Science.gov

Sample records for acquired hyperspectral images

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

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  2. 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.

  3. Hyperspectral image processing methods

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  4. 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.

  5. 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.

  6. Hyperspectral image classification using functional data analysis.

    PubMed

    Li, Hong; Xiao, Guangrun; Xia, Tian; Tang, Y Y; Li, Luoqing

    2014-09-01

    The large number of spectral bands acquired by hyperspectral imaging sensors allows us to better distinguish many subtle objects and materials. Unlike other classical hyperspectral image classification methods in the multivariate analysis framework, in this paper, a novel method using functional data analysis (FDA) for accurate classification of hyperspectral images has been proposed. The central idea of FDA is to treat multivariate data as continuous functions. From this perspective, the spectral curve of each pixel in the hyperspectral images is naturally viewed as a function. This can be beneficial for making full use of the abundant spectral information. The relevance between adjacent pixel elements in the hyperspectral images can also be utilized reasonably. Functional principal component analysis is applied to solve the classification problem of these functions. Experimental results on three hyperspectral images show that the proposed method can achieve higher classification accuracies in comparison to some state-of-the-art hyperspectral image classification methods.

  7. Medical hyperspectral imaging: a review.

    PubMed

    Lu, Guolan; Fei, Baowei

    2014-01-01

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

  8. 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

  9. 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.

  10. 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.

  11. Hyperspectral imager development at Army Research Laboratory

    NASA Astrophysics Data System (ADS)

    Gupta, Neelam

    2008-04-01

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

  12. Quantitative Hyperspectral Reflectance Imaging

    PubMed Central

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

    2008-01-01

    Hyperspectral imaging is a non-destructive optical analysis technique that can for instance be used to obtain information from cultural heritage objects unavailable with conventional colour or multi-spectral photography. This technique can be used to distinguish and recognize materials, to enhance the visibility of faint or obscured features, to detect signs of degradation and study the effect of environmental conditions on the object. We describe the basic concept, working principles, construction and performance of a laboratory instrument specifically developed for the analysis of historical documents. The instrument measures calibrated spectral reflectance images at 70 wavelengths ranging from 365 to 1100 nm (near-ultraviolet, visible and near-infrared). By using a wavelength tunable narrow-bandwidth light-source, the light energy used to illuminate the measured object is minimal, so that any light-induced degradation can be excluded. Basic analysis of the hyperspectral data includes a qualitative comparison of the spectral images and the extraction of quantitative data such as mean spectral reflectance curves and statistical information from user-defined regions-of-interest. More sophisticated mathematical feature extraction and classification techniques can be used to map areas on the document, where different types of ink had been applied or where one ink shows various degrees of degradation. The developed quantitative hyperspectral imager is currently in use by the Nationaal Archief (National Archives of The Netherlands) to study degradation effects of artificial samples and original documents, exposed in their permanent exhibition area or stored in their deposit rooms. PMID:27873831

  13. 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

  14. 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

  15. Hyperspectral image analysis. A tutorial.

    PubMed

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

    2015-10-08

    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.

  16. 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.

  17. New generation handheld hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Wu, Huawen (Owen); Li, Hui; Tang, Shengjun

    2016-10-01

    A miniaturized hyper-spectral 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 hyper-spectral imagers for decades. The result is a handheld spectral imager that can be installed on miniature UAV drones or conveyor belts in production lines. Eventually, small handhelds can be adapted for use in outpatient medical clinics for point-of-care diagnostics and other in-field applications.

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

    PubMed

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

    2016-09-01

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

  19. Dental caries imaging using hyperspectral stimulated Raman scattering microscopy

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  20. Illumination system characterization for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Katrašnik, Jaka; Pernuš, Franjo; Likar, Boštjan

    2011-03-01

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

  1. Hyperspectral imaging polarimeter design and calibration

    NASA Astrophysics Data System (ADS)

    Loe, Richard S.; Duggin, Michael J.

    2002-01-01

    The integration and calibration of a hyperspectral imaging polarimeter is described. The system was designed to exploit subtle spectral details in visible and near-IR hyperspectral polarimetric images. All of the system components were commercial-off-the-shelf. This device uses a tunable liquid crystal filter and 16-bit cooled CCD camera. The challenges of calibrating a hyperspectral polarimeter are discussed.

  2. Image capture: simulation of sensor responses from hyperspectral images.

    PubMed

    Vora, P L; Farrell, J E; Tietz, J D; Brainard, D H

    2001-01-01

    This paper describes the design and performance of an image capture simulator. The general model underlying the simulator assumes that the image capture device contains multiple classes of sensors with different spectral sensitivities and that each sensor responds in a known way to irradiance over most of its operating range. The input to the simulator is a set of narrow-band images of the scene taken with a custom-designed hyperspectral camera system. The parameters for the simulator are the number of sensor classes, the sensor spectral sensitivities, the noise statistics and number of quantization levels for each sensor class, the spatial arrangement of the sensors and the exposure duration. The output of the simulator is the raw image data that would have been acquired by the simulated image capture device. To test the simulator, we acquired images of the same scene both with the hyperspectral camera and with a calibrated Kodak DCS-200 digital color camera. We used the simulator to predict the DCS-200 output from the hyperspectral data. The agreement between simulated and acquired images validated the image capture response model and our simulator implementation. We believe the simulator will provide a useful tool for understanding the effect of varying the design parameters of an image capture device.

  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. Contextual Detection of Anomalies within Hyperspectral Images

    DTIC Science & Technology

    2011-03-01

    Hyperspectral Imagery (HSI), Unsupervised Target Detection, Target Identification, Contextual Anomaly Detection 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...processing. Hyperspectral imaging has a wide range of applications within remote sensing, not limited to terrain classification , environmental monitoring...Johnson, R. J. (2008). Improved feature extraction, feature selection, and identification techniques that create a fast unsupervised hyperspectral

  5. Single-pixel hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Suo, Jinli; Wang, Yuwang; Bian, Liheng; Dai, Qionghai

    2016-10-01

    Conventional multispectral imaging methods detect photons of a 3D hyperspectral data cube separately either in the spatial or spectral dimension using array detectors, and are thus photon inefficient and spectrum range limited. Besides, they are usually bulky and highly expensive. To address these issues, this paper presents single-pixel multispectral imaging techniques, which are of high sensitivity, wide spectrum range, low cost and light weight. Two mechanisms are proposed, and experimental validation are also reported.

  6. Hyperspectral Imaging of River Systems

    DTIC Science & Technology

    2010-09-30

    plume. The 300 m MERIS pixels do a much better job of imaging the river mouth. 3 The Hyperspectral Imager for the Coastal Ocean (HICO; Corson et...radiances, L1B data is supplied by NRL’s HICOTM team [ Corson 2010]. (b) At-sensor radiance for black pixel in Fig. 1 (a). The raw data is indicated...that goal. RELATED PROJECTS I continue to collaborate regularly with colleagues at the NRL Remote Sensing Division (Code 7200; Mike Corson and

  7. Hyperspectral imaging of ischemic wounds

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  8. Hyperspectral imaging of bruised skin

    NASA Astrophysics Data System (ADS)

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

    2006-02-01

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

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

    PubMed

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

    2014-03-21

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

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

    PubMed Central

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

    2014-01-01

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

  11. 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.

  12. 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...

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

    PubMed

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2016-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2016-03-01

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

  15. 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...

  16. Application of hyperspectral imaging for characterization of intramuscular fat distribution in beef

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this study, a hyperspectral imaging system in the spectral region of 400–1000 nm was used for visualization and determination of intramuscular fat concentration in beef samples. Hyperspectral images were acquired for beef samples, and spectral information was then extracted from each single sampl...

  17. Hyperspectral Imaging of human arm

    NASA Technical Reports Server (NTRS)

    2003-01-01

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

  18. Common hyperspectral image database design

    NASA Astrophysics Data System (ADS)

    Tian, Lixun; Liao, Ningfang; Chai, Ali

    2009-11-01

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

  19. 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.

  20. New Thermal Infrared Hyperspectral Imagers

    DTIC Science & Technology

    2009-10-01

    SET-151 Thermal Hyperspectral Imagery (Imagerie hyperspectrale thermique). Meeting Proceedings of Sensors and Electronics Panel (SET) Specialists...in hyperspectral instruments, where the optical power from the target is spread spectrally over tens of pixels, but the instrument radiation is not...because it also depends on temperature, emissivity and spectral features of the target . The well describing figure of merit for a hyperspectral

  1. 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.

  2. Reflectance and fluorescence hyperspectral elastic image registration

    NASA Astrophysics Data System (ADS)

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

    2004-05-01

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

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

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Tilton, James C.

    2011-01-01

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

  4. Perceptual Based Image Fusion with Applications to Hyperspectral Image Data.

    DTIC Science & Technology

    1994-12-01

    spectral bands from the AVIRIS hyperspectral sensor will be evaluated. 1.4 Approach/ Thesis Organization Chapter one described data processing problems...Based Image Fusion with Applications to Hyperspectral Image Data THESIS A o .:or \\Terry Allen Wilson NTS _ Captain, USAF DTIC Tf-, LI Unannou!c<ej LI...Applications to Hyperspectral Image Data THESIS Presented to the Faculty of the Graduate School of Engineering of the Air Force Institute of

  5. Content-based hyperspectral image retrieval using spectral unmixing

    NASA Astrophysics Data System (ADS)

    Plaza, Antonio J.

    2011-11-01

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

  6. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging.

    PubMed

    Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2016-02-27

    Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

  7. Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

    PubMed Central

    Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2016-01-01

    Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management. PMID:27656035

  8. Hyperspectral Fluorescence and Reflectance Imaging Instrument

    NASA Technical Reports Server (NTRS)

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

    2008-01-01

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

  9. Hyperspectral imaging and its applications

    NASA Astrophysics Data System (ADS)

    Serranti, S.; Bonifazi, G.

    2016-04-01

    Hyperspectral imaging (HSI) is an emerging technique that combines the imaging properties of a digital camera with the spectroscopic properties of a spectrometer able to detect the spectral attributes of each pixel in an image. For these characteristics, HSI allows to qualitatively and quantitatively evaluate the effects of the interactions of light with organic and/or inorganic materials. The results of this interaction are usually displayed as a spectral signature characterized by a sequence of energy values, in a pre-defined wavelength interval, for each of the investigated/collected wavelength. Following this approach, it is thus possible to collect, in a fast and reliable way, spectral information that are strictly linked to chemical-physical characteristics of the investigated materials and/or products. Considering that in an hyperspectral image the spectrum of each pixel can be analyzed, HSI can be considered as one of the best nondestructive technology allowing to perform the most accurate and detailed information extraction. HSI can be applied in different wavelength fields, the most common are the visible (VIS: 400-700 nm), the near infrared (NIR: 1000-1700 nm) and the short wave infrared (SWIR: 1000-2500 nm). It can be applied for inspections from micro- to macro-scale, up to remote sensing. HSI produces a large amount of information due to the great number of continuous collected spectral bands. Such an approach, when successful, is quite challenging being usually reliable, robust and characterized by lower costs, if compared with those usually associated to commonly applied analytical off-line and/or on-line analytical approaches. More and more applications have been thus developed and tested, in these last years, especially in food inspection, with a large range of investigated products, such as fruits and vegetables, meat, fish, eggs and cereals, but also in medicine and pharmaceutical sector, in cultural heritage, in material characterization and in

  10. 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.

