Science.gov

Sample records for hyperspectral imaging sensors

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

    NASA Astrophysics Data System (ADS)

    Hegyi, Alex N.; Martini, Joerg

    2015-06-01

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

  2. Onboard Image Processing System for Hyperspectral Sensor.

    PubMed

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

    2015-01-01

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

  3. Onboard Image Processing System for Hyperspectral Sensor

    PubMed Central

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

    2015-01-01

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

  4. 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.

  5. 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.

  6. 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.

  7. Observation planning strategy of a Japanese spaceborne sensor: hyperspectral imager suite (HISUI)

    NASA Astrophysics Data System (ADS)

    Ogawa, Kenta; Takenaka, Makoto; Matsunaga, Tsuneo; Yamamoto, Satoru; Kashimura, Osamu; Tachikawa, Tetsushi; Tsuchida, Satoshi; Tanii, Jun; Rokugawa, Shuichi

    2012-11-01

    Hyperspectral Imager Suite (HISUI) is a Japanese future spaceborne hyperspectral instrument being developed by Ministry of Economy, Trade, and Industry (METI) and will be launched in 2015 or later. HISUI's operation strategic study is described in this paper. In HISUI project, Operation Mission Planning (OMP) team will make long- and short-term observation strategy of the sensor. OMP is important for HISUI especially for hyperspectral sensor, and relationship between the limitations of sensor operation and the planned observation scenarios have to be studied. Major factors of the limitations are the combinations of downlink rate, observation time (15 minutes per orbit) and the swath of the sensor (30 km). The achievements of global mapping or regional monitoring need to be simulated precisely before launch. We have prepared daily global high resolution (30 second in latitude and longitude) climate data for the simulation.

  8. Pesticide residue quantification analysis by hyperspectral imaging sensors

    NASA Astrophysics Data System (ADS)

    Liao, Yuan-Hsun; Lo, Wei-Sheng; Guo, Horng-Yuh; Kao, Ching-Hua; Chou, Tau-Meu; Chen, Junne-Jih; Wen, Chia-Hsien; Lin, Chinsu; Chen, Hsian-Min; Ouyang, Yen-Chieh; Wu, Chao-Cheng; Chen, Shih-Yu; Chang, Chein-I.

    2015-05-01

    Pesticide residue detection in agriculture crops is a challenging issue and is even more difficult to quantify pesticide residue resident in agriculture produces and fruits. This paper conducts a series of base-line experiments which are particularly designed for three specific pesticides commonly used in Taiwan. The materials used for experiments are single leaves of vegetable produces which are being contaminated by various amount of concentration of pesticides. Two sensors are used to collected data. One is Fourier Transform Infrared (FTIR) spectroscopy. The other is a hyperspectral sensor, called Geophysical and Environmental Research (GER) 2600 spectroradiometer which is a batteryoperated field portable spectroradiometer with full real-time data acquisition from 350 nm to 2500 nm. In order to quantify data with different levels of pesticide residue concentration, several measures for spectral discrimination are developed. Mores specifically, new measures for calculating relative power between two sensors are particularly designed to be able to evaluate effectiveness of each of sensors in quantifying the used pesticide residues. The experimental results show that the GER is a better sensor than FTIR in the sense of pesticide residue quantification.

  9. 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

  10. 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.

  11. 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.

  12. Evaluation of onboard hyperspectral-image compression techniques for a parallel push-broom sensor

    SciTech Connect

    Briles, S.

    1996-04-01

    A single hyperspectral imaging sensor can produce frames with spatially-continuous rows of differing, but adjacent, spectral wavelength. If the frame sample-rate of the sensor is such that subsequent hyperspectral frames are spatially shifted by one row, then the sensor can be thought of as a parallel (in wavelength) push-broom sensor. An examination of data compression techniques for such a sensor is presented. The compression techniques are intended to be implemented onboard a space-based platform and to have implementation speeds that match the date rate of the sensor. Data partitions examined extend from individually operating on a single hyperspectral frame to operating on a data cube comprising the two spatial axes and the spectral axis. Compression algorithms investigated utilize JPEG-based image compression, wavelet-based compression and differential pulse code modulation. Algorithm performance is quantitatively presented in terms of root-mean-squared error and root-mean-squared correlation coefficient error. Implementation issues are considered in algorithm development.

  13. Hyperspectral fundus imager

    NASA Astrophysics Data System (ADS)

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

    2000-11-01

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

  14. Hyperspectral image processing

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  15. 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.

  16. 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

  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. Hyperspectral light field imaging

    NASA Astrophysics Data System (ADS)

    Leitner, Raimund; Kenda, Andreas; Tortschanoff, Andreas

    2015-05-01

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

  19. Hyperspectral imaging system for UAV

    NASA Astrophysics Data System (ADS)

    Zhang, Da; Zheng, Yuquan

    2015-10-01

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

  20. 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.

  1. Chemometric analysis of multi-sensor hyperspectral images of coarse mode aerosol particles for the image-based investigation on aerosol particles

    NASA Astrophysics Data System (ADS)

    Ofner, Johannes; Kamilli, Katharina A.; Eitenberger, Elisabeth; Friedbacher, Gernot; Lendl, Bernhard; Held, Andreas; Lohninger, Hans

    2015-04-01

    Multi-sensor hyperspectral imaging is a novel technique, which allows the determination of composition, chemical structure and pure components of laterally resolved samples by chemometric analysis of different hyperspectral datasets. These hyperspectral datasets are obtained by different imaging methods, analysing the same sample spot and superimposing the hyperspectral data to create a single multi-sensor dataset. Within this study, scanning electron microscopy (SEM), Raman and energy-dispersive X-ray spectroscopy (EDX) images were obtained from size-segregated aerosol particles, sampled above Western Australian salt lakes. The particles were collected on aluminum foils inside a 2350 L Teflon chamber using a Sioutas impactor, sampling aerosol particles of sizes between 250 nm and 10 µm. The complex composition of the coarse-mode particles can be linked to primary emissions of inorganic species as well as to oxidized volatile organic carbon (VOC) emissions. The oxidation products of VOC emissions are supposed to form an ultra-fine nucleation mode, which was observed during several field campaigns between 2006 and 2013. The aluminum foils were analysed using chemical imaging and electron microscopy. A Horiba LabRam 800HR Raman microscope was used for vibrational mapping of an area of about 100 µm x 100 µm of the foils at a resolution of about 1 µm. The same area was analysed using a Quanta FEI 200 electron microscope (about 250 nm resolution). In addition to the high-resolution image, the elemental composition could be investigated using energy-dispersive X-ray spectroscopy. The obtained hyperspectral images were combined into a multi-sensor dataset using the software package Imagelab (Epina Software Labs, www.imagelab.at). After pre-processing of the images, the multi-sensor hyperspectral dataset was analysed using several chemometric methods such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and other multivariate methods. Vertex component analysis (VCA) allowed the extraction of single spectra of pure components such as gypsum, NaNO3 and oxidized VOC). Different aerosol species related to the salt lakes (e.g. inorganic salts) and the surrounding land (e.g. silica particles) could be uncovered. Furthermore, the interaction of primary particles with volatile organic species, released from remains of the former eucalyptus forests and crop plants, could be identified. The application of multi-sensor imaging to size-segregated laterally resolved aerosol particles deepens the understanding of composition, linkage, interfaces and single components of complex mixtures of atmospheric aerosol particles, and therefore gives access to an intrinsic understanding of the nature of these particles.

  2. Longwave infrared compressive hyperspectral imager

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  3. Hyperspectral Image Compression

    NASA Astrophysics Data System (ADS)

    Wright, Stephanie; Miguel, Agnieszka, , Dr.; Ashbach, Jason

    2008-05-01

    Hyperspectral images gathered by satellites or aerial means provide a vast amount of data for geophysicists. A few applications include the exploration of minerals, research of land pollution, and military surveillance. NASA and other agencies are producing gigabytes of hyperspectral images which need to be transmitted and stored daily. As these images require high compression rates and preservation of data integrity, we are presented with an intriguing compression problem. In our research we investigate two compression algorithms: a near-lossless technique based on minimizing maximum absolute distortion (MAD) and a lossy based algorithm which minimizes mean squared error (MSE). Near-lossless algorithms provide high compression rates and a uniform distribution of error. Whereas MSE based algorithms yield high compression rates but a non-uniform distribution of error. Our goal is to determine which algorithm yields high compression rates and minimal data loss without modifying post processing of hyperspectral data. In order to compare these two compression algorithms and determine their effect on post processing we used ENVI's image processing tools. We classified the decompressed images for each algorithm and compared them to the classified original image.

  4. Multipurpose Hyperspectral Imaging System

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  5. 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.

  6. Simulation of Hyperspectral Images

    NASA Technical Reports Server (NTRS)

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

    2004-01-01

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

  7. 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.

  8. 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...

  9. Hyperspectral Imager-Tracker

    NASA Technical Reports Server (NTRS)

    Agurok, Llya

    2013-01-01

    The Hyperspectral Imager-Tracker (HIT) is a technique for visualization and tracking of low-contrast, fast-moving objects. The HIT architecture is based on an innovative and only recently developed concept in imaging optics. This innovative architecture will give the Light Prescriptions Innovators (LPI) HIT the possibility of simultaneously collecting the spectral band images (hyperspectral cube), IR images, and to operate with high-light-gathering power and high magnification for multiple fast- moving objects. Adaptive Spectral Filtering algorithms will efficiently increase the contrast of low-contrast scenes. The most hazardous parts of a space mission are the first stage of a launch and the last 10 kilometers of the landing trajectory. In general, a close watch on spacecraft operation is required at distances up to 70 km. Tracking at such distances is usually associated with the use of radar, but its milliradian angular resolution translates to 100- m spatial resolution at 70-km distance. With sufficient power, radar can track a spacecraft as a whole object, but will not provide detail in the case of an accident, particularly for small debris in the onemeter range, which can only be achieved optically. It will be important to track the debris, which could disintegrate further into more debris, all the way to the ground. Such fragmentation could cause ballistic predictions, based on observations using high-resolution but narrow-field optics for only the first few seconds of the event, to be inaccurate. No optical imager architecture exists to satisfy NASA requirements. The HIT was developed for space vehicle tracking, in-flight inspection, and in the case of an accident, a detailed recording of the event. The system is a combination of five subsystems: (1) a roving fovea telescope with a wide 30 field of regard; (2) narrow, high-resolution fovea field optics; (3) a Coude optics system for telescope output beam stabilization; (4) a hyperspectral-mutispectral imaging assembly; and (5) image analysis software with effective adaptive spectral filtering algorithm for real-time contrast enhancement.

  10. Hyperspectral image analysis. A tutorial.

    PubMed

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

    2015-10-01

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

  11. 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.

  12. 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

  13. 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

  14. Skin detection in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Sanchez, Stephanie M.; Velez-Reyes, Miguel

    2015-05-01

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

  15. 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.

  16. 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.

  17. JOINT BLIND DECONVOLUTION AND SPECTRAL UNMIXING OF HYPERSPECTRAL IMAGES

    E-print Network

    Plemmons, Robert J.

    JOINT BLIND DECONVOLUTION AND SPECTRAL UNMIXING OF HYPERSPECTRAL IMAGES Qiang Zhang Dept sensors can collect simultaneous images ranging from visible to LWIR. Multiframe blind deconvolution (MFBD. Among these techniques, blind deconvolution methods are often applied to jointly estimate both an object

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

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yu; Yamada, Yoshiro; Ishii, Juntaro

    2013-10-01

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

  19. 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.

  20. 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.

  1. 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...

  2. 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...

  3. 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.

  4. Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes

    E-print Network

    Lee, Juheon; Cai, Xiaohao; Schönlieb, Carola-Bibiane; Coomes, David A.

    2015-06-02

    with boresight calibrated hyperspectral sensors provides geocoordi- nates of each pixel in the hyperspectral imagery, which meant that the hyperspectral images could be orthorectified by digital elevation models (DEMs) from Advanced Spaceborne Thermal Emission... SENSING 1 Nonparametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes Juheon Lee, Xiaohao Cai, Carola-Bibiane Schönlieb, and David A. Coomes Abstract—There is much current interest in using multisen- sor...

  5. Sparse demixing of hyperspectral images.

    PubMed

    Greer, John B

    2012-01-01

    In the LMM for hyperspectral images, all the image spectra lie on a high-dimensional simplex with corners called endmembers. Given a set of endmembers, the standard calculation of fractional abundances with constrained least squares typically identifies the spectra as combinations of most, if not all, endmembers. We assume instead that pixels are combinations of only a few endmembers, yielding abundance vectors that are sparse. We introduce sparse demixing (SD), which is a method that is similar to orthogonal matching pursuit, for calculating these sparse abundances. We demonstrate that SD outperforms an existing L(1) demixing algorithm, which we prove to depend adversely on the angles between endmembers. We combine SD with dictionary learning methods to calculate automatically endmembers for a provided set of spectra. Applying it to an airborne visible/infrared imaging spectrometer image of Cuprite, NV, yields endmembers that compare favorably with signatures from the USGS spectral library. PMID:21693418

  6. Hyperspectral sensors and the conservation of monumental buildings

    NASA Astrophysics Data System (ADS)

    Camaiti, Mara; Benvenuti, Marco; Chiarantini, Leandro; Costagliola, Pilar; Moretti, Sandro; Paba, Francesca; Pecchioni, Elena; Vettori, Silvia

    2010-05-01

    The continuous control of the conservation state of outdoor materials is a good practice for timely planning conservative interventions and therefore to preserve historical buildings. The monitoring of surfaces composition, in order to characterize compounds of neo-formation and deposition, by traditional diagnostic campaigns, although gives accurate results, is a long and expensive method, and often micro-destructive analyses are required. On the other hand, hyperspectral analysis in the visible and near infrared (VNIR) region is a very common technique for determining the characteristics and properties of soils, air, and water in consideration of its capability to give information in a rapid, simultaneous and not-destructive way. VNIR Hypespectral analysis, which discriminate materials on the basis of their different patterns of absorption at specific wavelengths, are in fact successfully used for identifying minerals and rocks (1), as well as for detecting soil properties including moisture, organic content and salinity (2). Among the existing VNIR techniques (Laboratory Spectroscopy - LS, Portable Spectroscopy - PS and Imaging Spectroscopy - IS), PS and IS can play a crucial role in the characterization of components of exposed stone surfaces. In particular, the Imaging Spectroscopic (remote sensing), which uses sensors placed both on land or airborne, may contribute to the monitoring of large areas in consideration of its ability to produce large areal maps at relatively low costs. In this presentation the application of hyperspectral instruments (mainly PS and IS, not applied before in the field of monumental building diagnostic) to quantify the degradation of carbonate surfaces will be discussed. In particular, considering gypsum as the precursor symptom of damage, many factors which may affect the estimation of gypsum content on the surface will be taken into consideration. Two hyperspectral sensors will be considered: 1) A portable radiometer (ASD-FieldSpec FP Pro spectroradiometer), which continuously acquires punctual reflectance spectra in the range 350-2500 nm, both in natural light conditions and by a contact probe (fixed geometry of shot). This instrument is used on field for the identification of different materials, as well as for the definition of maps (e.g geological maps) if coupled with other hyperspectral instruments. 2) Hyperspectral sensor SIM-GA (Selex Galileo Multisensor Hyperspectral System), a system with spatial acquisition of data which may be used on an earth as well as on an airborne platform. SIM-GA consists of two electro-optical heads, which operate in the VNIR and SWIR regions, respectively, between 400-1000 nm and 1000-2500 nm (3). Although the spectral signature in the VNIR of many minerals is known, the co-presence of more minerals on a surface can affect the quantitative analysis of gypsum. Different minerals, such as gypsum, calcite, weddellite, whewellite, and other components (i.e. carbon particles in black crusts) are, in fact, commonly found on historical surfaces. In order to illustrate the complexity, but also the potentiality of hyperspectral sensors (portable or remote sensing) for the characterization of stone surfaces, a case study, the Facade of Santa Maria Novella in Florence - Italy, will be presented. References 1) R.N. Clark and G.A. Swayze, 1995, "Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice, and snow, and other materials: The USGS Tricorder Algorithm", in "Summaries of the Fifth Annual JPL Airborne Earth Science Workshop", JPL Publication 95-1,1,39-40 2) E. Ben-Dor, K. Patin, A. Banin and A. Karnieli, 2002, "Mapping of several soil properties using DATS-7915 hyperspectral scanner data. A case study over clayely soils in Israel", International Journal of Remote Sensing, 23(6), 1043-1062 3) S. Vettori, M. Benvenuti, M. Camaiti, L. Chiarantini, P. Costagliola, S. Moretti, E. Pecchioni, 2008, "Assessment of the deterioration status of historical buildings by Hyperspectral Imaging techniques&q

  7. 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.

  8. 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.

  9. 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.

  10. Hyperspectral imaging utility for transportation systems

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  11. Portable Hyperspectral Imaging Broadens Sensing Horizons

    NASA Technical Reports Server (NTRS)

    2007-01-01

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

  12. 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

  13. Phase congruency assesses hyperspectral image quality

    NASA Astrophysics Data System (ADS)

    Shao, Xiaopeng; Zhong, Cheng

    2012-10-01

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

  14. 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.

  15. 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...

  16. Research on hyperspectral polarization imaging technique

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

  17. Biometric study using hyperspectral imaging during stress

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

  18. 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.

  19. Information efficiency in hyperspectral imaging systems

    E-print Network

    Reichenbach, Stephen E.

    Information efficiency in hyperspectral imaging systems Stephen E. Reichenbach University systems, it is typically important to deliver as much information as possible about the scene radiance containing important information about a scene; however, in a system design, there are practical limitations

  20. 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.

  1. Non-uniform system response detection for hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

    Castorena, Juan; Morrison, Jason; Paliwal, Jitendra; Erkinbaev, Chyngyz

    2015-11-01

    Near infrared (NIR) hyperspectral imaging (HSI) has established itself as a powerful non-destructive tool for the chemical analysis of heterogeneous samples. However, one of the main disadvantages of NIR HSI is that the technique suffers from instrumentation-related problems, which in turn affect the acquired images. In general, focal plane array (FPA) based hyperspectral systems are affected by spatial and spectral non-uniform response, the presence of defective sensors (e.g. dead or saturated sensors), and temporal and spatial (e.g. dark current) noise. Another issue is each new camera system needs to be calibrated to assess its specific responses to light. To correct for these issues, we used known standards to measure the response of the sensors and capture the location of the field of view and defective sensors using linear and quadratic models. The parameters of these models were then used as input features for classification of sensor responses using a k-means algorithm. The results conclude that linear models are insufficiently precise for calibration but estimate sufficiently accurately the system's response and functionality. Specifically, it was shown that the classification method discriminates non-responsive regions effectively.

  2. Airborne Hyperspectral Imaging of Seagrass and Coral Reef

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  3. Hyperspectral imaging using RGB color for foodborne pathogen detection

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance sp...

  4. Nonnegative Matrix Factorization for Efficient Hyperspectral Image Projection

    NASA Technical Reports Server (NTRS)

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

    2015-01-01

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

  5. Manifold alignment for classification of multitemporal hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Yang, Hsiu-Han

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

  6. 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.

  7. A collection of hyperspectral images for imaging systems research

    NASA Astrophysics Data System (ADS)

    Skauli, Torbjørn; Farrell, Joyce

    2013-01-01

    A set of hyperspectral image data are made available, intended for use in modeling of imaging systems. The set contains images of faces, landscapes and buildings. The data cover wavelengths from 0.4 to 2.5 micrometers, spanning the visible, NIR and SWIR electromagnetic spectral ranges. The images have been recorded with two HySpex line-scan imaging spectrometers covering the spectral ranges 0.4 to 1 micrometers and 1 to 2.5 micrometers. The hyperspectral data set includes measured illuminants and software for converting the radiance data to estimated reflectance. The images are being made available for download at http://scien.stanford.edu

  8. 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.

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

    PubMed

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

    2014-03-12

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

  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 PHENOMENOLOGY FOR THE DETECTION AND TRACKING OF PEDESTRIANS

    E-print Network

    Kerekes, John

    HYPERSPECTRAL IMAGING PHENOMENOLOGY FOR THE DETECTION AND TRACKING OF PEDESTRIANS Jared Herweg ABSTRACT The popularity of hyperspectral imaging in remote sensing continues to to be adapted in novel ways to overcome chal- lenging imaging problems. This paper reports on some of the latest research efforts

  12. Segmentation for Hyperspectral Images with Jian Ye, Todd Wittman, Xavier Bresson, Stanley Osher

    E-print Network

    Ferguson, Thomas S.

    Segmentation for Hyperspectral Images with Priors Jian Ye, Todd Wittman, Xavier Bresson, Stanley, we extend the Chan-Vese model for image segmentation in [1] to hyperspectral image segmentation hyperspectral images. 1 Introduction A hyperspectral image is a high-dimensional image set that typically

  13. 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.

  14. 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.

  15. 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

  16. A low cost thermal infrared hyperspectral imager for small satellites

    NASA Astrophysics Data System (ADS)

    Crites, S. T.; Lucey, P. G.; Wright, R.; Garbeil, H.; Horton, K. A.; Wood, M.

    2012-06-01

    The growth of the small satellite market and launch opportunities for these satellites is creating a new niche for earth observations that contrasts with the long mission durations, high costs, and long development times associated with traditional space-based earth observations. Low-cost, short-lived missions made possible by this new approach provide an experimental platform for testing new sensor technologies that may transition to larger, more long-lived platforms. The low costs and short lifetimes also increase acceptable risk to sensors, enabling large decreases in cost using commercial off-the-shelf (COTS) parts and allowing early-career scientists and engineers to gain experience with these projects. We are building a low-cost long-wave infrared spectral sensor, funded by the NASA Experimental Project to Stimulate Competitive Research program (EPSCoR), to demonstrate ways in which a university's scientific and instrument development programs can fit into this niche. The sensor is a low-mass, power-efficient thermal hyperspectral imager with electronics contained in a pressure vessel to enable use of COTS electronics and will be compatible with small satellite platforms. The sensor, called Thermal Hyperspectral Imager (THI), is based on a Sagnac interferometer and uses an uncooled 320x256 microbolometer array. The sensor will collect calibrated radiance data at long-wave infrared (LWIR, 8-14 microns) wavelengths in 230 meter pixels with 20 wavenumber spectral resolution from a 400 km orbit. We are currently in the laboratory and airborne testing stage in order to demonstrate the spectro-radiometric quality of data that the instrument provides.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  18. Snapshot hyperspectral imaging in ophthalmology William R. Johnson

    E-print Network

    Arizona, University of

    Snapshot hyperspectral imaging in ophthalmology William R. Johnson Daniel W. Wilson Wolfgang Fink, California 91109 Abstract. Retinal imaging spectroscopy can provide functional maps using chromophore spectra a snapshot imaging spectrometer with far-reaching applicability that acquires a complete spatial

  19. Pork grade evaluation using hyperspectral imaging techniques

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

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

  20. 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...

  1. Unmixing Prior to Supervised Classification of Urban Hyperspectral Images

    E-print Network

    Plaza, Antonio J.

    Unmixing Prior to Supervised Classification of Urban Hyperspectral Images Inmaculada D, aplaza}@unex.es Abstract-- Supervised classification of urban hyperspectral images is a very challenging into the classification process, and does not penalize classes which are not relevant in terms of variance or signal-to-noise

  2. Kernel based orthogonalization for change detection in hyperspectral image data

    E-print Network

    Kernel based orthogonalization for change detection in hyperspectral image data Allan A. Nielsen are applied to change detection in hyperspectral image (HyMap) data. The kernel versions are based on so dimensional feature space of the original data. Via kernel substitution also known as the kernel trick

  3. 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...

  4. 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...

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  6. Research on ground-based LWIR hyperspectral imaging remote gas detection

    NASA Astrophysics Data System (ADS)

    Yang, Zhixiong; Yu, Chunchao; Zheng, Weijian; Lei, Zhenggang; Yan, Min; Yuan, Xiaochun; Zhang, Peizhong

    2015-10-01

    The new progress of ground-based long-wave infrared remote sensing is presented, which describes the windowing spatial and temporal modulation Fourier spectroscopy imaging in details. The prototype forms the interference fringes based on the corner-cube of spatial modulation of Michelson interferometer, using cooled long-wave infrared photovoltaic staring FPA (focal plane array) detector. The LWIR hyperspectral imaging is achieved by the process of collection, reorganization, correction, apodization, FFT etc. from data cube. Noise equivalent sensor response (NESR), which is the sensitivity index of CHIPED-1 LWIR hyperspectral imaging prototype, can reach 5.6×10-8W/(cm-1.sr.cm2) at single sampling. Hyperspectral imaging is used in the field of organic gas VOC infrared detection. Relative to wide band infrared imaging, it has some advantages. Such as, it has high sensitivity, the strong anti-interference ability, identify the variety, and so on.

  7. Ore minerals textural characterization by hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Bonifazi, Giuseppe; Picone, Nicoletta; Serranti, Silvia

    2013-02-01

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

  8. Use of Hyperspectral Images to Map Soil Carbon

    NASA Astrophysics Data System (ADS)

    McCarty, G.; Reeves, J.; Hively, D.; Lang, M.; Lund, E.; Weatherbee, O.; Serbin, G.

    2009-04-01

    Rapid methods of measuring soil carbon such as near-infrared (NIR) spectroscopy have gained interest but problems of accurate and precise measurement still persist resulting from the high spatial variability. Tillage and airborne-based spectral sensors can provide means to capture the spatial distribution of soil carbon in agricultural landscapes. We evaluated an airborne hyperspectral sensor covering the range from 450 to 2450 nm at 2.5-m spatial resolution and a tillage sensor covering the range from 920 to 2225 nm. We intensively sampled soils within five tilled (bare soil) agricultural fields within the flight path of the airborne sensor. The test fields were located on the Delmarva Peninsula in Maryland. The quality of spectral data acquired by these field-based sensors was compared to laboratory-acquired spectral data in both NIR (1000 to 2500 nm) and MIR (2500 to 25000 nm) spectral regions for the soil samples taken at 304 geo-referenced locations. Partial Least Squares (PLS) regression models developed from the three NIR spectral data sources were very comparable, indicating that the two field-based NIR sensors performed well for generating spatial data. Although the laboratory based MIR calibration was found to be substantially better than the laboratory derived NIR calibration, current instrumentation limitations favor the use of NIR for in field measurements. A 2.5 m resolution soil carbon map was produced for an agricultural field using the airborne hyperspectral image using PLS regression to develop the calibration model. This approach for mapping will permit better assessment of carbon sequestration in agricultural ecosystems.

  9. Hyperspectral imaging analysis for ophthalmic applications

    NASA Astrophysics Data System (ADS)

    Zamora, Gilberto; Truitt, Paul W.; Nemeth, Sheila C.; Raman, Balaji; Soliz, Peter

    2004-07-01

    A continuing clinical need exists to find diagnostic tools that will detect and characterize the extent of retinal abnormalities as early as possible with non-invasive, highly sensitive techniques. The objective of this paper was to demonstrate the utility of a Hyperspectral Fundus Imager and related analytical tools to detect and characterize retinal tissues based on their spectral signatures. In particular, the paper shows that this system can measure spectral differences between normal retinal tissue and clinically significant macular edema. Future work will lead to clinical studies focused on spectrally characterizing retinal tissue, its diseases, and on the detection and tracking of the progression of retinal disease.

  10. 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.

  11. Differentiating Biological Colours with Few and Many Sensors: Spectral Reconstruction with RGB and Hyperspectral Cameras

    PubMed Central

    Garcia, Jair E.; Girard, Madeline B.; Kasumovic, Michael; Petersen, Phred; Wilksch, Philip A.; Dyer, Adrian G.

    2015-01-01

    Background The ability to discriminate between two similar or progressively dissimilar colours is important for many animals as it allows for accurately interpreting visual signals produced by key target stimuli or distractor information. Spectrophotometry objectively measures the spectral characteristics of these signals, but is often limited to point samples that could underestimate spectral variability within a single sample. Algorithms for RGB images and digital imaging devices with many more than three channels, hyperspectral cameras, have been recently developed to produce image spectrophotometers to recover reflectance spectra at individual pixel locations. We compare a linearised RGB and a hyperspectral camera in terms of their individual capacities to discriminate between colour targets of varying perceptual similarity for a human observer. Main Findings (1) The colour discrimination power of the RGB device is dependent on colour similarity between the samples whilst the hyperspectral device enables the reconstruction of a unique spectrum for each sampled pixel location independently from their chromatic appearance. (2) Uncertainty associated with spectral reconstruction from RGB responses results from the joint effect of metamerism and spectral variability within a single sample. Conclusion (1) RGB devices give a valuable insight into the limitations of colour discrimination with a low number of photoreceptors, as the principles involved in the interpretation of photoreceptor signals in trichromatic animals also apply to RGB camera responses. (2) The hyperspectral camera architecture provides means to explore other important aspects of colour vision like the perception of certain types of camouflage and colour constancy where multiple, narrow-band sensors increase resolution. PMID:25965264

  12. 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.

