral analysis including Spectral Angle Mapper (SAM). The proposed method provides an alternative quantification method for hyperspectral imaging data, which can be easily implemented by basic PCA analysis. PMID:25360377

  15. An average enumeration method of hyperspectral imaging data for quantitative evaluation of medical device surface contamination

    PubMed Central

    Le, Hanh N. D.; Kim, Moon S.; Hwang, Jeeseong; Yang, Yi; Thainual, Paweena U; Kang, Jin U.; Kim, Do-Hyun

    2014-01-01

    We propose a quantification method called Mapped Average Principal component analysis Score (MAPS) to enumerate the contamination coverage on common medical device surfaces. The method was adapted from conventional Principal Component Analysis (PCA) on non-overlapped regions of a full frame hyperspectral image to resolve the percentage of contamination from the substrate. The concept was proven by using a controlled contamination sample with artificial test soil and color simulating organic mixture, and was further validated using a bacterial system including biofilm on stainless steel surface. We also validate the results of MAPS with other statistical spectral analysis including Spectral Angle Mapper (SAM). The proposed method provides an alternative quantification method for hyperspectral imaging data, which can be easily implemented by basic PCA analysis. PMID:25360377

  16. Optimization study on the deformable mirror support structure of the hyperspectral imaging system for food detection

    NASA Astrophysics Data System (ADS)

    Zhao, Fu; Wang, Ping; Gong, Yanjue; Zhang, Li; Meng, Chunling

    2010-10-01

    The deformable mirror (DM) is a critical optoelectronics component of the hyperspectral imaging system for food detection. It is very significant for the deformable mirror to design the best support structure with high dynamic stiffness. Based on the finite element analysis method, this paper discusses the DM support structure's mechanical principle and carries out optimal design. Three kinds of DM support structures with different sections are selected to compare their resonate frequencies. The type of the support structure with larger resonant frequency is picked up, then an optimal design solution has been introduced to determine a group of rational structure parameters which improve the resonate frequency of the support. Finally, the validity simulation analyses including random vibration and harmonic response are carried out to demonstrate that the optimization method is effective to improve the performance of the DM support structure of the hyperspectral imaging system for food detection.

  17. Hyperspectral Imaging on the International Space Station: An Innovative Approach to Commercial Development of Space

    NASA Technical Reports Server (NTRS)

    2003-01-01

    NASA s Space Partnership Division (SPD) was established to promote the commercial development of space by providing access to space ai opportunity to perform commercial research in the microgravity environment. NASA, through SPD, has established Research Partnership Centers (RPC s) that bring the government, universities at private industry together to perform research in space for commercial applica!.!lons. The SPD Office has fostered a re!ationship between an RPC and an aerospace company to perform hyperspectral imaging on the Window Observational Research Facility (WORF) on board the International Space Station (ISS). As a result of this relationship and M the capabilities of the WORF, the ISS will serve the private sector with platform to conduct hyperspectral imaging for commercial research.

  18. Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel

    NASA Astrophysics Data System (ADS)

    Huang, Min; Zhao, Weiyan; Wang, Qingguo; Zhang, Min; Zhu, Qibing

    2015-01-01

    Moisture content uniformity is one of critical parameters to evaluate the quality of dried products and the drying technique. The potential of the hyperspectral imaging technique for evaluating the moisture content uniformity of maize kernels during the drying process was investigated. Predicting models were established using the partial least squares regression method. Two methods, using the prediction value of moisture content to calculate the uniformity (indirect) and predicting the moisture content uniformity directly, were investigated. Better prediction results were achieved using the direct method (with correlation coefficients RP = 0.848 and root-mean-square error of prediction RMSEP = 2.73) than the indirect method (RP = 0.521 and RMSEP = 10.96). The hyperspectral imaging technique showed significant potential in evaluating moisture content uniformity of maize kernels during the drying process.