  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. A novel substrate for multisensor hyperspectral imaging.

    PubMed

    Ofner, J; Kirschner, J; Eitenberger, E; Friedbacher, G; Kasper-Giebl, A; Lohninger, H; Eisenmenger-Sittner, C; Lendl, B

    2017-03-01

    The quality of chemical imaging, especially multisensor hyperspectral imaging, strongly depends on sample preparation techniques and instrumental infrastructure but also on the choice of an appropriate imaging substrate. To optimize the combined imaging of Raman microspectroscopy, scanning-electron microscopy and energy-dispersive X-ray spectroscopy, a novel substrate was developed based on sputtering of highly purified aluminium onto classical microscope slides. The novel aluminium substrate overcomes several disadvantages of classical substrates like impurities of the substrate material and contamination of the surface as well as surface roughness and homogeneity. Therefore, it provides excellent conditions for various hyperspectral imaging techniques and enables high-quality multisensor hyperspectral chemical imaging at submicron lateral resolutions.

  13. 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.

  14. Hyperspectral laser-induced autofluorescence imaging of dental caries

    NASA Astrophysics Data System (ADS)

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

    2012-01-01

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

  15. Hyperspectral vibrational photoacoustic imaging of lipids and collagen

    NASA Astrophysics Data System (ADS)

    Wang, Pu; Wang, Ping; Wang, Han-Wei; Cheng, Ji-Xin

    2012-02-01

    The recently developed vibrational photoacoustic (VPA) microscopy allows bond-selective imaging of deep tissues by taking advantage of intrinsic contrast from harmonic vibration of C-H bonds. Due to the spectral similarity of molecules in the overtone vibration region, the compositional information is not available from VPA images acquired by single wavelength excitation. Here we demonstrate that lipids and collagen, two critical markers in many kinds of diseases, can be distinguished by hyperspectral VPA imaging. A phantom consisted of rat tail tendon (collagen) and fat tissue (lipids) was constructed. Wavelengths between 1650 and 1850 nm were scanned to excite the first overtone/combination vibration of C-H bond. B-scan hyperspectral VPA images, in which each pixel contains a spectrum, was analyzed by a Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) algorism to recover the spatial distribution of two chemical components in the phantom.

  16. Hyperspectral imaging results from the NRL PHILLS instrument

    NASA Astrophysics Data System (ADS)

    Baumback, Mark M.; Antoniades, John A.; Bowles, Jeffrey H.; Palmadesso, Peter J.; Rickard, Lee J.

    1995-09-01

    The portable hyperspectral imager for low light spectroscopy (PHILLS) instrument consists of several modules containing analog and digital imaging spectrometers, two of which are intensified, covering the 200 nm to 1100 nm wavelength range with over 100 wavelength bands. The PHILLS instrument is usually flown aboard P-3 Orion aircraft at altitudes from 500 feet to 10,000 feet. PHILLS ground images are acquired with > 70 degrees FOV and 2-5 meter spatial resolution, and 0.5-1 nm wavelength resolution. Hyperspectral data cubes are processed using a combination of spectral matching techniques and the filter vector algorithm, to produce terrain separation and spectral dimensionality; i.e. variability of the spectral signatures for individual 'substances' in the image. Data obtained on flights over the Florida Keys showing land and underwater features are presented.

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

    PubMed

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

    2008-10-01

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

  18. MEMS FPI-based smartphone hyperspectral imager

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  19. Infrared hyperspectral imaging polarimeter using birefringent prisms.

    PubMed

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

    2011-03-10

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

  20. Tongue Tumor Detection in Medical Hyperspectral Images

    PubMed Central

    Liu, Zhi; Wang, Hongjun; Li, Qingli

    2012-01-01

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

  1. A new hyperspectral image compression paradigm based on fusion

    NASA Astrophysics Data System (ADS)

    Guerra, Raúl; Melián, José; López, Sebastián.; Sarmiento, Roberto

    2016-10-01

    The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor. Hence, this process must be performed by space qualified hardware, having area, power and speed limitations. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompress image. In this manuscript we proposed a new methodology for compressing hyperspectral images based on hyperspectral image fusion concepts. The proposed compression process has two independent steps. The first one is to spatially degrade the remote sensed hyperspectral image to obtain a low resolution hyperspectral image. The second step is to spectrally degrade the remote sensed hyperspectral image to obtain a high resolution multispectral image. These two degraded images are then send to the earth surface, where they must be fused using a fusion algorithm for hyperspectral and multispectral image, in order to recover the remote sensed hyperspectral image. The main advantage of the proposed methodology for compressing remote sensed hyperspectral images is that the compression process, which must be performed on-board, becomes very simple, being the fusion process used to reconstruct image the more complex one. An extra advantage is that the compression ratio can be fixed in advanced. Many simulations have been performed using different fusion algorithms and different methodologies for degrading the hyperspectral image. The results obtained in the simulations performed corroborate the benefits of the proposed methodology.

  2. Platforms for hyperspectral imaging, in-situ optical and acoustical imaging in urbanized regions

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R.; Oney, Taylor

    2016-10-01

    Hyperspectral measurements of the water surface of urban coastal waters are presented. Oblique bidirectional reflectance factor imagery was acquired made in a turbid coastal sub estuary of the Indian River Lagoon, Florida and along coastal surf zone waters of the nearby Atlantic Ocean. Imagery was also collected using a pushbroom hyperspectral imager mounted on a fixed platform with a calibrated circular mechatronic rotation stage. Oblique imagery of the shoreline and subsurface features clearly shows subsurface bottom features and rip current features within the surf zone water column. In-situ hyperspectral optical signatures were acquired from a vessel as a function of depth to determine the attenuation spectrum in Palm Bay. A unique stationary platform methodology to acquire subsurface acoustic images showing the presence of moving bottom boundary nephelometric layers passing through the acoustic fan beam. The acoustic fan beam imagery indicated the presence of oscillatory subsurface waves in the urbanized coastal estuary. Hyperspectral imaging using the fixed platform techniques are being used to collect hyperspectral bidirectional reflectance factor (BRF) measurements from locations at buildings and bridges in order to provide new opportunities to advance our scientific understanding of aquatic environments in urbanized regions.

  3. 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

  4. Hyperspectral Image Recovery via Hybrid Regularization

    NASA Astrophysics Data System (ADS)

    Arablouei, Reza; de Hoog, Frank

    2016-12-01

    Natural images tend to mostly consist of smooth regions with individual pixels having highly correlated spectra. This information can be exploited to recover hyperspectral images of natural scenes from their incomplete and noisy measurements. To perform the recovery while taking full advantage of the prior knowledge, we formulate a composite cost function containing a square-error data-fitting term and two distinct regularization terms pertaining to spatial and spectral domains. The regularization for the spatial domain is the sum of total-variation of the image frames corresponding to all spectral bands. The regularization for the spectral domain is the l1-norm of the coefficient matrix obtained by applying a suitable sparsifying transform to the spectra of the pixels. We use an accelerated proximal-subgradient method to minimize the formulated cost function. We analyze the performance of the proposed algorithm and prove its convergence. Numerical simulations using real hyperspectral images exhibit that the proposed algorithm offers an excellent recovery performance with a number of measurements that is only a small fraction of the hyperspectral image data size. Simulation results also show that the proposed algorithm significantly outperforms an accelerated proximal-gradient algorithm that solves the classical basis-pursuit denoising problem to recover the hyperspectral image.

  5. Hyperspectral Image Recovery via Hybrid Regularization.

    PubMed

    Arablouei, Reza; de Hoog, Frank

    2016-09-27

    Natural images tend to mostly consist of smooth regions with individual pixels having highly correlated spectra. This information can be exploited to recover hyperspectral images of natural scenes from their incomplete and noisy measurements. To perform the recovery while taking full advantage of the prior knowledge, we formulate a composite cost function containing a square-error data-fitting term and two distinct regularization terms pertaining to spatial and spectral domains. The regularization for the spatial domain is the sum of total-variation of the image frames corresponding to all spectral bands. The regularization for the spectral domain is the ��������-norm of the coefficient matrix obtained by applying a suitable sparsifying transform to the spectra of the pixels. We use an accelerated proximal-subgradient method to minimize the formulated cost function. We analyse the performance of the proposed algorithm and prove its convergence. Numerical simulations using real hyperspectral images exhibit that the proposed algorithm offers an excellent recovery performance with a number of measurements that is only a small fraction of the hyperspectral image data size. Simulation results also show that the proposed algorithm significantly outperforms an accelerated proximal-gradient algorithm that solves the classical basis-pursuit denoising problem to recover the hyperspectral image.

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

    NASA Astrophysics Data System (ADS)

    Mehrubeoglu, Mehrube; Zemlan, Michael; Henry, Sam

    2015-09-01

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

  7. 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.

  8. 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.

  9. LIFTERS-hyperspectral imaging at LLNL

    SciTech Connect

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

    1994-11-15

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

  10. 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.

  11. [Spectral calibration of hyperspectral imager based on spectral absorption target].

    PubMed

    Gou, Zhi-Yang; Yan, Lei; Chen, Wei; Zhao, Hong-Ying; Yin, Zhong-Yi; Duan, Yi-Ni

    2013-02-01

    Retrieval of center wavelength and bandwidth is a key step for quantitative analysis of hyperspectral data. The present paper proposes a spectral calibration method of hyperspectral imager, whose spectrum covers visible and near-infrared band, using spectral absorption target. Ground calibration experiment was designed for a hyperspectral imager with a bandwidth of 6 nm. Hyperspectral imager and ASD spectrometer measured the same spectral absorption target synchronously. Reflectance spectrum was derived from the different data set. Center wavelength and bandwidth were retrieved by matching the reflectance data from hyperspectral imager and ASD spectrometer. The experiment result shows that this method can be applied in spectral calibration of hyperspectral imagers to improve the quantitative studies on hyperspectral imagery.

  12. ICER-3D Hyperspectral Image Compression Software

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  13. 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...

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

  15. Nonnegative matrix factorization for efficient hyperspectral image projection

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

    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.

  16. 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.