  13. 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.

  14. 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.

  15. Image visualization of hyperspectral spectrum for LWIR

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  16. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and

    E-print Network

    Schuerger, Andrew C.

    Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments hyperspectral imagers, laser-induced fluorescence spectroscopy (LIFS), and laser-induced fluorescence imaging reserved. Keywords: Heavy metals; Spectral reflectance; Hyperspectral imaging; Laser-induced fluorescence

  17. Hyperspectral Fluorescence and Reflectance Imaging Instrument

    NASA Technical Reports Server (NTRS)

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

    2008-01-01

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

  18. Pattern recognition in hyperspectral persistent imaging

    NASA Astrophysics Data System (ADS)

    Rosario, Dalton; Romano, Joao; Borel, Christoph

    2015-05-01

    We give updates on a persistent imaging experiment dataset, being considered for public release in a foreseeable future, and present additional observations analyzing a subset of the dataset. The experiment is a long-term collaborative effort among the Army Research Laboratory, Army Armament RDEC, and Air Force Institute of Technology that focuses on the collection and exploitation of longwave infrared (LWIR) hyperspectral imagery. We emphasize the inherent challenges associated with using remotely sensed LWIR hyperspectral imagery for material recognition, and show that this data type violates key data assumptions conventionally used in the scientific community to develop detection/ID algorithms, i.e., normality, independence, identical distribution. We treat LWIR hyperspectral imagery as Longitudinal Data and aim at proposing a more realistic framework for material recognition as a function of spectral evolution through time, and discuss limitations. The defining characteristic of a longitudinal study is that objects are measured repeatedly through time and, as a result, data are dependent. This is in contrast to cross-sectional studies in which the outcomes of a specific event are observed by randomly sampling from a large population of relevant objects in which data are assumed independent. Researchers in the remote sensing community generally assume the problem of object recognition to be cross-sectional. But through a longitudinal analysis of a fixed site with multiple material types, we quantify and argue that, as data evolve through a full diurnal cycle, pattern recognition problems are longitudinal in nature and that by applying this knowledge may lead to better algorithms.

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

    NASA Technical Reports Server (NTRS)

    Fisher, Kevin (Inventor)

    2013-01-01

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

  20. 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

  1. Multi-channel morphological profiles for classification of hyperspectral images using support vector machines.

    PubMed

    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

  2. Point-and-stare operation and high-speed image acquisition in real-time hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Driver, Richard D.; Bannon, David P.; Ciccone, Domenic; Hill, Sam L.

    2010-04-01

    The design and optical performance of a small-footprint, low-power, turnkey, Point-And-Stare hyperspectral analyzer, capable of fully automated field deployment in remote and harsh environments, is described. The unit is packaged for outdoor operation in an IP56 protected air-conditioned enclosure and includes a mechanically ruggedized fully reflective, aberration-corrected hyperspectral VNIR (400-1000 nm) spectrometer with a board-level detector optimized for point and stare operation, an on-board computer capable of full system data-acquisition and control, and a fully functioning internal hyperspectral calibration system for in-situ system spectral calibration and verification. Performance data on the unit under extremes of real-time survey operation and high spatial and high spectral resolution will be discussed. Hyperspectral acquisition including full parameter tracking is achieved by the addition of a fiber-optic based downwelling spectral channel for solar illumination tracking during hyperspectral acquisition and the use of other sensors for spatial and directional tracking to pinpoint view location. The system is mounted on a Pan-And-Tilt device, automatically controlled from the analyzer's on-board computer, making the HyperspecTM particularly adaptable for base security, border protection and remote deployments. A hyperspectral macro library has been developed to control hyperspectral image acquisition, system calibration and scene location control. The software allows the system to be operated in a fully automatic mode or under direct operator control through a GigE interface.

  3. Metric Learning for Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. Tests for the elliptical symmetry of hyperspectral imaging data

    E-print Network

    Niu, Sidi

    Accurate statistical models for hyperspectral imaging (HSI) data distribution are useful for many applications. A family of elliptically contoured distribution (ECD) has been investigated to model the unimodal ground cover ...

  9. HYPERSPECTRAL IMAGING PHENOMENOLOGY OF GENETICALLY ENGINEERED PLANT SENTINELS

    E-print Network

    Kerekes, John

    HYPERSPECTRAL IMAGING PHENOMENOLOGY OF GENETICALLY ENGINEERED PLANT SENTINELS D. Simmonsa , J genetically engineered plants that display a visible reaction to chemical inducers in their environment Institute of Technology b Gitam Technologies, Inc., c Electrical Engineering Department, Wright State

  10. 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.

  11. LiCHI - Liquid Crystal Hyperspectral Imager for simultaneous multispectral imaging in aeronomy.

    PubMed

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

    2015-07-13

    A four channel hyperspectral imager using Liquid Crystal Fabry-Perot (LCFP) etalons has been built and tested. This imager is capable of making measurements simultaneously in four wavelength ranges in the visible spectrum. The instrument was designed to make measurements of natural airglow and auroral emissions in the upper atmosphere of the Earth and was installed and tested at the Poker Flat Research Range in Fairbanks, Alaska from February to April 2014. The results demonstrate the capabilities and challenges this instrument presents as a sensor for aeronomical studies. PMID:26191839

  12. 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 ...

  13. URBAN AREA PRODUCT SIMULATION FOR THE ENMAP HYPERSPECTRAL SENSOR , A. Villa ,

    E-print Network

    Plaza, Antonio J.

    URBAN AREA PRODUCT SIMULATION FOR THE ENMAP HYPERSPECTRAL SENSOR P. Gamba , A. Villa , , A. Plaza for remote sensing classification, especially in a urban environment. In this work, we will focus on the simulation of urban area environment at a low spatial resolution, comparable to the new hyperspectral sensors

  14. 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.

  15. Directly Estimating Endmembers for Compressive Hyperspectral Images

    PubMed Central

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

    2015-01-01

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

  16. Unmixing hyperspectral images using Markov random fields

    SciTech Connect

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

    2011-03-14

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

  17. Directly estimating endmembers for compressive hyperspectral images.

    PubMed

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

    2015-01-01

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

  18. Improved Scanners for Microscopic Hyperspectral Imaging

    NASA Technical Reports Server (NTRS)

    Mao, Chengye

    2009-01-01

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

  19. Camouflage target reconnaissance based on hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

    Hua, Wenshen; Guo, Tong; Liu, Xun

    2015-08-01

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

  20. 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

  1. A Bayesian MRF framework for labeling terrain using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Neher, Robert E., Jr.

    We explore the non-Gaussianity of hyperspectral data and present probability models that capture variability of hyperspectral images. In particular, we present a nonparametric probability distribution that models the distribution of the hyperspectral data after reducing the dimension of the data via principal components or Fisher's discriminant analysis. We also explore the directional differences in observed images and present two parametric distributions, the generalized Laplacian and the Bessel K form, that well model the non-Gaussian behavior of directional differences. We then propose a model that labels each spatial site, using Bayesian inference and Markov random fields, that incorporates the information of the nonparametric distribution of the data and the parametric distributions of the directional differences, along with a prior distribution that favors smooth labeling. We then test our model on actual hyperspectral data and present the results of our model, using the Washington D.C. Mall and Indian Springs rural area data sets.

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

    PubMed

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

    2012-01-01

    During the last two decades, a number of methods have been developed to objectively measure meat quality attributes. Hyperspectral imaging technique as one of these methods has been regarded as a smart and promising analytical tool for analyses conducted in research and industries. Recently there has been a renewed interest in using hyperspectral imaging in quality evaluation of different food products. The main inducement for developing the hyperspectral imaging system is to integrate both spectroscopy and imaging techniques in one system to make direct identification of different components and their spatial distribution in the tested product. By combining spatial and spectral details together, hyperspectral imaging has proved to be a promising technology for objective meat quality evaluation. The literature presented in this paper clearly reveals that hyperspectral imaging approaches have a huge potential for gaining rapid information about the chemical structure and related physical properties of all types of meat. In addition to its ability for effectively quantifying and characterizing quality attributes of some important visual features of meat such as color, quality grade, marbling, maturity, and texture, it is able to measure multiple chemical constituents simultaneously without monotonous sample preparation. Although this technology has not yet been sufficiently exploited in meat process and quality assessment, its potential is promising. Developing a quality evaluation system based on hyperspectral imaging technology to assess the meat quality parameters and to ensure its authentication would bring economical benefits to the meat industry by increasing consumer confidence in the quality of the meat products. This paper provides a detailed overview of the recently developed approaches and latest research efforts exerted in hyperspectral imaging technology developed for evaluating the quality of different meat products and the possibility of its widespread deployment. PMID:22591341

  3. 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.

  4. Hyperspectral image analysis using artificial color

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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...

  10. 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 Information ABSTRACT: Raman microscopy is a quantitative, label-free, and noninvasive optical imaging

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

    E-print Network

    . For example, the Compact Reconnaissance Imaging Spectrometer (CRISM) aboard the Mars Reconnaissance Or- biter inherent in the spectral data of each new image. Purely unsupervised results are not always physically meanSPARSE SUPERPIXEL UNMIXING FOR EXPLORATORY ANALYSIS OF CRISM HYPERSPECTRAL IMAGES David R. Thompson

  12. 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.

  13. Hyperspectral imaging is an emerging technique in remote sensing data processing that expands and improves capability of multispectral image analysis. It takes advantage

    E-print Network

    Chang, Chein-I

    role than does noise in hyperspectral image analysis. More importantly, the detection techniques in hyperspectral image analysis, specifically, subpixel detection and mixed pixel classificationvii PREFACE Hyperspectral imaging is an emerging technique in remote sensing data processing

  14. Accurate accommodation of scan-mirror distortion in the registration of hyperspectral image cubes

    NASA Astrophysics Data System (ADS)

    Conover, Damon M.; Delaney, John K.; Loew, Murray H.

    2013-05-01

    To improve the spatial sampling of scanning hyperspectral cameras, it is often necessary to capture numerous overlapping image cubes and later mosaic them to form the overall image cube. For hyperspectral camera systems having broad-area coverage, whisk-broom scanning using an external mirror is often employed. Creating the final image cube mosaic requires sub-pixel correction of the scan-mirror distortion, as well as alignment of the individual image cubes. For systems lacking geo-positional information that relates sensor to scene, alignment of the image scans is nontrivial. Here we present a novel algorithm that removes scan distortion and aligns hyperspectral image cubes based on correlation of the cubes' image content with a reference image. The algorithm is able to provide robust results by recognizing that the cubes' image content will not always match identically with that of the reference image. For example, in cultural heritage applications, the reference color image of the finished painting need not match the under-painting seen in the SWIR. Our approach is to identify a corresponding set of points between the cubes and the reference image, using a subset of wavelet scales, and then filtering out matches that are inconsistent with a map of the distortion. The filtering is performed by removing points iteratively according to their proximity to a function fit to their disparity (distance between the matched points). Our method will be demonstrated and our results validated using hyperspectral image cubes (976-1680 nm) and visible reference images from the fields of remote sensing and cultural heritage preservation.

  15. Random projection and SVD methods in hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Zhang, Jiani

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

  16. A compact, active hyperspectral imaging system for the detection of concealed targets

    E-print Network

    Kerekes, John

    A compact, active hyperspectral imaging system for the detection of concealed targets Bernadette with hyperspectral imaging for the detection of concealed targets in natural terrain. The active illuminator-brightness, subnanosecond-pulse-length output spanning the visible through near-infrared spectral range. The hyperspectral-imaging

  17. A Machine Learning Approach for Material Detection in Hyperspectral Images Raphael Maree

    E-print Network

    Liège, Université de

    of industrial gaseous pollutants [5]. In the biomedical field, hyperspectral image analysis approaches could a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images of specific gaseous traces in ther- mal infra red (TIR) hyperspectral images. Although invis- ible

  18. SparseCEM and SparseACE for Hyperspectral Image Target Detection

    E-print Network

    Ferguson, Thomas S.

    detection has received considerable attention. In a hyperspectral image, each pixel has a nearly continuous1 SparseCEM and SparseACE for Hyperspectral Image Target Detection Shuo Yang, Zhenwei Shi, Member hyperspectral remote sensing images, targets of interest usually occupy only a few pixels (or even sub- pixels

  19. Subspace method for multispectral, hyperspectral, and SAR image classification

    NASA Astrophysics Data System (ADS)

    Bagan, H.; Yamagata, Y.

    2011-12-01

    Various types of remotely sensed data (i.e., difference sensors, spatial resolutions, num of bands) collected over a span of years available today require advanced and innovative techniques to extract information and thematic maps useful for observing and characterizing both natural and human-induced changes over large areas. Land cover classification is one of the most important and typical applications of remote sensing. In the last years, new methods based on optimization and neural network algorithms have been proposed, and among them, subspace methods are very promising [1]-[3]. They are particularly useful for high dimensional and multisource data analysis, which is generally difficult to accomplish with classical statistical methods. The objective of subspace methods is to represent high-dimensional data in a low-dimensional subspaces. Classification then takes place on the chosen subspaces. In this paper, we carried out experiments with three types of remote sensing images, a Landsat ETM+ data, the AVIRIS hyperspectral data and CASI-3 hyperspectral data, and a recent lunched fully polarimetric Phased Array-type L-band Synthetic Aperture Radar (PALSAR) data. Experimental results show that subspace method is a valid and effective alternative to other pattern recognition approaches for the classification of various types of remote sensing data. The advantages of the subspace method are: (1) only 3 parameters are required to be set, and these can easily be determined by an automatic procedure; (2) The computational speed is faster than SVM and similar to the MLC; and (3) Could be a promising tool for future land cover classifications. References [1] H. Bagan, et al., "Classification of airborne hyperspectral data based on the average learning subspace method", IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3. 2008. [2] H. Bagan, Y. Yamagata. "Improved subspace classification method for multispectral remote sensing image classification". PE & RS, vol. 76, No.11, 2010. [3] H. Bagan, T. Kinoshita, Y.Yamagata, "Combination of AVNIR-2, PALSAR, and Polarimetric Parameters for Land Cover Classification", IEEE Trans. Geosci. Remote Sens. in press.

  20. 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.

  1. 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.

  2. 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.

  3. Resolving mixed algal species in hyperspectral images.

    PubMed

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

    2013-01-01

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

  4. 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

  5. Ground-based hyperspectral imaging for the mapping of geological outcrop composition

    NASA Astrophysics Data System (ADS)

    Kurz, Tobias; Buckley, Simon; Schneider, Danilo; Howell, John

    2010-05-01

    The use of high resolution surveying techniques has increased dramatically in earth science applications over the last decade. New products, software solutions and an increased attention to "usability" have made terrestrial laser scanning (lidar) and digital photogrammetry popular methods for obtaining more detailed geometric data for many applications. Geology, especially the study of outcrops, is one such application area where the introduction of laser scanning in particular has benefitted, by allowing an increasingly quantitative approach at a variety of scales. Despite this, most of the contribution of modern surveying techniques has been related to the capture of topographic detail - the shape and form of outcrops - while the remote mapping of outcrop lithology has yet to be satisfactorily addressed. Ground-based spectral imaging offers new possibilities for an improved understanding of outcrop composition, by mapping lithology and the distribution of mineralogy with high resolution and increased automation. Advances in airborne and spaceborne multispectral and hyperspectral sensors have been successful for mineral prospecting and the regional mapping of rock types. However, because of the nadir viewing angle of the sensor, such a configuration is of limited value for near-vertical cliff sections. A new generation of close range hyperspectral imagers is now becoming available, with capabilities of measuring in the short-wave infra-red (SWIR) part of the electromagnetic spectrum suitable for detecting absorption features exhibited by many minerals found in sedimentary rocks. This research uses a ground-based hyperspectral sensor to acquire spectral images of geological outcrops, with the aim of remotely determining the distribution of lithologies. The method was applied to case studies from carbonate and siliciclastic rocks. The images were processed to obtain spectral classification maps of the distribution of representative rock types. To increase the quantitative approach, the spectral data were integrated with photorealistic 3D models derived from terrestrial laser scanning and conventional image acquisition. Because the push-broom hyperspectral sensor recorded panoramic rather than planar images, the integration was performed using a cylindrical camera model. Using this approach, it was possible to relate the pixels of the spectral images to a real-world coordinate system, aiding analysis and validation. In addition, the spectral images could be superimposed on the lidar-derived photorealistic models, allowing a simultaneous visualisation of multiple thematic results together with the conventional digital camera imagery. For the case studies used, encouraging results were produced, allowing the mapping of features that were not easily visible in conventional images. It is therefore concluded that ground-based hyperspectral imaging is an important method that may be applicable to many earth science applications.

  6. A comparison of noise reduction methods for image enhancement in classification of hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Morillo-Contreras, Shirley; Velez-Reyes, Miguel; Hunt, Shawn D.

    2005-06-01

    A particular challenge in hyperspectral remote sensing of benthic habitats is that the signal exiting from the water is a small component of the overall signal received at the satellite or airborne sensor. Therefore, in order to be able to discriminate different ecological areas in benthic habitats, it is important to have a high signal to noise ratio (SNR). The SNR can be improved by building better sensors; SNR improvements however, we believe, are also achievable by means of signal processing and by taking advantage of the unique characteristics of hyperspectral sensors. One approach for SNR improvement is based on signal oversampling. Another approach for SNR improvement is Reduced Rank Filtering (RRF) where the small Singular Values of the image are discarded and then reconstruct a lower rank approximation to the original image. This paper presents a comparison in the use of oversampling filtering (OF) versus RRF as SNR enhancement methods in terms of classification accuracy and class separability when used as a pre-processing step in a classification system. Overall results show that OF does a better job improving the classification accuracy than RRF and at much lower computational cost, making it an attractive technique for Hyperspectral Image Processing.

  7. 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.

  8. Identification of seedling cabbages and weeds using hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  9. RECONSTRUCTING AND SEGMENTING HYPERSPECTRAL IMAGES FROM COMPRESSED MEASUREMENTS

    E-print Network

    Plemmons, Robert J.

    and other vegetation, crop health, mineral and soil composition, moisture content of soils and vegetation. In fact, hyperspectral imaging was used following the attack on the twin towers and the hurricane Katrina target domain). This spar- sity assumption suggests approaching the imaging problem by using

  10. Textural Analysis of Hyperspectral Images for Improving Detection Accuracy

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  11. 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...

  12. MULTISCALE STOCHASTIC WATERSHED FOR UNSUPERVISED HYPERSPECTRAL IMAGE SEGMENTATION

    E-print Network

    Angulo,Jesús

    MULTISCALE STOCHASTIC WATERSHED FOR UNSUPERVISED HYPERSPECTRAL IMAGE SEGMENTATION J. Angulo, S on the stochastic watershed, an approach to estimate a probability density function (pdf) of contours of an image time a multiscale framework for the computation of the pdf of contours using the stochastic watershed

  13. 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...

  14. Supplemental Blue LED Lighting Array to Improve the Signal Quality in Hyperspectral Imaging of Plants

    PubMed Central

    Mahlein, Anne-Katrin; Hammersley, Simon; Oerke, Erich-Christian; Dehne, Heinz-Wilhelm; Goldbach, Heiner; Grieve, Bruce

    2015-01-01

    Hyperspectral imaging systems used in plant science or agriculture often have suboptimal signal-to-noise ratio in the blue region (400–500 nm) of the electromagnetic spectrum. Typically there are two principal reasons for this effect, the low sensitivity of the imaging sensor and the low amount of light available from the illuminating source. In plant science, the blue region contains relevant information about the physiology and the health status of a plant. We report on the improvement in sensitivity of a hyperspectral imaging system in the blue region of the spectrum by using supplemental illumination provided by an array of high brightness light emitting diodes (LEDs) with an emission peak at 470 nm. PMID:26039423

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  16. Hyperspectral imaging with a liquid crystal polarization interferometer.

    PubMed

    Hegyi, Alex; Martini, Joerg

    2015-11-01

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

  17. Applying region growing algorithm to hyperspectral image for oil segmentation

    NASA Astrophysics Data System (ADS)

    Song, Mei-ping; Xu, Xing-wei; Lu, Shuangyang; Xu, Wei; Bao, Hai-mo

    2014-05-01

    Region growing is one of the popular segmentation algorithms for 2-D image, which comes up a continuous interested region. How to extent this method to hyperspectral image processing effectively is a problem needs to be discussed deeply. Here in this paper, three ways of using region growing in hyperspectral scenario are explored to separate oil from sea water. Furthermore, in order to release the influence of sunlight, a modification to growing rule is prompted, considering the property of local region. At last, a normalized ATGP is used to obtain more potential target. The experiment results show that combining unmixing techniques with region growing is better than other methods.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  19. 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.

  20. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

  2. 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.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-09-01

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

  5. Hyperspectral image analysis for plant stress detection

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  7. 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 ...

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  9. Edge Detection in Hyperspectral Imaging: Multivariate Statistical Approaches

    E-print Network

    Duin, Robert P.W.

    Edge Detection in Hyperspectral Imaging: Multivariate Statistical Approaches Sergey Verzakov, Pavel Pacl´ik, and Robert P.W. Duin Information and Communication Theory Group Faculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology Mekelweg 4, 2628 CD Delft

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

    SciTech Connect

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

    2010-11-01

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

  11. 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...

  12. HyperCam: Hyperspectral Imaging for Ubiquitous Computing Applications

    E-print Network

    into three bands of color (although with slightly different spectral responses). When two materials look by the human trichromatic color vision system. They can still have very different spectral properties in some, hyperspectral imaging is already being used in the food and agriculture industries [3,23,24], astronomy [16

  13. 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...

  14. 3D Reconstruction from Hyperspectral Images Yongsheng Gao3

    E-print Network

    Zhou, Jun

    - construct a 3D model from hyperspectral images. Our pro- posed method first generates 3D point sets from industry [18]. 3D model was generated based on depth data captured by a laser scanner, with hy- perspectral- formation as 3D model. They first constructed 3D model using two different hardware and then mapped

  15. 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...

  16. 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 (...

  17. A ROBUST TEST FOR NONLINEAR MIXTURE DETECTION IN HYPERSPECTRAL IMAGES

    E-print Network

    Tourneret, Jean-Yves

    . The proposed detection strategy considers the distance between an ob- served pixel and the hyperplane spannedA ROBUST TEST FOR NONLINEAR MIXTURE DETECTION IN HYPERSPECTRAL IMAGES Y. Altmann , N. Dobigeon University of Santa Catarina Florian´opolis, SC, Brazil ABSTRACT This paper studies a pixel by pixel

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

    PubMed

    Koprowski, Robert

    2015-11-01

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

  19. 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.

  20. Hyperspectral imaging applied to end-of-life concrete recycling

    NASA Astrophysics Data System (ADS)

    Serranti, Silvia; Bonifazi, Giuseppe

    2014-03-01

    In this paper a new technology, based on HyperSpectral Imaging (HSI) sensors, and related detection architectures, is investigated in order to develop suitable and low cost strategies addressed to: i) preliminary detection and characterization of the composition of the structure to dismantle and ii) definition and implementation of innovative smart detection engines for sorting and/or demolition waste flow stream quality control. The proposed sensing architecture is fast, accurate, affordable and it can strongly contribute to bring down the economic threshold above which recycling is cost efficient. Investigations have been carried out utilizing an HSI device working in the range 1000-1700 nm: NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Spectral data analysis was carried out utilizing the PLS_Toolbox (Version 6.5.1, Eigenvector Research, Inc.) running inside Matlab® (Version 7.11.1, The Mathworks, Inc.), applying different chemometric techniques, selected depending on the materials under investigation. The developed procedure allows assessing the characteristics, in terms of materials identification, such as recycled aggregates and related contaminants, as resulting from end-of-life concrete processing. A good classification of the different classes of material was obtained, being the model able to distinguish aggregates from other materials (i.e. glass, plastic, tiles, paper, cardboard, wood, brick, gypsum, etc.).

  1. "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.

  2. Parallel hyperspectral image processing on distributed multicluster systems

    NASA Astrophysics Data System (ADS)

    Liu, Fangbin; Seinstra, Frank J.; Plaza, Antonio

    2011-01-01

    Computationally efficient processing of hyperspectral image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, and defense/security. As individual cluster computers often cannot satisfy the computational demands of emerging problems in hyperspectral imaging, there is a growing need for distributed supercomputing using multicluster systems. A well-known manner of obtaining speedups in hyperspectral imaging is to apply data parallel approaches, in which commonly used data structures (e.g., the image cubes) are being scattered among the available compute nodes. Such approaches work well for individual compute clusters, but--due to the inherently large wide-area communication overheads--these are generally not applied in distributed multi-cluster systems. Given the nature of many algorithmic approaches in hyperspectral imaging, however, and due to the increasing availability of high-bandwidth optical networks, wide-area data parallel execution may well be a feasible acceleration approach. This paper discusses the wide-area data parallel execution of two realistic and state-of-the-art algorithms for endmember extraction in hyperspectral unmixing applications: automatic morphological endmember extraction and orthogonal subspace projection. It presents experimental results obtained on a real-world multicluster system, and provides a feasibility analysis of the applied parallelization approaches. The two parallel algorithms evaluated in this work had been developed before for single-cluster execution, and were not changed. Because no further implementation efforts were required, the proposed methodology is easy to apply to already available algorithms, thus reducing complexity and enhancing standardization.

  3. 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...

  4. 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...

  5. 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

  6. In-vivo microvasculature visualization using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Shah, Bhavesh B.; Cavanagh, H. D.; Petroll, W. M.; Kothare, Amogh D.; Gundabhat, Prasad; Behbehani, Khosrow; Zuzak, Karel J.

    2006-02-01

    We describe a new noninvasive microscopic near infrared reflectance hyperspectral imaging method for visualizing, in vivo, spatially distributed contributions of oxyhemoglobin perfusing the microvasculature within dermal tissue. Microscopic images of the dermis are acquired, generating a series of spectroscopic images formatted as a function of wavelength consisting of one spectral and two spatial dimensions; a hyperspectral image data cube. The data thus collected can be considered as a series of spatially resolved spectra. For data collection, images are acquired by a system consisting of a near infrared liquid crystal tunable filter (LCTF) and a Focal plane array detector (FPA) integrated with a microscope. The LCTF is continuously tunable over a useful near infrared spectral range (650-950 nm) with an average full width at half-height bandwidth of 6.78 nm. To provide high quantum efficiency without etaloning we utilized a back-illumination FPA with deep -depletion technology. A 30W halogen light source illuminates a dermal tissue area of approximately 18 mm in diameter. Reflected light from the dermal tissue is first passed through the microscope, the LCTF, and then imaged onto the FPA. The acquired hyperspectral data is deconvoluted using a multivariate least squares approach that requires at least two reference spectra, oxy- and deoxyhemoglobin. The resulting images are gray scale encoded to directly represent the varying spatial distributions of oxyhemoglobin contribution. As a proof of principle example, we examined a clinical model of vascular occlusion and reperfusion.

  7. A hyperspectral image analysis workbench for environmental science applications

    SciTech Connect

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

    1992-01-01

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

  8. A hyperspectral image analysis workbench for environmental science applications

    SciTech Connect

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

    1992-10-01

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

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

    NASA Astrophysics Data System (ADS)

    Bjorgan, Asgeir; Randeberg, Lise L.

    2015-07-01

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

  10. 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.

  11. Advanced shortwave infrared and Raman hyperspectral sensors for homeland security and law enforcement operations

    NASA Astrophysics Data System (ADS)

    Klueva, Oksana; Nelson, Matthew P.; Gardner, Charles W.; Gomer, Nathaniel R.