  19. Automatic method for the dermatological diagnosis of selected hand skin features in hyperspectral imaging

    PubMed Central

    2014-01-01

    Introduction Hyperspectral imaging has been used in dermatology for many years. The enrichment of hyperspectral imaging with image analysis broadens considerably the possibility of reproducible, quantitative evaluation of, for example, melanin and haemoglobin at any location in the patient's skin. The dedicated image analysis method proposed by the authors enables to automatically perform this type of measurement. Material and method As part of the study, an algorithm for the analysis of hyperspectral images of healthy human skin acquired with the use of the Specim camera was proposed. Images were collected from the dorsal side of the hand. The frequency ? of the data obtained ranged from 397 to 1030 nm. A total of 4'000 2D images were obtained for 5 hyperspectral images. The method proposed in the paper uses dedicated image analysis based on human anthropometric data, mathematical morphology, median filtration, normalization and others. The algorithm was implemented in Matlab and C programs and is used in practice. Results The algorithm of image analysis and processing proposed by the authors enables segmentation of any region of the hand (fingers, wrist) in a reproducible manner. In addition, the method allows to quantify the frequency content in different regions of interest which are determined automatically. Owing to this, it is possible to perform analyses for melanin in the frequency range ? E ?(450,600) nm and for haemoglobin in the range ? H ?(397,500) nm extending into the ultraviolet for the type of camera used. In these ranges, there are 189 images for melanin and 126 images for haemoglobin. For six areas of the left and right sides of the little finger (digitus minimus manus), the mean values of melanin and haemoglobin content were 17% and 15% respectively compared to the pattern. Conclusions The obtained results confirmed the usefulness of the proposed new method of image analysis and processing in dermatology of the hand as it enables reproducible, quantitative assessment of any fragment of this body part. Each image in a sequence was analysed in this way in no more than 100 ms using Intel Core i5 CPU M460 @2.5 GHz 4 GB RAM. PMID:24755183

  20. Mapping SO 2 Frost on Io by the Modeling of NIMS Hyperspectral Images

    Microsoft Academic Search

    Sylvain Douté; Bernard Schmitt; Rosaly Lopes-Gautier; Robert Carlson; Laurence Soderblom; James Shirley

    2001-01-01

    We analyze a collection of hyperspectral images of Io acquired by the near infrared mapping spectrometer (NIMS) of Galileo during the G2 to E16 orbits of Jupiter. This analysis leads to the geographical distribution and physical characterization of SO2 frost deposits over about three-fourths of Io's surface. These deposits are excellent tracers of various phenomena, including volcanic production and emission,

  1. Using hyperspectral imaging to determine germination of native Australian plant seeds.

    PubMed

    Nansen, Christian; Zhao, Genpin; Dakin, Nicole; Zhao, Chunhui; Turner, Shane R

    2015-04-01

    We investigated the ability to accurately and non-destructively determine the germination of three native Australian tree species, Acacia cowleana Tate (Fabaceae), Banksia prionotes L.F. (Proteaceae), and Corymbia calophylla (Lindl.) K.D. Hill & L.A.S. Johnson (Myrtaceae) based on hyperspectral imaging data. While similar studies have been conducted on agricultural and horticultural seeds, we are unaware of any published studies involving reflectance-based assessments of the germination of tree seeds. Hyperspectral imaging data (110 narrow spectral bands from 423.6nm to 878.9nm) were acquired of individual seeds after 0, 1, 2, 5, 10, 20, 30, and 50days of standardized rapid ageing. At each time point, seeds were subjected to hyperspectral imaging to obtain reflectance profiles from individual seeds. A standard germination test was performed, and we predicted that loss of germination was associated with a significant change in seed coat reflectance profiles. Forward linear discriminant analysis (LDA) was used to select the 10 spectral bands with the highest contribution to classifications of the three species. In all species, germination decreased from over 90% to below 20% in about 10-30days of experimental ageing. P50 values (equal to 50% germination) for each species were 19.3 (A. cowleana), 7.0 (B. prionotes) and 22.9 (C. calophylla) days. Based on independent validation of classifications of hyperspectral imaging data, we found that germination of Acacia and Corymbia seeds could be classified with over 85% accuracy, while it was about 80% for Banksia seeds. The selected spectral bands in each LDA-based classification were located near known pigment peaks involved in photosynthesis and/or near spectral bands used in published indices to predict chlorophyll or nitrogen content in leaves. The results suggested that seed germination may be successfully classified (predicted) based on reflectance in narrow spectral bands associated with the primary metabolism function and performance of plants. PMID:25752861

  2. Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal

    Microsoft Academic Search

    Mingmin Chi; Lorenzo Bruzzone

    2007-01-01

    This paper addresses classification of hyperspectral remote sensing images with kernel-based methods defined in the framework of semisupervised support vector machines (S3VMs). In particular, we analyzed the critical problem of the nonconvexity of the cost function associated with the learning phase of S3VMs by considering different (S3VMs) techniques that solve optimization directly in the primal formulation of the objective function.