  17. A hyperspectral image optimizing method based on sub-pixel MTF analysis

    NASA Astrophysics Data System (ADS)

    Wang, Yun; Li, Kai; Wang, Jinqiang; Zhu, Yajie

    2015-04-01

    Hyperspectral imaging is used to collect tens or hundreds of images continuously divided across electromagnetic spectrum so that the details under different wavelengths could be represented. A popular hyperspectral imaging methods uses a tunable optical band-pass filter settled in front of the focal plane to acquire images of different wavelengths. In order to alleviate the influence of chromatic aberration in some segments in a hyperspectral series, in this paper, a hyperspectral optimizing method uses sub-pixel MTF to evaluate image blurring quality was provided. This method acquired the edge feature in the target window by means of the line spread function (LSF) to calculate the reliable position of the edge feature, then the evaluation grid in each line was interpolated by the real pixel value based on its relative position to the optimal edge and the sub-pixel MTF was used to analyze the image in frequency domain, by which MTF calculation dimension was increased. The sub-pixel MTF evaluation was reliable, since no image rotation and pixel value estimation was needed, and no artificial information was introduced. With theoretical analysis, the method proposed in this paper is reliable and efficient when evaluation the common images with edges of small tilt angle in real scene. It also provided a direction for the following hyperspectral image blurring evaluation and the real-time focal plane adjustment in real time in related imaging system.

  18. Hyperspectral image data compression based on DSP

    NASA Astrophysics Data System (ADS)

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

    2010-11-01

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

  19. Infrared hyperspectral imaging for chemical vapour detection

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  20. Spectral mixture analyses of hyperspectral data acquired using a tethered balloon

    USGS Publications Warehouse

    Chen, Xuexia; Vierling, Lee

    2006-01-01

    Tethered balloon remote sensing platforms can be used to study radiometric issues in terrestrial ecosystems by effectively bridging the spatial gap between measurements made on the ground and those acquired via airplane or satellite. In this study, the Short Wave Aerostat-Mounted Imager (SWAMI) tethered balloon-mounted platform was utilized to evaluate linear and nonlinear spectral mixture analysis (SMA) for a grassland-conifer forest ecotone during the summer of 2003. Hyperspectral measurement of a 74-m diameter ground instantaneous field of view (GIFOV) attained by the SWAMI was studied. Hyperspectral spectra of four common endmembers, bare soil, grass, tree, and shadow, were collected in situ, and images captured via video camera were interpreted into accurate areal ground cover fractions for evaluating the mixture models. The comparison between the SWAMI spectrum and the spectrum derived by combining in situ spectral data with video-derived areal fractions indicated that nonlinear effects occurred in the near infrared (NIR) region, while nonlinear influences were minimal in the visible region. The evaluation of hyperspectral and multispectral mixture models indicated that nonlinear mixture model-derived areal fractions were sensitive to the model input data, while the linear mixture model performed more stably. Areal fractions of bare soil were overestimated in all models due to the increased radiance of bare soil resulting from side scattering of NIR radiation by adjacent grass and trees. Unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. In addition, though an apparent endmember assemblage could be derived using linear approaches to yield low residual error, the tree and shade endmember fractions calculated using this technique were erroneous and therefore separate treatment of endmembers subject to high amounts of multiple scattering (i.e. shadows and trees) must be done with caution. Including the

  1. 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.

  2. Miniaturization of a SWIR hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Warren, Christopher P.; Pfister, William; Even, Detlev; Velasco, Arleen; Yee, Selwyn; Breitwieser, David; Naungayan, Joseph

    2011-05-01

    A new approach for the design and fabrication of a miniaturized SWIR Hyperspectral imager is described. Previously, good results were obtained with a VNIR Hyperspectral imager, by use of light propagation within bonded solid blocks of fused silica. These designs use the Offner design form, providing excellent, low distortion imaging. The same idea is applied to the SWIR Hyperspectral imager here, resulting in a microHSITM SWIR Hyperspectral sensor, capable of operating in the 850-1700 nm wavelength range. The microHSI spectrometer weighs 910 g from slit input to camera output. This spectrometer can accommodate custom foreoptics to adapt to a wide range of fields-of-view (FOV). The current application calls for a 15 degree FOV, and utilizes an InGaAs image sensor with a spatial format of 640 x 25 micron pixels. This results in a slit length of 16 mm, and a foreoptics focal length of 61 mm, operating at F# = 2.8. The resulting IFOV is 417 μrad for this application, and a spectral dispersion of 4.17 nm/pixel. A prototype SWIR microHSI was fabricated, and the blazed diffraction grating was embedded within the optical blocks, resulting in a 72% diffraction efficiency at the wavelength of 1020 nm. This spectrometer design is capable of accommodating slit lengths of up to 25.6 mm, which opens up a wide variety of applications. The microHSI concepts can be extended to other wavelength regions, and a miniaturized LWIR microHSI sensor is in the conceptual design stage.

  3. Spectral-spatial hyperspectral image classification using super-pixel-based spatial pyramid representation

    NASA Astrophysics Data System (ADS)

    Fan, Jiayuan; Tan, Hui Li; Toomik, Maria; Lu, Shijian

    2016-10-01

    Spatial pyramid matching has demonstrated its power for image recognition task by pooling features from spatially increasingly fine sub-regions. Motivated by the concept of feature pooling at multiple pyramid levels, we propose a novel spectral-spatial hyperspectral image classification approach using superpixel-based spatial pyramid representation. This technique first generates multiple superpixel maps by decreasing the superpixel number gradually along with the increased spatial regions for labelled samples. By using every superpixel map, sparse representation of pixels within every spatial region is then computed through local max pooling. Finally, features learned from training samples are aggregated and trained by a support vector machine (SVM) classifier. The proposed spectral-spatial hyperspectral image classification technique has been evaluated on two public hyperspectral datasets, including the Indian Pines image containing 16 different agricultural scene categories with a 20m resolution acquired by AVIRIS and the University of Pavia image containing 9 land-use categories with a 1.3m spatial resolution acquired by the ROSIS-03 sensor. Experimental results show significantly improved performance compared with the state-of-the-art works. The major contributions of this proposed technique include (1) a new spectral-spatial classification approach to generate feature representation for hyperspectral image, (2) a complementary yet effective feature pooling approach, i.e. the superpixel-based spatial pyramid representation that is used for the spatial correlation study, (3) evaluation on two public hyperspectral image datasets with superior image classification performance.

  4. Compressive Hyperspectral Imaging and Anomaly Detection

    DTIC Science & Technology

    2010-02-01

    the desired jointly sparse a"s, one shall adjust a and b. 4.4 Hyperspectral Image Reconstruction and Denoising We apply the model x* = Da’ + e! to...iteration for compressive sensing and sparse denoising ,’" Communications in Mathematical Sciences , 2008. W. Yin, "Analysis and generalizations of...Aharon, M. Elad, and A. Bruckstein, "K- SVD : An algorithm for designing overcomplete dictionaries for sparse representation,’" IEEE Transactions on Signal

  5. Polarimetric Hyperspectral Imaging Systems and Applications

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

  6. Excitation-scanning hyperspectral imaging microscope

    PubMed Central

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

    2014-01-01

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

  7. Excitation-scanning hyperspectral imaging microscope.

    PubMed

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

    2014-04-01

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

  8. Hyperspectral imaging of skin and lung cancers

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  9. Combined hyperspatial and hyperspectral imaging spectrometer concept

    NASA Technical Reports Server (NTRS)

    Burke, Ian; Zwick, Harold

    1995-01-01

    There is a user need for increasing spatial and spectral resolution in Earth Observation (EO) optical instrumentation. Higher spectral resolution will be achieved by the introduction of spaceborne imaging spectrometers. Higher spatial resolutions of 1 - 3m will be achieved also, but at the expense of sensor redesign, higher communications bandwidth, high data processing volumes, and therefore, at the risk of time delays due to large volume data-handling bottlenecks. This paper discusses a design concept whereby the hyperspectral properties of a spaceborne imaging spectrometer can be used to increase the image spatial resolution, without such adverse cost impact.

  10. Sparse representations for online-learning-based hyperspectral image compression.

    PubMed

    Ülkü, İrem; Töreyin, Behçet Uğur

    2015-10-10

    Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio.

  11. 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.

  12. 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

    2012-12-03

    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.

  13. Onboard Image Processing System for Hyperspectral Sensor.

    PubMed

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

    2015-09-25

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

  14. 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

  15. 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...

  16. 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.

  17. 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...

  18. 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.

  19. Raman hyperspectral imaging of iron transport across membranes in cells

    NASA Astrophysics Data System (ADS)

    Das, Anupam; Costa, Xavier Felipe; Khmaladze, Alexander; Barroso, Margarida; Sharikova, Anna

    2016-09-01

    Raman scattering microscopy is a powerful imaging technique used to identify chemical composition, structural and conformational state of molecules of complex samples in biology, biophysics, medicine and materials science. In this work, we have shown that Raman techniques allow the measurement of the iron content in protein mixtures and cells. Since the mechanisms of iron acquisition, storage, and excretion by cells are not completely understood, improved knowledge of iron metabolism can offer insight into many diseases in which iron plays a role in the pathogenic process, such as diabetes, neurodegenerative diseases, cancer, and metabolic syndrome. Understanding of the processes involved in cellular iron metabolism will improve our knowledge of cell functioning. It will also have a big impact on treatment of diseases caused by iron deficiency (anemias) and iron overload (hereditary hemochromatosis). Previously, Raman studies have shown substantial differences in spectra of transferrin with and without bound iron, thus proving that it is an appropriate technique to determine the levels of bound iron in the protein mixture. We have extended these studies to obtain hyperspectral images of transferrin in cells. By employing a Raman scanning microscope together with spectral detection by a highly sensitive back-illuminated cooled CCD camera, we were able to rapidly acquire and process images of fixed cells with chemical selectivity. We discuss and compare various methods of hyperspectral Raman image analysis and demonstrate the use of these methods to characterize cellular iron content without the need for dye labeling.

  20. A computational hyperspectral imaging technique

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

    A novel spectral imaging technique is introduced based on a highly dispersive imaging lens system. The chromatic aberration of the lens system is utilized to spread the spectral content of the object over a focal distance. Two three-dimensional surface reconstruction algorithms, depth from focus and depth from defocus, are applied to images captured by dispersive lens system. Using these algorithms, the spectral imager is able to relate either the location of focused image or the amount of defocus at the imaging detector to the spectral content of the object. A spectral imager with ~5 nm spectral resolution is designed based on this technique. The spectral and spatial resolutions of the introduced technique are independent and can be improved simultaneously. Simulation and experimental results are presented.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The objective of this research was to measure the absorption and reduced scattering coefficients 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 hyperspect...