    2015-05-01

    Proliferation of chemical and explosive threats as well as illicit drugs continues to be an escalating danger to civilian and military personnel. Conventional means of detecting and identifying hazardous materials often require the use of reagents and/or physical sampling, which is a time-consuming, costly and often dangerous process. Stand-off detection allows the operator to detect threat residues from a safer distance minimizing danger to people and equipment. Current fielded technologies for standoff detection of chemical and explosive threats are challenged by low area search rates, poor targeting efficiency, lack of sensitivity and specificity or use of costly and potentially unsafe equipment such as lasers. A demand exists for stand-off systems that are fast, safe, reliable and user-friendly. To address this need, ChemImage Sensor Systems™ (CISS) has developed reagent-less, non-contact, non-destructive sensors for the real-time detection of hazardous materials based on widefield shortwave infrared (SWIR) and Raman hyperspectral imaging (HSI). Hyperspectral imaging enables automated target detection displayed in the form of image making result analysis intuitive and user-friendly. Application of the CISS' SWIR-HSI and Raman sensing technologies to Homeland Security and Law Enforcement for standoff detection of homemade explosives and illicit drugs and their precursors in vehicle and personnel checkpoints is discussed. Sensing technologies include a portable, robot-mounted and standalone variants of the technology. Test data is shown that supports the use of SWIR and Raman HSI for explosive and drug screening at checkpoints as well as screening for explosives and drugs at suspected clandestine manufacturing facilities.

  12. Hyperspectral image-based methods for spectral diversity

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  13. Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

    PubMed

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

    2015-01-01

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

  14. 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.

  15. Classification of hyperspectral images based on conditional random fields

    NASA Astrophysics Data System (ADS)

    Hu, Yang; Saber, Eli; Monteiro, Sildomar T.; Cahill, Nathan D.; Messinger, David W.

    2015-02-01

    A significant increase in the availability of high resolution hyperspectral images has led to the need for developing pertinent techniques in image analysis, such as classification. Hyperspectral images that are correlated spatially and spectrally provide ample information across the bands to benefit this purpose. Conditional Random Fields (CRFs) are discriminative models that carry several advantages over conventional techniques: no requirement of the independence assumption for observations, flexibility in defining local and pairwise potentials, and an independence between the modules of feature selection and parameter leaning. In this paper we present a framework for classifying remotely sensed imagery based on CRFs. We apply a Support Vector Machine (SVM) classifier to raw remotely sensed imagery data in order to generate more meaningful feature potentials to the CRFs model. This approach produces promising results when tested with publicly available AVIRIS Indian Pine imagery.

  16. 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

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

    PubMed

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

    2014-01-01

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

  18. Multidimensional feature extraction from 3D hyperspectral images

    NASA Astrophysics Data System (ADS)

    Mehrubeoglu, Mehrube; McLauchlan, Lifford

    2011-09-01

    A hyperspectral imaging system has been set up and used to capture hyperspectral image cubes from various samples in the 400-1000 nm spectral region. The system consists of an imaging spectrometer attached to a CCD camera with fiber optic light source as the illuminator. The significance of this system lies in its capability to capture 3D spectral and spatial data that can then be analyzed to extract information about the underlying samples, monitor the variations in their response to perturbation or changing environmental conditions, and compare optical properties. In this paper preliminary results are presented that analyze the 3D spatial and spectral data in reflection mode to extract features to differentiate among different classes of interest using biological and metallic samples. Studied biological samples possess homogenous as well as non-homogenous properties. Metals are analyzed for their response to different surface treatments, including polishing. Similarities and differences in the feature extraction process and results are presented. The mathematical approach taken is discussed. The hyperspectral imaging system offers a unique imaging modality that captures both spatial and spectral information that can then be correlated for future sample predictions.

  19. Modelling the appearance of chromatic environment using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Fomins, S.; Ozolinsh, M.

    2013-11-01

    Color of objects is a spectral composition of incident light source, reflection properties of the object itself, and spectral tuning of the eye. Light sources with different spectral characteristics can produce metameric representation of color; however most variable in this regard is vision. Pigments of color vision are continuously bleached by different stimuli and optical density of the pigment is changed, while continuous conditions provide an adaptation and perception of white. Special cases are color vision deficiencies which cover almost 8 % of male population in Europe. Hyperspectral imaging allows obtaining the spectra of the environment and modelling the performance of the dichromatic, anomalous trichromatic, as also normal trichromatic adapted behavior. First, CRI Nuance hyperspectral imaging system was spectrally calibrated for natural continuous spectral illumination of high color rendering index and narrow band fluorescent light sources. Full-scale images of color deficiency tests were acquired in the range of 420 to 720 nm to evaluate the modelling capacity for dichromatic and anomalous trichromatic vision. Hyperspectral images were turned to cone excitation images according to Stockman and Sharpe (2000) 1. Further, model was extended for anomalous trichromacy conditions. Cone sensitivity spectra were shifted by 4 nm according to each anomaly type. LWS and SWS cone signals were balanced in each condition to provide the appropriate appearance of colors in CIE system.

  20. Hyperspectral imaging for detection of cholesterol in human skin

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  1. The challenges of analysing blood stains with hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

  2. 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

  3. 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.

  4. Making digital phantoms with spectral and spatial light modulators for quantitative applications of hyperspectral optical medical imaging devices

    NASA Astrophysics Data System (ADS)

    Chon, Bonghwan; Tokumasu, Fuyuki; Lee, Ji Youn; Allen, David W.; Rice, Joseph P.; Hwang, Jeeseong

    2015-03-01

    We present a procedure to generate digital phantoms with a hyperspectral image projector (HIP) consisting of two liquid crystal on silicon (LCoS) spatial light modulators (SLMs). The digital phantoms are 3D image data cubes of the spatial distribution of spectrally resolved abundances of intracellular light-absorbing oxy-hemoglobin molecules in single erythrocytes. Spectrally and spatially resolved image data indistinguishable from the real scene may be used as standards to calibrate image sensors and validate image analysis algorithms for their measurement quality, performance consistency, and inter-laboratory comparisons for quantitative biomedical imaging applications.

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

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

    SciTech Connect

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

    1994-08-01

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

  7. Hyperspectral imaging technique for determination of pork freshness attributes

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

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

  8. Hyperspectral cathodoluminescence imaging of modern and fossil carbonate shells

    NASA Astrophysics Data System (ADS)

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

    2006-09-01

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

  9. 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.

  10. Computationally efficient strategies to perform anomaly detection in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Rossi, Alessandro; Acito, Nicola; Diani, Marco; Corsini, Giovanni

    2012-11-01

    In remote sensing, hyperspectral sensors are effectively used for target detection and recognition because of their high spectral resolution that allows discrimination of different materials in the sensed scene. When a priori information about the spectrum of the targets of interest is not available, target detection turns into anomaly detection (AD), i.e. searching for objects that are anomalous with respect to the scene background. In the field of AD, anomalies can be generally associated to observations that statistically move away from background clutter, being this latter intended as a local neighborhood surrounding the observed pixel or as a large part of the image. In this context, many efforts have been put to reduce the computational load of AD algorithms so as to furnish information for real-time decision making. In this work, a sub-class of AD methods is considered that aim at detecting small rare objects that are anomalous with respect to their local background. Such techniques not only are characterized by mathematical tractability but also allow the design of real-time strategies for AD. Within these methods, one of the most-established anomaly detectors is the RX algorithm which is based on a local Gaussian model for background modeling. In the literature, the RX decision rule has been employed to develop computationally efficient algorithms implemented in real-time systems. In this work, a survey of computationally efficient methods to implement the RX detector is presented where advanced algebraic strategies are exploited to speed up the estimate of the covariance matrix and of its inverse. The comparison of the overall number of operations required by the different implementations of the RX algorithms is given and discussed by varying the RX parameters in order to show the computational improvements achieved with the introduced algebraic strategy.

  11. 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. ...

  12. 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...

  13. 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, ...

  14. Compact Infrared Hyperspectral Imaging Polarimeter Julia Craven, Michael W. Kudenov, Maryn G. Stapelbroek, Eustace L. Dereniak

    E-print Network

    Dereniak, Eustace L.

    Compact Infrared Hyperspectral Imaging Polarimeter Julia Craven, Michael W. Kudenov, Maryn G Blvd., Tucson, AZ 85721 ABSTRACT A compact SWIR/MWIR infrared hyperspectral imaging polarimeter (IHIP a pair of sapphire Wollaston prisms and high order retarders to form an imaging birefringent Fourier

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  16. 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.

  17. 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.

  18. Beam imaging sensor

    DOEpatents

    McAninch, Michael D; Root, Jeffrey J

    2015-03-31

    The present invention relates generally to the field of sensors for beam imaging and, in particular, to a new and useful beam imaging sensor for use in determining, for example, the power density distribution of a beam including, but not limited to, an electron beam or an ion beam. In one embodiment, the beam imaging sensor of the present invention comprises, among other items, a circumferential slit that is either circular, elliptical or polygonal in nature.

  19. Detection of early plant stress responses in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Behmann, Jan; Steinrücken, Jörg; Plümer, Lutz

    2014-07-01

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

  20. Subpixel target detection and enhancement in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Tiwari, K. C.; Arora, M.; Singh, D.

    2011-06-01

    Hyperspectral data due to its higher information content afforded by higher spectral resolution is increasingly being used for various remote sensing applications including information extraction at subpixel level. There is however usually a lack of matching fine spatial resolution data particularly for target detection applications. Thus, there always exists a tradeoff between the spectral and spatial resolutions due to considerations of type of application, its cost and other associated analytical and computational complexities. Typically whenever an object, either manmade, natural or any ground cover class (called target, endmembers, components or class) gets spectrally resolved but not spatially, mixed pixels in the image result. Thus, numerous manmade and/or natural disparate substances may occur inside such mixed pixels giving rise to mixed pixel classification or subpixel target detection problems. Various spectral unmixing models such as Linear Mixture Modeling (LMM) are in vogue to recover components of a mixed pixel. Spectral unmixing outputs both the endmember spectrum and their corresponding abundance fractions inside the pixel. It, however, does not provide spatial distribution of these abundance fractions within a pixel. This limits the applicability of hyperspectral data for subpixel target detection. In this paper, a new inverse Euclidean distance based super-resolution mapping method has been presented that achieves subpixel target detection in hyperspectral images by adjusting spatial distribution of abundance fraction within a pixel. Results obtained at different resolutions indicate that super-resolution mapping may effectively aid subpixel target detection.

  1. 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.

  2. 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.

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

    PubMed

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

    2014-01-01

    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 x 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. PMID:24967840

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

    SciTech Connect

    Bernacki, Bruce E.; Phillips, Mark C.

    2010-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

  7. Spatial regularization for the unmixing of hyperspectral images

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  8. Superpixel-Augmented Endmember Detection for Hyperspectral Images

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

    Superpixels are homogeneous image regions comprised of several contiguous pixels. They are produced by shattering the image into contiguous, homogeneous regions that each cover between 20 and 100 image pixels. The segmentation aims for a many-to-one mapping from superpixels to image features; each image feature could contain several superpixels, but each superpixel occupies no more than one image feature. This conservative segmentation is relatively easy to automate in a robust fashion. Superpixel processing is related to the more general idea of improving hyperspectral analysis through spatial constraints, which can recognize subtle features at or below the level of noise by exploiting the fact that their spectral signatures are found in neighboring pixels. Recent work has explored spatial constraints for endmember extraction, showing significant advantages over techniques that ignore pixels relative positions. Methods such as AMEE (automated morphological endmember extraction) express spatial influence using fixed isometric relationships a local square window or Euclidean distance in pixel coordinates. In other words, two pixels covariances are based on their spatial proximity, but are independent of their absolute location in the scene. These isometric spatial constraints are most appropriate when spectral variation is smooth and constant over the image. Superpixels are simple to implement, efficient to compute, and are empirically effective. They can be used as a preprocessing step with any desired endmember extraction technique. Superpixels also have a solid theoretical basis in the hyperspectral linear mixing model, making them a principled approach for improving endmember extraction. Unlike existing approaches, superpixels can accommodate non-isometric covariance between image pixels (characteristic of discrete image features separated by step discontinuities). These kinds of image features are common in natural scenes. Analysts can substitute superpixels for image pixels during endmember analysis that leverages the spatial contiguity of scene features to enhance subtle spectral features. Superpixels define populations of image pixels that are independent samples from each image feature, permitting robust estimation of spectral properties, and reducing measurement noise in proportion to the area of the superpixel. This permits improved endmember extraction, and enables automated search for novel and constituent minerals in very noisy, hyperspatial images. This innovation begins with a graph-based segmentation based on the work of Felzenszwalb et al., but then expands their approach to the hyperspectral image domain with a Euclidean distance metric. Then, the mean spectrum of each segment is computed, and the resulting data cloud is used as input into sequential maximum angle convex cone (SMACC) endmember extraction.

  9. Embedded Bone Fragment Detection in Chicken Fillets using Transmittance Image Enhancement and Hyperspectral Reflectance Imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper is concerned with the detection of bone fragments embedded in compressed de-boned skinless chicken breast fillets by enhancing single-band transmittance images generated by back-lighting and exploiting spectral information from hyperspectral reflectance images. Optical imaging of chicken ...

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed Central

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

    2015-01-01

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

  12. Hyperspectral microscope imaging methods to classify gram-positive and gram-negative foodborne pathogenic bacteria

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An acousto-optic tunable filter-based hyperspectral microscope imaging method has potential for identification of foodborne pathogenic bacteria from microcolony rapidly with a single cell level. We have successfully developed the method to acquire quality hyperspectral microscopic images from variou...

  13. 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...

  14. Hyperspectral imaging-based classification and wavebands selection for internal defect detection of pickling cucumbers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging is useful for detecting internal defect of pickling cucumbers. The technique, however, is not yet suitable for high-speed online implementation due to the challenges for analyzing large-scale hyperspectral images. This research was aimed to select the optimal wavebands from the...

  15. 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...

  16. 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...

  17. HICO and RAIDS Experiment Payload - Hyperspectral Imager for the Coastal Ocean

    NASA Technical Reports Server (NTRS)

    Corson, Mike

    2009-01-01

    HICO and RAIDS Experiment Payload - Hyperspectral Imager For The Coastal Ocean (HREP-HICO) will operate a visible and near-infrared (VNIR) Maritime Hyperspectral Imaging (MHSI) system, to detect, identify and quantify coastal geophysical features from the International Space Station.

  18. On the Statistics of Hyperspectral Imaging Data Dimitris Manolakis, David Marden, John Kerekes and Gary Shaw

    E-print Network

    Kerekes, John

    On the Statistics of Hyperspectral Imaging Data Dimitris Manolakis, David Marden, John Kerekes of the joint (among wavebands) probability density function (pdf) of hyperspectral imaging (HSI) data detection and classification algorithms depend upon the joint pdf of the data and the vector

  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. Hyperspectral imaging for detection of arthritis: feasibility and prospects

    NASA Astrophysics Data System (ADS)

    Milanic, Matija; Paluchowski, Lukasz A.; Randeberg, Lise L.

    2015-09-01

    Rheumatoid arthritis (RA) is a disease that frequently leads to joint destruction. It has a high incidence rate worldwide, and the disease significantly reduces patients' quality of life. Detecting and treating inflammatory arthritis before structural damage to the joint has occurred is known to be essential for preventing patient disability and pain. Existing diagnostic technologies are expensive, time consuming, and require trained personnel to collect and interpret data. Optical techniques might be a fast, noninvasive alternative. Hyperspectral imaging (HSI) is a noncontact optical technique which provides both spectral and spatial information in one measurement. In this study, the feasibility of HSI in arthritis diagnostics was explored by numerical simulations and optimal imaging parameters were identified. Hyperspectral reflectance and transmission images of RA and normal human joint models were simulated using the Monte Carlo method. The spectral range was 600 to 1100 nm. Characteristic spatial patterns for RA joints and two spectral windows with transmission were identified. The study demonstrated that transmittance images of human joints could be used as one parameter for discrimination between arthritic and unaffected joints. The presented work shows that HSI is a promising imaging modality for the diagnostics and follow-up monitoring of arthritis in small joints.

  2. Multimodal confocal hyperspectral imaging microscopy with wavelength sweeping source

    NASA Astrophysics Data System (ADS)

    Kim, Young-Duk; Do, Dukho; Yoo, Hongki; Gweon, DaeGab

    2015-02-01

    There exist microscopes that are able to obtain the chemical properties of a sample, because there are some cases in which it is difficult to find out causality of a phenomenon by using only the structural information of a sample. Obtaining the chemical properties of a sample is important in biomedical imaging, because most biological phenomena include changes in the chemical properties of the sample. Hyperspectral imaging (HSI) is one of the popular imaging methods for characterizing materials and biological samples by measuring the reflectance or emission spectrum of the sample. Because all materials have a unique reflectance spectrum, it is possible to analyze material properties and detect changes in the chemical properties of a sample by measuring the spectral changes with respect to the original spectrum. Because of its ability to measure the spectrum of a sample, HSI is widely used in materials identification applications such as aerial reconnaissance and is the subject of various studies in microscopy. Although there are many advantages to using the method, conventional HSI has some limitations because of its complex configuration and slow speed. In this research we propose a new type of multimodal confocal hyperspectral imaging microscopy with fast image acquisition and a simple configuration that is capable of both confocal and HSI microscopies.

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

    SciTech Connect

    Sinclair, Michael B.; Melgaard, David Kennett; Reichardt, Thomas A.; Timlin, Jerilyn Ann; Garcia, Omar Fidel; Luk, Ting Shan; Jones, Howland D. T.; Collins, Aaron M.

    2010-10-01

    A considerable amount research is being conducted on microalgae, since microalgae are becoming a promising source of renewable energy. Most of this research is centered on lipid production in microalgae because microalgae produce triacylglycerol which is ideal for biodiesel fuels. Although we are interested in research to increase lipid production in algae, we are also interested in research to sustain healthy algal cultures in large scale biomass production farms or facilities. The early detection of fluctuations in algal health, productivity, and invasive predators must be developed to ensure that algae are an efficient and cost-effective source of biofuel. Therefore we are developing technologies to monitor the health of algae using spectroscopic measurements in the field. To do this, we have proposed to spectroscopically monitor large algal cultivations using LIDAR (Light Detection And Ranging) remote sensing technology. Before we can deploy this type of technology, we must first characterize the spectral bio-signatures that are related to algal health. Recently, we have adapted our confocal hyperspectral imaging microscope at Sandia to have two-photon excitation capabilities using a chameleon tunable laser. We are using this microscope to understand the spectroscopic signatures necessary to characterize microalgae at the cellular level prior to using these signatures to classify the health of bulk samples, with the eventual goal of using of LIDAR to monitor large scale ponds and raceways. By imaging algal cultures using a tunable laser to excite at several different wavelengths we will be able to select the optimal excitation/emission wavelengths needed to characterize algal cultures. To analyze the hyperspectral images generated from this two-photon microscope, we are using Multivariate Curve Resolution (MCR) algorithms to extract the spectral signatures and their associated relative intensities from the data. For this presentation, I will show our two-photon hyperspectral imaging results on a variety of microalgae species and show how these results can be used to characterize algal ponds and raceways.

  4. Experimental evaluation of a hyperspectral imager for near-infrared fluorescent contrast agent studies

    NASA Astrophysics Data System (ADS)

    Luthman, A. S.; Bohndiek, Sarah E.

    2015-03-01

    Hyperspectral imaging (HSI) systems have the potential to combine morphological and spectral information to provide detailed and high sensitivity readouts in biological and medical applications. As HSI enables simultaneous detection in several spectral bands, the technology has significant potential for use in real-time multiplexed contrast agent studies. Examples include tumor detection in intraoperative and endoscopic imaging as well as histopathology. A multiplexed readout from multiple disease targets, such as cell surface receptors overexpressed in cancer cells, could improve both sensitivity and specificity of tumor identification. Here, we evaluate a commercial, compact, near-infrared HSI sensor that has the potential to enable low cost, video rate HSI for multiplexed fluorescent contrast agent studies in biomedical applications. The hyperspectral imager, based on a monolithically integrated Fabry-Perot etalon, has 70 spectral bands between 600-900 nm, making it ideal for this application. Initial calibration of the imager was performed to determine wavelength band response, quantum efficiency and the effect of F-number on the spectral response. A platform for wide-field fluorescence imaging in reflectance using fluorophore specific LED excitation was then developed. The applicability of the imaging platform for simultaneous readout of multiple fluorophore signals was demonstrated using a dilution series of Alexa Fluor 594 and Alexa Fluor 647, showing that nanomolar fluorophore concentrations can be detected. Our results show that the HSI system can clearly resolve the emission spectra of the two fluorophores in mixtures of concentrations across several orders of magnitude, indicating a high dynamic range performance. We therefore conclude that the HSI sensor tested here is suitable for detecting fluorescence in biomedical imaging applications.

  5. The development of a wide-field, high-resolution UV Raman hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Gomer, Nathaniel R.; Nelson, Matthew P.; Angel, S. M.

    2015-05-01

    Raman spectroscopy is a valuable tool for the investigation and analysis of explosive and biological analytes because it provides a unique molecular fingerprint that allows for unambiguous target identification. Raman can be advantageous when utilized with deep UV excitation, but typical deep UV Raman systems have numerous limitations that hinder their performance and make their potential integration onto a field portable platform difficult. These systems typically offer very low throughput, are physically large and heavy, and can only probe an area the size of a tightly focused laser, severely diminishing the ability of the system to investigate large areas efficiently. The majority of these limitations are directly related to a system's spectrometer, which is typically dispersive grating based and requires a very narrow slit width and long focal length optics to achieve high spectral resolution. To address these shortcomings, ChemImage Sensor Systems (CISS), teaming with the University of South Carolina, are developing a revolutionary wide-field Raman hyperspectral imaging system capable of providing wide-area, high resolution measurements with greatly increased throughput in a small form factor, which would revolutionize the way Raman is conducted and applied. The innovation couples a spatial heterodyne spectrometer (SHS), a novel slit-less spectrometer that operates similar to Michelson interferometer, with a fiber array spectral translator (FAST) fiber array, a two-dimensional imaging fiber for hyperspectral imagery. This combination of technologies creates a novel wide-field, high throughput Raman hyperspectral imager capable of yielding very high spectral resolution measurements using defocused excitation, giving the system a greater area coverage and faster search rate than traditional Raman systems. This paper will focus on the need for an innovative UV Raman system, provide an overview of spatial heterodyne Raman spectroscopy, and discuss the development of the system.

  6. Wide-field hyperspectral 3D imaging of functionalized gold nanoparticles targeting cancer cells by reflected light microscopy.

    PubMed

    Patskovsky, Sergiy; Bergeron, Eric; Rioux, David; Meunier, Michel

    2015-05-01

    We present a new hyperspectral reflected light microscopy system with a scanned broadband supercontinuum light source. This wide-field and low phototoxic hyperspectral imaging system has been successful for performing spectral three-dimensional (3D) localization and spectroscopic identification of CD44-targeted PEGylated AuNPs in fixed cell preparations. Such spatial and spectral information is essential for the improvement of nanoplasmonic-based imaging, disease detection and treatment in complex biological environment. The presented system can be used for real-time 3D NP tracking as spectral sensors, thus providing new avenues in the spatio-temporal characterization and detection of bioanalytes. 3D image of the distribution of functionalized AuNPs attached to CD44-expressing MDA-MB-231 human cancer cells. PMID:24961507

  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. 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.

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

    NASA Astrophysics Data System (ADS)

    Wen, Jia; Zhao, Junsuo; Cailing, Wang

    2015-12-01

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

  10. 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.

  11. 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.

  12. DLP hyperspectral imaging for surgical and clinical utility

    NASA Astrophysics Data System (ADS)

    Zuzak, Karel J.; Francis, Robert P.; Wehner, Eleanor F.; Smith, Jack; Litorja, Maritoni; Allen, David W.; Tracy, Chad; Cadeddu, Jeffrey; Livingston, Edward

    2009-02-01

    We describe a novel digital light processing, DLP hyperspectral imaging system for visualizing chemical composition of in vivo tissues during surgical procedures non-invasively and at near video rate. The novelty of the DLP hyperspectral imaging system resides in (1) its ability to conform light to rapidly sweep through a series of preprogrammed spectral illuminations as simple as a set of contiguous bandpasses to any number of complex spectra, and (2) processing the reflected spectroscopic image data using unique supervised and unsupervised chemometric methods that color encode molecular content of tissue at each image detector pixel providing an optical biopsy. Spectral illumination of tissue is accomplished utilizing a DLP® based spectral illuminator incorporating a series of bandpass spectra and measuring the reflectance image with a CCD array detector. Wavelength dependent images are post processed with a multivariate least squares analysis method using known reference spectra of oxy- and deoxyhemoglobin. Alternatively, illuminating with complex reference spectra reduces the number of spectral images required for generating chemically relevant images color encoded for relative percentage of oxyhemoglobin are collected and displayed in real time near-video rate, (3 to 4) frames per second (fps). As a proof of principle application, a kidney of an anesthetized pig was imaged before and after renal vasculature occlusion showing the clamped kidney to be 61% of the unclamped kidney percentage of oxyhemoglobin. Using the "3-Shot" spectral illumination method and gathering data at (3 to 4) fps shows a non-linear exponential de-oxygenation of hemoglobin reaching steady state within 30 seconds post occlusion.

  13. 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

  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. Material characterization using a hyperspectral infrared imaging spectrometer

    SciTech Connect

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

    1998-10-30

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

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

    NASA Astrophysics Data System (ADS)

    Yang, Chun-Chieh; Jun, Won; Kim, Moon S.; Chao, Kaunglin; Kang, Sukwon; Chan, Diane E.; Lefcourt, Alan

    2010-04-01

    This paper reported the development of hyperspectral fluorescence imaging system using ultraviolet-A excitation (320-400 nm) for detection of bovine fecal contaminants on the abaxial and adaxial surfaces of romaine lettuce and baby spinach leaves. Six spots of fecal contamination were applied to each of 40 lettuce and 40 spinach leaves. In this study, the wavebands at 666 nm and 680 nm were selected by the correlation analysis. The two-band ratio, 666 nm / 680 nm, of fluorescence intensity was used to differentiate the contaminated spots from uncontaminated leaf area. The proposed method could accurately detect all of the contaminated spots.

  17. 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

  18. Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability.

    PubMed

    Halimi, Abderrahim; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2015-12-01

    This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing, accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to consider their variability in the image. An additive noise is also considered in the proposed model, generalizing the normal compositional model. The proposed algorithm exploits the whole image to benefit from both spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed Bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated through simulations conducted on both synthetic and real data. PMID:26302517

  19. Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation

    NASA Astrophysics Data System (ADS)

    Wei, Qi; Bioucas-Dias, Jose; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2015-07-01

    This paper presents a variational based approach to fusing hyperspectral and multispectral images. The fusion process is formulated as an inverse problem whose solution is the target image assumed to live in a much lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the corresponding supports of active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the state-of-the-art fusion methods.

  20. Extended hyperspectral imaging system modeling and implementation for subpixel target detection

    NASA Astrophysics Data System (ADS)

    Ding, Bo; Kerekes, John P.

    2013-09-01

    For hyperspectral imaging system design and parameter trade-o research, an analytical model to simulate the remote sensing system has been developed and is in progress to be made available to the community. The analytical model includes scene, sensor and target characteristics, and also atmospheric features, background spectral statistics, sensor speci cations and target spectral statistics. The model is being implemented as a web-based application through an RIT-hosted website. Predicting system performance has been veri ed by real world data collected during the RIT SHARE 2012 collection and the data shows consistency with the simulated results on calibration tarps and grass. Also, subpixel target spectral statistics are predicted by this model. Some parameter trade-o examples are given and analyzed to explain the utility of this model.

  1. 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.

  2. Lossless compression of hyperspectral images using hybrid context prediction.

    PubMed

    Liang, Yuan; Li, Jianping; Guo, Ke

    2012-03-26

    In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost. PMID:22453490

  3. 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).

  4. 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...

  5. HYPERSPECTRAL REFLECTANCE AND FLUORESCENCE LINE-SCAN IMAGING FOR ONLINE QUALITY AND SAFETY INSPECTION OF APPLES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Instrumentation and Sensing Laboratory has recently developed a rapid online line-scan imaging system capable of both hyperspectral Vis/NIR reflectance and fluorescence in the Vis with UV-A excitation. The hyperspectral online line-scan system integrated with a commercial apple-sorting machine ...

  6. Time-domain surface profile imaging via a hyperspectral Fourier transform spectrometer

    E-print Network

    the HS-FTS. © 2008 Optical Society of America OCIS codes: 240.0310, 110.4234, 120.2830. The development using a hyperspectral Fourier transform spectrometer (HS-FTS). This technique measures the frequency of a hyperspectral Fourier trans- form spectrometer (HS-FTS) for surface profile imag- ing with a primary application

  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. 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.

  9. 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.)

  10. Digital Compressive Quantitation and Hyperspectral Imaging

    E-print Network

    2013-07-25

    (classify) pairs of liquids in time scales of microseconds to milliseconds (and that it .... con guration ensures that the laser spot always remains centered at the back .... an on-board counter that is used for counting the TTL pulses output by the ..... chemical image of plant tissue.15 However, the latter study obtained chemical ...