  3. A solid-state hyperspectral imager for real-time standoff explosives detection using shortwave infrared imaging

    Microsoft Academic Search

    Bora M. Onat; Gary Carver; Mark Itzler

    2009-01-01

    We present a new and innovative short-wave infrared (SWIR) hyperspectral imaging focal plane array (FPA) concept for bulk and trace standoff explosives detection. The proposed technology combines conventional uncooled InGaAs based SWIR imaging with the wavelength selectivity of a monolithically integrated solid-state Fabry-Perot interferometer. Each pixel of the array consists of a group of sub-pixels in which each sub-pixel is

  4. 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.

  5. Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images

    PubMed Central

    Huang, Fenghua; Yan, Luming

    2014-01-01

    To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1) different radial basis kernel functions (RBFs) are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF) is proposed by combining them in a weighted manner; (2) the binary decision tree-based multiclass SMO (BDT-SMO) is used in the classification of hyperspectral fused images; (3) experiments are carried out, where the single radial basis function- (SRBF-) based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA) are used to optimize the kernel parameters of the classifiers. The results show that, compared with SRBF, CRBF-based BDT-SMO classifiers display greater classification accuracy and efficiency. PMID:25243224

  6. Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

    PubMed

    Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

    2015-08-24

    There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods. PMID:24689555

  7. Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Poole, Kristin M.; Patil, Chetan A.; Nelson, Christopher E.; McCormack, Devin R.; Madonna, Megan C.; Duvall, Craig L.; Skala, Melissa C.

    2014-03-01

    Peripheral arterial disease (PAD) is an atherosclerotic disease of the extremities that leads to high rates of myocardial infarction and stroke, increased mortality, and reduced quality of life. PAD is especially prevalent in diabetic patients, and is commonly modeled by hind limb ischemia in mice to study collateral vessel development and test novel therapies. Current techniques used to assess recovery cannot obtain quantitative, physiological data non-invasively. Here, we have applied hyperspectral imaging and swept source optical coherence tomography (OCT) to study longitudinal changes in blood oxygenation and vascular morphology, respectively, intravitally in the diabetic mouse hind limb ischemia model. Additionally, recommended ranges for controlling physiological variability in blood oxygenation with respect to respiration rate and body core temperature were determined from a control animal experiment. In the longitudinal study with diabetic mice, hyperspectral imaging data revealed the dynamics of blood oxygenation recovery distally in the ischemic footpad. In diabetic mice, there is an early increase in oxygenation that is not sustained in the long term. Quantitative analysis of vascular morphology obtained from Hessian-filtered speckle variance OCT volumes revealed temporal dynamics in vascular density, total vessel length, and vessel diameter distribution in the adductor muscle of the ischemic limb. The combination of hyperspectral imaging and speckle variance OCT enabled acquisition of novel functional and morphological endpoints from individual animals, and provides a more robust platform for future preclinical evaluations of novel therapies for PAD.

  8. Hyperspectral imaging for the age estimation of blood stains at the crime scene.

    PubMed

    Edelman, Gerda; van Leeuwen, Ton G; Aalders, Maurice C G

    2012-11-30

    The age estimation of blood stains can provide important information on the temporal aspects of a crime. As previously shown, visible spectroscopy of blood stains can successfully be used for their age estimation. In the present study we evaluated the feasibility to use hyperspectral imaging for this purpose. Visible reflectance spectra of blood stains were recorded using a pushbroom hyperspectral imaging system. From these spectra, the relative amounts of oxyhemoglobin, methemoglobin and hemichrome within the blood stains were derived. By comparison of the hemoglobin derivative fractions with a reference dataset, the age of blood stains up to 200 days old was estimated. The absolute error of the age estimation task increased with age, with a median relative error of 13.4% of the actual age. To test the practical applicability of this method, a simulated crime scene was analyzed, in which blood stains of several ages were deposited. Hyperspectral imaging combined with the proposed analysis provided insight in the absolute age of the blood stains. Additionally, the blood stains were clustered based on their hemoglobin derivative fractions, without the use of a reference dataset. Results demonstrated that the order of formation of blood stains can be determined, even under unknown environmental circumstances, when no proficient reference dataset is available. These findings are an important step toward the practical implementation of blood stain age estimation in forensic casework. PMID:22938693

  9. Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.