  2. Developing a portable GPU library for hyperspectral image processing

    NASA Astrophysics Data System (ADS)

    Pérez-Irizarry, Gabriel J.; De La Cruz-Sanchez, Francisco; Landrón-Rivera, Brian A.; Santiago, Nayda G.; Velez-Reyes, Miguel

    2012-06-01

    The increasing volume of data produced by hyperspectral image sensors have forced researches and developers to seek out new and more ecient ways of analyzing the data as quick as possible. Medical, scientic, and military applications present performance requirements for tools that perform operations on hyperspectral sensor data. By providing a hyperspectral image analysis library, we aim to accelerate hyperspectral image application development. Development of a cross-platform library, Libdect, with GPU support for hyperspectral image analysis is presented. Coupling library development with ecient hyperspectral algorithms escalates into a signicant time invest- ment in many projects or prototypes. Provided a solution to these issues, developers can implement hyperspectral image analysis applications in less time. Developers will not be focused on implementing target detection code and potential issues related to platform or GPU architecture dierences. Libdect's development team counts with previously implemented detection algorithms. By utilizing proven tools, such as CMake and CTest, to develop Libdect's infrastructure, we were able to develop and test a prototype library that provides target detection code with GPU support on Linux platforms. As a whole, Libdect is an early prototype of an open and documented example of Software Engineering practices and tools. They are put together in an eort to increase developer productivity and encourage new developers into the eld of hyperspectral image application development.

  3. Evaluation of copyright protection schemes for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Minguillon, Julia; Herrera-Joancomarti, Jordi; Megias, David; Serra-Sagrista, Joan

    2004-02-01

    In this paper we evaluate the performance of several image watermarking schemes applied to hyperspectral imaging. An image watermarking scheme based on JPEG2000 which can be also used to store and manipulate hyperspectral images is also described. Different watermarking schemes are tested in order to determine the suitability of each one for a specific hyperspectral image environment. The impact of classical GIS operations (namely zooming, cropping and compression) on the performance of each watermarking scheme is measured in terms of capacity and robustness. In order to do so, we study several possibilities for watermarking hyperspectral images, as all hyperspectral image bands should be taken into account. We also study the impact of watermarking in image quality, measured as usual by PSNR, but also by the degradation of classification performance. Compression, classification and watermarking are closely related to each other as decisions taken in one subject have a large impact on the others. Our results show that the newcomer JPEG2000 standard is a useful tool for both hyperspectral imaging and copyright protection purposes. The proposed watermarking scheme, which takes advantage of JPEG2000 standard capabilities, can be considered to be robust under the constraints defined by the integration of hyperspectral imaging with geographical information systems. JPEG2000 extensions defined by the standard related to this work are also considered.

  4. Reconfigurable Hardware for Compressing Hyperspectral Image Data

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  5. Spectral-Spatial Hyperspectral Image Classification Based on KNN

    NASA Astrophysics Data System (ADS)

    Huang, Kunshan; Li, Shutao; Kang, Xudong; Fang, Leyuan

    2016-12-01

    Fusion of spectral and spatial information is an effective way in improving the accuracy of hyperspectral image classification. In this paper, a novel spectral-spatial hyperspectral image classification method based on K nearest neighbor (KNN) is proposed, which consists of the following steps. First, the support vector machine is adopted to obtain the initial classification probability maps which reflect the probability that each hyperspectral pixel belongs to different classes. Then, the obtained pixel-wise probability maps are refined with the proposed KNN filtering algorithm that is based on matching and averaging nonlocal neighborhoods. The proposed method does not need sophisticated segmentation and optimization strategies while still being able to make full use of the nonlocal principle of real images by using KNN, and thus, providing competitive classification with fast computation. Experiments performed on two real hyperspectral data sets show that the classification results obtained by the proposed method are comparable to several recently proposed hyperspectral image classification methods.

  6. Detecting pits in tart cherries by hyperspectral transmission imaging

    NASA Astrophysics Data System (ADS)

    Qin, Jianwei; Lu, Renfu

    2004-11-01

    The presence of pits in processed cherry products causes safety concerns for consumers and imposes potential liability for the food industry. The objective of this research was to investigate a hyperspectral transmission imaging technique for detecting the pit in tart cherries. A hyperspectral imaging system was used to acquire transmission images from individual cherry fruit for four orientations before and after pits were removed over the spectral region between 450 nm and 1,000 nm. Cherries of three size groups (small, intermediate, and large), each with two color classes (light red and dark red) were used for determining the effect of fruit orientation, size, and color on the pit detection accuracy. Additional cherries were studied for the effect of defect (i.e., bruises) on the pit detection. Computer algorithms were developed using the neural network (NN) method to classify the cherries with and without the pit. Two types of data inputs, i.e., single spectra and selected regions of interest (ROIs), were compared. The spectral region between 690 nm and 850 nm was most appropriate for cherry pit detection. The NN with inputs of ROIs achieved higher pit detection rates ranging from 90.6% to 100%, with the average correct rate of 98.4%. Fruit orientation and color had a small effect (less than 1%) on pit detection. Fruit size and defect affected pit detection and their effect could be minimized by training the NN with properly selected cherry samples.

  7. 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.

  8. Sparse Superpixel Unmixing for Hyperspectral Image Analysis

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  9. Hyperspectral imaging from space: Warfighter-1

    NASA Astrophysics Data System (ADS)

    Cooley, Thomas; Seigel, Gary; Thorsos, Ivan

    1999-01-01

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

  10. SWIR hyperspectral imaging detector for surface residues

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  11. Using hyperspectral imaging technology to identify diseased tomato leaves

    NASA Astrophysics Data System (ADS)

    Li, Cuiling; Wang, Xiu; Zhao, Xueguan; Meng, Zhijun; Zou, Wei

    2016-11-01

    In the process of tomato plants growth, due to the effect of plants genetic factors, poor environment factors, or disoperation of parasites, there will generate a series of unusual symptoms on tomato plants from physiology, organization structure and external form, as a result, they cannot grow normally, and further to influence the tomato yield and economic benefits. Hyperspectral image usually has high spectral resolution, not only contains spectral information, but also contains the image information, so this study adopted hyperspectral imaging technology to identify diseased tomato leaves, and developed a simple hyperspectral imaging system, including a halogen lamp light source unit, a hyperspectral image acquisition unit and a data processing unit. Spectrometer detection wavelength ranged from 400nm to 1000nm. After hyperspectral images of tomato leaves being captured, it was needed to calibrate hyperspectral images. This research used spectrum angle matching method and spectral red edge parameters discriminant method respectively to identify diseased tomato leaves. Using spectral red edge parameters discriminant method produced higher recognition accuracy, the accuracy was higher than 90%. Research results have shown that using hyperspectral imaging technology to identify diseased tomato leaves is feasible, and provides the discriminant basis for subsequent disease control of tomato plants.

  12. Research on hyperspectral dynamic scene and image sequence simulation

    NASA Astrophysics Data System (ADS)

    Sun, Dandan; Gao, Jiaobo; Sun, Kefeng; Hu, Yu; Li, Yu; Xie, Junhu; Zhang, Lei

    2016-10-01

    This paper presents a simulation method of hyper-spectral dynamic scene and image sequence for hyper-spectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyper-spectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyper-spectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyper-spectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyper-spectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyper-spectral images are consistent with the theoretical analysis results.

  13. Research on hyperspectral dynamic scene and image sequence simulation

    NASA Astrophysics Data System (ADS)

    Sun, Dandan; Liu, Fang; Gao, Jiaobo; Sun, Kefeng; Hu, Yu; Li, Yu; Xie, Junhu; Zhang, Lei

    2016-10-01

    This paper presents a simulation method of hyperspectral dynamic scene and image sequence for hyperspectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyperspectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyperspectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyperspectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyperspectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyperspectral images are consistent with the theoretical analysis results.

  14. Mapping pigment distribution in mud samples through hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

  15. Recent applications of hyperspectral imaging in microbiology.

    PubMed

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

    2015-05-01

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

  16. 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.

  17. Modular hyperspectral imager enables multiple research applications

    NASA Astrophysics Data System (ADS)

    Hô, Nicolas; Prel, Florent; Moreau, Louis; Lavoie, Hugo; Bouffard, François; Dubé, Denis; Thériault, Jean-Marc; Vallières, Christian; Roy, Claude

    2012-09-01

    The MR-i spectroradiometer can support a wide range of applications from its architecture suited to multiple configurations. Its modular 4-port FTIR spectroradiometer architecture allows the simultaneous use of two different detector modules, direct or differential input(s) and multiple telescopes. In a given configuration, MR-i can combine a MWIR focal plane array and a LWIR focal plane array to provide an extended spectral range from the two imaging sensors. The two detector array modules are imaging the same scene allowing synchronized pixel-to-pixel spectral range combination. In another configuration, MR-i can combine two identical focal plane arrays with different attenuation factors and two interleaved integration times per detector array. This configuration generates four sets of hyperspectral data cubes with different dynamic ranges that can be combined to produce a single hyperspectral cube with unmatched dynamic range. This configuration is particularly well suited for high-speed, high-dynamic range characterization of targets such as aircrafts, flares, and explosions. In a third configuration, named iCATSI, the spectroradiometer is used in differential input configuration to provide efficient optical background subtraction. The iCATSI configuration features an MCT detectors array with spectral cutoff near 14 µm. This extended spectral range and high sensitivity allows the detection and identification of a wide range of chemicals.

  18. 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.

  19. Rapid discrimination of main red meat species based on near-infrared hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

    Qiao, Lu; Peng, Yankun; Chao, Kuanglin; Qin, Jianwei

    2016-05-01

    Meat is the necessary source of essential nutrients for people including protein, fat, and so on. The discrimination of meat species and the determination of meat authenticity have been an important issue in the meat industry. The objective of this study is to realize the fast and accurate identification of three main red meats containing beef, lamb and pork by using near-infrared hyperspectral imaging (HSI) technology. After acquiring the hyperspectral images of meat samples, the calibration of acquired images and selection of the region of interest (ROI) were carried out. Then spectral preprocessing method of standard normal variate correction (SNV) was used to reduce the light scattering and random noise before the spectral analysis. Finally, characteristic wavelengths were extracted by principal component analysis (PCA), and the Fisher linear discriminant method was applied to establish Fisher discriminant functions to identify the meat species. All the samples were collected from different batches in order to improve the coverage of the models. In addition to the validation of sample itself in train set and cross validation, three different meat samples were sliced at the size of 2cm×2cm×2 cm approximately and were spliced together in one interface to be scanned by HSI system. The acquired hyperspectral data was applied to further validate the discriminant model. The results demonstrated that the near-infrared hyperspectral imaging technology could be applied as an effective, rapid and non-destructive discrimination method for main red meats.

  20. Locality Preserving Projection Based on Endmember Extraction for Hyperspectral Image Dimensionality Reduction and Target Detection.

    PubMed

    Wang, Yiting; Huang, Shiqi; Liu, Zhigang; Wang, Hongxia; Liu, Daizhi

    2016-09-01

    In order to reduce the effect of spectral variability on calculation precision for the weighted matrix in the locality preserving projection (LPP) algorithm, an improved dimensionality reduction method named endmember extraction-based locality preserving projection (EE-LPP) is proposed in this paper. The method primarily uses the vertex component analysis (VCA) method to extract endmember spectra from hyperspectral imagery. It then calculates the similarity between pixel spectra and the endmember spectra by using the spectral angle distance, and uses it as the basis for selecting neighboring pixels in the image and constructs a weighted matrix between pixels. Finally, based on the weighted matrix, the idea of the LPP algorithm is applied to reduce the dimensions of hyperspectral image data. Experimental results of real hyperspectral data demonstrate that the low-dimensional features acquired by the proposed methods can fully reflect the characteristics of the original image and further improve target detection accuracy.