  11. 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.

  12. 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.

  13. Classification of oat and groat kernels using NIR hyperspectral imaging.

    PubMed

    Serranti, Silvia; Cesare, Daniela; Marini, Federico; Bonifazi, Giuseppe

    2013-01-15

    An innovative procedure to classify oat and groat kernels based on coupling hyperspectral imaging (HSI) in the near infrared (NIR) range (1006-1650 nm) and chemometrics was designed, developed and validated. According to market requirements, the amount of groat, that is the hull-less oat kernels, is one of the most important quality characteristics of oats. Hyperspectral images of oat and groat samples have been acquired by using a NIR spectral camera (Specim, Finland) and the resulting data hypercubes were analyzed applying Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares-Discriminant Analysis (PLS-DA) to build the classification models to discriminate the two kernel typologies. Results showed that it is possible to accurately recognize oat and groat single kernels by HSI (prediction accuracy was almost 100%). The study demonstrated also that good classification results could be obtained using only three wavelengths (1132, 1195 and 1608 nm), selected by means of a bootstrap-VIP procedure, allowing to speed up the classification processing for industrial applications. The developed objective and non-destructive method based on HSI can be utilized for quality control purposes and/or for the definition of innovative sorting logics of oat grains. PMID:23200388

  14. Superpixel Segmentation for Endmember Detection in Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Thompson, D. R.; de Granville, C.; Gilmore, M. S.; Castano, R.

    2009-12-01

    "Superpixel segmentation" is a novel approach to facilitate statistical analyses of hyperspectral image data with high spatial resolution and subtle spectral features. The method oversegments the image into homogeneous regions each comprised of several contiguous pixels. This can reduce noise by exploiting scene features' spatial contiguity: isolated spectral features are likely to be noise, but spectral features that appear in several adjacent pixels probably indicate real materials in the scene. The mean spectra from each superpixel define a smaller, noise-reduced dataset. This preprocessing step improves endmember detection for the images in our study. Our endmember detection approach presumes a linear (geographic) mixing model for image spectra. We generate superpixels with the Felzenszwalb/Huttenlocher graph-based segmentation [1] with a Euclidean distance metric. This segmentation shatters the image into thousands of superpixels, each with an area of approximately 20 image pixels. We then apply Symmetric Maximum Angle Convex Cone (SMACC) endmember detection algorithm to the data set consisting of the mean spectrum from all superpixels. We evaluated the approach for several images from the Compact Reconnaissance Imaging Spectrometer (CRISM) [2]. We used the 1000-2500nm wavelengths of images frt00003e12 and frt00003fb9. We cleaned the images with atmospheric correction based on Olympus Mons spectra [3] and preprocessed with a radius-1 median filter in the spectral domain. Endmembers produced with and without the superpixel reduction are compared to the representative (mean) spectra of five representative mineral classes identified in an expert analysis of each scene. Expert-identified minerals include mafic minerals and phyllosilicate deposits that in some cases subtended just a few tens of pixels. Only the endmembers from the superpixel approach reflected all major mineral constituents in the images. Additionally, the superpixel endmembers are more quantitatively more faithful to the mean mineral spectra, reducing average squared error over all wavelengths for comparisons between endmembers and the closest-matching mineral spectrum (Figures). We conclude that superpixel segmentation holds promise for improving signal-to-noise for statistical analyses of hyperspectral images with high spatial resolution. References [1] P.F. Felzenszwalb and D.P. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of Computer Vision, vol. 59:2, pp. 167-181, 2004. [2] S. Murchie et al., “Compact reconnaissance imaging spectrometer for Mars (CRISM) on Mars reconnaissance orbiter,” J. Geophys. Res, vol. 112, no. 5, 2007. [3] F. Morgan et al., “CAT tutorial,” CRISM Data User’s Workshop, Lunar and Planetary Science Conference 2009.

  15. 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.

  16. Image and spectral fidelity study of hyperspectral remote sensing image scaling up based on wavelet transform

    NASA Astrophysics Data System (ADS)

    An, Ni; Ma, Yi; Bao, Yuhai

    2015-08-01

    Wavelet transform is a kind of effective image-scale transformation method, which can achieve multi-scale transformation by distinguishing the low-frequency information and the high-frequency information. Hyperspectral remote sensing data combining image with spectrum has almost continuous spectrum that is the important premise of extracting hyperspectral image information, while scale transformation will inevitably lead to the change of image and spectra. Therefore, it is important to study the image and spectral fidelity after wavelet transform. In this paper, the Proba CHRIS hyperspectral remote sensing image of Yellow River Estuary Wetland is used to investigate the image and spectral fidelity of image transformed by wavelet which remained the low-frequency information. The level 1-3 of up-scale images are obtained and then compared with the original. Then image and spectral fidelity is quantitatively analyzed. The results show that the image fidelity is slightly reduced by up-scale transformation, but near-infrared images have a larger distortion than other bands. With the increasing scaling up, the distortion of spectrum is more and more great, but spectral fidelity is overall well. For the typical wetland objects, Phragmites austrialis has the best spectral correlation, Spartina has a small spectra change, and aquaculture water spectral distortion is most remarkable.

  17. 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.

  18. 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.

  19. ASTRAL, a hyperspectral imaging DNA sequencer

    NASA Astrophysics Data System (ADS)

    O'Brien, Kevin M.; Wren, Jonathan; Davé, Varshal K.; Bai, Diane; Anderson, Richard D.; Rayner, Simon; Evans, Glen A.; Dabiri, Ali E.; Garner, Harold R.

    1998-05-01

    We are developing a prototype automatic DNA sequencer which utilizes polyacrylamide slab gels imaged through a novel optical detection system. The design of this prototype sequencer allows the ability to perform direct optical coupling over the entire read area of the gel and hyperspectrographic separation and detection of the fluorescence emission. The machine has no moving parts. All the major components incorporated in this prototype are all currently available "off the shelf," thus reducing equipment development time and decreasing costs. Software developed for data acquisition, analysis, and conversion to other standard formats facilitates compatibility.

  20. Hyperspectral image visualization based on a human visual model

    NASA Astrophysics Data System (ADS)

    Zhang, Hongqin; Peng, Honghong; Fairchild, Mark D.; Montag, Ethan D.

    2008-02-01

    Hyperspectral image data can provide very fine spectral resolution with more than 200 bands, yet presents challenges for visualization techniques for displaying such rich information on a tristimulus monitor. This study developed a visualization technique by taking advantage of both the consistent natural appearance of a true color image and the feature separation of a PCA image based on a biologically inspired visual attention model. The key part is to extract the informative regions in the scene. The model takes into account human contrast sensitivity functions and generates a topographic saliency map for both images. This is accomplished using a set of linear "center-surround" operations simulating visual receptive fields as the difference between fine and coarse scales. A difference map between the saliency map of the true color image and that of the PCA image is derived and used as a mask on the true color image to select a small number of interesting locations where the PCA image has more salient features than available in the visible bands. The resulting representations preserve hue for vegetation, water, road etc., while the selected attentional locations may be analyzed by more advanced algorithms.

  1. Hyperspectral vital sign signal analysis for medical data

    NASA Astrophysics Data System (ADS)

    Gao, Cheng; Li, Yao; Li, Hsiao-Chi; Chang, Chein-I.; Hu, Peter; Mackenzie, Colin

    2015-05-01

    This paper develops a completely new technology,) from a hyperspectral imaging perspective, called Hyperspectral Vital Sign Signal Analysis (HyVSSA. A hyperspectral image is generally acquired by hundreds of contiguous spectral bands, each of which is an optical sensor specified by a particular wavelength. In medical application, we can consider a patient with different vital sign signals as a pixel vector in hyperspectral image and each vital sign signal as a particular band. In light of this interpretation, a revolutionary concept is developed, which translates medical data to hyperspectral data in such a way that hyperspectral technology can be readily applied to medical data analysis. One of most useful techniques in hyperspectral data processing is, Anomaly Detection (AD) which in this medical application is used to predict outcomes such as transfusion, length of stay (LOS) and mortality using various vital signs. This study compared transfusion prediction performance of Anomaly Detection (AD) and Logistic Regression (LR).

  2. Spectral homogenization techniques for the hyperspectral image projector

    NASA Astrophysics Data System (ADS)

    Hillberry, Logan E.; Rice, Joseph P.

    2015-05-01

    In an effort to improve technology for performance testing and calibration of multispectral and hyperspectral imagers, the National Institute of Standards and Technology (NIST) has been developing a Hyperspectral Image Projector (HIP) capable of projecting dynamic scenes than include distinct, programmable spectra in each of its 1024x768 spatial pixels. The HIP is comprised of a spectral engine, which is a light source capable generating the spectra in the scene, coupled to a spatial engine, capable of projecting the spectra into the correct locations of the scene. In the prototype HIP, the light exiting the Visible-Near-Infrared (VNIR) / Short-Wavelength Infrared (SWIR) spectral engine is spectrally dispersed and needs to be spectrally homogenized before it enters the spatial engine. In this paper we describe the results from a study of several different techniques for performing this spectral homogenization. These techniques include an integrating sphere, a liquid light guide, a randomized fiber bundle, and an engineered diffuser, in various combinations. The spectral uniformity of projected HIP scenes is measured and analyzed using the spectral angle mapper (SAM) algorithm over the VNIR spectral range. The SAM provides a way to analyze the spectral uniformity independently from the radiometric uniformity. The goal of the homogenizer is a spectrally uniform and bright projected image. An integrating sphere provides the most spectrally uniform image, but at a great loss of light compared with the other methods. The randomized fiber bundle generally outperforms the liquid light guide in both spectral homogenization and brightness. Using an engineered diffuser with the randomized fiber bundle increases the spectral uniformity by a factor of five, with a decrease in brightness by a factor of five, compared with the randomized fiber bundle alone. The combination of an engineered diffuser with a randomized fiber bundle provides comparable spectral uniformity to the integrating sphere while enabling 40 times greater brightness.

  3. Detection of hypercholesterolemia using hyperspectral imaging of human skin

    NASA Astrophysics Data System (ADS)

    Milanic, Matija; Bjorgan, Asgeir; Larsson, Marcus; Strömberg, Tomas; Randeberg, Lise L.

    2015-07-01

    Hypercholesterolemia is characterized by high blood levels of cholesterol and is associated with increased risk of atherosclerosis and cardiovascular disease. Xanthelasma is a subcutaneous lesion appearing in the skin around the eyes. Xanthelasma is related to hypercholesterolemia. Identifying micro-xanthelasma can thereforeprovide a mean for early detection of hypercholesterolemia and prevent onset and progress of disease. The goal of this study was to investigate spectral and spatial characteristics of hypercholesterolemia in facial skin. Optical techniques like hyperspectral imaging (HSI) might be a suitable tool for such characterization as it simultaneously provides high resolution spatial and spectral information. In this study a 3D Monte Carlo model of lipid inclusions in human skin was developed to create hyperspectral images in the spectral range 400-1090 nm. Four lesions with diameters 0.12-1.0 mm were simulated for three different skin types. The simulations were analyzed using three algorithms: the Tissue Indices (TI), the two layer Diffusion Approximation (DA), and the Minimum Noise Fraction transform (MNF). The simulated lesions were detected by all methods, but the best performance was obtained by the MNF algorithm. The results were verified using data from 11 volunteers with known cholesterol levels. The face of the volunteers was imaged by a LCTF system (400- 720 nm), and the images were analyzed using the previously mentioned algorithms. The identified features were then compared to the known cholesterol levels of the subjects. Significant correlation was obtained for the MNF algorithm only. This study demonstrates that HSI can be a promising, rapid modality for detection of hypercholesterolemia.

  4. 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.

  5. Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

    PubMed

    Khan, Zohaib; Shafait, Faisal; Mian, Ajmal

    2015-12-01

    A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition. PMID:26316126

  6. 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.

  7. Proceedings of SPIE Vol. 4816, Imaging Spectrometry VIII, S. Shen ed., 415425, 2002. Thermal Infrared Hyperspectral Imaging from

    E-print Network

    Kirkland, Laurel

    Infrared Hyperspectral Imaging from Vehicle-carried Instrumentation Laurel E. Kirklanda, Kenneth C. Herrb University, salisburys@worldnet.att.net ABSTRACT Stand-off identification in the field using thermal infrared has a mature program for field hyperspectral measurements using van-mounted thermal- infrared

  8. 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.

  9. 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.

  10. Hyperspectral imaging for the detection of retinal disease

    NASA Astrophysics Data System (ADS)

    Harvey, Andrew R.; Lawlor, Joanne; McNaught, Andrew I.; Williams, John W.; Fletcher-Holmes, David W.

    2002-11-01

    Hyperspectral imaging (HSI) shows great promise for the detection and classification of several diseases, particularly in the fields of "optical biopsy" as applied to oncology, and functional retinal imaging in ophthalmology. In this paper, we discuss the application of HSI to the detection of retinal diseases and technological solutions that address some of the fundamental difficulties of spectral imaging within the eye. HSI of the retina offers a route to non-invasively deduce biochemical and metabolic processes within the retina. For example it shows promise for the mapping of retinal blood perfusion using spectral signatures of oxygenated and deoxygenated hemoglobin. Compared with other techniques using just a few spectral measurements, it offers improved classification in the presence of spectral cross-contamination by pigments and other components within the retina. There are potential applications for this imaging technique in the investigation and treatment of the eye complications of diabetes, and other diseases involving disturbances to the retinal, or optic-nerve-head circulation. It is well known that high-performance HSI requires high signal-to-noise ratios (SNR) whereas the application of any imaging technique within the eye must cope with the twin limitations of the small numerical aperture provided by the entrance pupil to the eye and the limit on the radiant power at the retina. We advocate the use of spectrally-multiplexed spectral imaging techniques (the traditional filter wheel is a traditional example). These approaches enable a flexible approach to spectral imaging, with wider spectral range, higher SNRs and lower light intensity at the retina than could be achieved using a Fourier-transform (FT) approach. We report the use of spectral imaging to provide calibrated spectral albedo images of healthy and diseased retinas and the use of this data for screening purposes. These images clearly demonstrate the ability to distinguish between oxygenated and deoxygenated hemoglobin using spectral imaging and this shows promise for the early detection of various retinopathies.

  11. Extended Hyperspectral Imaging System Modeling and Implementation for Subpixel Target Detection

    E-print Network

    Kerekes, John

    of Technology 54 Lomb Memorial Drive, Rochester, NY 14623 USA ABSTRACT For hyperspectral imaging system design space and used with target detection algorithms to generate probability of detection versus false alarm

  12. 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 ...

  13. MINERAL EXPLORATION BY USING HYPERSPECTRAL IMAGE CLASSIFICATION AND \\DOMING" DELINEATION 1

    E-print Network

    Merényi, Erzsébet

    MINERAL EXPLORATION BY USING HYPERSPECTRAL IMAGE CLASSIFICATION AND \\DOMING" DELINEATION 1 Erzsebet that o ers promise for mineral exploration. Two fully independent data analyses are combined. \\Doming" delineation, which predicts the locations of endogenetic mineralization from topographic features

  14. DETECTION OF BACTERIAL BIOFILM ON STAINLESS STEEL BY HYPERSPECTRAL FLUORESCENCE IMAGING

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this study, hyperspectral fluorescence imaging techniques were investigated for detection of microbial biofilm on stainless steel plates typically used to manufacture food processing equipment. Stainless steel coupons were immersed in bacterium cultures consisting of nonpathogenic E. coli, Pseudo...

  15. 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.

  16. 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.

  17. 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

  18. An automatic blood cell segmentation method based on hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

    Wang, Qian; Li, Qingli; Liu, Hongying; Zhou, Mei; Guo, Fangmin

    2015-08-01

    Hyperspectral blood image has been utilized in biomedical field for a period of time. However, identifying and segmenting blood cells is still a tricky issue. Thus, this paper proposed a new method based on support vector machine (SVM) to solve this issue from hyperspectral images. Then post-processing of holes-filling and noise removing are performed on the segmented results to get completed cell. The experimental results proved the accuracy and accommodation for this new proposed method.

  19. Kernel weighted joint collaborative representation for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Du, Qian; Li, Wei

    2015-05-01

    Collaborative representation classifier (CRC) has been applied to hyperspectral image classification, which intends to use all the atoms in a dictionary to represent a testing pixel for label assignment. However, some atoms that are very dissimilar to the testing pixel should not participate in the representation, or their contribution should be very little. The regularized version of CRC imposes strong penalty to prevent dissimilar atoms with having large representation coefficients. To utilize spatial information, the weighted sum of local spatial neighbors is considered as a joint spatial-spectral feature, which is actually for regularized CRC-based classification. This paper proposes its kernel version to further improve classification accuracy, which can be higher than those from the traditional support vector machine with composite kernel and the kernel version of sparse representation classifier.

  20. Compressive source separation: theory and methods for hyperspectral imaging.

    PubMed

    Golbabaee, Mohammad; Arberet, Simon; Vandergheynst, Pierre

    2013-12-01

    We propose and analyze a new model for hyperspectral images (HSIs) based on the assumption that the whole signal is composed of a linear combination of few sources, each of which has a specific spectral signature, and that the spatial abundance maps of these sources are themselves piecewise smooth and therefore efficiently encoded via typical sparse models. We derive new sampling schemes exploiting this assumption and give theoretical lower bounds on the number of measurements required to reconstruct HSI data and recover their source model parameters. This allows us to segment HSIs into their source abundance maps directly from compressed measurements. We also propose efficient optimization algorithms and perform extensive experimentation on synthetic and real datasets, which reveals that our approach can be used to encode HSI with far less measurements and computational effort than traditional compressive sensing methods. PMID:24043385

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

    SciTech Connect

    Baba, Justin S; Boudreaux, Philip R

    2012-01-01

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

  2. Rapid prototyping of biomimetic vascular phantoms for hyperspectral reflectance imaging.

    PubMed

    Ghassemi, Pejhman; Wang, Jianting; Melchiorri, Anthony J; Ramella-Roman, Jessica C; Mathews, Scott A; Coburn, James C; Sorg, Brian S; Chen, Yu; Pfefer, T Joshua

    2015-12-01

    The emerging technique of rapid prototyping with three-dimensional (3-D) printers provides a simple yet revolutionary method for fabricating objects with arbitrary geometry. The use of 3-D printing for generating morphologically biomimetic tissue phantoms based on medical images represents a potentially major advance over existing phantom approaches. Toward the goal of image-defined phantoms, we converted a segmented fundus image of the human retina into a matrix format and edited it to achieve a geometry suitable for printing. Phantoms with vessel-simulating channels were then printed using a photoreactive resin providing biologically relevant turbidity, as determined by spectrophotometry. The morphology of printed vessels was validated by x-ray microcomputed tomography. Channels were filled with hemoglobin (Hb) solutions undergoing desaturation, and phantoms were imaged with a near-infrared hyperspectral reflectance imaging system. Additionally, a phantom was printed incorporating two disjoint vascular networks at different depths, each filled with Hb solutions at different saturation levels. Light propagation effects noted during these measurements—including the influence of vessel density and depth on Hb concentration and saturation estimates, and the effect of wavelength on vessel visualization depth—were evaluated. Overall, our findings indicated that 3-D-printed biomimetic phantoms hold significant potential as realistic and practical tools for elucidating light–tissue interactions and characterizing biophotonic system performance. PMID:26662064

  3. 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

  4. 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.

  5. [Harmonic analysis fusion of hyperspectral image and its spectral information fidelity evaluation].

    PubMed

    Yang, Keming; Zhang, Tao; Wang, Li-bo; Qian, Xiao-li; Wang, Lin-wei; Liu, Shi-wen

    2013-09-01

    Combined with the Hyperion hyperspectral image and ALI high spatial resolution band of the EO-1 satellite, the paper puts forward the harmonic analysis fusion (HAF) algorithm of hyperspectral image and the derivative spectral d-value's information entropy (DSD-IE) model of the spectral-fused information fidelity evaluation. Through calculating and evaluating some parameters such as the DSD-IE values, average gradient and standard deviation of the sample spectra meanwhile compared with the fused hyperspectral images by the traditional methods like the principal component analysis (PCA), Gram-Schmidt and wavelet, the fused hyperspectral iamge by the HAF has proved to have the higher information degree of spatial integration and spectral fidelity, and the better superiorities in the reliability, accuracy and applicability. PMID:24369660

  6. Geometric correction method of rotary scanning hyperspectral image in agriculture application

    NASA Astrophysics Data System (ADS)

    Wan, Peng; Yang, Guijun; Xu, Bo; Feng, Haikuan; Yu, Haiyang

    2015-04-01

    In order to meet the demand of farmland plot experiments hyperspectral images acquisition, an equipment that incorporating an aerial lift vehicle with hyperspectral imager was proposed. In this manner, high spatial resolution (in millimeter) imageries were collected, which meets the need of spatial resolution on farm experiments, but also improves the efficiency of image acquisition. In allusion to the image circular geometric distortion which produced by telescopic arm rotation, an image rectification method that based on mounted position and orientation system was proposed. Experimental results shows that the image rectification method is effective.

  7. Interactive transmission of spectrally wavelet-transformed hyperspectral images

    NASA Astrophysics Data System (ADS)

    Monteagudo-Pereira, José Lino; Bartrina-Rapesta, Joan; Aulí-Llinàs, Francesc; Serra-Sagristà, Joan; Zabala, Alaitz; Pons, Xavier

    2008-08-01

    The size of images used in remote sensing scenarios has constantly increased in the last years. Remote sensing images are not only stored, but also processed and transmitted, raising the need for more resources and bandwidth. On another side, hyperspectral remote sensing images have a large number of components with a significant inter-component redundancy, which is usually taken into account by many image coding systems to improve the coding performance. The main approaches used to decorrelate the spectral dimension are the Karhunen Loeve-Transform and the Discrete Wavelet Transform (DWT). This paper is focused on DWT decorrelators because they have a lower computational complexity, and because they provide interesting features such as component and resolution scalability and progressive transmission. Influence of the spectral transform is investigated, considering the DWT kernel applied and the number of decomposition levels. In addition, a JPIP compliant application, CADI, is introduced. It may be useful to test new protocols, techniques, or coding systems, without requiring significant changes on the application. CADI can be run in most computer platforms and devices thanks to the use of JAVA and the configuration of a light-version, suitable for devices with constrained resources.

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

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

    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 representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown in Petri dishes of Rainbow agar. The purpose of the feasibility study was to evaluate whether a DSLR camera (Nikon D700) could be used to predict hyperspectral images in the wavelength range from 400 to 1,000 nm and even to predict the types of pathogens using a hyperspectral STEC classification algorithm that was previously developed. Unlike many other studies using color charts with known and noise-free spectra for training reconstruction models, this work used hyperspectral and color images, separately measured by a hyperspectral imaging spectrometer and the DSLR color camera. The color images were calibrated (i.e. normalized) to relative reflectance, subsampled and spatially registered to match with counterpart pixels in hyperspectral images that were also calibrated to relative reflectance. Polynomial multivariate least-squares regression (PMLR) was previously developed with simulated color images. In this study, partial least squares regression (PLSR) was also evaluated as a spectral recovery technique to minimize multicollinearity and overfitting. The two spectral recovery models (PMLR and PLSR) and their parameters were evaluated by cross-validation. The QR decomposition was used to find a numerically more stable solution of the regression equation. The preliminary results showed that PLSR was more effective especially with higher order polynomial regressions than PMLR. The best classification accuracy measured with an independent test set was about 90%. The results suggest the potential of cost-effective color imaging using hyperspectral image classification algorithms for rapidly differentiating pathogens in agar plates.

  9. 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.

  10. Multiplexed hyperspectral imaging and spectrometry using spatial light modulators

    NASA Astrophysics Data System (ADS)

    Deverse, Richard Andrew

    1999-11-01

    The signal-noise-ratio, (SNR) is often the primary figure of merit when evaluating instrument performance. Multiplexing is one method employed in spectrometry and imaging to increase the magnitude of a signal received by a detector during a period of measurement. Optical masks employing Hadamard encoding matrices for multiplexing can be used to increase the SNR of some spectrometric and hyperspectrometric imaging measurements. A transmissive/opaque metal-etched optical mask and a reflective micro-mirror optical mask were used to investigate Hadamard transform (HT) multiplexing in spectrometric and hyperspectrometric imaging measurements. HT spectrometry (HTS) is a combination of dispersive and multiplexing spectrometry employing a one- dimensional (1D) optical mask. Dividing and folding the ID Hadamard, encoded optical mask used for spectral encoding in HT multiplexing spectrometry results in a two-dimensional (2D) mask that can be used for spatial encoding in Hadamard transform imaging (HTI). An etched, mechanically translated 2D optical mask and a digital micro-mirror array (DMA) encoded by a cyclic S255 matrix were successfully employed in the investigation of spatial multiplexing in HT Raman chemical mapping systems. Imaging heterogeneous liquid and solid samples using a 2D Hadamard encoded metal- etched moving mask was successfully demonstrated using conventional Raman spectroscopic equipment. The DMA is an improvement in Hadamard optical mask technology over moving optical masks and was successfully applied to visible Raman hyperspectral imaging of heterogeneous liquid and solid samples. The DMA was also successfully demonstrated to be effective for improving SNR in near- infrared (NIR) FITS as a 1D Hadamard encoded optical mask.

  11. 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.

  12. 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...

  13. Visible hyperspectral imaging evaluating the cutaneous response to ultraviolet radiation

    NASA Astrophysics Data System (ADS)

    Ilias, Michail A.; Häggblad, Erik; Anderson, Chris; Salerud, E. Göran

    2007-02-01

    In vivo diagnostics of skin diseases as well as understanding of the skin biology constitute a field demanding characterization of physiological and anatomical parameters. Biomedical optics has been successfully used, to qualitatively and quantitatively estimate the microcirculatory conditions of superficial skin. Capillaroscopy, laser Doppler techniques and spectroscopy, all elucidate different aspects of microcirculation, e.g. capillary anatomy and distribution, tissue perfusion and hemoglobin oxygenation. We demonstrate the use of a diffuse reflectance hyperspectral imaging system for spatial and temporal characterization of tissue oxygenation, important to skin viability. The system comprises: light source, liquid crystal tunable filter, camera objective, CCD camera, and the decomposition of the spectral signature into relative amounts of oxy- and deoxygenized hemoglobin as well as melanin in every pixel resulting in tissue chromophore images. To validate the system, we used a phototesting model, creating a graded inflammatory response of a known geometry, in order to evaluate the ability to register spatially resolved reflectance spectra. The obtained results demonstrate the possibility to describe the UV inflammatory response by calculating the change in tissue oxygen level, intimately connected to a tissue's metabolism. Preliminary results on the estimation of melanin content are also presented.

  14. Hyperspectral imaging applied to complex particulate solids systems

    NASA Astrophysics Data System (ADS)

    Bonifazi, Giuseppe; Serranti, Silvia

    2008-04-01

    HyperSpectral Imaging (HSI) is based on the utilization of an integrated hardware and software (HW&SW) platform embedding conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool, for non-destructive analysis, in many research and industrial sectors. The possibility to apply on-line HSI based techniques in order to identify and quantify specific particulate solid systems characteristics is presented and critically evaluated. The originally developed HSI based logics can be profitably applied in order to develop fast, reliable and lowcost strategies for: i) quality control of particulate products that must comply with specific chemical, physical and biological constraints, ii) performance evaluation of manufacturing strategies related to processing chains and/or realtime tuning of operative variables and iii) classification-sorting actions addressed to recognize and separate different particulate solid products. Case studies, related to recent advances in the application of HSI to different industrial sectors, as agriculture, food, pharmaceuticals, solid waste handling and recycling, etc. and addressed to specific goals as contaminant detection, defect identification, constituent analysis and quality evaluation are described, according to authors' originally developed application.

  15. 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.

  16. New Cloud Detection Algorithm for Multispectral and Hyperspectral Images: Application to

    E-print Network

    Camps-Valls, Gustavo

    New Cloud Detection Algorithm for Multispectral and Hyperspectral Images: Application to ENVISAT that faces the problem of accurate identification of location and abundance of clouds in multispectral images inevitable that many of these images present cloud covers. The objective of this work is to develop

  17. 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...

  18. 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.