    PubMed

    Xie, Chuanqi; Li, Xiaoli; Shao, Yongni; He, Yong

    2014-01-01

    This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (?L*, ?a* and ?b*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ?L*, 0.849 for ?a*and 0.917 for ?b*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods. PMID:25546335

  10. Color Measurement of Tea Leaves at Different Drying Periods Using Hyperspectral Imaging Technique

    PubMed Central

    Xie, Chuanqi; Li, Xiaoli; Shao, Yongni; He, Yong

    2014-01-01

    This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (?L*, ?a* and ?b*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380–1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ?L*, 0.849 for ?a*and 0.917 for ?b*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods. PMID:25546335

  11. Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral microscopy

    PubMed Central

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

    2010-01-01

    A snapshot Image Mapping Spectrometer (IMS) with high sampling density is developed for hyperspectral microscopy, measuring a datacube of dimensions 285 × 285 × 60 (x, y, ?). The spatial resolution is ~0.45 µm with a FOV of 100 × 100 µm2. The measured spectrum is from 450 nm to 650 nm and is sampled by 60 spectral channels with average sampling interval ~3.3 nm. The channel’s spectral resolution is ~8nm. The spectral imaging results demonstrate the potential of the IMS for real-time cellular fluorescence imaging. PMID:20639917

  12. The mineralogic evaluation of mining sites in Wyoming using hyperspectral field and imaging systems

    NASA Astrophysics Data System (ADS)

    Wawrzynski, Alecia Lucille

    1999-11-01

    Two mining sites in Wyoming were analyzed to evaluate different methods of processing and interpreting data from hyperspectral field and imaging systems. This resulted in two projects being undertaken, one to compare the performance of data processing techniques on hyperspectral imagery, and the other to investigate the usefulness of field spectroscopy for identifying alteration. The first project, located near Atlantic City, Wyoming, applied the USGS Tetracorder and the ENVI software package to AVIRIS data of the site. The results from both methods were checked against field maps and field spectroscopy data collected for the study. Several rock units in the Atlantic City image were successfully identified, including supracrustal rocks, granite and granite-peiss, and some sedimentary units. By comparing the results from each processing method, the strong and weak points of each program were identified and improvements suggested. The Silver Crown project was undertaken to characterize alteration associated with a gold-copper ore deposit located near Cheyenne, Wyoming. This was accomplished using spectral analyses of drill hole samples and airborne spectroscopic data. Analyses of drill hole samples with reflectance spectroscopy techniques are relatively undeveloped and undocumented, so new methods had to be derived, Several alteration zones in the Silver Crown area were successfully identified by spectral analysis of cutting samples. The results suggest the spectral analysis of such samples can facilitate the geologic and mineralogic assessment of a prospect area. Results confirm the potential of hyperspectral imaging and spectrometry as significant new tools for geologic mapping and mineral analysis. The results also indicate important limitations resulting from varying field conditions, methods of data collection, and different processing routines. Both the positive and negative results may serve as a guide to the future development and application of hyperspectral techniques and speed future users in their efforts to produce mineralogic maps and identify alteration at mining sites.

  13. Hyperspectral imaging in quality control of inkjet printed personalised dosage forms.

    PubMed

    Vakili, Hossein; Kolakovic, Ruzica; Genina, Natalja; Marmion, Mathieu; Salo, Harri; Ihalainen, Petri; Peltonen, Jouko; Sandler, Niklas

    2015-04-10

    The aim of the study was to investigate applicability of near infra-red (NIR) hyperspectral imaging technique in quality control of printed personalised dosage forms. Inkjet printing technology was utilized to fabricate escalating doses of an active pharmaceutical ingredient (API). A solution containing anhydrous theophylline as the model drug was developed as a printable formulation. Single units solid dosage forms (SDFs) were prepared by jetting the solution onto 1cm×1cm areas on carrier substrate with multiple printing passes. It was found that the number of printing passes was in excellent correlation (R(2)=0.9994) with the amount of the dispensed drug (?gcm(-2)) based on the UV calibration plot. The API dose escalation was approximately 7.5?gcm(-2) for each printing pass concluding that inkjet printing technology can optimally provide solutions to accurate deposition of active substances with a potential for personalized dosing. Principal component analysis (PCA) was carried out in order to visualize the trends in the hyperspectral data. Subsequently, a quantitative partial least squares (PLS) regression model was created. NIR hyperspectral imaging proved (R(2)=0.9767) to be a reliable, rapid and non-destructive method to optimize quality control of these planar printed dosage forms. PMID:25527212