  1. Hyperspectral image-based analysis of weathering sensitivity for safety diagnosis of Seongsan Ilchulbong Peak

    NASA Astrophysics Data System (ADS)

    Kim, Sungho; Kim, Heekang

    2016-10-01

    This paper presents a weathering sensitivity analysis method for the safety diagnosis of Seongsan Ilchulbong Peak using hyperspectral images. Remote sensing-based safety diagnosis is important for preventing accidents in famous mountains. A hyperspectral correlation-based method is proposed to evaluate the weathering sensitivity. The three issues are how to reduce the illumination effect, how to remove camera motion while acquiring images on a boat, and how to define the weathering sensitivity index. A novel minimum subtraction and maximum normalization (MSM-norm) method is proposed to solve the shadow and specular illumination problem. Geometrically distorted hyperspectral images are corrected by estimating the borderline of the mountain and sea surface. The final issue is solved by proposing a weathering sensitivity index (WS-Index) based on a spectral angle mapper. Real experiments on the Seongsan Ilchulbong Peak (UNESCO, World Natural Heritage) highlighted the feasibility of the proposed method in safety diagnosis by the weathering sensitivity index.

  2. 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.

  3. 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.

  4. Hyperspectral imaging fluorescence excitation scanning for detecting colorectal cancer: pilot study

    NASA Astrophysics Data System (ADS)

    Leavesley, Silas J.; Wheeler, Mikayla; Lopez, Carmen; Baker, Thomas; Favreau, Peter F.; Rich, Thomas C.; Rider, Paul F.; Boudreaux, Carole W.

    2016-03-01

    Optical spectroscopy and hyperspectral imaging have shown the theoretical potential to discriminate between cancerous and non-cancerous tissue with high sensitivity and specificity. To date, these techniques have not been able to be effectively translated to endoscope platforms. Hyperspectral imaging of the fluorescence excitation spectrum represents a new technology that may be well-suited for endoscopic implementation. However, the feasibility of detecting differences between normal and cancerous mucosa using fluorescence excitation-scanning hyperspectral imaging has not been evaluated. The objective of this pilot study was to evaluate the changes in the fluorescence excitation spectrum of resected specimen pairs of colorectal adenocarcinoma and normal colorectal mucosa. Patients being treated for colorectal adenocarcinoma were enrolled. Representative adenocarcinoma and normal colonic mucosa specimens were collected from each case. Specimens were flash frozen in liquid nitrogen. Adenocarcinoma was confirmed by histologic evaluation of H&E permanent sections. Hyperspectral image data of the fluorescence excitation of adenocarcinoma and surrounding normal tissue were acquired using a custom microscope configuration previously developed in our lab. Results demonstrated consistent spectral differences between normal and cancerous tissues over the fluorescence excitation spectral range of 390-450 nm. We conclude that fluorescence excitation-scanning hyperspectral imaging may offer an alternative approach for differentiating adenocarcinoma and surrounding normal mucosa of the colon. Future work will focus on expanding the number of specimen pairs analyzed and will utilize fresh tissues where possible, as flash freezing and reconstituting tissues may have altered the autofluorescence properties.

  5. Mid-infrared hyperspectral imaging of painting materials

    NASA Astrophysics Data System (ADS)

    Rosi, Francesca; Harig, Roland; Miliani, Costanza; Braun, René; Sali, Diego; Daveri, Alessia; Brunetti, Brunetto G.; Sgamellotti, Antonio

    2013-05-01

    A novel hyperspectral imaging system (HI90, Bruker Optics), working in the mid-infrared range and recently developed for the remote identification and mapping of hazardous compounds, has here been optimized for investigating painting surfaces. The painting Sestante 10 (1982) by Alberto Burri has been spectrally and spatially investigated with the HI90 system revealing the distribution of inorganic materials constituting the artworks. In order to validate the results obtainable by the imager for the pigment identification previous tests on laboratory models were performed. Yellow, white and blue pigments painted with different binders (namely egg, alkyd, acrylic and vinyl) were investigated by the HI90. Afterwards, the polychrome painting Sestante 10 was investigated focusing the attention on the inorganic material distribution revealing the presence of different extenders (kaolin, BaSO4, CaSO4) mixed with the various silica-based pigments present in the painting. The brightness temperature spectra collected by HI90 have also been compared to single point reflection spectra acquired by a conventional portable FTIR spectrometer (Alpha-R by Bruker Optics) highlighting the good spectral quality of the imaging system. This comparison permitted also to evaluate the spectral response and the diagnostic strengths of the spectral range available by the HI90 imaging (1300-860 cm-1), validating the reliability of the obtained chemical images. This study clearly highlights the high potential of the new hyperspectral imaging system and opens up new perspectives in the current scientific interest devoted to the application of mapping and imaging methods for the study of painting surfaces.

  6. Polarized hyperspectral imaging system for in vivo detection of vulvar lichen sclerosis

    NASA Astrophysics Data System (ADS)

    Qu, Yingjie; Ren, Wenqi; Liu, Songde; Liu, Peng; Xie, Lan; Zhang, Xiaoyuan; Zhang, Shiwu; Chang, Shufang; Xu, Ronald

    2016-03-01

    Vulvar lichen sclerosis (VLS) is a chronic, inflammatory and mucocutaneous disease of extragenital skin, which often goes undetected for years. The underlying causes are associated with the decrease of VEGF that reduces the blood oxygenation of vulva and the structural changes in the collagen fibrils, which can lead to scarring of the affected area. However, few methods are available for quantitative detection of VLS. Clinician's examinations are subjective and may lead to misdiagnosis. Spectroscopy is a potentially effective method for noninvasive detection of VLS. In this paper, we developed a polarized, hyperspectral imaging system for quantitative assessment. The system utilized a hyperspectral camera to collect the reflectance images of the entire vulva under Xenon lamp illumination with and without a polarizer in front of the fiber. One image (Ipar) acquired with the AOTF parallel to the polarization of illumination and the other image (Iper) acquired with the AOTF perpendicular to the illumination. This paper compares polarized images of VLS in a pilot clinical study. The collected reflectance data under Xenon lamp illumination without a polarizer are calibrated and the hyperspectral signals are extracted. An IRB approved clinical trial was carried out to evaluate the clinical utility for VLS detection. Our pilot study has demonstrated the technical potential of using this polarized hyperspectral imaging system for in vivo detection of vulvar lichen sclerosis.

  7. 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.

  8. Compressive hyperspectral and multispectral imaging fusion

    NASA Astrophysics Data System (ADS)

    Espitia, Óscar; Castillo, Sergio; Arguello, Henry

    2016-05-01

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

  9. Massively parallel processing of remotely sensed hyperspectral images

    NASA Astrophysics Data System (ADS)

    Plaza, Javier; Plaza, Antonio; Valencia, David; Paz, Abel

    2009-08-01

    In this paper, we develop several parallel techniques for hyperspectral image processing that have been specifically designed to be run on massively parallel systems. The techniques developed cover the three relevant areas of hyperspectral image processing: 1) spectral mixture analysis, a popular approach to characterize mixed pixels in hyperspectral data addressed in this work via efficient implementation of a morphological algorithm for automatic identification of pure spectral signatures or endmembers from the input data; 2) supervised classification of hyperspectral data using multi-layer perceptron neural networks with back-propagation learning; and 3) automatic target detection in the hyperspectral data using orthogonal subspace projection concepts. The scalability of the proposed parallel techniques is investigated using Barcelona Supercomputing Center's MareNostrum facility, one of the most powerful supercomputers in Europe.

  10. Hyperspectral image reconstruction for diffuse optical tomography

    PubMed Central

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

    2011-01-01

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

  11. Hyperspectral fluorescence imaging system for biomedical diagnostics

    NASA Astrophysics Data System (ADS)

    Martin, Matthew E.; Wabuyele, Musundi B.; Panjehpour, Masoud; Phan, Mary N.; Overholt, Bergein F.; Vo-Dinh, Tuan

    2006-02-01

    An advanced hyper-spectral imaging (HSI) system has been developed for use in medical diagnostics. One such diagnostic, esophageal cancer is diagnosed currently through biopsy and subsequent pathology. The end goal of this research is to develop an optical-based technique to assist or replace biopsy. In this paper, we demonstrate an instrument that has the capability to optically diagnose cancer in laboratory mice. We have developed a real-time HSI system based on state-of-the-art liquid crystal tunable filter (LCTF) technology coupled to an endoscope. This unique HSI technology is being developed to obtain spatially resolved images of the slight differences in luminescent properties of normal versus tumorous tissues. In this report, an in-vivo mouse study is shown. A predictive measure of cancer for the mice studied is developed and shown. It is hoped that the results of this study will lead to advances in the optical diagnosis of esophageal cancer in humans.

  12. Metric Learning for Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

  13. Detecting and Characterizing Nighttime Lighting Using Multispectral and Hyperspectral Imaging

    DTIC Science & Technology

    2012-12-01

    astronaut color photography and 8-, 6-, and 4-band MSI generated by modeling high spectral resolution hyperspectral imagery (HSI) data to lower spectral... photography and 8-, 6- and 4-band MSI generated by modeling high spectral resolution hyperspectral imagery (HSI) data to lower spectral resolution were...acquired from NASA’s Gateway to Astronaut Photography of Earth (http://www.eol.jsc.nasa.gov) .......................................27  Figure 20

  14. Hyperspectral image super-resolution: a hybrid color mapping approach

    NASA Astrophysics Data System (ADS)

    Zhou, Jin; Kwan, Chiman; Budavari, Bence

    2016-07-01

    NASA has been planning a hyperspectral infrared imager mission which will provide global coverage using a hyperspectral imager with 60-m resolution. In some practical applications, such as special crop monitoring or mineral mapping, 60-m resolution may still be too coarse. There have been many pansharpening algorithms for hyperspectral images by fusing high-resolution (HR) panchromatic or multispectral images with low-resolution (LR) hyperspectral images. We propose an approach to generating HR hyperspectral images by fusing high spatial resolution color images with low spatial resolution hyperspectral images. The idea is called hybrid color mapping (HCM) and involves a mapping between a high spatial resolution color image and a low spatial resolution hyperspectral image. Several variants of the color mapping idea, including global, local, and hybrid, are proposed and investigated. It was found that the local HCM yielded the best performance. Comparison of the local HCM with >10 state-of-the-art algorithms using five performance metrics has been carried out using actual images from the air force and NASA. Although our HCM method does not require a point spread function (PSF), our results are comparable to or better than those methods that do require PSF. More importantly, our performance is better than most if not all methods that do not require PSF. After applying our HCM algorithm, not only the visual performance of the hyperspectral image has been significantly improved, but the target classification performance has also been improved. Another advantage of our technique is that it is very efficient and can be easily parallelized. Hence, our algorithm is very suitable for real-time applications.