  19. The Thermal Infrared Compact Imaging Spectrometer (TIRCIS): a follow-on to the Space Ultra Compact Hyperspectral Imager (SUCHI)

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

    The Thermal Infrared Compact Imaging Spectrometer (TIRCIS) is a long wave infrared (LWIR, 8-14 microns) hyperspectral imager designed as the follow-on to the University of Hawaii's SUCHI (Space Ultra Compact Hyperspectral Imager). SUCHI is a low-mass (<9kg), low-volume (10x12x40cm3) LWIR spectrometer designed as the primary payload on the University of Hawaii-built 'HiakaSat' microsatellite. SUCHI is based on a variable-gap Fabry Perot interferometer employed as a Fourier transform spectrometer with images collected by a commercial off-the-shelf microbolometer contained inside a 1-atm sealed vessel. The sensor has been fully integrated with the HiakaSat microsatellite and is awaiting launch in 2015. The TIRCIS instrument is based on the same principles but takes lessons learned from SUCHI and applies them to a new design with improvements in spatial resolution, spectral resolution and spectral responsivity. The TIRCIS instrument is based on an uncooled microbolometer array with custom detector coatings to enhance responsivity towards 7 microns. Like SUCHI, TIRCIS utilizes a variable-gap Fabry Perot interferometer to create the spectra, but three different interferometer wedges with varying slopes resulting in spectral resolution ranging from 44 cm-1 to 6.5 cm-1 will be tested to explore tradeoffs between spectral resolution and sensitivity. TIRCIS is designed to achieve 120 m spatial resolution, compared with 230 m for SUCHI, from a theoretical 500 km orbit. It will be used for ground and aircraft data collection but will undergo environmental testing to demonstrate its relevance to the space environment. TIRCIS has been fully designed and is entering fabrication, with an operational instrument to be delivered in October, 2015.

  20. 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

  1. Distributed Compressive Sensing of Hyperspectral Images Using Low Rank and Structure Similarity Property

    NASA Astrophysics Data System (ADS)

    Huang, Bingchao; Xu, Ke; Wan, Jianwei; Liu, Xu

    2015-11-01

    An efficient method and system for distributed compressive sensing of hyperspectral images is presented, which exploit the low rank and structure similarity property of hyperspectral imagery. In this paper, by integrating the respective characteristics of DSC and CS, a distributed compressive sensing framework is proposed to simultaneously capture and compress hyperspectral images. At the encoder, every band image is measured independently, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. It is simple to operate and easy to hardware implementation. At the decoder, each band image is reconstructed by the method of total variation norm minimize. During each band reconstruction, the low rand structure of band images and spectrum structure similarity are used to give birth to the new regularizers. With combining the new regularizers and other regularizer, we can sufficiently exploit the spatial correlation, spectral correlation and spectral structural redundancy in hyperspectral imagery. A numerical optimization algorithm is also proposed to solve the reconstruction model by augmented Lagrangian multiplier method. Experimental results show that this method can effectively improve the reconstruction quality of hyperspectral images.

  2. A pixel to pixel hyperspectral synthetic image model inter-comparison study

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R., Jr.; Bassetti, Luce

    2006-09-01

    The purpose of this paper is to present simulation in order to compare a Hyperspectral Monte Carlo Model (MC) which generates synthetic images with realistic water wave surface to an iterative layered radiative transfer model used to generate hyperspectral synthetic images with realistic water wave surfaces. The MC model developed by Bostater and Gimond (2002) and Bostater and Chiang (2002) is divided into 5 steps: (1) Generation of the photons, (2) tracking of the photon optical path and simultaneously (3) recording of the photon's location within the water column, (4) then a tabulation of the sampling and its conversion to meaningful radiometric quantities and finally (5) a calculation and processing of the event probabilities between successive photons. This model will then be compared to the ILRT which is analytical and uses an iterative method to converge on the solution to a layered, iterative two flow radiative transfer model developed by (Bostater et al., 2002). The purpose of this research and the presentation will be to describe the effects of spectrally derived wave facets and the foam estimation coverage in order to assess the differences between the above modeling approaches, and to develop a better scientific understanding of the influence of water waves on the remote sensing signal from 400 to 750 nm, as well as the coupled influence of water waves and shallow bottom reflectance effects due to benthic aquatic habitat features such as submerged vegetation, corals, and other objects submerged within the water column as well as effects due to waves at the air-sea interface. The spectral wave models used include the wave (Phillips, Jonswap, Pierson-Moskowitz and TMA) that will help to simulate what a sensor sees from a low flying aircraft. In order to evaluate the wave models the Inverse Fast Fourier Transform (IFFT) is applied and results described.

  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. 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.

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

    NASA Astrophysics Data System (ADS)

    Hill, Samuel L.; Clemens, Peter

    2015-06-01

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

  6. Hyperspectral image classification for mapping agricultural tillage practices

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An efficient classification framework for mapping agricultural tillage practice using hyperspectral remote sensing imagery is proposed, which has the potential to be implemented practically to provide rapid, accurate, and objective surveying data for precision agricultural management and appraisal f...

  7. Hyperspectral imaging based procedures applied to bottom ash characterization

    NASA Astrophysics Data System (ADS)

    Bonifazi, Giuseppe; Serranti, Silvia

    2007-09-01

    Bottom ash from Municipal Solid Waste Incinerators (MSWIs) is mainly land filled or used as material for the foundation of road in European countries. Bottom ash is usually first crushed to below 40 mm and separated magnetically to recover the steel scrap. The remaining material contains predominantly sand, sinters and pieces of stone, glass and ceramics, which could be used as building material if strict technical and environmental requirements are respected. The main problem is the presence of residual organic matter in the ash and the large surface area presented by the fine fraction that creates leaching values, for elements such as copper, that are above the accepted levels for standard building materials. Main aim of the study was to evaluate the possibility offered by hyperspectral imaging to identify organic matter inside the residues in order to develop control/selection strategies to be implemented inside the bottom ash recycling plant. Reflectance spectra of selected bottom ash samples have been acquired in the VIS-NIR field (400- 1000 nm). Results showed as the organic content of the different samples influences the spectral signatures, in particular an inverse correlation between reflectance level and organic matter content was found.

  8. Optical hyperspectral imaging in microscopy and spectroscopy - a review of data acquisition.

    PubMed

    Gao, Liang; Smith, R Theodore

    2015-06-01

    Rather than simply acting as a photographic camera capturing two-dimensional (x, y) intensity images or a spectrometer acquiring spectra (?), a hyperspectral imager measures entire three-dimensional (x, y, ?) datacubes for multivariate analysis, providing structural, molecular, and functional information about biological cells or tissue with unprecedented detail. Such data also gives clinical insights for disease diagnosis and treatment. We summarize the principles underpinning this technology, highlight its practical implementation, and discuss its recent applications at microscopic to macroscopic scales. Datacube acquisition strategies in hyperspectral imaging x, y, spatial coordinates; ?, wavelength. PMID:25186815

  9. 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.

  10. A long-wave infrared hyperspectral sensor for Shadow class UAVs

    NASA Astrophysics Data System (ADS)

    Lucey, P. G.; Akagi, Jason T.; Hinrichs, John L.; Crites, S. T.; Wright, R.

    2013-05-01

    The University of Hawaii has developed a concept to ruggedize an existing thermal infrared hyperspectral system for use in the NASA SIERRA UAV. The Hawaii Institute of Geophysics and Planetology has developed a suite of instruments that acquire high spectral resolution thermal infrared image data with low mass and power consumption by combining microbolometers with stationary interferometers, allowing us to achieve hyperspectral resolution (20 wavenumbers between 8 and 14 micrometers), with signal to noise ratios as high as 1500:1. Several similar instruments have been developed and flown by our research group. One recent iteration, developed under NASA EPSCoR funding and designed for inclusion on a microsatellite (Thermal Hyperspectral Imager; THI), has a mass of 11 kg. Making THI ready for deployment on the SIERRA will involve incorporating improvements made in building nine thermal interferometric hyperspectral systems for commercial and government sponsors as part of HIGP's wider program. This includes: a) hardening the system for operation in the SIERRA environment, b) compact design for the calibration system, c) reconfiguring software for autonomous operation, d) incorporating HIGP-developed detectors with increased responsiveness at the 8 micron end of the TIR range, and e) an improved interferometer to increase SNR for imaging at the SIERRA's air speed. UAVs provide a unique platform for science investigations that the proposed instrument, UAVTHI, will be well placed to facilitate (e.g. very high temporal resolution measurements of temporally dynamic phenomena, such as wildfires and volcanic ash clouds). Its spectral range is suited to measuring gas plumes, including sulfur dioxide and carbon dioxide, which exhibit absorption features in the 8 to 14 micron range.

  11. Detection of mechanical injury on pickling cucumbers using near-infrared hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Ariana, D.; Lu, R.; Guyer, D.

    2005-11-01

    Automated detection of defects on freshly harvested pickling cucumbers will help the pickle industry provide higher 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 mechanical stress. A near-infrared hyperspectral imaging system was used to capture both spatial and spectral information from cucumbers in the spectral region of 900 - 1700 nm. The system consisted of an imaging spectrograph attached to an InGaAs camera with line-light fiber bundles as an illumination source. Cucumber samples were subjected to two forms of mechanical loading, dropping and rolling, to simulate stress caused by mechanical harvesting. Hyperspectral images were acquired from the cucumbers over time periods of 0, 1, 2, 3, and 6 days after mechanical stress. Hyperspectral image processing methods, including principal component analysis and wavelength selection, were developed to separate normal and mechanically injured cucumbers. Results showed that reflectance from normal or non-bruised cucumbers was consistently higher than that from bruised cucumbers. The spectral region between 950 and 1350 nm was found to be most effective for bruise detection. The hyperspectral imaging system detected all mechanically injured cucumbers immediately after they were bruised. The overall detection accuracy was 97% within two hours of bruising and it was lower as time progressed. Lower detection accuracies for the prolonged times after bruising were attributed to the self- healing of the bruised tissue after mechanical injury. This research demonstrated that hyperspectral imaging is useful for detecting mechanical injury on pickling cucumbers.

  12. Handling large datasets of hyperspectral images: reducing data size without loss of useful information.

    PubMed

    Ferrari, Carlotta; Foca, Giorgia; Ulrici, Alessandro

    2013-11-13

    Hyperspectral Imaging (HSI) is gaining increasing interest in the field of analytical chemistry, since this fast and non-destructive technique allows one to easily acquire a large amount of spectral and spatial information on a wide number of samples in very short times. However, the large size of hyperspectral image data often limits the possible uses of this technique, due to the difficulty of evaluating many samples altogether, for example when one needs to consider a representative number of samples for the implementation of on-line applications. In order to solve this problem, we propose a novel chemometric strategy aimed to significantly reduce the dataset size, which allows to analyze in a completely automated way from tens up to hundreds of hyperspectral images altogether, without losing neither spectral nor spatial information. The approach essentially consists in compressing each hyperspectral image into a signal, named hyperspectrogram, which is created by combining several quantities obtained by applying PCA to each single hyperspectral image. Hyperspectrograms can then be used as a compact set of descriptors and subjected to blind analysis techniques. Moreover, a further improvement of both data compression and calibration/classification performances can be achieved by applying proper variable selection methods to the hyperspectrograms. A visual evaluation of the correctness of the choices made by the algorithm can be obtained by representing the selected features back into the original image domain. Likewise, the interpretation of the chemical information underlying the selected regions of the hyperspectrograms related to the loadings is enabled by projecting them in the original spectral domain. Examples of applications of the hyperspectrogram-based approach to hyperspectral images of food samples in the NIR range (1000-1700 nm) and in the vis-NIR range (400-1000 nm), facing a calibration and a defect detection issue respectively, demonstrate the effectiveness of the proposed approach. PMID:24176502

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

    PubMed Central

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

    2014-01-01

    Abstract. 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. PMID:25277147

  14. TWO-STAGE DENOISING METHOD FOR HYPERSPECTRAL IMAGES COMBINING KPCA AND TOTAL VARIATION

    E-print Network

    Pizurica, Aleksandra

    , image noise is unavoidably present, which can affect information re- trieval and content interpretation.g. classification, target detection, unmixing, etc. Many techniques were reported for noise reduction in hy the spectral dimension and noise in hyperspectral images. Othman and Qian [2] proposed a hybrid spatial

  15. Kernel Methods in Orthogonalization of Near-Infrared Hyperspectral Images of

    E-print Network

    background. Application areas include hyperspectral NIR images for food quality control, analysis of simple multispectral data in terms of image quality with implications for noise removal," IEEE Transactions- versity Press, 2004. [4] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. [5] W. H

  16. A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on leafy greens

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Ensuring the supply of safe, contaminant free fresh fruit and vegetables is of importance to consumers, suppliers and governments worldwide. In this study, three hyperspectral imaging (HSI) configurations coupled with two multivariate image analysis techniques are compared for detection of fecal con...

  17. 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 ...

  18. 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...

  19. Sub-pixel Hyperspectral Image Analysis using Piece-wise Convex Spectral Unmixing

    E-print Network

    Pivovarov, Peter

    for detecting and classifying dif- ferent materials in an image scene down to the sub-pixel level. Sub-pixelSub-pixel Hyperspectral Image Analysis using Piece-wise Convex Spectral Unmixing Alina Zare (MU and explosive object detection, trace explosives detection and medical applications such as tissue or cell

  20. 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.

  1. Feasibility of detecting Aflatoxin B1 in single maize kernels using hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The feasibility of detecting Aflatoxin B1 (AFB1) in single maize kernel inoculated with Aspergillus flavus conidia in the field, as well as its spatial distribution in the kernels, was assessed using near-infrared hyperspectral imaging (HSI) technique. Firstly, an image mask was applied to a pixel-b...

  2. 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...

  3. 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...

  4. 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...

  5. 4904 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 12, DECEMBER 2015 Unsupervised Unmixing of Hyperspectral Images

    E-print Network

    Dobigeon, Nicolas

    . However, the endmembers are assumed random to consider their variability in the image. An additive noise. Index Terms--Hyperspectral imagery, endmember variability, image classification, spectral unmixing4904 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 12, DECEMBER 2015 Unsupervised Unmixing

  6. 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.

  7. Person detection in hyperspectral images via skin segmentation using an active learning approach

    NASA Astrophysics Data System (ADS)

    Marqués, Ion; Graña, Manuel; Sanchez, Stephanie M.; Alkhatib, Mohammed Q.; Velez-Reyes, Miguel

    2015-05-01

    Human skin detection is a computer vision problem that has been widely researched in color images. In this article we deal with this task as an interactive segmentation problem in hyperspectral outdoor images. We have focused on the problem of skin identification in hyperspectral cameras allowing a fine sampling of the light spectrum, so that the information gathered at each pixel is a high dimensional vector. The problem is treated as a classification problem, where we make use of active learning strategies to provide an interactive robust solution reaching high accuracy in a short training/testing cycle.

  8. 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.

  9. Quantification and spatial characterization of moisture and NaCl content of Iberian dry-cured ham slices using NIR hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging technology is increasingly regarded as a powerful tool for the classification and spatial quantification of a wide range of agrofood product properties. Taking into account the difficulties involved in validating hyperspectral calibrations, the models constructed here proved mo...

  10. An FPGA implementation of image space reconstruction algorithm for hyperspectral imaging analysis

    NASA Astrophysics Data System (ADS)

    Morales, Javier; Santiago, Nayda G.; Fernandez, Alejandro

    2007-04-01

    The Image Space Reconstruction Algorithm (ISRA) has been used in hyperspectral imaging applications to monitor changes in the environment and specifically, changes in coral reef, mangrove, and sand in coastal areas. This algorithm is one of a set of iterative methods used in the hyperspectral imaging area to estimate abundance. However, ISRA is highly computational, making it difficult to obtain results in a timely manner. We present the use of specialized hardware in the implementation of this algorithm, specifically the use of VHDL and FPGAs in order to reduce the execution time. The implementation of ISRA algorithm has been divided into hardware and software units. The hardware units were implemented on a Xilinx Virtex II Pro XC2VP30 FPGA and the software was implemented on the Xilinx Microblaze soft processor. This case study illustrates the feasibility of this alternative design for iterative hyperspectral imaging algorithms. The main bottleneck found in this implementations was data transfer. In order to reduce or eliminate this bottleneck we introduced the use of block-rams (BRAMS) to buffer data and have data readily available to the ISRA algorithm. The memory combination of DDR and BRAMS improved the speed of the implementation. Results demonstrate that the C language implementation is better than both FPGA's implementations. Nevertheless, taking a detailed look at the improvements in the results, FPGA results are similar to results obtained in the C language implementation and could further be improved by adding memory capabilities to the FPGA board. Results obtained with these two implementations do not have significant differences in terms of execution time.

  11. 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.

  12. Target detection algorithm for airborne thermal hyperspectral data

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  13. SYSTEMATIC APPROACH FOR USING HYPERSPECTRAL IMAGING DATA TO DEVELOP MULTISPECTRAL IMAGINING SYSTEMS: DETECTION OF FECES ON APPLES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The large size of data sets generated using hyperspectral imaging techniques significantly increases both the ability and difficulty of designing detection and classification systems. Of particular interest is the confluence with increasing use of multispectral imaging in machine vision, particularl...

  14. 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

  15. 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

  16. 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

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

    PubMed

    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. PMID:24898603

  18. Line-scan hyperspectral imaging for real-time poultry fecal detection

    NASA Astrophysics Data System (ADS)

    Park, Bosoon; Yoon, Seung-Chul; Windham, William R.; Lawrence, Kurt C.; Heitschmidt, G. W.; Kim, Moon S.; Chao, Kaunglin

    2010-04-01

    The ARS multispectral imaging system with three-band common aperture camera was able to inspect fecal contaminants in real-time mode during poultry processing. Recent study has demonstrated several image processing methods including binning, cuticle removal filter, median filter, and morphological analysis in real-time mode could remove false positive errors. The ARS research groups and their industry partner are now merging the fecal detection and systemically disease detection systems onto a common platform using line-scan hyperspectral imaging system. This system will aid in commercialization by creating one hyperspectral imaging system with user-defined wavelengths that can be installed in different locations of the processing line to solve significant food safety problems. Therefore, this research demonstrated the feasibility of line-scan hyperspectral imaging system in terms of processing speed and detection accuracy for a real-time, on-line fecal detection at current processing speed (140 birds per minute) of commercial poultry plant. The newly developed line-scan hyperspectral imaging system could improve Food Safety Inspection Service (FSIS)'s poultry safety inspection program significantly.

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

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  20. Multispectral MWIR imaging sensor

    NASA Astrophysics Data System (ADS)

    Svensson, Thomas; Renhorn, Ingmar G. E.

    2003-01-01

    Multispectral data in the mid-wave spectral region can provide significant information for the characterization of both manmade objects and natural background. Processing multi-spectral images is still in its infancy, complicated by the fact that spectral characteristics change from place to place in the scene. A multispectral high performance imaging sensor for the spectral region 1.5 - 5.2 ´m has been specified and obtained from AEG INFRAROT-MODULE GmbH. The instrument is equipped with a spinning filter wheel containing four filters closely related to the atmospheric windows. The spectral bands are also selected to distinguish between reflected and emitted radiation and also to distinguish between targets of different temperature and emissivity. The full frame rate is 100 Hz, and up to 800 Hz is possible using sub-windowing. Applications include missile warning, reconnaissance, surveillance, tracking and identification.

  1. Mapping the land cover in coastal Gabes oases using the EO-1 HYPERION hyperspectral sensor

    NASA Astrophysics Data System (ADS)

    Ben-Arfa, Jouda; Bergès, Jean Claude; Beltrando, Gérard; Rim, Katlane; Zargouni, Fouad

    2015-04-01

    Gabes region is characterized by unique maritime oases in Mediterranean basin. Unfortunately these oases are sensitive areas due to a harsh competition for land and water between different user groups (urban, industry, agriculture). An industrial complex is now located in center of this region, cultivation practices have shifted from a traditional multi-layer plant association system and moreover the Gabes city itself is expanding in the very core of oases. The oases of Gabes are transformed into city oases; they undergo multiform interactions whose amplify their environmental dynamic. A proper management of this environment should be based on a fine cartography of land use and remote sensing plays a major role in this issue. However the use of legacy natural resource remote sensing data is disappointing. The crop production strategies rely on a fine scale ground split among various uses and the ground resolution of these satellites is not adequate. Our study relies on hyperspectral images in order to cartography oases boundaries and land use. We tested the potential of Hyperion hyperspectral satellite imagery for mapping dynamics oases covered. We have the opportunity to access EO1/Hyperion data on seven different dates on 2009 and 2010. This dataset allows us to compare various hyperspectral based processing both on the basis on information pertinence and time stability. In this frame some index appear as significantly efficient: cellulose index, vegetation mask, water presence index. On another side spectral unmixing looks as more sensitive to slight ground changes. These results raise the issue of compared interest of enhancing spatial resolution versus spectral resolution. Whereas high resolution ground observation satellites are obviously more appropriate for visual recognition process, reliable information could be extracted from hyperspectral information through a fully automatic process.

  2. Validation of ocean color sensors using a profiling hyperspectral radiometer

    NASA Astrophysics Data System (ADS)

    Ondrusek, M. E.; Stengel, E.; Rella, M. A.; Goode, W.; Ladner, S.; Feinholz, M.

    2014-05-01

    Validation measurements of satellite ocean color sensors require in situ measurements that are accurate, repeatable and traceable enough to distinguish variability between in situ measurements and variability in the signal being observed on orbit. The utility of using a Satlantic Profiler II equipped with HyperOCR radiometers (Hyperpro) for validating ocean color sensors is tested by assessing the stability of the calibration coefficients and by comparing Hyperpro in situ measurements to other instruments and between different Hyperpros in a variety of water types. Calibration and characterization of the NOAA Satlantic Hyperpro instrument is described and concurrent measurements of water-leaving radiances conducted during cruises are presented between this profiling instrument and other profiling, above-water and moored instruments. The moored optical instruments are the US operated Marine Optical BuoY (MOBY) and the French operated Boussole Buoy. In addition, Satlantic processing versions are described in terms of accuracy and consistency. A new multi-cast approach is compared to the most commonly used single cast method. Analysis comparisons are conducted in turbid and blue water conditions. Examples of validation matchups with VIIRS ocean color data are presented. With careful data collection and analysis, the Satlantic Hyperpro profiling radiometer has proven to be a reliable and consistent tool for satellite ocean color validation.

  3. 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 and at the San Jose Lagoon (SJL), the results of the survey reflected presence of toxic sediments deposited TP concentration in the lagoon waters. A water quality model was used to verify the spectral results

  4. 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.

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

    PubMed Central

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

    2014-01-01

    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

  6. Pushbroom hyperspectral imaging system with selectable region of interest for medical imaging.

    PubMed

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2015-04-01

    A spatial-scanning pushbroom hyperspectral imaging (HSI) system incorporating a video camera (VC) which is not only used for direct video imaging but also for the selection of the region of interest within the VC’s full field-of-view is presented. Using a VC for these two applications brings many benefits to a pushbroom HSI system, such as a minimized data acquisition time and smaller data storage requirement. A detailed description of the system followed by the methods and formulas used for calibration and electronic hardware interfacing were discussed and analyzed using United States Air Force resolution chart, chicken breast tissue, and fluorescent targets as test samples. The proposed concepts and developed system can find potential biomedical imaging applications and can be extended to endoscopic imaging applications as well. PMID:25900146

  7. 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.

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

    SciTech Connect

    Love, Steven P

    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.

  9. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 4, APRIL 2013 1267 Nonlinearity Detection in Hyperspectral Images

    E-print Network

    Dobigeon, Nicolas

    IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 4, APRIL 2013 1267 Nonlinearity Detection a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated

  10. Fast Hyperspectral Imaging Using a Mid-Infrared Tunable External Cavity Quantum Cascade Laser

    SciTech Connect

    Phillips, Mark C.; Ho, Nicolas

    2008-04-23

    An active hyperspectral imaging system using an external cavity quantum cascade laser and a focal plane array acquiring images at 25 Hz from 985 cm-1 to 1075 cm-1 with a resolution of 0.3 cm 1 is demonstrated. The chemical imaging of gases is demonstrated in both static and dynamic cases. The system was also used to analyze liquid and solid samples.

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. DETECTION OF INTERNAL DEFECT IN PICKLING CUCUMBERS USING HYPERSPECTRAL TRANSMITTANCE IMAGING

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Internal quality is an important aspect in the quality control and assurance of pickled products. A rapid and nondestructive method for internal defect detection would be of value to the pickle industry. A hyperspectral transmittance imaging technique was developed to detect internal defect in the ...

  16. 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 ...

  17. APPLICATION OF HYPERSPECTRAL FLUORESCENCE IMAGING FOR DETECTION OF SKIN TUMORS ON CHICKEN CARCASSES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper describes a method for detecting skin tumors on chicken carcasses. It utilizes both spectral and spatial information in hyperspectral fluorescence images to increase detection rate in a synergistic manner. Since real-time processing is a key issue in implementation, the proposed method ...

  18. 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.

  19. 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...

  20. 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...

  1. 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...

  2. Classification of gram-positive and gram-negative foodborne pathogenic bacteria with hyperspectral microscope imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Optical method with hyperspectral microscope imaging (HMI) has potential for identification of foodborne pathogenic bacteria from microcolonies rapidly with a cell level. A HMI system that provides both spatial and spectral information could be an effective tool for analyzing spectral characteristic...

  3. Acousto-Optic Tunable Filter Hyperspectral Microscope Imaging Method for Characterizing Spectra from Foodborne Pathogens.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral microscope imaging (HMI) method, which provides both spatial and spectral characteristics of samples, 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 Salmon...

  4. Characterization of Lung Tissues using Liquid-Crystal Tunable Filter and Hyperspectral Imaging System

    E-print Network

    Won, Chang-Hee

    Characterization of Lung Tissues using Liquid-Crystal Tunable Filter and Hyperspectral Imaging to characterize lung tissue for detecting emphysematous tissues in lung volume reduction surgery. The system, the spectral signature of healthy lung tissue and simulated smokers lung tissue is obtained and compared

  5. Internal defect detection of whole pickles using hyperspectral reflectance and transmittance imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging technique in simultaneous reflectance and transmittance modes was investigated for detection of hollow or bloater damage on whole pickles that was caused by mechanical injury during harvesting and handling or developed during the brining process. Normal and bloated pickle sampl...

  6. Online screening of fruits and vegetables using hyperspectral line-scan imaging techniques

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Over the last fifteen years, myriads of hyperspectral research studies have focused and reported on finding spectral imaging approaches suitable for potential online implementation to evaluate food and agricultural samples of interest. ARS researchers in Beltsville, Maryland, pioneered the online im...

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

    E-print Network

    Plaza, Antonio J.

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

  8. Comparison between visible/ NIR spectroscopy and hyperspectral imaging for detecting surface contaminants on poultry carcasses

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The U. S. Department of Agriculture, Agricultural Research Service has been developing a method and system to detect fecal contamination on processed poultry carcasses with hyperspectral and multispectral imaging systems. The patented method utilizes a three step approach to contaminant detection. S...

  9. 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

  10. 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...

  11. 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...

  12. 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...

  13. 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....

  14. 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...

  15. 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 ...

  16. 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...

  17. Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model

    E-print Network

    Dobigeon, Nicolas

    unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear1 Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model if the observed pixel results from the commonly used linear mixing model or from a more general nonlinear mixture

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Widespread implementation of precision agriculture will require methods for efficiently and economically characterizing variations in soil properties and other factors that affect crop yields. In this research the potential of using airborne hyperspectral images to estimate within-field soil variab...

  19. Near-infrared hyperspectral imaging for detecting Aflatoxin B1 of maize kernels

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The feasibility of detecting the Aflatoxin B1 in maize kernels inoculated with Aspergillus flavus conidia in the field was assessed using near-infrared hyperspectral imaging technique. After pixel-level calibration, wavelength dependent offset, the masking method was adopted to reduce the noise and ...

  20. 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...

  1. 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

  2. Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant.

    PubMed

    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

  3. 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

  4. Hyperspectral imaging-based wound analysis using mixture-tuned matched filtering classification method

    NASA Astrophysics Data System (ADS)

    Calin, Mihaela Antonina; Coman, Toma; Parasca, Sorin Viorel; Bercaru, Nicolae; Savastru, Roxana; Manea, Dragos

    2015-04-01

    Hyperspectral imaging is a technology that is beginning to occupy an important place in medical research with good prospects in future clinical applications. We evaluated the role of hyperspectral imaging in association with a mixture-tuned matched filtering method in the characterization of open wounds. The methodology and the processing steps of the hyperspectral image that have been performed in order to obtain the most useful information about the wound are described in detail. Correlations between the hyperspectral image and clinical examination are described, leading to a pattern that permits relative evaluation of the square area of the wound and its different components in comparison with the surrounding normal skin. Our results showed that the described method can identify different types of tissues that are present in the wounded area and can objectively measure their respective abundance, which proves its value in wound characterization. In conclusion, the method that was described in this preliminary case presentation shows promising results, but needs further evaluation in order to become a reliable and useful tool.