  14. A DMD-based hyperspectral imaging system using compressive sensing method

    NASA Astrophysics Data System (ADS)

    Sun, Zhongqiu; Chen, Bo; Cheng, Chengqi

    2014-11-01

    Hyperspectral Imaging Systems (HIS) are widely applied in many fields. However, in the traditional design of HIS, it is much time-consuming to acquire an integrated hyperspectral image. Compressive sensing is an efficient method to process sparse data, and a single-pixel camera which used the digital micromirror device (DMD) for accomplishing the CS algorithms had been developed. Nowadays, DMD achieved great development. The size of mirror array is increasing while switch speed of a single mirror becomes very fast. Consequently, researchers make efforts to design a HIS using CS method. CS method is a method to scale down the spatial information but the hyperspectral datacubes are still huge because of the thousands of bands. In this paper, we design a DMD-based spectrometer architecture using the method of compressed sensing principle, combined with DMD's spectral filter characteristics. In the new architecture, there are two DMDs. One is used for implementing the CS pattern, the other for filtering the bands. It has spectral simply adjustable advantages. With this new technology, we can reduce the amount of information which needs to be transmitted and processed in both spatial and spectral domain. We also present some simulation results of implementation procedures.

  15. [Hyperspectral remote sensing image classification based on radical basis function neural network].

    PubMed

    Tan, Kun; Du, Pei-jun

    2008-09-01

    Based on the radial basis function neural network (RBFNN) theory and the specialty of hyperspectral remote sensing data, the effective feature extraction model was designed, and those extracted features were connected to the input layer of RBFNN, finally the classifier based on radial basis function neural network was constructed. The hyperspectral image with 64 bands of OMIS II made by Chinese was experimented, and the case study area was zhongguancun in Beijing. Minimum noise fraction (MNF) was conducted, and the former 20 components were extracted for further processing. The original data (20 dimension) of extraction by MNF, the texture transformation data (20 dimension) extracted from the former 20 components after MNF, and the principal component analysis data (20 dimension) of extraction were combined to 60 dimension. For classification by RBFNN, the sizes of training samples were less than 6.13% of the whole image. That classifier has a simple structure and fast convergence capacity, and can be easily trained. The classification precision of radial basis function neural network classifier is up to 69.27% in contrast with the 51.20% of back propagation neural network (BPNN) and 40. 88% of traditional minimum distance classification (MDC), so RBFNN classifier performs better than the other three classifiers. It proves that RBFNN is of validity in hyperspectral remote sensing classification. PMID:19093550

  16. [Effectively predicting soluble solids content in apple based on hyperspectral imaging].

    PubMed

    Huang, Wen-Qian; Li, Jiang-Bo; Chen, Li-Ping; Guo, Zhi-Ming

    2013-10-01

    It is very important to extract effective wavelengths for quantitative analysis of fruit internal quality based on hyperspectral imaging. In the present study, genetic algorithm (GA), successive projections algorithm (SPA) and GA-SPA combining algorithm were used for extracting effective wavelengths from 400-1 000 nm hyperspectral images of Yantai "Fuji" apples, respectively. Based on the effective wavelengths selected by GA, SPA and GA-SPA, different models were built and compared for predicting soluble solids content (SSC) of apple using partial least squares (PLS), least squared support vector machine (LS-SVM) and multiple linear regression (MLR), respectively. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Among all the models, the SPA-MLR achieved the best results, where Rp(2), RMSEP and RPD were 0.950 1, 0.308 7 and 4.476 6 respectively. Results showed that SPA can be effectively used for selecting the effective wavelengths from hyperspectral data. And, SPA-MLR is an optimal modeling method for prediction of apple SSC. Furthermore, less effective wavelengths and simple and easily-interpreted MLR model show that the SPA-MLR model has a great potential for online detection of apple SSC and development of a portable instrument. PMID:24409747

  17. Recovery of macular pigment spectrum in vivo using hyperspectral image analysis

    PubMed Central

    Fawzi, Amani A.; Lee, Noah; Acton, Jennifer H.; Laine, Andrew F.; Smith, R. Theodore