  15. 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

  16. A survey of landmine detection using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Makki, Ihab; Younes, Rafic; Francis, Clovis; Bianchi, Tiziano; Zucchetti, Massimo

    2017-02-01

    Hyperspectral imaging is a trending technique in remote sensing that finds its application in many different areas, such as agriculture, mapping, target detection, food quality monitoring, etc. This technique gives the ability to remotely identify the composition of each pixel of the image. Therefore, it is a natural candidate for the purpose of landmine detection, thanks to its inherent safety and fast response time. In this paper, we will present the results of several studies that employed hyperspectral imaging for the purpose of landmine detection, discussing the different signal processing techniques used in this framework for hyperspectral image processing and target detection. Our purpose is to highlight the progresses attained in the detection of landmines using hyperspectral imaging and to identify possible perspectives for future work, in order to achieve a better detection in real-time operation mode.

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

    PubMed

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

    2015-02-01

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

  18. Hyperspectral imaging simulation of object under sea-sky background

    NASA Astrophysics Data System (ADS)

    Wang, Biao; Lin, Jia-xuan; Gao, Wei; Yue, Hui

    2016-10-01

    Remote sensing image simulation plays an important role in spaceborne/airborne load demonstration and algorithm development. Hyperspectral imaging is valuable in marine monitoring, search and rescue. On the demand of spectral imaging of objects under the complex sea scene, physics based simulation method of spectral image of object under sea scene is proposed. On the development of an imaging simulation model considering object, background, atmosphere conditions, sensor, it is able to examine the influence of wind speed, atmosphere conditions and other environment factors change on spectral image quality under complex sea scene. Firstly, the sea scattering model is established based on the Philips sea spectral model, the rough surface scattering theory and the water volume scattering characteristics. The measured bi directional reflectance distribution function (BRDF) data of objects is fit to the statistical model. MODTRAN software is used to obtain solar illumination on the sea, sky brightness, the atmosphere transmittance from sea to sensor and atmosphere backscattered radiance, and Monte Carlo ray tracing method is used to calculate the sea surface object composite scattering and spectral image. Finally, the object spectrum is acquired by the space transformation, radiation degradation and adding the noise. The model connects the spectrum image with the environmental parameters, the object parameters, and the sensor parameters, which provide a tool for the load demonstration and algorithm development.

  19. 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.

  20. How to predict the sugariness and hardness of melons: A near-infrared hyperspectral imaging method.

    PubMed

    Sun, Meijun; Zhang, Dong; Liu, Li; Wang, Zheng

    2017-03-01

    Hyperspectral imaging (HSI) in the near-infrared (NIR) region (900-1700nm) was used for non-intrusive quality measurements (of sweetness and texture) in melons. First, HSI data from melon samples were acquired to extract the spectral signatures. The corresponding sample sweetness and hardness values were recorded using traditional intrusive methods. Partial least squares regression (PLSR), principal component analysis (PCA), support vector machine (SVM), and artificial neural network (ANN) models were created to predict melon sweetness and hardness values from the hyperspectral data. Experimental results for the three types of melons show that PLSR produces the most accurate results. To reduce the high dimensionality of the hyperspectral data, the weighted regression coefficients of the resulting PLSR models were used to identify the most important wavelengths. On the basis of these wavelengths, each image pixel was used to visualize the sweetness and hardness in all the portions of each sample.

  1. Hyperspectral Image Classification via Kernel Sparse Representation

    DTIC Science & Technology

    2013-01-01

    and sparse representations, image processing, wavelets, multirate systems , and filter banks . Nasser M. Nasrabadi (S’80–M’84–SM’92–F’01) received the...ery, sampling, multirate systems , filter banks , transforms, wavelets, and their applications in signal analysis, compression, processing, and...University of Pavia and the Center of Pavia images, are urban images acquired by the Reflective Optics System Imaging Spectrom- eter (ROSIS). The ROSIS

  2. Remote sensing of shorelines using data fusion of hyperspectral and multispectral imagery acquired from mobile and fixed platforms

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R.; Frystacky, Heather

    2012-06-01

    An optimized data fusion methodology is presented and makes use of airborne and vessel mounted hyperspectral and multispectral imagery acquired at littoral zones in Florida and the northern Gulf of Mexico. The results demonstrate the use of hyperspectral-multispectral data fusion anomaly detection along shorelines and in surface and subsurface waters. Hyperspectral imagery utilized in the data fusion analysis was collected using a 64-1024 channel, 1376 pixel swath width; temperature stabilized sensing system; an integrated inertial motion unit; and differential GPS. The imaging system is calibrated using dual 18 inch calibration spheres, spectral line sources, and custom line targets. Simultaneously collected multispectral three band imagery used in the data fusion analysis was derived either a 12 inch focal length large format camera using 9 inch high speed AGFA color negative film, a 12.3 megapixel digital camera or dual high speed full definition video cameras. Pushbroom sensor imagery is corrected using Kalman filtering and smoothing in order to correct images for airborne platform motions or motions of a small vessel. Custom software developed for the hyperspectral system and the optimized data fusion process allows for post processing using atmospherically corrected and georeferenced reflectance imagery. The optimized data fusion approach allows for detecting spectral anomalies in the resolution enhanced data cubes. Spectral-spatial anomaly detection is demonstrated using simulated embedded targets in actual imagery. The approach allows one to utilize spectral signature anomalies to identify features and targets that would otherwise not be possible. The optimized data fusion techniques and software has been developed in order to perform sensitivity analysis of the synthetic images in order to optimize the singular value decomposition model building process and the 2-D Butterworth cutoff frequency selection process, using the concept of user defined "feature

  3. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-07-01

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

  5. Hyperspectral imaging fluorescence excitation scanning for colon cancer detection

    NASA Astrophysics Data System (ADS)

    Leavesley, Silas J.; Walters, Mikayla; Lopez, Carmen; Baker, Thomas; Favreau, Peter F.; Rich, Thomas C.; Rider, Paul F.; Boudreaux, Carole W.

    2016-10-01

    Optical spectroscopy and hyperspectral imaging have shown the potential to discriminate between cancerous and noncancerous tissue with high sensitivity and specificity. However, to date, these techniques have not been effectively translated to real-time endoscope platforms. Hyperspectral imaging of the fluorescence excitation spectrum represents new technology that may be well suited for endoscopic implementation. However, the feasibility of detecting differences between normal and cancerous mucosa using fluorescence excitation-scanning hyperspectral imaging has not been evaluated. The goal of this study was to evaluate the initial feasibility of using fluorescence excitation-scanning hyperspectral imaging for measuring changes in fluorescence excitation spectrum concurrent with colonic adenocarcinoma using a small pre-pilot-scale sample size. Ex vivo analysis was performed using resected pairs of colorectal adenocarcinoma and normal mucosa. Adenocarcinoma was confirmed by histologic evaluation of hematoxylin and eosin (H&E) permanent sections. Specimens were imaged using a custom hyperspectral imaging fluorescence excitation-scanning microscope system. Results demonstrated consistent spectral differences between normal and cancerous tissues over the fluorescence excitation range of 390 to 450 nm that could be the basis for wavelength-dependent detection of colorectal cancers. Hence, excitation-scanning hyperspectral imaging may offer an alternative approach for discriminating adenocarcinoma from surrounding normal colonic mucosa, but further studies will be required to evaluate the accuracy of this approach using a larger patient cohort.

  6. [Detection of Late Blight Disease on Potato Leaves Using Hyperspectral Imaging Technique].

    PubMed

    Hu, Yao-hua; Ping, Xue-wen; Xu, Ming-zhu; Shan, Wei-xing; He, Yong

    2016-02-01

    Hyperspectral imaging feature on potato leaves stressed by late blight was studied in the present paper. The experiment used 60 potato leaves. Among those 60 potato leaves, 48 leaves were vitro inoculated with pathogen of potato late blight, the rest 12 leaves were used as control samples. The leaves were observed for 7 continuous days before and after inoculated and samples including healthy and infested were acquired. Hyperspectral data of healthy and infected potato samples of different disease severity were obtained by the hyperspectral imaging system from 374 to 1,018 nm and then extract spectral data of region of interest (ROI) from those hyperspectral data by the ENVI software. In order to improve the signal-to-noise ratio, the spectral data were preprocessed using different pretreatment methods such as moving average smoothing, normalization, derivative, baseline etc. The least squares-support vector machine(LS-SVM) models were developed based on the raw and those preprocessed data. Among the nine models, the model that used the raw data and the data after the spectroscopic transformation performed best with the discrimination of 94.87%. It was demonstrated that it is realized to determine the potato late blight disease of different disease severity using hyperspectral imaging technique.

  7. 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.

  8. 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

  9. 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.

  10. Accuracy assessment of blind and semi-blind restoration methods for hyperspectral images

    NASA Astrophysics Data System (ADS)

    Zhang, Mo; Vozel, Benoit; Chehdi, Kacem; Uss, Mykhail; Abramov, Sergey; Lukin, Vladimir

    2016-10-01

    Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. When no knowledge is available about the degradations present or the original image, blind restoration methods must be considered. Otherwise, when a partial information is needed, semi-blind restoration methods can be considered. Numerous semi-blind and quite advanced methods are available in the literature. So to get better insights and feedback on the applicability and potential efficiency of a representative set of four semi-blind methods recently proposed, we have performed a comparative study of these methods in objective terms of blur filter and original image error estimation accuracy. In particular, we have paid special attention to the accurate recovering in the spectral dimension of original spectral signatures. We have analyzed peculiarities and factors restricting the applicability of these methods. Our tests are performed on a synthetic hyperspectral image, degraded with various synthetic blurs (out-of-focus, gaussian, motion) and with signal independent noise of typical levels such as those encountered in real hyperspectral images. This synthetic image has been built from various samples from classified areas of a real-life hyperspectral image, in order to benefit from realistic reference spectral signatures to recover after synthetic degradation. Conclusions, practical recommendations and perspectives are drawn from the results experimentally obtained.

  11. Infrared hyperspectral imaging sensor for gas detection

    NASA Astrophysics Data System (ADS)

    Hinnrichs, Michele

    2000-11-01

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

  12. [Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

    PubMed

    Liu, De-hua; Zhang, Shu-juan; Wang, Bin; Yu, Ke-qiang; Zhao, Yan-ru; He, Yong

    2015-11-01

    Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect

  13. Hyperspectral Image Classification using a Self-Organizing Map

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  14. 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

  15. 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.

  16. Hyperspectral imaging technology for pharmaceutical analysis

    NASA Astrophysics Data System (ADS)

    Hamilton, Sara J.; Lodder, Robert A.