  5. 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...

  6. 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.

  7. GPU implementation of target and anomaly detection algorithms for remotely sensed hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Paz, Abel; Plaza, Antonio

    2010-08-01

    Automatic target and anomaly detection are considered very important tasks for hyperspectral data exploitation. These techniques are now routinely applied in many application domains, including defence and intelligence, public safety, precision agriculture, geology, or forestry. Many of these applications require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. However, with the recent explosion in the amount and dimensionality of hyperspectral imagery, this problem calls for the incorporation of parallel computing techniques. In the past, clusters of computers have offered an attractive solution for fast anomaly and target detection in hyperspectral data sets already transmitted to Earth. However, these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time, i.e., at the same time as the data is collected by the sensor. An exciting new development in the field of commodity computing is the emergence of commodity graphics processing units (GPUs), which can now bridge the gap towards on-board processing of remotely sensed hyperspectral data. In this paper, we describe several new GPU-based implementations of target and anomaly detection algorithms for hyperspectral data exploitation. The parallel algorithms are implemented on latest-generation Tesla C1060 GPU architectures, and quantitatively evaluated using hyperspectral data collected by NASA's AVIRIS system over the World Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in the WTC complex.

  8. Spectral measurements of muzzle flash with multispectral and hyperspectral sensor

    NASA Astrophysics Data System (ADS)

    Kastek, M.; Dulski, R.; Trzaskawka, P.; Pi?tkowski, T.; Polakowski, H.

    2011-08-01

    The paper presents some practical aspects of the measurements of muzzle flash signatures. Selected signatures of sniper shot in typical scenarios has been presented. Signatures registered during all phases of muzzle flash were analyzed. High precision laboratory measurements were made in a special ballistic laboratory and as a result several flash patterns were registered. The field measurements of a muzzle flash were also performed. During the tests several infrared cameras were used, including the measurement class devices with high accuracy and frame rates. The registrations were made in NWIR, SWIR and LWIR spectral bands simultaneously. An ultra fast visual camera was also used for visible spectra registration. Some typical infrared shot signatures were presented. Beside the cameras, the LWIR imaging spectroradiometer HyperCam was also used during the laboratory experiments and the field tests. The signatures collected by the HyperCam device were useful for the determination of spectral characteristics of the muzzle flash, whereas the analysis of thermal images registered during the tests provided the data on temperature distribution in the flash area. As a result of the measurement session the signatures of several types handguns, machine guns and sniper rifles were obtained which will be used in the development of passive infrared systems for sniper detection.

  9. 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.

  10. 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.

  11. Detection of fruit fly infestation in pickling cucumbers using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Lu, Renfu; Ariana, Diwan P.

    2011-06-01

    Fruit fly infestation can be a serious problem in pickling cucumber production. In the United States and many other countries, there is zero tolerance for fruit flies in pickled products. Currently, processors rely on manual inspection to detect and remove fruit fly-infested cucumbers, which is labor intensive and also prone to error due to human fatigue and the difficulty of visually detecting infestation that is hidden inside the fruit. In this research, a laboratory hyperspectral imaging system was used to detect fruit fly-infested pickling cucumbers. Hyperspectral reflectance (450-740 nm) and transmittance (740-1,000 nm) images were acquired simultaneously for 329 normal (infestation free) and fruit flyinfested pickling cucumbers of three size classes with the mean diameters of 16.8, 22.1, and 27.6 mm, respectively. Mean spectra were extracted from the hyperspectral image of each cucumber, and they were then corrected for the fruit size effect using a diameter correction equation. Partial least squares discriminant analyses for the reflectance, transmittance and their combined data were performed for differentiating normal and infested pickling cucumbers. With reflectance mode, the overall classification accuracies for the three size classes and mixed class were between 82% and 88%, whereas transmittance achieved better classification results with the overall accuracies of 88%-93%. Integration of reflectance and transmittance did not result in noticeable improvements, compared to transmittance mode. Overall, the hyperspectral imaging system performed better than manual inspection, which had an overall accuracy of 75% and decreased significantly for smaller size cucumbers. This research demonstrated that hyperspectral imaging is potentially useful for detecting fruit fly-infested pickling cucumbers.

  12. 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.

  13. [Application of Hyperspectral Imaging for Visualization of Nitrogen Content in Pepper Leaf with Different Positions].

    PubMed

    Yu, Ke-qiang; Zhao, Yan-ru; Li, Xiao-li; Ding, Xi-bin; Zhuang, Zai-chun; He, Yong

    2015-03-01

    In order to estimate pepper plant growth rapidly and accurately, hyperspectral imaging technology combined with chemometrics methods were employed to realize visualization of nitrogen content (NC) distribution. First, pepper leaves were picked up with the leaf number based on different leaf positions, and hyperspectral data of these leaves were acquired. Then, SPAD and NC value of leaves were measured, respectively. After acquirement of pepper leaves' spectral information, random-frog (RF) algorithm was chosen to extract characteristic wavelengths. Finally, five characteristic wavelengths were selected respectively, and then those characteristic wavelengths and full spectra were used to establish partial least squares regression (PLSR) models, respectively. As a result, SPAD predicted model had an excellent performance of R(C) = 0.970, R(CV) = 0.965, R(P) = 0.934, meanwhile evaluation parameters of NC predicted model were R(C) = 0.857, R(CV) = 0.806, R(P) = 0.839. Lastly, according to the optimal models, SPAD and NC of each pixel in hyperspectral images of pepper leaves were calculated and their distribution was mapped. In fact, SPAD in plant can reflect the NC. In this research, the change trend of both was similar, so the conclusions of this research were proved to be corrected. The results revealed that it was feasible to apply hyperspectral imaging technology for mapping SPAD and NC in pepper leaf, which provided a theoretical foundation for monitoring plant growth and distribution of nutrients. PMID:26117891

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Confocal Raman microscopy and hyperspectral dark field microscopy imaging of chemical and biological systems

    NASA Astrophysics Data System (ADS)

    Toma, Henrique E.; da Silva Shinohara, Jorge; Grasseschi, Daniel

    2015-03-01

    Hyperspectral imaging can provide accurate information on the distribution of the chemical species in materials and biological samples, based on the analysis of their electronic and vibrational profiles. In special, confocal Raman microscopy is one of the best ways to access the chemical distribution of molecules, especially under resonance Raman or SERS conditions. On the other hand, enhanced dark field optical microscopy can be employed for hyperspectral imaging in the visible and near-IR region, while extending the optical resolution up to the nanoscale dimension. It allows the detection of gold or silver single nanoparticles, as well as spectral monitoring from the characteristic surface plasmon bands. These two hyperspectral microscopies can be conveniently combined to provide nanoscale electronic and vibrational information of the species present in a wide variety of chemical and biological systems. A case study focusing on the improvement of the classical spot-test analysis of nickel(II) ions with dithizone is here detailed. A great enhancement of sensitivity in the detection of nickel(II) ions, by at least 4 orders of magnitude, has been observed in this work. Hyperspectral measurements allowed the mapping of the gold nanoparticles (AuNP) distribution on cellulose fibers and on glass, and the evaluation of their extinction and SERS spectra for analytical purposes.

  20. Evaluation of cross-polarized near infrared hyperspectral imaging for early detection of dental caries

    NASA Astrophysics Data System (ADS)

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

    2012-01-01

    Despite major improvements in dental healthcare and oral hygiene, dental caries remains one of the most prevalent oral diseases and represents the primary cause of oral pain and tooth loss. The initial stages of dental caries are characterized by demineralization of enamel crystals and are difficult to diagnose. Near infrared (NIR) hyperspectral imaging is a new promising technique for detection of early changes in the surfaces of carious teeth. This noninvasive imaging technique can characterize and differentiate between the sound tooth surface and initial or advanced tooth caries. The absorbing and scattering properties of dental tissues reflect in distinct spectral features, which can be measured, quantified and used to accurately classify and map different dental tissues. Specular reflections from the tooth surface, which appear as bright spots, mostly located around the edges and the crests of the teeth, act as a noise factor which can significantly interfere with the spectral measurements and analysis of the acquired images, degrading the accuracy of the classification and diagnosis. Employing cross-polarized imaging setup can solve this problem, however has yet to be systematically evaluated, especially in broadband hyperspectral imaging setups. In this paper, we employ cross-polarized illumination setup utilizing state-of-the-art high-contrast broadband wire-grid polarizers in the spectral range from 900 nm to 1700 nm for hyperspectral imaging of natural and artificial carious lesions of various degrees.

  1. Automatic image analysis and spot classification for detection of fruit fly infestation in hyperspectral images of mangoes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An algorithm has been developed to identify spots generated in hyperspectral images of mangoes infested with fruit fly larvae. The algorithm incorporates background removal, application of a Gaussian blur, thresholding, and particle count analysis to identify locations of infestations. Each of the f...

  2. Construction of filter vectors for the information-efficient spectral imaging sensor

    SciTech Connect

    Stallard, B.R.; Gentry, S.M.

    1998-12-01

    The information-efficient spectral imaging sensor (ISIS) seeks to improve system performance by processing hyperspectral information in the optical hardware. Its output may be a gray scale image in which one attempts to maximize the contrast between a given target and the background. Alternatively, its output may be a small number of images, rather than a full data cube, that retain the essential information required in the application. The principal advantage of ISIS is that it offers the discrimination of hyperspectral imaging while achieving the signal-to-noise ratio of multispectral imaging. The paper focuses on construction of the filter vectors that are needed to program ISIS. The instrument produces an image which is essentially a dot product of the scene and the filter vector. Both single vector and multiple vector approaches are considered. Also, they discuss some subtle points related to optimizing the filter vectors.

  3. 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

  4. Multi sensor satellite imagers for commercial remote sensing

    NASA Astrophysics Data System (ADS)

    Cronje, T.; Burger, H.; Du Plessis, J.; Du Toit, J. F.; Marais, L.; Strumpfer, F.

    2005-10-01

    This paper will discuss and compare recent refractive and catodioptric imager designs developed and manufactured at SunSpace for Multi Sensor Satellite Imagers with Panchromatic, Multi-spectral, Area and Hyperspectral sensors on a single Focal Plane Array (FPA). These satellite optical systems were designed with applications to monitor food supplies, crop yield and disaster monitoring in mind. The aim of these imagers is to achieve medium to high resolution (2.5m to 15m) spatial sampling, wide swaths (up to 45km) and noise equivalent reflectance (NER) values of less than 0.5%. State-of-the-art FPA designs are discussed and address the choice of detectors to achieve these performances. Special attention is given to thermal robustness and compactness, the use of folding prisms to place multiple detectors in a large FPA and a specially developed process to customize the spectral selection with the need to minimize mass, power and cost. A refractive imager with up to 6 spectral bands (6.25m GSD) and a catodioptric imager with panchromatic (2.7m GSD), multi-spectral (6 bands, 4.6m GSD), hyperspectral (400nm to 2.35?m, 200 bands, 15m GSD) sensors on the same FPA will be discussed. Both of these imagers are also equipped with real time video view finding capabilities. The electronic units could be subdivided into the Front-End Electronics and Control Electronics with analogue and digital signal processing. A dedicated Analogue Front-End is used for Correlated Double Sampling (CDS), black level correction, variable gain and up to 12-bit digitizing and high speed LVDS data link to a mass memory unit.

  5. 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.

  6. Parsimonious model development for real-time monitoring of moisture in red meat using hyperspectral imaging.

    PubMed

    Kamruzzaman, Mohammed; Makino, Yoshio; Oshita, Seiichi

    2016-04-01

    A hyperspectral imaging system in the spectral range of 400-1000nm was investigated to develop a multispectral real-time imaging system allowing the meat industry to determine moisture content in red meat including beef, lamb, and pork. Multivariate calibration models were developed using partial least-squares regression (PLSR) and least-squares support vector machines (LS-SVM) in the full spectral range. Instead of selection of different sets of feature wavelengths for beef, lamb, and pork, a set of 10 feature wavelengths was selected for convenient industrial application for the determination of moisture content in red meat. A quantitative linear function was then established using MLR based on these key feature wavelengths for predicting moisture content of red meat in an online system and creating moisture distribution maps. The results reveal that the combination of hyperspectral imaging and multivariate has great potential in the meat industry for real-time determination of moisture content. PMID:26593592

  7. 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.

  8. Hyperspectral imaging utilizing LCTF and DLP technology for surgical and clinical applications

    NASA Astrophysics Data System (ADS)

    Zuzak, Karel J.; Francis, Robert P.; Wehner, Eleanor F.; Smith, Jack; Litorja, Maritoni; Allen, David W.; Tracy, Chad; Cadeddu, Jeffrey; Livingston, Edward

    2009-02-01

    Two different, already characterized, hyperspectral imaging systems created for visualizing the spatial distribution of tissue oxygenation non-invasively for in vivo clinical use are described. Individual components of both liquid crystal tunable filter (LCTF) and digital light processing (DLP) systems were characterized, calibrated, and found to be well within manufacturer specifications. Coupling LCTF with charge coupled device (CCD) technology and acquiring images at multiple, contiguous wavelengths and at narrow bandwidths are formatted into a hyperspectral data cube consisting of one spectral and two spatial dimensions. DLP® technology has the novel ability to conform light to any desired spectral illumination scheme. Subsequently the collected multispectral data are processed into chemically relevant images that are color encoded at each pixel detector for the relative percentage of oxyhemoglobin. Using spectral illumination methods unique to the DLP hyperspectral imager results in producing chemically relevant images at near video rate; 4 frames per second. As an example, both systems are used to collect spectral data from a 27.22 kg porcine kidney whose renal artery has been occluded for 60 minutes. Both systems return nearly identical spectra collected from the surface of the kidney, with a root mean square deviation between the two spectra of 0.02.

  9. Development of the hyperspectral cellular imaging system to apply to regenerative medicine

    NASA Astrophysics Data System (ADS)

    Ishihara, Miya; Sato, Masato; Matsumura, Kouji; Mochida, Joji; Kikuchi, Makoto

    2010-02-01

    Regenerative medicine by the transplantation of differentiated cells or tissue stem cells has been clinically performed, particularly in the form of cell sheets. To ensure the safety and effectiveness of cell therapy, the efficient selection of desired cells with high quality is a critical issue, which requires the development of a new evaluation method to discriminate cells non-invasively with high throughput. There were many ways to characterize cells and their components, among which the optical spectral analysis has a powerful potential for this purpose. We developed a cellular hyperspectral imaging system, which captured both spatial and spectral information in a single pixel. Hyperspectral data are composed of continual spectral bands, whereas multispectral data are usually composed of about 5 to 10 discrete bands of large bandwidths. The hyperspectral imaging system which we developed was set up by a commonly-used inverted light microscope for cell culture experiments, and the time-lapse imaging system with automatic focus correction. Spectral line imaging device with EMCCD was employed for spectral imaging. The system finally enabled to acquire 5 dimensional (x, y, z, time, wavelength) data sets and cell-by-cell evaluation. In this study, we optimized the protocol for the creation of cellular spectral database under biological understanding. We enabled to confirm spectrum of autofluorescence of collagen, absorption of specific molecules in the cultural sample and increase of scattering signal due to cell components although detail spectral analyses have not been performed.

  10. Characterizing Hyperspectral Imagery (AVIRIS) Using Fractal Technique

    NASA Technical Reports Server (NTRS)

    Qiu, Hong-Lie; Lam, Nina Siu-Ngan; Quattrochi, Dale

    1997-01-01

    With the rapid increase in hyperspectral data acquired by various experimental hyperspectral imaging sensors, it is necessary to develop efficient and innovative tools to handle and analyze these data. The objective of this study is to seek effective spatial analytical tools for summarizing the spatial patterns of hyperspectral imaging data. In this paper, we (1) examine how fractal dimension D changes across spectral bands of hyperspectral imaging data and (2) determine the relationships between fractal dimension and image content. It has been documented that fractal dimension changes across spectral bands for the Landsat-TM data and its value [(D)] is largely a function of the complexity of the landscape under study. The newly available hyperspectral imaging data such as that from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) which has 224 bands, covers a wider spectral range with a much finer spectral resolution. Our preliminary result shows that fractal dimension values of AVIRIS scenes from the Santa Monica Mountains in California vary between 2.25 and 2.99. However, high fractal dimension values (D > 2.8) are found only from spectral bands with high noise level and bands with good image quality have a fairly stable dimension value (D = 2.5 - 2.6). This suggests that D can also be used as a summary statistics to represent the image quality or content of spectral bands.

  11. Mapping of TBARS distribution in frozen-thawed pork using NIR hyperspectral imaging.

    PubMed

    Wu, Xiang; Song, Xinglin; Qiu, Zhengjun; He, Yong

    2016-03-01

    In this study, NIR hyperspectral imaging technology was applied to determine the distribution of TBARS in frozen-thawed pork. A total of 240 fresh pork samples were assigned to 4 treatment groups (0, 1, 3, 5 frozen-thawed cycles). For each sample, a hyperspectral image (874-1734nm) was collected, followed by chemical TBARS analysis. Successive projection algorithm (SPA) was applied to choose effective wavelengths (EWs). The selected 13 EWs of the calibration set and relevant TBARS value were used as inputs of partial least squares regression (PLSR) model, yielding correlation coefficient of prediction of 0.81 and root mean square error of prediction of 0.33. The developed PLSR model were applied pixel-wise to produce chemical maps of TBARS for 24 selected samples in the prediction set. The results indicated that NIR hyperspectral imaging combined with image processing has the potential to visualize TBARS distribution in frozen-thawed pork. This technique could be useful in real-time quality monitoring in meat industry. PMID:26630204

  12. 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.

  13. Unsupervised mis-registration noise estimation in multi-temporal hyperspectral images

    NASA Astrophysics Data System (ADS)

    Resta, Salvatore; Acito, Nicola; Diani, Marco; Corsini, Giovanni

    2012-11-01

    In this work, we focus on Anomalous Change Detection (ACD), whose goal is the detection of small changes occurred between two hyperspectral images (HSI) of the same scene. When data are collected by airborne platforms, perfect registration between images is very difficult to achieve, and therefore a residual mis-registration (RMR) error should be taken into account in developing ACD techniques. Recently, the Local Co-Registration Adjustment (LCRA) approach has been proposed to deal with the performance reduction due to the RMR, providing excellent performance in ACD tasks. In this paper, we propose a method to estimate the first and second order statistics of the RMR. The RMR is modeled as a unimodal bivariate random variable whose mean value and covariance matrix have to be estimated from the data. In order to estimate the RMR statistics, a feature description of each image is provided in terms of interest points extending the Scale Invariant Feature Transform (SIFT) algorithm to hyperspectral images, and false matches between descriptors belonging to different features are filtered by means of a highly robust estimator of multivariate location, based on the Minimum Covariance Determinant (MCD) algorithm. In order to assess the performance of the method, an experimental analysis has been carried out on a real hyperspectral dataset with high spatial resolution. The results highlighted the effectiveness of the proposed approach, providing reliable and very accurate estimation of the RMR statistics.

  14. Multivariate curve resolution for hyperspectral image analysis :applications to microarray technology.

    SciTech Connect

    Van Benthem, Mark Hilary; Sinclair, Michael B.; Haaland, David Michael; Martinez, M. Juanita (University of New Mexico, Albuquerque, NM); Timlin, Jerilyn Ann; Werner-Washburne, Margaret C. (University of New Mexico, Albuquerque, NM); Aragon, Anthony D. (University of New Mexico, Albuquerque, NM)

    2003-01-01

    Multivariate curve resolution (MCR) using constrained alternating least squares algorithms represents a powerful analysis capability for a quantitative analysis of hyperspectral image data. We will demonstrate the application of MCR using data from a new hyperspectral fluorescence imaging microarray scanner for monitoring gene expression in cells from thousands of genes on the array. The new scanner collects the entire fluorescence spectrum from each pixel of the scanned microarray. Application of MCR with nonnegativity and equality constraints reveals several sources of undesired fluorescence that emit in the same wavelength range as the reporter fluorphores. MCR analysis of the hyperspectral images confirms that one of the sources of fluorescence is due to contaminant fluorescence under the printed DNA spots that is spot localized. Thus, traditional background subtraction methods used with data collected from the current commercial microarray scanners will lead to errors in determining the relative expression of low-expressed genes. With the new scanner and MCR analysis, we generate relative concentration maps of the background, impurity, and fluorescent labels over the entire image. Since the concentration maps of the fluorescent labels are relatively unaffected by the presence of background and impurity emissions, the accuracy and useful dynamic range of the gene expression data are both greatly improved over those obtained by commercial microarray scanners.

  15. Large size MOEMS Fabry-Perot interferometer filter for focal plane array hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Chee, J.; Hwu, J.; Kim, T. S.; Kubby, J.; Velicu, S.; Gupta, N.

    2015-02-01

    Focal plane array (FPA) technology is mature and is widely used for imaging applications. However, FPAs have broadband responses which limit their ability to provide high performance in hyperspectral applications such as detection of buried explosives, and identifying the presence of explosive chemicals and their concentrations. EPIR is currently developing Micro-Opto-Electro-Mechanical System (MOEMS) Fabry-Perot interferometer filter (FPF) devices for FPAs. In this paper, we present our approach to MOEMS FPF design and fabrication that will meet the size requirements for large format FPA hyperspectral imaging. We also report the performance of our FPF resonance cavity, capable of up to 3 ?m change gap in tens of nanometer increments.

  16. 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

  17. 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.

  18. A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination

    NASA Astrophysics Data System (ADS)

    Tan, Kun; Hu, Jun; Li, Jun; Du, Peijun

    2015-07-01

    In the process of semi-supervised hyperspectral image classification, spatial neighborhood information of training samples is widely applied to solve the small sample size problem. However, the neighborhood information of unlabeled samples is usually ignored. In this paper, we propose a new algorithm for hyperspectral image semi-supervised classification in which the spatial neighborhood information is combined with classifier to enhance the classification ability in determining the class label of the selected unlabeled samples. There are two key points in this algorithm: (1) it is considered that the correct label should appear in the spatial neighborhood of unlabeled samples; (2) the combination of classifier can obtains better results. Two classifiers multinomial logistic regression (MLR) and k-nearest neighbor (KNN) are combined together in the above way to further improve the performance. The performance of the proposed approach was assessed with two real hyperspectral data sets, and the obtained results indicate that the proposed approach is effective for hyperspectral classification.

  19. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging.

    PubMed

    Xie, Chuanqi; Shao, Yongni; Li, Xiaoli; He, Yong

    2015-01-01

    This study investigated the potential of using hyperspectral imaging for detecting different diseases on tomato leaves. One hundred and twenty healthy, one hundred and twenty early blight and seventy late blight diseased leaves were selected to obtain hyperspectral images covering spectral wavelengths from 380 to 1023?nm. An extreme learning machine (ELM) classifier model was established based on full wavelengths. Successive projections algorithm (SPA) was used to identify the most important wavelengths. Based on the five selected wavelengths (442, 508, 573, 696 and 715?nm), an ELM model was re-established. Then, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) at the five effective wavelengths were extracted to establish detection models. Among the models which were established based on spectral information, all performed excellently with the overall classification accuracy ranging from 97.1% to 100% in testing sets. Among the eight texture features, dissimilarity, second moment and entropy carried most of the effective information with the classification accuracy of 71.8%, 70.9% and 69.9% in the ELM models. The results demonstrated that hyperspectral imaging has the potential as a non-invasive method to identify early blight and late blight diseases on tomato leaves. PMID:26572857

  20. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging

    PubMed Central

    Xie, Chuanqi; Shao, Yongni; Li, Xiaoli; He, Yong

    2015-01-01

    This study investigated the potential of using hyperspectral imaging for detecting different diseases on tomato leaves. One hundred and twenty healthy, one hundred and twenty early blight and seventy late blight diseased leaves were selected to obtain hyperspectral images covering spectral wavelengths from 380 to 1023?nm. An extreme learning machine (ELM) classifier model was established based on full wavelengths. Successive projections algorithm (SPA) was used to identify the most important wavelengths. Based on the five selected wavelengths (442, 508, 573, 696 and 715?nm), an ELM model was re-established. Then, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) at the five effective wavelengths were extracted to establish detection models. Among the models which were established based on spectral information, all performed excellently with the overall classification accuracy ranging from 97.1% to 100% in testing sets. Among the eight texture features, dissimilarity, second moment and entropy carried most of the effective information with the classification accuracy of 71.8%, 70.9% and 69.9% in the ELM models. The results demonstrated that hyperspectral imaging has the potential as a non-invasive method to identify early blight and late blight diseases on tomato leaves. PMID:26572857

  1. 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

  2. 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.

  3. Image-based occupancy sensor

    DOEpatents

    Polese, Luigi Gentile; Brackney, Larry

    2015-05-19

    An image-based occupancy sensor includes a motion detection module that receives and processes an image signal to generate a motion detection signal, a people detection module that receives the image signal and processes the image signal to generate a people detection signal, a face detection module that receives the image signal and processes the image signal to generate a face detection signal, and a sensor integration module that receives the motion detection signal from the motion detection module, receives the people detection signal from the people detection module, receives the face detection signal from the face detection module, and generates an occupancy signal using the motion detection signal, the people detection signal, and the face detection signal, with the occupancy signal indicating vacancy or occupancy, with an occupancy indication specifying that one or more people are detected within the monitored volume.

  4. Infrared hyperspectral tunable filter imaging spectrometer for remote leak detection, chemical speciation, and stack/vent analysis applications

    NASA Astrophysics Data System (ADS)

    Hinnrichs, Michele

    2002-02-01

    With support from the Department of Energy, the State of California and the Gas Technology Institute, Pacific Advanced Technology is developing a small field portable infrared imaging spectrometer (Sherlock) based on the advances in hyperspectral tunable filter technology, that will be applied to the detection of fugitive gas leaks. This imaging spectrometer uses the Image Multi-spectral Sensing (IMSS) diffractive optic tunable filter invented by Pacific Advanced Technology . The Sherlock has an embedded digital signal processor for real time detection of the gas leak while surrounded by severe background noise. The infrared sensor engine is a 256 x 320 midwave cooled focal plane array which spans the spectral range from 3 to 5 microns, ideal for most hydrocarbon leaks. The technology is by no means limited to this spectral region, and can just as easily work in the longwave infrared from 8 to 12 microns for chemical detection applications. This paper will present the design of the Sherlock camera as well as processed data collected at a gas processing plant and an instrumented kiln at LSU using the prototype camera. The processed data shows that the IMSS imaging spectrometer, using an all passive approach, has the sensitivity to detect methane gas leaks at short range with a flow rate as low as 0.01 scfm2. In addition, the IMSS imaging spectrometer can measure hot gas plumes at longer ranges. As will be shown in this paper the IMSS can detect and image warm species gas additives of methane and propane in the Kiln exhaust stack. The methane injected gas with a concentration of 72 ppm and the propane with a concentration of 49 ppm (as seen by the IMSS sensor) at a range of 60 meters. The atmospheric path was a stressing environment, being hot and humid, for any imaging infrared spectrometer.

  5. Instrumentation challenges of a pushbroom hyperspectral imaging system for currency counterfeit applications

    NASA Astrophysics Data System (ADS)

    Lim, Hoong-Ta; Vadakke Matham, Murukeshan

    2015-07-01

    Hyperspectral imaging allows the intensity of narrow and adjacent spectral bands over a large spectral range to be recorded, giving rich spectral information for each pixel in the imaged region. The spectral characteristics of each point in the imaged region can thus be detected, which is useful for quantification and classification. Hyperspectral imaging has been used in many applications such as remote sensing, quality assessment of agro-food products, biomedical imaging and document counterfeit application. This paper presents a pushbroom spatial-scanning imager, which gives a higher spectral resolution over a broad spectral range. Although a spatial-scanning imager may be slower due to the need to perform mechanical scanning, such a high spectral resolution is especially important in applications where the capability to perform classification is much more important than speed. The application of this system is demonstrated for currency counterfeit detection applications. The high spectral resolution of a pushbroom imager is able to capture fine spectral details of the samples used in this research, providing important information required for classification. Using this technique, the reflectance is acquired from specific regions of a genuine and counterfeit note. The spectra of the same region from both notes are then compared to distinguish and delineate the differences between them. The spectrum acquired from a genuine note can then be used as a reference from which future comparison can be based upon for identifying currency counterfeit and related relevant applications.

  6. Automated Registration Of Images From Multiple Sensors

    NASA Technical Reports Server (NTRS)

    Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.; Pang, Shirley S. N.