    2011-01-01

    We investigated the feasibility of a novel method for hyperspectral mapping of macular pigment (MP) in vivo. Six healthy subjects were recruited for noninvasive imaging using a snapshot hyperspectral system. The three-dimensional full spatial-spectral data cube was analyzed using non-negative matrix factorization (NMF), wherein the data was decomposed to give spectral signatures and spatial distribution, in search for the MP absorbance spectrum. The NMF was initialized with the in vitro MP spectrum and rank 4 spectral signature decomposition was used to recover the MP spectrum and optical density in vivo. The recovered MP spectra showed two peaks in the blue spectrum, characteristic of MP, giving a detailed in vivo demonstration of these absorbance peaks. The peak MP optical densities ranged from 0.08 to 0.22 (mean 0.15+/?0.05) and became spatially negligible at diameters 1100 to 1760 ?m (4 to 6 deg) in the normal subjects. This objective method was able to exploit prior knowledge (the in vitro MP spectrum) in order to extract an accurate in vivo spectral analysis and full MP spatial profile, while separating the MP spectra from other ocular absorbers. Snapshot hyperspectral imaging in combination with advanced mathematical analysis provides a simple cost-effective approach for MP mapping in vivo. PMID:22029355

  18. Methyl green and nitrotetrazolium blue chloride co-expression in colon tissue: A hyperspectral microscopic imaging analysis

    NASA Astrophysics Data System (ADS)

    Li, Qingli; Liu, Hongying; Wang, Yiting; Sun, Zhen; Guo, Fangmin; Zhu, Jianzhong

    2014-12-01

    Histological observation of dual-stained colon sections is usually performed by visual observation under a light microscope, or by viewing on a computer screen with the assistance of image processing software in both research and clinical settings. These traditional methods are usually not sufficient to reliably differentiate spatially overlapping chromogens generated by different dyes. Hyperspectral microscopic imaging technology offers a solution for these constraints as the hyperspectral microscopic images contain information that allows differentiation between spatially co-located chromogens with similar but different spectra. In this paper, a hyperspectral microscopic imaging (HMI) system is used to identify methyl green and nitrotetrazolium blue chloride in dual-stained colon sections. Hyperspectral microscopic images are captured and the normalized score algorithm is proposed to identify the stains and generate the co-expression results. Experimental results show that the proposed normalized score algorithm can generate more accurate co-localization results than the spectral angle mapper algorithm. The hyperspectral microscopic imaging technology can enhance the visualization of dual-stained colon sections, improve the contrast and legibility of each stain using their spectral signatures, which is helpful for pathologist performing histological analyses.

  19. Hyperspectral face recognition for homeland security

    NASA Astrophysics Data System (ADS)

    Pan, Zhihong; Healey, Glenn E.; Prasad, Manish; Tromberg, Bruce J.

    2003-09-01

    Hyperspectral sensors provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. Near-infrared spectral measurements allow the sensing of subsurface tissue structure which is significantly different from person to person but relatively stable over time. The spectral properties of human tissue are also nearly invariant to changes in face orientation which bring significant degradation to most other face recognition algorithms. We examine the utility of using near-infrared hyperspectral images for the recognition of human subjects over a database of 200 subjects. The face recognition algorithm exploits spectral measurements for individual facial tissue types and combinations of facial tissue types. We demonstrate experimentally that hyperspectral imaging promises to support face recognition independent of facial expression and orientation.

  20. MightySat II.1 Fourier-transform hyperspectral imager payload performance

    NASA Astrophysics Data System (ADS)

    Otten, Leonard J.; Sellar, R. Glenn; Rafert, J. Bruce

    1995-12-01

    Using a new microsat called MightySat II as a platform, Kestrel Corporation is designing and building the first Fourier transform hyperspectral imager (FTHSI) to be operated from a spacecraft. This payload will also be the first to fly on the Phillips Laboratory MightySat II spacecraft series, a new, innovative approach, to affordable space testing of high risk, high payoff technologies. Performance enhancements offered by the Fourier transform approach have shown it to be one of the more promising spaceborne hyperspectral concepts. Simulations of the payload's performance have shown that the instrument is capable of separating a wide range of subtle spectral differences. Variations in the return from the Georges Bank and shoals are discernible and various types of coastal grasses (sea oats and spartina) can be isolated against a sand background.