    2002-06-01

    The sensitivity and spatial resolution of hyperspectral imaging instruments are tested in this paper using pharmaceutical applications. The first experiment tested the hypothesis that a near-IR tunable diode-based remote sensing system is capable of monitoring degradation of hard gelatin capsules at a relatively long distance. Spectra from the capsules were used to differentiate among capsules exposed to an atmosphere containing imaging spectrometry of tablets permits the identification and composition of multiple individual tables to be determined simultaneously. A near-IR camera was used to collect thousands of spectra simultaneously from a field of blister-packaged tablets. The number of tablets that a typical near-IR camera can currently analyze simultaneously form a field of blister- packaged tablets. The number of tablets that a typical near- IR camera can currently analyze simultaneously was estimated to be approximately 1300. The bootstrap error-adjusted single-sample technique chemometric-imaging algorithm was used to draw probability-density contour plots that revealed tablet composition. The single-capsule analysis provides an indication of how far apart the sample and instrumentation can be and still maintain adequate S/N, while the multiple- sample imaging experiment gives an indication of how many samples can be analyzed simultaneously while maintaining an adequate S/N and pixel coverage on each sample.

  17. Practical issues of hyperspectral imaging analysis of solid dosage forms.

    PubMed

    Amigo, José Manuel

    2010-09-01

    Hyperspectral imaging techniques have widely demonstrated their usefulness in different areas of interest in pharmaceutical research during the last decade. In particular, middle infrared, near infrared, and Raman methods have gained special relevance. This rapid increase has been promoted by the capability of hyperspectral techniques to provide robust and reliable chemical and spatial information on the distribution of components in pharmaceutical solid dosage forms. Furthermore, the valuable combination of hyperspectral imaging devices with adequate data processing techniques offers the perfect landscape for developing new methods for scanning and analyzing surfaces. Nevertheless, the instrumentation and subsequent data analysis are not exempt from issues that must be thoughtfully considered. This paper describes and discusses the main advantages and drawbacks of the measurements and data analysis of hyperspectral imaging techniques in the development of solid dosage forms.

  18. Improved Scanners for Microscopic Hyperspectral Imaging

    NASA Technical Reports Server (NTRS)

    Mao, Chengye

    2009-01-01

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

  19. Near infrared hyperspectral imaging in quality and safety evaluation of cereals.

    PubMed

    Sendin, Kate; Williams, Paul J; Manley, Marena

    2016-09-13

    The requirements of cereal research, as well as grading and evaluation of food products, have encouraged the development of non-destructive, rapid and accurate analytical techniques to evaluate grain quality and safety. NIR hyperspectral imaging integrates spectroscopy and imaging techniques in one analytical system, allowing direct identification of chemical components and their distribution within the sample. It is a promising technique that may be implemented on-line, enabling the cereal industry to move away from subjective, manual classification and measuring methods. NIR hyperspectral imaging has gained popularity for rapidly acquiring information to enable the quantification, identification or differentiation of a variety of cereal properties. The technique can potentially replace multiple conventional chemical, microbial or physical tests with a single, automated image acquisition. Individual kernels can be analysed non-destructively, enabling one to follow changes in the same kernel over time (e.g. fungal development). Although NIR hyperspectral imaging has not been extensively implemented in industry, it shows great potential for the development of an evaluation system to assess cereal grains, especially regarding variety discrimination and grading/classification properties. This review outlines the theory and principles of NIR hyperspectral imaging, and focuses specifically on its application in cereal science research and industry.

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

    PubMed

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

    2012-12-12

    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.

  1. 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

  2. Determining Spectral Reflectance Coefficients from Hyperspectral Images Obtained from Low Altitudes

    NASA Astrophysics Data System (ADS)

    Walczykowski, P.; Jenerowicz, A.; Orych, A.; Siok, K.

    2016-06-01

    Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an

  3. Hyperspectral Imaging and Obstacle Detection for Robotics Navigation

    DTIC Science & Technology

    2005-09-01

    such as anomaly or target detection , based on imaging sensor data can enhance UGVs’ capability to safely maneuver unknown terrain with increased speed...process; otherwise the deconvoluted images become very noisy , significantly affecting detection performance. • For SECOTS the image drift should be...Hyperspectral Imaging and Obstacle Detection for Robotics Navigation by Heesung Kwon, Dalton Rosario, Neelam Gupta, Matthew Thielke

  4. Hyperspectral range imaging for transportation systems evaluation

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  5. Advanced Airborne Hyperspectral Imaging System (AAHIS)

    NASA Astrophysics Data System (ADS)

    Topping, Miles Q.; Pfeiffer, Joel E.; Sparks, Andrew W.; Jim, Kevin T. C.; Yoon, Dugan

    2002-11-01

    The design, operation, and performance of the fourth generation of Science and Technology International's Advanced Airborne Hyperspectral Imaging Sensors (AAHIS) are described. These imaging spectrometers have a variable bandwidth ranging from 390-840 nm. A three-axis image stabilization provides spatially and spectrally coherent imagery by damping most of the airborne platform's random motion. A wide 40-degree field of view coupled with sub-pixel detection allows for a large area coverage rate. A software controlled variable aperture, spectral shaping filters, and high quantum efficiency, back-illuminated CCD's contribute to the excellent sensitivity of the sensors. AAHIS sensors have been operated on a variety of fixed and rotary wing platforms, achieving ground-sampling distances ranging from 6.5 cm to 2 m. While these sensors have been primarily designed for use over littoral zones, they are able to operate over both land and water. AAHIS has been used for detecting and locating submarines, mines, tanks, divers, camouflage and disturbed earth. Civilian applications include search and rescue on land and at sea, agricultural analysis, environmental time-series, coral reef assessment, effluent plume detection, coastal mapping, damage assessment, and seasonal whale population monitoring

  6. 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.

  7. 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

  8. Lossless compression of hyperspectral images using C-DPCM-APL with reference bands selection

    NASA Astrophysics Data System (ADS)

    Wang, Keyan; Liao, Huilin; Li, Yunsong; Zhang, Shanshan; Wu, Xianyun

    2014-05-01

    The availability of hyperspectral images has increased in recent years, which is used in military and civilian applications, such as target recognition, surveillance, geological mapping and environmental monitoring. Because of its abundant data quantity and special importance, now it exists lossless compression methods of hyperspectral images mainly exploiting the strong spatial or spectral correlation. C-DPCM-APL is a method that achieves highest lossless compression ratio on the CCSDS hyperspectral images acquired in 2006 but consuming longest processing time among existing lossless compression methods to determine the optimal prediction length for each band. C-DPCM-APL gets best compression performance mainly via using optimal prediction length but ignoring the correlationship between reference bands and the current band which is a crucial factor that influences the precision of prediction. Considering this, we propose a method that selects reference bands according to the atmospheric absorption characteristic of hyperspectral images. Experiments on CCSDS 2006 images data set show that the proposed reduces the computation complexity heavily without decaying its lossless compression performance when compared to C-DPCM-APL.

  9. [Simultaneous Detection of External and Internal Quality Parameters of Huping Jujube Fruits using Hyperspectral Imaging Technology].

    PubMed

    Xue, Jian-xin; Zhang, Shu-juan; Zhang, Jing-jing

    2015-08-01

    Nondestructive detection of external and internal quality parameters of jujube is crucial for improving jujube's shelf life and industry production. Hyperspectral imaging is an emerging technique that integrates conventional imaging and spectroscopy to acquire both spatial and spectral information from a sample. It takes the advantages of the conventional RGB, near-infrared spectroscopy, and multi-spectral imaging. In this work, hyperspectral imaging technology covered the range of 450~1000 nm has been evaluated for nondestructive determination of "natural defects" (shrink, crack, insect damage and peck injury) and soluble solids content (SSC) in Huping jujube fruit. 400 RGB images were acquired through four different defect (50 for each stage) and normal (200) classes of the Huping jujube samples. After acquiring hyperspectral images of Huping jujube fruits, the spectral data were extracted from region of interests (ROIs). Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (280) and test set (120) according to the proportion of 3:1. Seven principal components (PCs) were selected based on principal component analysis (PCA), and seven textural feature variables (contrast, correlation, energy, homogeneity, variance, mean and entropy) were extracted by gray level co-occurrence matrix (GLCM). The least squares support vector machine (LS-SVM) models were built based on the PCs spectral, textural, combined PCs and textural features, respectively. The satisfactory results show the correct discrimination rate of 92.5% for the prediction samples, as well as correlation coefficient (Rp) of 0.944 for the prediction set to calculate SSC content based on PCs and textural features. The study demonstrated that hyperspectral image technique can be a reliable tool to simultaneous detection of external ("natural defects") and internal (SSC) quality parameters of Huping jujube fruits, which provided a theoretical reference for nondestructive

  10. Raman Hyperspectral Imaging of Microfossils: Potential Pitfalls

    PubMed Central

    Olcott Marshall, Alison

    2013-01-01

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

  11. 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.

  12. Geographical classification of apple based on hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  13. Standoff midwave infrared hyperspectral imaging of ship plumes

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  14. Standoff midwave infrared hyperspectral imaging of ship plumes

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  16. 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

  17. Coherent anti-Stokes Raman scattering hyperspectral tissue imaging with a wavelength-swept system

    PubMed Central

    Bégin, Steve; Burgoyne, Bryan; Mercier, Vincent; Villeneuve, Alain; Vallée, Réal; Côté, Daniel

    2011-01-01

    We present a wavelength-swept coherent anti-Stokes Raman scattering (WS-CARS) spectroscopy system for hyperspectral imaging in thick tissue. We use a strategy where the Raman lines are excited sequentially, circumventing the need for a spectrometer. This fibre laser system, consisting of a pump laser synchronized with a rapidly tunable programmable laser (PL), can access Raman lines over a significant fraction of the high wavenumber region (2700–2950 cm−1) at rates of up to 10,000 spectral points per second. To demonstrate its capabilities, we have acquired WS-CARS spectra of several samples as well as images and hyperspectral images (HSI) of thick tissue both in forward and epi-detection. This instrument should be especially useful in providing local biochemical information with surrounding context supplied by imaging. PMID:21559141

  18. On the usefulness of hyperspectral imaging for face recognition

    NASA Astrophysics Data System (ADS)

    Bianco, Simone

    2016-11-01

    Hyperspectral cameras provide additional information in terms of multiple sampling of the visible spectrum, holding information that could be potentially useful for biometric applications. This paper investigates whether the performance of hyperspectral face recognition algorithms can be improved by considering single and multiple one-dimensional (1-D) projections of the whole spectral data along the spectral dimension. Three different projections are investigated and found by optimization: single-spectral band selection, nonnegative spectral band combination, and unbounded spectral band combination. Since 1-D projections can be performed directly on the imaging device with color filters, projections are also restricted to be physically plausible. The experiments are performed on a standard hyperspectral dataset and the obtained results outperform eight existing hyperspectral face recognition algorithms.