    1994-01-01

    Images of terrain scanned in common by multiple Earth-orbiting remote sensors registered automatically with each other and, where possible, on geographic coordinate grid. Simulated image of terrain viewed by sensor computed from ancillary data, viewing geometry, and mathematical model of physics of imaging. In proposed registration algorithm, simulated and actual sensor images matched by area-correlation technique.

  7. Recovery of macular pigment spectrum in vivo using hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Fawzi, Amani A.; Lee, Noah; Acton, Jennifer H.; Laine, Andrew F.; Smith, R. Theodore

    2011-10-01

    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.

  8. Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis

    NASA Astrophysics Data System (ADS)

    Zhu, Fengle; Yao, Haibo; Hruska, Zuzana; Kincaid, Russell; Brown, Robert; Bhatnagar, Deepak; Cleveland, Thomas

    2015-05-01

    Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive. This study employed fluorescence and reflectance visible near-infrared (VNIR) hyperspectral images to classify aflatoxin contaminated corn kernels rapidly and non-destructively. Corn ears were artificially inoculated in the field with toxigenic A. flavus spores at the early dough stage of kernel development. After harvest, a total of 300 kernels were collected from the inoculated ears. Fluorescence hyperspectral imagery with UV excitation and reflectance hyperspectral imagery with halogen illumination were acquired on both endosperm and germ sides of kernels. All kernels were then subjected to chemical analysis individually to determine aflatoxin concentrations. A region of interest (ROI) was created for each kernel to extract averaged spectra. Compared with healthy kernels, fluorescence spectral peaks for contaminated kernels shifted to longer wavelengths with lower intensity, and reflectance values for contaminated kernels were lower with a different spectral shape in 700-800 nm region. Principal component analysis was applied for data compression before classifying kernels into contaminated and healthy based on a 20 ppb threshold utilizing the K-nearest neighbors algorithm. The best overall accuracy achieved was 92.67% for germ side in the fluorescence data analysis. The germ side generally performed better than endosperm side. Fluorescence and reflectance image data achieved similar accuracy.

  9. Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions

    NASA Astrophysics Data System (ADS)

    Tuia, Devis; Flamary, Rémi; Courty, Nicolas

    2015-07-01

    In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters. We define an active set feature learner that includes in the model only features that improve the classifier. To this end, we consider a fast and linear classifier, multiclass logistic classification, and show that with a good representation (the filters discovered), such a simple classifier can reach at least state of the art performances. We apply the proposed active set learner in four hyperspectral image classification problems, including agricultural and urban classification at different resolutions, as well as multimodal data. We also propose a hierarchical setting, which allows to generate more complex banks of features that can better describe the nonlinearities present in the data.

  10. 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.

  11. Support vector machine with adaptive composite kernel for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Li, Wei; Du, Qian

    2015-05-01

    With the improvement of spatial resolution of hyperspectral imagery, it is more reasonable to include spatial information in classification. The resulting spectral-spatial classification outperforms the traditional hyperspectral image classification with spectral information only. Among many spectral-spatial classifiers, support vector machine with composite kernel (SVM-CK) can provide superior performance, with one kernel for spectral information and the other for spatial information. In the original SVM-CK, the spatial information is retrieved by spatial averaging of pixels in a local neighborhood, and used in classifying the central pixel. Obviously, not all the pixels in such a local neighborhood may belong to the same class. Thus, we investigate the performance of Gaussian lowpass filter and an adaptive filter with weights being assigned based on the similarity to the central pixel. The adaptive filter can significantly improve classification accuracy while the Gaussian lowpass filter is less time-consuming and less sensitive to the window size.

  12. Photogrammetry & Machine Vision 1. Image sensors

    E-print Network

    Giger, Christine

    Photogrammetry & Machine Vision 1. Image sensors (a) Fundamentals of image sensors (b) CCD image. Remondino, N. D'Apuzzo Photogrammetry and Machine Vision ­ 1. Measurement in images (b) Camera calibration of Photogrammetry and Machine Vision Fully understand: 1. Image based 3D and 4D measurement 2. Image based 3D

  13. Hyperspectral imaging in the quality control of herbal medicines - the case of neurotoxic Japanese star anise.

    PubMed

    Vermaak, Ilze; Viljoen, Alvaro; Lindström, Susanne Wiklund

    2013-03-01

    Illicium verum (Chinese star anise) dried fruit is popularly used as a remedy to treat infant colic. However, instances of life-threatening adverse events in infants have been recorded after use, in some cases due to substitution and/or adulteration of I. verum with Illicium anisatum (Japanese star anise), which is toxic. It is evident that rapid and efficient quality control methods are of utmost importance to prevent re-occurrence of such dire consequences. The potential of short wave infrared (SWIR) hyperspectral imaging and image analysis as a rapid quality control method to distinguish between I. anisatum and I. verum whole dried fruit was investigated. Images were acquired using a sisuChema SWIR hyperspectral pushbroom imaging system with a spectral range of 920-2514 nm. Principal component analysis (PCA) was applied to the images to reduce the high dimensionality of the data, remove unwanted background and to visualise the data. A classification model with 4 principal components and an R²X_cum of 0.84 and R²Y_cum of 0.81 was developed for the 2 species using partial least squares discriminant analysis (PLS-DA). The model was subsequently used to accurately predict the identity of I. anisatum (98.42%) and I. verum (97.85%) introduced into the model as an external dataset. The results show that SWIR hyperspectral imaging is an objective and non-destructive quality control method that can be successfully used to identify whole dried fruit of I. anisatum and I. verum. In addition, this method has the potential to detect I. anisatum whole dried fruits within large batches of I. verum through upscaling to a conveyor belt system. PMID:23277152

  14. Active infrared hyperspectral imaging system using a broadly tunable optical parametric oscillator

    NASA Astrophysics Data System (ADS)

    Malcolm, G. P. A.; Maker, G. T.; Robertson, G.; Dunn, M. H.; Stothard, D. J. M.

    2009-09-01

    The in situ identification and spatial location of gases, discrete liquid droplets and residues on surfaces is a technically challenging problem. Active Infrared (IR) hyperspectral imaging is a powerful technique that combines real-time imaging and optical spectroscopy for "standoff" detection of suspected chemical substances, including chemical warfare agents, toxic industrial chemicals, explosives and narcotics. An active IR hyperspectral imaging system requires a coherent, broadly tunable IR light source of high spectral purity, in order to detect a broad range of target substances. In this paper we outline a compact and power-efficient IR illumination source with high stability, efficiency, tuning range and spectral purity based upon an optical parametric oscillator (OPO). The fusion of established OPO technology with novel diode-pumped laser technology and electro-mechanical scanning has enabled a broadly applicable imaging system. This system is capable of hyperspectral imaging at both Near-IR (1.3 - 1.9 ?m) and Mid-IR (2.3 - 4.6 ?m) wavelengths simultaneously with a line width of < 3 cm-1. System size and complexity are minimised by using a dual InGaAs/InSb single element detector, and images are acquired by raster scanning the coaxial signal and idler beams simultaneously, at ranges up to 20 m. Reflection, absorption and scatter of incident radiation by chemical targets and their surroundings provide a method for spatial location, and characteristic spectra obtained from each sample can be used to identify targets uniquely. To date, we have recognized liquids in sample sizes as small 20 ?l-and gases with sensitivity as high as 10ppm.m-at detection standoff distances > 10 m.

  15. Weighted Chebyshev distance classification method for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Demirci, S.; Erer, I.; Ersoy, O.

    2015-06-01

    The main objective of classification is to partition the surface materials into non-overlapping regions by using some decision rules. For supervised classification, the hyperspectral imagery (HSI) is compared with the reflectance spectra of the material containing similar spectral characteristic. As being a spectral similarity based classification method, prediction of different level of upper and lower spectral boundaries of all classes spectral signatures across spectral bands constitutes the basic principles of the Multi-Scale Vector Tunnel Algorithm (MS-VTA) classification algorithm. The vector tunnel (VT) scaling parameters obtained from means and standard deviations of the class references are used. In this study, MS-VT method is improved and a spectral similarity based technique referred to as Weighted Chebyshev Distance (WCD) method for the supervised classification of HSI is introduced. This is also shown to be equivalent to the use of the WCD in which the weights are chosen as an inverse power of the standard deviation per spectral band. The use of WCD measures in terms of the inverse power of standard deviations and optimization of power parameter constitute the most important side of the study. The algorithms are trained with the same kinds of training sets, and their performances are calculated for the power of the standard deviation. During these studies, various levels of the power parameters are evaluated based on the efficiency of the algorithms for choosing the best values of the weights.

  16. Impact of Feature Reduction Techniques on Target Detection Methods for Hyperspectral Image Classification by Multiple Classifier System

    NASA Astrophysics Data System (ADS)

    Bhushan, Bharath; Rao Nidamanuri, Rama

    2012-07-01

    Hyperspectral imaging provides with immense amount of data while offering possibilities for detailed information extraction from remote sensing data. Feature reduction techniques have been used to eliminate redundant information and reduce high dimensionality that is typical to hyperspectral data. Apart from the data capability, accuracy of hyperspectral image classifications depend upon the ability of the classifiers to trade on the relationship between available information classes, training samples, and the complexity of the classifier itself. The best land cover maps can be achieved by combing different feature reduction techniques and classifiers. Multiple classifier system has evolved as new pattern recognition method to overcome the limitations of single classifier. The objective of this work was to evaluate the impact of various feature reduction techniques on the performance of a multiple classifier system (MCS) designed for classifying a hyperspectral image for discriminating various land cover categories. The widely used three categories of target detection methods- spectral matching methods, covariance based methods and subspace model are used as the base classifiers in the MCS. The impact of feature reduction techniques on the MCS was analyzed in terms of overall accuracy supported by two-tail statistical significance test with 95% confidence interval. The relationship and sensitivity between the target detection methods and the feature reduction techniques are discussed. The experimental analysis shows that there is a need for adaptive techniques to achieve the best classification maps. Key words: hyperspectral image, feature extraction methods, multiple classifier system, image classification, target detection methods.

  17. Stimulated Raman hyperspectral imaging based on spectral filtering of broadband fiber laser pulses.

    PubMed

    Ozeki, Yasuyuki; Umemura, Wataru; Sumimura, Kazuhiko; Nishizawa, Norihiko; Fukui, Kiichi; Itoh, Kazuyoshi

    2012-02-01

    We demonstrate a technique of hyperspectral imaging in stimulated Raman scattering (SRS) microscopy using a tunable optical filter, whose transmission wavelength can be varied quickly by a galvanometer mirror. Experimentally, broadband Yb fiber laser pulses are synchronized with picosecond Ti:sapphire pulses, and then spectrally filtered out by the filter. After amplification by fiber amplifiers, we obtain narrowband pulses with a spectral width of <3.3 cm(-1) and a wavelength tunability of >225 cm(-1). By using these pulses, we accomplish SRS imaging of polymer beads with spectral information. PMID:22297376

  18. Method of selecting the best classification bands from hyperspectral images based on genetic algorithm and rough set

    NASA Astrophysics Data System (ADS)

    Sun, Lixin; Gao, Wen

    1998-08-01

    Selecting the best classification bands from hyperspectral images for particular remote sensing application is one of the most important problems in utilizing hyperspectral images. In this paper, the best classification bands selection problem is regarded as optimal feature subset selection problem and the bands in original bands set are divided into redundant and irrelevant. In order to eliminate these two type bands, a multi-level optimal classification bands selection model from hyperspectral images based on genetic algorithm and rough set theory is proposed. Through the initial two steps of the multi-level model, the dimension reduction step and the genetic algorithm based filter step, most of redundant and irrelevant bands are deleted from the original images bands set. From the machine learning perspective, the multi-level model can take both advantages of the filter and wrapper models.

  19. Optical coherence tomography and hyperspectral imaging of vascular recovery in a model of peripheral arterial disease

    NASA Astrophysics Data System (ADS)

    Poole, Kristin M.; Sit, Wesley W.; Tucker-Schwartz, Jason M.; Duvall, Craig L.; Skala, Melissa C.

    2013-03-01

    Peripheral arterial disease (PAD) leads to an increased risk of myocardial infarction and stroke, increased mortality, and reduced quality of life. The mouse hind limb ischemia (HLI) model is the most commonly used system for studying the mechanisms of collateral vessel formation and for testing new PAD therapies, but there is a lack of techniques for acquiring physiologically-relevant, quantitative data intravitally in this model. In this work, non-invasive, quantitative optical imaging techniques were applied to the mouse HLI model over a time course. Optical coherence tomography (OCT) imaged changes in blood flow (Doppler OCT) and microvessel morphology (speckle variance OCT) through the skin of haired mice with high resolution. Hyperspectral imaging was also used to quantify blood oxygenation. In ischemic limbs, blood oxygenation in the footpad was substantially reduced after induction of ischemia followed by complete recovery by three weeks, consistent with standard measures. Three dimensional images of the vasculature distal to vessel occlusion acquired with speckle variance OCT revealed changes in OCT flow signal and vessel morphology. Taken together, OCT and hyperspectral imaging enable intravital acquisition of both functional and morphological data which fill critical gaps in understanding structure-function relationships that contribute to recovery in the mouse HLI model. Therefore, these optical imaging methods hold promise as tools for studying the mechanisms of vascular recovery and evaluating novel therapeutic treatments in preclinical studies.

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

    NASA Astrophysics Data System (ADS)

    Jun, Won; Lee, Kangjin; Millner, Patricia; Sharma, Manan; Chao, Kuanglin; Kim, Moon S.

    2008-04-01

    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 used in the manufacture of food processing equipment. Stainless steel coupons were immersed in bacterium cultures, such as E. coli, Pseudomonas pertucinogena, Erwinia chrysanthemi, and Listeria innocula. Following a 1-week exposure, biofilm formations were assessed using fluorescence imaging. In addition, the effects on biofilm formation from both tryptic soy broth (TSB) and M9 medium with casamino acids (M9C) were examined. TSB grown cells enhance biofilm production compared with M9C-grown cells. Hyperspectral fluorescence images of the biofilm samples, in response to ultraviolet-A (320 to 400 nm) excitation, were acquired from approximately 416 to 700 nm. Visual evaluation of individual images at emission peak wavelengths in the blue revealed the most contrast between biofilms and stainless steel coupons. Two-band ratios compared with the single-band images increased the contrast between the biofilm forming area and stainless steel coupon surfaces. The 444/588 nm ratio images exhibited the greatest contrast between the biofilm formations and stainless coupon surfaces.

  1. DMD based hyperspectral augmentation for multi-object tracking systems

    NASA Astrophysics Data System (ADS)

    Neumann, Jonathan G.

    2009-02-01

    Current wide area pan-chromatic or dual band persistent surveillance systems often do not provide enough observable data to maintain accurate and unique tracks of multiple objects over a large field of regard. Previous experiments have shown augmentation with hyperspectral imagery can enhance tracking performance. However, hyperspectral imagers have significantly slower coverage rates than persistent surveillance systems, essentially because a hyperspectral pixel contains vector data while a standard image pixel is typically either scalar or RGB. This coverage rate gap is a fundamental mismatch between the two systems and presents a technological hurdle to the practical use of hyperspectral imagers for tracking multiple objects spread over the entire field of regard of persistent surveillance systems. In this non-mapping hyperspectral application, we assume much of the information in a hyperspectral data cube is superfluous background and need not be processed or even collected. The effective coverage rate of the hyperspectral imager can be made compatible with modern persistent surveillance systems, at least for the objects of interest, by using a DMD to judiciously select which data are collected and processed. A proof-of-concept sensor has been developed and preliminary results are presented.

  2. Data processing method applying principal component analysis and spectral angle mapper for imaging spectroscopic sensors

    NASA Astrophysics Data System (ADS)

    García-Allende, P. B.; Conde, O. M.; Mirapeix, J.; Cubillas, A. M.; López-Higuera, J. M.

    2007-07-01

    A data processing method for hyperspectral images is presented. Each image contains the whole diffuse reflectance spectra of the analyzed material for all the spatial positions along a specific line of vision. This data processing method is composed of two blocks: data compression and classification unit. Data compression is performed by means of Principal Component Analysis (PCA) and the spectral interpretation algorithm for classification is the Spectral Angle Mapper (SAM). This strategy of classification applying PCA and SAM has been successfully tested on the raw material on-line characterization in the tobacco industry. In this application case the desired raw material (tobacco leaves) should be discriminated from other unwanted spurious materials, such as plastic, cardboard, leather, candy paper, etc. Hyperspectral images are recorded by a spectroscopic sensor consisting of a monochromatic camera and a passive Prism- Grating-Prism device. Performance results are compared with a spectral interpretation algorithm based on Artificial Neural Networks (ANN).

  3. Wavelet-based nearest-regularized subspace for noise-robust hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Li, Wei; Liu, Kui; Su, Hongjun

    2014-01-01

    A wavelet-based nearest-regularized-subspace classifier is proposed for noise-robust hyperspectral image (HSI) classification. The nearest-regularized subspace, coupling the nearest-subspace classification with a distance-weighted Tikhonov regularization, was designed to only consider the original spectral bands. Recent research found that the multiscale wavelet features [e.g., extracted by redundant discrete wavelet transformation (RDWT)] of each hyperspectral pixel are potentially very useful and less sensitive to noise. An integration of wavelet-based features and the nearest-regularized-subspace classifier to improve the classification performance in noisy environments is proposed. Specifically, wealthy noise-robust features provided by RDWT based on hyperspectral spectrum are employed in a decision-fusion system or as preprocessing for the nearest-regularized-subspace (NRS) classifier. Improved performance of the proposed method over the conventional approaches, such as support vector machine, is shown by testing several HSIs. For example, the NRS classifier performed with an accuracy of 65.38% for the AVIRIS Indian Pines data with 75 training samples per class under noisy conditions (signal-to-noise ratio=36.87 dB), while the wavelet-based classifier can obtain an accuracy of 71.60%, resulting in an improvement of approximately 6%.

  4. Metro Maps of Plant Disease Dynamics—Automated Mining of Differences Using Hyperspectral Images

    PubMed Central

    Wahabzada, Mirwaes; Mahlein, Anne-Katrin; Bauckhage, Christian; Steiner, Ulrike; Oerke, Erich-Christian; Kersting, Kristian

    2015-01-01

    Understanding the response dynamics of plants to biotic stress is essential to improve management practices and breeding strategies of crops and thus to proceed towards a more sustainable agriculture in the coming decades. In this context, hyperspectral imaging offers a particularly promising approach since it provides non-destructive measurements of plants correlated with internal structure and biochemical compounds. In this paper, we present a cascade of data mining techniques for fast and reliable data-driven sketching of complex hyperspectral dynamics in plant science and plant phenotyping. To achieve this, we build on top of a recent linear time matrix factorization technique, called Simplex Volume Maximization, in order to automatically discover archetypal hyperspectral signatures that are characteristic for particular diseases. The methods were applied on a data set of barley leaves (Hordeum vulgare) diseased with foliar plant pathogens Pyrenophora teres, Puccinia hordei and Blumeria graminis hordei. Towards more intuitive visualizations of plant disease dynamics, we use the archetypal signatures to create structured summaries that are inspired by metro maps, i.e. schematic diagrams of public transport networks. Metro maps of plant disease dynamics produced on several real-world data sets conform to plant physiological knowledge and explicitly illustrate the interaction between diseases and plants. Most importantly, they provide an abstract and interpretable view on plant disease progression. PMID:25621489

  5. Evaluation of nitrogen content in cabbage seedlings using hyper-spectral images

    NASA Astrophysics Data System (ADS)

    Chen, Suming; Chen, Chia-Tseng; Wang, Ching-Yin; Yang, I.-Chang; Hsiao, Shih-Chieh

    2007-09-01

    Monitoring of nutrient status of crops is essential for better management of crop production. Nitrogen is one of the most important elements in fertilizer for the growth and yield of vegetable crops. In this study, nitrogen content of cabbage seedlings was evaluated using hyper-spectral images. Cabbage seedlings, cultured at five nitrogen fertilization levels, were planted in the 128-cell plug trays and grown in a phytotron at National Taiwan University. The images, ranged from 410 to 1090 nm, of cabbage seedlings were analyzed by a hyper-spectral imaging system consisting of CCD cameras with liquid crystal tunable filters (LCTF), which was developed in this study. The digital images of seedling canopies were processed including image segmentation, gray level calibration and absorbance conversion. Models including modified partial least square regression (MPLSR), step-wise multi-linear regression (SMLR) and artificial neural network with cross-learning strategy (ANN-CL) were developed for the determination of the nitrogen content in cabbage seedlings. The three significant wavelengths derived from SMLR model are 470, 710, and 1080; and the best result is obtained by ANN-CL model, in which r c=0.89, SEC=6.41 mg/g, r v=0.87, and SEV=6.96 mg/g. The ANN-CL model is more suitable for the remote sensing in precision agriculture applications because not only its model accuracy but also only 3 wavelengths are needed.

  6. New method for detection of gastric cancer by hyperspectral imaging: a pilot study

    NASA Astrophysics Data System (ADS)

    Kiyotoki, Shu; Nishikawa, Jun; Okamoto, Takeshi; Hamabe, Kouichi; Saito, Mari; Goto, Atsushi; Fujita, Yusuke; Hamamoto, Yoshihiko; Takeuchi, Yusuke; Satori, Shin; Sakaida, Isao

    2013-02-01

    We developed a new, easy, and objective method to detect gastric cancer using hyperspectral imaging (HSI) technology combining spectroscopy and imaging A total of 16 gastroduodenal tumors removed by endoscopic resection or surgery from 14 patients at Yamaguchi University Hospital, Japan, were recorded using a hyperspectral camera (HSC) equipped with HSI technology Corrected spectral reflectance was obtained from 10 samples of normal mucosa and 10 samples of tumors for each case The 16 cases were divided into eight training cases (160 training samples) and eight test cases (160 test samples) We established a diagnostic algorithm with training samples and evaluated it with test samples Diagnostic capability of the algorithm for each tumor was validated, and enhancement of tumors by image processing using the HSC was evaluated The diagnostic algorithm used the 726-nm wavelength, with a cutoff point established from training samples The sensitivity, specificity, and accuracy rates of the algorithm's diagnostic capability in the test samples were 78.8% (63/80), 92.5% (74/80), and 85.6% (137/160), respectively Tumors in HSC images of 13 (81.3%) cases were well enhanced by image processing Differences in spectral reflectance between tumors and normal mucosa suggested that tumors can be clearly distinguished from background mucosa with HSI technology.

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

    PubMed

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

    2015-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Debret, Maxime; Copard, Yoann; Van Exem, Antonin; Butz, Christoph; Vannière, Boris; Sabatier, Pierre; Grosjean, Martin; Reyss, Jean-Louis; Desmet, Marc

    2015-04-01

    Lacustrine sediments contain a wide range of proxies that permit paleoenvironmental reconstructions such as paleofire at very high temporal resolution. However, to achieve this, it is necessary to develop fast, non-destructive and high-resolution analysis methods. In this study, we develop a new fire proxy by studying a lacustrine core sampled in the Esterel Massif, SE France, an area that was affected by two recent fire events in 1987 and 2003. For this purpose, we searched for charcoal deposited and preserved in lake sediments by combining a number of complementary methods including: classical macrocharcoal tallying, scanning spectrophotometry and scanning hyperspectral image analyses. Macrocharcoal quantification is destructive and time-consuming, and only provides intermediate-resolution (1 cm) data. Spectrophotometry, used classically to quantify colour, is non-destructive, very fast and provides data with high resolution (1 mm according to the device). Hyperspectral data have the same advantages as spectrophotometry but offer higher spatial resolution (57-µm pixel size) with high spectral resolution (3 nm). Our study focused on a new fire proxy (hyperspectral index) obtained through hyperspectral investigations: the trough area method. The method involves the calculation of the area between the reflectance values and the continuum between 650 -700 nm, which corresponds to the quantification of a trough in red reflectance produced by chlorophyll a and its by-products. First derivative spectra allowed the quantification of red reflectance around 675 nm linked to chlorophyll a and its diagenetic products. Moreover, first derivative spectra show this wavelength is also affected by the presence of altered organic matter, because the reflectance at 675 nm decreases with organic matter alteration processes, such as combustion. The trough area method is suitable for detecting burned organic matter by quantifying the chloropyll signal dilution by charcoal signal. Thus, this adaptation of trough area could be applied in fire reconstruction studies.

  9. ENABLING EUROPA SCIENCE THROUGH ONBOARD FEATURE DETECTION IN HYPER-SPECTRAL IMAGES. M. Bunte1,2

    E-print Network

    ENABLING EUROPA SCIENCE THROUGH ONBOARD FEATURE DETECTION IN HYPER- SPECTRAL IMAGES. M. Bunte1 plains and dark linear features, the two major morpho- logical feature types for Europa. Spectra- sion lifespan. This is a particular concern for a future mission to Europa. Hyperspectral images might

  10. Fertility and embryo development of broiler hatching eggs evaluated with a hyperspectral imaging and predictive modeling system

    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 eggs were collected (12 fertile, 12 infertile) for each of 8 replicate trials (n=192) and imaged on Days 0, 1...

  11. Persistent hyperspectral adaptive multi-modal feature-aided tracking

    NASA Astrophysics Data System (ADS)

    Rice, Andrew C.; Vasquez, Juan R.; Kerekes, John; Mendenhall, Michael J.

    2009-05-01

    An architecture and implementation is presented regarding persistent, hyperspectral, adaptive, multi-modal, feature-aided tracking within the urban context. A novel remote-sensing imager has been designed which employs a micro-mirror array at the focal plane for per-pixel adaptation. A suite of end-to-end synthetic experiments have been conducted, which include high-fidelity moving-target urban vignettes, DIRSIG hyperspectral rendering, and full image-chain treatment of the prototype adaptive sensor. Corresponding algorithm development has focused on: motion segmentation, spectral feature modeling, classification, fused kinematic/spectral association, and adaptive sensor feedback/control.

  12. Use of Hyperspectral Images to Map Soil Carbon

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Rapid methods of measuring soil carbon such as near-infrared (NIR) spectroscopy have gained interest but problems of accurate and precise measurement still persist resulting from the high spatial variability. Tillage and airborne-based spectral sensors can provide means to capture the spatial distr...

  13. Analysis of reflectance spectra from hyperspectral images of poultry carcasses for fecal and ingesta detection

    NASA Astrophysics Data System (ADS)

    Windham, William R.; Lawrence, Kurt C.; Park, Bosoon; Smith, Doug P.; Poole, Gavin

    2002-11-01

    Identification and separation of poultry carcasses contaminated by feces and/or crop ingesta are very important to protect the consumer from a potential source of food poisoning. A transportable hyperspectral imaging system was developed to detect fecal and ingesta contamination on the surface of poultry carcasses. Detection algorithms used with the imaging system were developed from visible/near infrared monochromator spectra and with contaminates from birds fed a corn/soybean meal diet. The objectives of this study were to investigate using regions of interest reflectance spectra from hyperspectral images to determine optimal wavelengths for fecal detection algorithms from images of birds fed corn, wheat and milo diets. Spectral and spatial data between 400 and 900 nm with a 1.0 nm spectral resolution were acquired from uncontaminated and fecal and ingesta contaminated poultry carcasses. Regions of interest (ROIs) were defined for fecal and ingesta contaminated and uncontaminated skin (i.e. breast, thigh, and wing). Average reflectance spectra of the ROIs were extracted for analysis. Reflectance spectra of contaminants and uncontaminated skin differed. Spectral data pre-processing treatments with a single-term, linear regression program to select wavelengths for optimum calibration coefficients to detect contamination were developed. Fecal and ingesta detection models, specifically a quotient of 2 and/or 3-wavelengths was 100% successful in classification of contaminates.

  14. Passive shortwave infrared broadband and hyperspectral imaging in a maritime environment

    NASA Astrophysics Data System (ADS)

    Judd, K. Peter; Nichols, Jonathan M.; Howard, J. Grant; Waterman, James R.; Vilardebo, Kenneth M.

    2012-01-01

    This work offers a comparison of broadband shortwave infrared, defined as the spectral band from 0.9 to 1.7 ?m, and hyperspectral shortwave infrared imagers in a marine environment under various daylight conditions. Both imagers are built around a Raytheon Vision Systems large format (1024×1280) indium-gallium-arsenide focal plane array with high dynamic range and low noise electronics. Sample imagery from a variety of objects and scenes indicates roughly the same visual performance between the two systems. However, we show that the more detailed spectral information provided by the hyperspectral system allows for object detection and discrimination. A vessel was equipped with panels coated with a variety of paints that possessed spectral differences in the 0.9 to 1.7 ?m waveband. The vessel was imaged at various ranges, states of background clutter, and times of the day. Using a standard correlation receiver, it is demonstrated that image pixels containing the paint can be easily identified. During the exercise, it was also observed that both bow waves and near-field wakes from a wide variety of vessel traffic provide a spectral signature in the shortwave infrared waveband that could potentially be used for object tracking.

  15. Research on marine and freshwater fish identification model based on hyper-spectral imaging technology

    NASA Astrophysics Data System (ADS)

    Fu, Yan; Guo, Pei-yuan; Xiang, Ling-zi; Bao, Man; Chen, Xing-hai

    2013-08-01

    With the gradually mature of hyper spectral image technology, the application of the meat nondestructive detection and recognition has become one of the current research focuses. This paper for the study of marine and freshwater fish by the pre-processing and feature extraction of the collected spectral curve data, combined with BP network structure and LVQ network structure, a predictive model of hyper spectral image data of marine and freshwater fish has been initially established and finally realized the qualitative analysis and identification of marine and freshwater fish quality. The results of this study show that hyper spectral imaging technology combined with the BP and LVQ Artificial Neural Network Model can be used for the identification of marine and freshwater fish detection. Hyper-spectral data acquisition can be carried out without any pretreatment of the samples, thus hyper-spectral imaging technique is the lossless, high- accuracy and rapid detection method for quality of fish. In this study, only 30 samples are used for the exploratory qualitative identification of research, although the ideal study results are achieved, we will further increase the sample capacity to take the analysis of quantitative identification and verify the feasibility of this theory.

  16. Mapping of cellular iron using hyperspectral fluorescence imaging in a cellular model of Parkinson's disease

    NASA Astrophysics Data System (ADS)

    Oh, Eung Seok; Heo, Chaejeong; Kim, Ji Seon; Lee, Young Hee; Kim, Jong Min

    2013-05-01

    Parkinson's disease (PD) is characterized by progressive dopaminergic cell loss in the substantianigra (SN) and elevated iron levels demonstrated by autopsy and with 7-Tesla magnetic resonance imaging. Direct visualization of iron with live imaging techniques has not yet been successful. The aim of this study is to visualize and quantify the distribution of cellular iron using an intrinsic iron hyperspectral fluorescence signal. The 1-methyl-4-phenylpyridinium (MPP+)-induced cellular model of PD was established in SHSY5Y cells. The cells were exposed to iron by treatment with ferric ammonium citrate (FAC, 100 ?M) for up to 6 hours. The hyperspectral fluorescence imaging signal of iron was examined usinga high- resolution dark-field optical microscope system with signal absorption for the visible/ near infrared (VNIR) spectral range. The 6-hour group showed heavy cellular iron deposition compared with the small amount of iron accumulation in the 1-hour group. The cellular iron was dispersed in a small, particulate form, whereas extracellular iron was detected in an aggregated form. In addition, iron particles were found to be concentrated on the cell membrane/edge of shrunken cells. The cellular iron accumulation readily occurred in MPP+-induced cells, which is consistent with previous studies demonstrating elevated iron levels in the SN in PD. This direct iron imaging methodology could be applied to analyze the physiological role of iron in PD, and its application might be expanded to various neurological disorders involving other metals, such as copper, manganese or zinc.

  17. Dark-field hyperspectral X-ray imaging

    PubMed Central

    Egan, Christopher K.; Jacques, Simon D. M.; Connolley, Thomas; Wilson, Matthew D.; Veale, Matthew C.; Seller, Paul; Cernik, Robert J.

    2014-01-01

    In recent times, there has been a drive to develop non-destructive X-ray imaging techniques that provide chemical or physical insight. To date, these methods have generally been limited; either requiring raster scanning of pencil beams, using narrow bandwidth radiation and/or limited to small samples. We have developed a novel full-field radiographic imaging technique that enables the entire physio-chemical state of an object to be imaged in a single snapshot. The method is sensitive to emitted and scattered radiation, using a spectral imaging detector and polychromatic hard X-radiation, making it particularly useful for studying large dense samples for materials science and engineering applications. The method and its extension to three-dimensional imaging is validated with a series of test objects and demonstrated to directly image the crystallographic preferred orientation and formed precipitates across an aluminium alloy friction stir weld section. PMID:24808753

  18. Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy)

    PubMed Central

    Cavalli, Rosa Maria; Fusilli, Lorenzo; Pascucci, Simone; Pignatti, Stefano; Santini, Federico

    2008-01-01

    This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials.

  19. [Variety recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning].

    PubMed

    Cheng, Shu-Xi; Kong, Wen-Wen; Zhang, Chu; Liu, Fei; He, Yong

    2014-09-01

    The variety of Chinese cabbage seeds were recognized using hyperspectral imaging with 256 bands from 874 to 1,734 nm in the present paper. A total of 239 Chinese cabbage seed samples including 8 varieties were acquired by hyperspectral image system, 158 for calibration and the rest 81 for validation. A region of 15 pixel x 15 pixel was selected as region of interest (ROI) and the average spectral information of ROI was obtained as sample spectral information. Multiplicative scatter correction was selected as pretreatment method to reduce the noise of spectrum. The performance of four classification algorithms including Ada-boost algorithm, extreme learning machine (ELM), random forest (RF) and support vector machine (SVM) were examined in this study. In order to simplify the input variables, 10 effective wavelengths (EMS) including 1,002, 1,005, 1,015, 1,019, 1,022, 1,103, 1,106, 1,167, 1,237 and 1,409 nm were selected by analysis of variable load distribution in PLS model. The reflectance of effective wavelengths was taken as the input variables to build effective wavelengths based models. The results indicated that the classification accuracy of the four models based on full-spectral were over 90%, the optimal models were extreme learning machine and random forest, and the classification accuracy achieved 100%. The classification accuracy of effective wavelengths based models declined slightly but the input variables compressed greatly, the efficiency of data processing was improved, and the classification accuracy of EW-ELM model achieved 100%. ELM performed well both in full-spectral model and in effective wavelength based model in this study, it was proven to be a useful tool for spectral analysis. So rapid and nondestructive recognition of Chinese cabbage seeds by hyperspectral imaging combined with machine learning is feasible, and it provides a new method for on line batch variety recognition of Chinese cabbage seeds. PMID:25532356

  20. Peach maturity/quality assessment using hyperspectral imaging-based spatially resolved technique

    NASA Astrophysics Data System (ADS)

    Cen, Haiyan; Lu, Renfu; Mendoza, Fernando A.; Ariana, Diwan P.

    2011-06-01

    The objective of this research was to measure the absorption (?a) and reduced scattering coefficients (?s') of peaches, using a hyperspectral imaging-based spatially-resolved method, for their maturity/quality assessment. A newly developed optical property measuring instrument was used for acquiring hyperspectral reflectance images of 500 'Redstar' peaches. ?a and ?s' spectra for 515-1,000 nm were extracted from the spatially-resolved reflectance profiles using a diffusion model coupled with an inverse algorithm. The absorption spectra of peach fruit presented several absorption peaks around 525 nm for anthocyanin, 620 nm for chlorophyll-b, 675 nm for chlorophyll-a, and 970 nm for water, while ?s' decreased consistently with the increase of wavelength for most of the tested samples. Both ?a and ?s' were correlated with peach firmness, soluble solids content (SSC), and skin and flesh color parameters. Better prediction results for partial least squares models were obtained using the combined values of ?a and ?s' (i.e., ?a × ?s' and ?eff) than using ?a or ?s', where ?eff = [3 ?a (?a + ?s')]1/2 is the effective attenuation coefficient. The results were further improved using least squares support vector machine models with values of the best correlation coefficient for firmness, SSC, skin lightness and flesh lightness being 0.749 (standard error of prediction or SEP = 17.39 N), 0.504 (SEP = 0.92 °Brix), 0.898 (SEP = 3.45), and 0.741 (SEP = 3.27), respectively. These results compared favorably to acoustic and impact firmness measurements with the correlation coefficient of 0.639 and 0.631, respectively. Hyperspectral imaging-based spatially-resolved technique is useful for measuring the optical properties of peach fruit, and it also has good potential for assessing fruit maturity/quality attributes.

  1. Hyperspectral Thermal Emission Spectrometer: Engineering Flight Campaign

    NASA Technical Reports Server (NTRS)

    Johnson, William R.; Hook, Simon J.; Shoen, Steven S.; Eng, Bjorn T.

    2013-01-01

    The Hyperspectral Thermal Emission Spectrometer (HyTES) successfully completed its first set of engineering test flights. HyTES was developed in support of the Hyperspectral Infrared Imager (HyspIRI). HyspIRI is one of the Tier II Decadal Survey missions. HyTES currently provides both high spectral resolution (17 nm) and high spatial resolution (2-5m) data in the thermal infrared (7.5-12 micron) part of the electromagnetic spectrum. HyTES data will be used to help determine the optimum band positions for the HyspIRI Thermal Infrared (TIR) sensor and provide antecedent data for HyspIRI related studies.

  2. Real-time hyperspectral imaging for food safety applications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multispectral imaging systems with selected bands can commonly be used for real-time applications of food processing. Recent research has demonstrated several image processing methods including binning, noise removal filter, and appropriate morphological analysis in real-time mode can remove most fa...

  3. Assessment of renal oxygenation during partial nephrectomy using DLP hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Best, Sara L.; Thapa, Abhas; Holzer, Michael S.; Jackson, Neil; Mir, Saad A.; Donnally, Chester J.; Wehner, Eleanor; Raj, Ganesh V.; Livingston, Edward; Cadeddu, Jeffrey A.; Zuzak, Karel J.

    2011-03-01

    Digital Light Processing (DLP®) hyperspectral imaging (HsI) is a non-invasive method used to construct a highly sensitive, real-time tissue oxygenation map through the measurement of the percentage of oxyhemoglobin. We have demonstrated that this technology can detect the oxyhemoglobin in the blood vessels on the surface of the kidney and we have used this to monitor renal perfusion during kidney cancer operations, where the blood supply to the kidney is interrupted for a period of time. This technology may allow us to "personalize" surgery based on the oxygenation profile.

  4. Hyperspectral fluorescence lidar imaging at the Colosseum, Rome: elucidating past conservation interventions.

    PubMed

    Palombi, L; Lognoli, D; Raimondi, V; Cecchi, G; Hällström, J; Barup, K; Conti, C; Grönlund, R; Johansson, A; Svanberg, S

    2008-05-12

    Fluorescence lidar techniques offer considerable potential for remote, non-invasive diagnostics of stone cultural heritage in the outdoor environment. Here we present the results of a joint Italian-Swedish experiment, deploying two hyperspectral fluorescence lidar imaging systems, for the documentation of past conservation interventions on the Colosseum, Rome. Several portions of the monument were scanned and we show that it was possible to discriminate among masonry materials, reinforcement structures and protective coatings inserted during past conservation interventions, on the basis of their fluorescence signatures, providing useful information for a first quick, large-scale in situ screening of the monument. PMID:18545382

  5. [Rapid detection of nitrogen content and distribution in oilseed rape leaves based on hyperspectral imaging].

    PubMed

    Zhang, Xiao-Lei; Liu, Fei; Nie, Peng-Cheng; He, Yong; Bao, Yi-Dan

    2014-09-01

    Visible and near infrared (Vis-NIR) hyperspectral imaging system was carried out to rapidly determinate the content and estimate the distribution of nitrogen (N) in oilseed rape leaves. Hyperspectral images of 420 leaf samples were acquired at seedling, flowering and pod stages. The spectral data of rape leaves were extracted from the region of interest (ROI) in the wave- length range of 380-1,030 nm. Different spectra preprocessing including Savitzky-Golay smoothing (SG), standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivatives were applied to improve the signal to noise ratio. Among 471 wavelengths, only twelve wavelengths (467, 557, 665, 686, 706, 752, 874, 879, 886, 900, 978 and 995 nm) were selected by successive projections algorithm(SPA) as the effective wavelengths for N prediction. Based on these effective wavelengths, partial least squares(PLS) and least-squares support vector machines (LS-SVM) calibration models were established for the determination of N content. Reasonable estimation accuracy was obtained, with Rp of 0.807 and RMSEP of 0.387 by PLS and Rp of 0.836 and RMSEP of 0.358 by LS-SVM, respectively. Considering the simple structure and satisfying results of PLS model, SPA-PLS model was used to generate the distribution maps of N content in rape leaves. The concentrations of N were calculated at each pixel of hyperspectral images at the selected effective wavelengths by inputting its correspond- ing spectrum into the established SPA-PLS model. Different colour represented the change in N content in the rape leaves under different fertilizer treatments. By including all pixels within the selected ROI, the average N status can be displayed in more detail and visualised. The visualization of N distribution could be helpful to understanding the change in N content in rape leaves during rape growth period and facilitate discovering the difference of N content within one sample as well as among the samples from different fertilising plots. The overall results revealed that hyperspectral imaging is a promising technique to detect N content and distribution within oilseed rape leaves rapidly and nondestructively. PMID:25532355

  6. Hyperspectral microscope for in vivo imaging of microstructures and cells in tissues

    DOEpatents

    Demos; Stavros G. (Livermore, CA)

    2011-05-17

    An optical hyperspectral/multimodal imaging method and apparatus is utilized to provide high signal sensitivity for implementation of various optical imaging approaches. Such a system utilizes long working distance microscope objectives so as to enable off-axis illumination of predetermined tissue thereby allowing for excitation at any optical wavelength, simplifies design, reduces required optical elements, significantly reduces spectral noise from the optical elements and allows for fast image acquisition enabling high quality imaging in-vivo. Such a technology provides a means of detecting disease at the single cell level such as cancer, precancer, ischemic, traumatic or other type of injury, infection, or other diseases or conditions causing alterations in cells and tissue micro structures.

  7. Compact Image Slicing Spectrometer (ISS) for hyperspectral fluorescence microscopy

    PubMed Central

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

    2009-01-01

    An image slicing spectrometer (ISS) for microscopy applications is presented. Its principle is based on the redirecting of image zones by specially organized thin mirrors within a custom fabricated component termed an image slicer. The demonstrated prototype can simultaneously acquire a 140nm spectral range within its 2D field of view from a single image. The spectral resolution of the system is 5.6nm. The FOV and spatial resolution of the ISS depend on the selected microscope objective and for the results presented is 45×45?m2 and 0.45?m respectively. This proof-of-concept system can be easily improved in the future for higher (both spectral and spatial) resolution imaging. The system requires no scanning and minimal post data processing. In addition, the reflective nature of the image slicer and use of prisms for spectral dispersion make the system light efficient. Both of the above features are highly valuable for real time fluorescent-spectral imaging in biological and diagnostic applications. PMID:19654631

  8. Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis.

    PubMed

    Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J

    2015-02-20

    The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 ?g cm(-2). PMID:25682242

  9. Aflatoxin contaminated chili pepper detection by hyperspectral imaging and machine learning

    NASA Astrophysics Data System (ADS)

    Atas, Musa; Yardimci, Yasemin; Temizel, Alptekin

    2011-06-01

    Mycotoxins are toxic secondary metabolites produced by fungi. They have been demonstrated to cause various health problems in humans, including immunosuppression and cancer. A class of mycotoxins, aflatoxins, has been studied extensively because they have caused many deaths particularly in developing countries. Chili pepper is also prone to aflatoxin contamination during harvesting, production and storage periods. Chemical methods to detect aflatoxins are quite accurate but expensive and destructive in nature. Hyperspectral and multispectral imaging are becoming increasingly important for rapid and nondestructive testing for the presence of such contaminants. We propose a compact machine vision system based on hyperspectral imaging and machine learning for detection of aflatoxin contaminated chili peppers. We used the difference images of consecutive spectral bands along with individual band energies to classify chili peppers into aflatoxin contaminated and uncontaminated classes. Both UV and halogen illumination sources were used in the experiments. The significant bands that provide better discrimination were selected based on their neural network connection weights. Higher classification rates were achieved with fewer numbers of spectral bands. This selection scheme was compared with an information-theoretic approach and it demonstrated robust performance with higher classification accuracy.

  10. Para-GMRF: parallel algorithm for anomaly detection of hyperspectral image

    NASA Astrophysics Data System (ADS)

    Dong, Chao; Zhao, Huijie; Li, Na; Wang, Wei

    2007-12-01

    The hyperspectral imager is capable of collecting hundreds of images corresponding to different wavelength channels for the observed area simultaneously, which make it possible to discriminate man-made objects from natural background. However, the price paid for the wealthy information is the enormous amounts of data, usually hundreds of Gigabytes per day. Turning the huge volume data into useful information and knowledge in real time is critical for geoscientists. In this paper, the proposed parallel Gaussian-Markov random field (Para-GMRF) anomaly detection algorithm is an attempt of applying parallel computing technology to solve the problem. Based on the locality of GMRF algorithm, we partition the 3-D hyperspectral image cube in spatial domain and distribute data blocks to multiple computers for concurrent detection. Meanwhile, to achieve load balance, a work pool scheduler is designed for task assignment. The Para-GMRF algorithm is organized in master-slave architecture, coded in C programming language using message passing interface (MPI) library and tested on a Beowulf cluster. Experimental results show that Para-GMRF algorithm successfully conquers the challenge and can be used in time sensitive areas, such as environmental monitoring and battlefield reconnaissance.

  11. Hyperspectral imaging for diagnosis and quality control in agri-food and industrial sectors

    NASA Astrophysics Data System (ADS)

    García-Allende, P. Beatriz; Conde, Olga M.; Mirapeix, Jesus; Cobo, Adolfo; Lopez-Higuera, Jose M.

    2010-04-01

    Optical spectroscopy has been utilized in various fields of science, industry and medicine, since each substance is discernible from all others by its spectral properties. However, optical spectroscopy traditionally generates information on the bulk properties of the whole sample, and mainly in the agri-food industry some product properties result from the heterogeneity in its composition. This monitoring is considerably more challenging and can be successfully achieved by the so-called hyperspectral imaging technology, which allows the simultaneous determination of the optical spectrum and the spatial location of an object in a surface. In addition, it is a nonintrusive and non-contact technique which gives rise to a great potential for industrial applications and it does not require any particular preparation of the samples, which is a primary concern in food monitoring. This work illustrates an overview of approaches based on this technology to address different problems in agri-food and industrial sectors. The hyperspectral system was originally designed and tested for raw material on-line discrimination, which is a key factor in the input stages of many industrial sectors. The combination of the acquisition of the spectral information across transversal lines while materials are being transported on a conveyor belt, and appropriate image analyses have been successfully validated in the tobacco industry. Lastly, the use of imaging spectroscopy applied to online welding quality monitoring is discussed and compared with traditional spectroscopic approaches in this regard.

  12. Beam scanning for rapid coherent Raman hyperspectral imaging.

    PubMed

    Ryu, Ian Seungwan; Camp, Charles H; Jin, Ying; Cicerone, Marcus T; Lee, Young Jong

    2015-12-15

    Coherent Raman imaging requires high-peak power laser pulses to maximize the nonlinear multiphoton signal generation, but accompanying photo-induced sample damage often poses a challenge to microscopic imaging studies. We demonstrate that beam scanning by a 3.5-kHz resonant mirror in a broadband coherent anti-Stokes Raman scattering (BCARS) imaging system can reduce photo-induced damage without compromising signal intensity. Additionally, beam scanning enables slit acquisition, in which spectra from a thin line of sample illumination are acquired in parallel during a single charge-coupled device exposure. Reflective mirrors are employed in the beam-scanning assembly to minimize chromatic aberration and temporal dispersion. The combined approach of beam scanning and slit acquisition is compared with the sample-scanning mode in terms of spatial resolution, photo-induced damage, and imaging speed at the maximum laser power below the sample-damage threshold. We show that the beam-scanning BCARS imaging method can reduce photodamage probability in biological cells and tissues, enabling faster imaging speed by using a higher excitation laser power than could be achieved without beam scanning. PMID:26670522

  13. Non-Invasive Survey of Old Paintings Using Vnir Hyperspectral Sensor

    NASA Astrophysics Data System (ADS)

    Matouskova, E.; Pavelka, K.; Svadlenkova, Z.

    2013-07-01

    Hyperspectral imaging is relatively new method developed primarily for army applications with respect to detection of possible chemical weapon existence and as an efficient assistant for a geological survey. The method is based on recording spectral profile for many hundreds of narrow spectral band. The technique gives full spectral curve of explored pixel which is an unparalleled signature of pixels material. Spectral signatures can then be compared with pre-defined spectral libraries or they can be created with respect to application. A new project named "New Modern Methods of Non-invasive Survey of Historical Site Objects" started at CTU in Prague with the New Year. The project is designed for 4 years and is funded by the Ministry of Culture in the Czech Republic. It is focused on material and chemical composition, damage diagnostics, condition description of paintings, images, construction components and whole structure object analysis in cultural heritage domain. This paper shows first results of the project on painting documentation field as well as used instrument. Hyperspec VNIR by Headwall Photonics was used for this analysis. It operates in the spectral range between 400 and 1000 nm. Comparison with infrared photography is discussed. The goal of this contribution is a non-destructive deep exploration of specific paintings. Two original 17th century paintings by Flemish authors Thomas van Apshoven ("On the Road") and David Teniers the Younger ("The Interior of a Mill") were chosen for the first analysis with a kind permission of academic painter Mr. M. Martan. Both paintings oil painted on wooden panel. This combination was chosen because of the possibility of underdrawing visualization which is supposed to be the most uncomplicated painting combination for this type of analysis.

  14. [Study on identification the crack feature of fresh jujube using hyperspectral imaging].

    PubMed

    Yu, Ke-Qiang; Zhao, Yan-Ru; Li, Xiao-Li; Zhang, Shu-Juan; He, Yong

    2014-02-01

    Crack is one of the most important indicators to evaluate the quality of fresh jujube. Crack not only accelerates the decay of fresh jujube, but also diminishes the shelf life and reduces the economic value severely. In this study, the potential of hyperspectral imaging covered the range of 380 - 1030 nm was evaluated for discrimination crack feature (location and area) of fresh jujube. Regression coefficients of partial least squares regression (PLSR), successive projection analysis (SPA) and principal component analysis (PCA) based full-bands image were adopted to extract sensitive bands of crack of fresh jujube. Then least-squares support vector machine (LS-SVM) discriminant models using the selected sensitive bands for calibration set (132 samples)" were established for identification the prediction set (44 samples). ROC curve was used to judge the discriminant models of PLSR-LS-SVM, SPA-LS-SVM and PCA-LS-SVM which are established by sensitive bands of crack of fresh jujube. The results demonstrated that PLSR-LS-SVM model had an optimal effect (area=1, std=0) to discriminate crack feature of fresh jujube. Next, images corresponding to five sensitive bands (467, 544, 639, 673 and 682 nm) selected by PLSR were executed to PCA. Finally, the image of PC4 was employed to identify the location and area of crack feature through imaging processing. The results revealed that hyperspectral imaging technique combined with image processing could achieve the qualitative discrimination and quantitative identification of crack feature of fresh jujube, which provided a theoretical reference and basis for develop instrument of discrimination of crack of jujube in further work. PMID:24822434

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

    NASA Astrophysics Data System (ADS)

    Nidamanuri, Rama Rao; Zbell, Bernd

    2011-09-01

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

  16. Jovian Ammonia Cloud Identification and Color Analyses from Hyperspectral Imaging

    NASA Astrophysics Data System (ADS)

    Strycker, Paul D.; Chanover, N.; Voelz, D.; Simon-Miller, A.

    2008-09-01

    Narrowband visible and near-infrared images of Jupiter were acquired at the Apache Point Observatory 3.5-meter telescope from 26-27 June and 04 July 2007 with the New Mexico State University Acousto-optic Imaging Camera (NAIC) to study atmospheric spectral characteristics. Over 3000 images were collected and map-projected, yielding spectral image cubes that span 480-900 nm with 2 nm resolution (1 nm resolution after deconvolution) and spatially cover 85% of jovian longitudes. We report the detection of spectrally identifiable ammonia clouds (SIACs) through the ammonia absorption features centered at 647 nm and 790 nm, which may be the first identification of SIACs using narrowband visible imaging. The observed SIACs predominately reside in the range of 1° to 4° N latitude (planetographic) and are also found in the turbulent wake region northwest of the Great Red Spot (GRS), which is in agreement with the analysis of Galileo NIMS observations by Baines et al. (2002, Icarus 159, 74-94). SIAC size and spatial distributions and temporal evolution are discussed. Additionally, color analyses were conducted for a jovian chromophore investigation using principle component analysis and nonnegative matrix factorization. Results are compared to a color analysis of HST observations from May-July 2008 of the passage of the GRS and Oval BA (Simon-Miller, this meeting). This work is funded by NSF award AST0628919.

  17. Detection of microbial biofilms on food processing surfaces: hyperspectral fluorescence imaging study

    NASA Astrophysics Data System (ADS)

    Jun, Won; Kim, Moon S.; Chao, Kaunglin; Lefcourt, Alan M.; Roberts, Michael S.; McNaughton, James L.

    2009-05-01

    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 investigation was to determine a minimal number of spectral bands suitable to differentiate microbial biofilm formation from the four background materials typically used during food processing. Ultimately, the resultant spectral information will be used in development of handheld portable imaging devices that can be used as visual aid tools for sanitation and safety inspection (microbial contamination) of the food processing surfaces. Pathogenic E. coli O157:H7 and Salmonella cells were grown in low strength M9 minimal medium on various surfaces at 22 +/- 2 °C for 2 days for biofilm formation. Biofilm autofluorescence under UV excitation (320 to 400 nm) obtained by hyperspectral fluorescence imaging system showed broad emissions in the blue-green regions of the spectrum with emission maxima at approximately 480 nm for both E. coli O157:H7 and Salmonella biofilms. Fluorescence images at 480 nm revealed that for background materials with near-uniform fluorescence responses such as stainless steel and formica cutting board, regardless of the background intensity, biofilm formation can be distinguished. This suggested that a broad spectral band in the blue-green regions can be used for handheld imaging devices for sanitation inspection of stainless, cutting board, and formica surfaces. The non-uniform fluorescence responses of granite make distinctions between biofilm and background difficult. To further investigate potential detection of the biofilm formations on granite surfaces with multispectral approaches, principal component analysis (PCA) was performed using the hyperspectral fluorescence image data. The resultant PCA score images revealed distinct contrast between biofilms and granite surfaces. This investigation demonstrated that biofilm formations on food processing surfaces, even for background materials with heterogeneous fluorescence responses, can be detected. Furthermore, a multispectral approach in developing handheld inspection devices may be needed to inspect surface materials that exhibit non-uniform fluorescence.

  18. Preliminary study of detection of buried landmines using a programmable hyperspectral imager

    NASA Astrophysics Data System (ADS)

    McFee, John E.; Ripley, Herb T.; Buxton, Roger; Thriscutt, Andrew M.

    1996-05-01

    Experiments were conducted to determine if buried mines could be detected by measuring the change in reflectance spectra of vegetation above mine burial sites. Mines were laid using hand methods and simulated mechanical methods and spectral images were obtained over a three month period using a casi hyperspectral imager scanned from a personnel lift. Mines were not detectable by measurement of the shift of the red edge of vegetative spectra. By calculating the linear correlation coefficient image, some mines in light vegetative cover (grass, grass/blueberries) were apparently detected, but mines buried in heavy vegetation cover (deep ferns) were not detectable. Due to problems with ground truthing, accurate probabilities of detection and false alarm rates were not obtained.

  19. Recent developments and applications of hyperspectral imaging for quality evaluation of agricultural products: a review.

    PubMed

    Liu, Dan; Zeng, Xin-An; Sun, Da-Wen

    2015-01-01

    Food quality and safety is the foremost issue for consumers, retailers as well as regulatory authorities. Most quality parameters are assessed by traditional methods, which are time consuming, laborious, and associated with inconsistency and variability. Non-destructive methods have been developed to objectively measure quality attributes for various kinds of food. In recent years, hyperspectral imaging (HSI) has matured into one of the most powerful tools for quality evaluation of agricultural and food products. HSI allows characterization of a sample's chemical composition (spectroscopic component) and external features (imaging component) in each point of the image with full spectral information. In order to track the latest research developments of this technology, this paper gives a detailed overview of the theory and fundamentals behind this technology and discusses its applications in the field of quality evaluation of agricultural products. Additionally, future potentials of HSI are also reported. PMID:24915395

  20. Anomaly detection and compensation for hyperspectral imagery

    E-print Network

    Cho, Choongyeun, 1973-

    2005-01-01

    Hyperspectral sensors observe hundreds or thousands of narrow contiguous spectral bands. The use of hyperspectral imagery for remote sensing applications is new and promising, yet the characterization and analysis of such ...