  19. Hyperspectral Imaging of fecal contamination on chickens

    NASA Technical Reports Server (NTRS)

    2003-01-01

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

  20. Food inspection using hyperspectral imaging and SVDD

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  1. Assessment of spatial information for hyperspectral imaging of lesion

    NASA Astrophysics Data System (ADS)

    Yang, Xue; Li, Gang; Lin, Ling

    2016-10-01

    Multiple diseases such as breast tumor poses a great threat to women's health and life, while the traditional detection method is complex, costly and unsuitable for frequently self-examination, therefore, an inexpensive, convenient and efficient method for tumor self-inspection is needed urgently, and lesion localization is an important step. This paper proposes an self-examination method for positioning of a lesion. The method adopts transillumination to acquire the hyperspectral images and to assess the spatial information of lesion. Firstly, multi-wavelength sources are modulated with frequency division, which is advantageous to separate images of different wavelength, meanwhile, the source serves as fill light to each other to improve the sensitivity in the low-lightlevel imaging. Secondly, the signal-to-noise ratio of transmitted images after demodulation are improved by frame accumulation technology. Next, gray distributions of transmitted images are analyzed. The gray-level differences is constituted by the actual transmitted images and fitting transmitted images of tissue without lesion, which is to rule out individual differences. Due to scattering effect, there will be transition zones between tissue and lesion, and the zone changes with wavelength change, which will help to identify the structure details of lesion. Finally, image segmentation is adopted to extract the lesion and the transition zones, and the spatial features of lesion are confirmed according to the transition zones and the differences of transmitted light intensity distributions. Experiment using flat-shaped tissue as an example shows that the proposed method can extract the space information of lesion.

  2. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle

    SciTech Connect

    Hruska, Ryan; Mitchell, Jessica; Anderson, Matthew; Glenn, Nancy F.

    2012-09-17

    During the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral in-flight calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the U.S. Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 meters (based on RMSE).

  3. Spectral light source distribution variations to enhance discrimination of the common bile duct from surroundings in reflectance hyperspectral images

    NASA Astrophysics Data System (ADS)

    Litorja, Maritoni; Fein, Mira; Wehner, Eleanor; Schwarz, Roderich; Zuzak, Karel; Livingston, Edward

    2013-03-01

    The classification of anatomical features using hyperspectral imaging has been a common goal in biomedical hyperspectral imaging. Identification and location of the common bile duct is critical in cholecystectomies, one of the most common surgical procedures. In this study, surgical images where the common bile duct is visible to the surgeon during open surgeries of patients with normal bile ducts were acquired. The effect of the spectral distribution of simulated light sources on the scene color are explored with the objective of providing the optimum spectral light distribution that can enhance contrast between the common bile duct and surrounding tissue through luminance and color differences.

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

    PubMed

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

    2013-07-01

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

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

    PubMed

    Nogami, Satoru; Ohnuki, Shinsuke; Ohya, Yoshikazu

    2014-10-01

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

  6. 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.

  7. Hyperspectral imaging using the single-pixel Fourier transform technique

    NASA Astrophysics Data System (ADS)

    Jin, Senlin; Hui, Wangwei; Wang, Yunlong; Huang, Kaicheng; Shi, Qiushuai; Ying, Cuifeng; Liu, Dongqi; Ye, Qing; Zhou, Wenyuan; Tian, Jianguo

    2017-03-01

    Hyperspectral imaging technology is playing an increasingly important role in the fields of food analysis, medicine and biotechnology. To improve the speed of operation and increase the light throughput in a compact equipment structure, a Fourier transform hyperspectral imaging system based on a single-pixel technique is proposed in this study. Compared with current imaging spectrometry approaches, the proposed system has a wider spectral range (400–1100 nm), a better spectral resolution (1 nm) and requires fewer measurement data (a sample rate of 6.25%). The performance of this system was verified by its application to the non-destructive testing of potatoes.

  8. GRIN-optics-based hyperspectral imaging micro-sensor

    NASA Astrophysics Data System (ADS)

    Zhou, Wei; Leger, James

    2007-09-01

    By utilizing diffractive, refractive and graded-index optics technology, a miniature (1 mm x 1 mm x 2 mm) Computer-Tomography Imaging Spectrometer (CTIS) sensor has been designed with 16 independent optical channels working in a snap-shot mode for hyper-spectral imaging. The designed prototype covers a 400~700 nm wavelength range. One optical channel has been fabricated and characterized. By azimuthally rotating this optical channel along the optical axis and collecting different dispersed images to simulate the full sensor read-out, the full hyperspectral detection scheme has been demonstrated.

  9. Hyperspectral imaging using the single-pixel Fourier transform technique

    PubMed Central

    Jin, Senlin; Hui, Wangwei; Wang, Yunlong; Huang, Kaicheng; Shi, Qiushuai; Ying, Cuifeng; Liu, Dongqi; Ye, Qing; Zhou, Wenyuan; Tian, Jianguo

    2017-01-01

    Hyperspectral imaging technology is playing an increasingly important role in the fields of food analysis, medicine and biotechnology. To improve the speed of operation and increase the light throughput in a compact equipment structure, a Fourier transform hyperspectral imaging system based on a single-pixel technique is proposed in this study. Compared with current imaging spectrometry approaches, the proposed system has a wider spectral range (400–1100 nm), a better spectral resolution (1 nm) and requires fewer measurement data (a sample rate of 6.25%). The performance of this system was verified by its application to the non-destructive testing of potatoes. PMID:28338100

  10. Single-pixel hyperspectral imaging for real-time cancer detection: detecting damage in ex vivo porcine tissue samples

    NASA Astrophysics Data System (ADS)

    Peller, Joseph; Farahi, Faramarz; Trammell, Susan R.

    2016-03-01

    We are developing a single-pixel hyperspectral imaging system based on compressive sensing that acquires spatial and spectral information simultaneously. Our spectral imaging system uses autofluorescencent emission from collagen (400 nm) and NAD(P)H (475 nm), as well as, differences in the optical reflectance spectra as diagnostics for differentiating between healthy and diseased tissue. In this study, we demonstrate the ability of our imaging system to discriminate between healthy and damaged porcine epidermal tissue. Healthy porcine epidermal tissue samples (n=11) were imaged ex vivo using our hyperspectral system. The amount of NAD(P)H emission and the reflectance properties were approximately constant across the surface of healthy tissue samples. The tissue samples were then thermally damaged using an 1850 nm thulium fiber laser and re-imaged after laser irradiation. The damaged regions were clearly visible in the hyperspectral images as the thermal damage altered the fluorescent emission of NAD(P)H and changed the scattering properties of the tissue. The extent of the damaged regions was determined based on the hyperspectral images and these estimates were compared to damage extents measured in white light images acquired with a traditional camera. The extent of damage determined via hyperspectral imaging was in good agreement with estimates based on white light imaging indicating that our system is capable of differentiating between healthy and damaged tissue. Possible applications of our single pixel hyperspectral imaging system range from real-time determination of tumor margins during surgery to the use of this technique in the pathology lab to aid with cancer diagnosis and staging.

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  12. 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.

  13. The enhanced MODIS airborne simulator hyperspectral imager

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

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

  14. Hyperspectral fluorescence imaging with multi wavelength LED excitation

    NASA Astrophysics Data System (ADS)

    Luthman, A. Siri; Dumitru, Sebastian; Quirós-Gonzalez, Isabel; Bohndiek, Sarah E.

    2016-04-01

    Hyperspectral imaging (HSI) can combine morphological and molecular information, yielding potential for real-time and high throughput multiplexed fluorescent contrast agent imaging. Multiplexed readout from targets, such as cell surface receptors overexpressed in cancer cells, could improve both sensitivity and specificity of tumor identification. There remains, however, a need for compact and cost effective implementations of the technology. We have implemented a low-cost wide-field multiplexed fluorescence imaging system, which combines LED excitation at 590, 655 and 740 nm with a compact commercial solid state HSI system operating in the range 600 - 1000 nm. A key challenge for using reflectance-based HSI is the separation of contrast agent fluorescence from the reflectance of the excitation light. Here, we illustrate how it is possible to address this challenge in software, using two offline reflectance removal methods, prior to least-squares spectral unmixing. We made a quantitative comparison of the methods using data acquired from dilutions of contrast agents prepared in well-plates. We then established the capability of our HSI system for non-invasive in vivo fluorescence imaging in small animals using the optimal reflectance removal method. The HSI presented here enables quantitative unmixing of at least four fluorescent contrast agents (Alexa Fluor 610, 647, 700 and 750) simultaneously in living mice. A successful unmixing of the four fluorescent contrast agents was possible both using the pure contrast agents and with mixtures. The system could in principle also be applied to imaging of ex vivo tissue or intraoperative imaging in a clinical setting. These data suggest a promising approach for developing clinical applications of HSI based on multiplexed fluorescence contrast agent imaging.

  15. LED lighting for use in multispectral and hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  16. 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...

  17. [Hyperspectral image compression technology research based on EZW].

    PubMed

    Wei, Jun-Xia; Xiangli, Bin; Duan, Xiao-Feng; Xu, Zhao-Hui; Xue, Li-Jun

    2011-08-01

    Along with the development of hyperspectral remote sensing technology, hyperspectral imaging technology has been applied in the aspect of aviation and spaceflight, which is different from multispectral imaging, and with the band width of nanoscale spectral imaging the target continuously, the image resolution is very high. However, with the increasing number of band, spectral data quantity will be more and more, and these data storage and transmission is the problem that the authors must face. Along with the development of wavelet compression technology, in field of image compression, many people adopted and improved EZW, the present paper used the method in hyperspectral spatial dimension compression, but does not involved the spectrum dimension compression. From hyperspectral image compression reconstruction results, whether from the peak signal-to-noise ratio (PSNR) and spectral curve or from the subjective comparison of source and reconstruction image, the effect is well. If the first compression of image from spectrum dimension is made, then compression on space dimension, the authors believe the effect will be better.

  18. A Framework of Hyperspectral Image Compression using Neural Networks

    SciTech Connect

    Masalmah, Yahya M.; Martínez Nieves, Christian; Rivera Soto, Rafael; Velez, Carlos; Gonzalez, Jenipher

    2015-01-01

    Hyperspectral image analysis has gained great attention due to its wide range of applications. Hyperspectral images provide a vast amount of information about underlying objects in an image by using a large range of the electromagnetic spectrum for each pixel. However, since the same image is taken multiple times using distinct electromagnetic bands, the size of such images tend to be significant, which leads to greater processing requirements. The aim of this paper is to present a proposed framework for image compression and to study the possible effects of spatial compression on quality of unmixing results. Image compression allows us to reduce the dimensionality of an image while still preserving most of the original information, which could lead to faster image processing. Lastly, this paper presents preliminary results of different training techniques used in Artificial Neural Network (ANN) based compression algorithm.

  19. A Framework of Hyperspectral Image Compression using Neural Networks

    DOE PAGES

    Masalmah, Yahya M.; Martínez Nieves, Christian; Rivera Soto, Rafael; ...

    2015-01-01

    Hyperspectral image analysis has gained great attention due to its wide range of applications. Hyperspectral images provide a vast amount of information about underlying objects in an image by using a large range of the electromagnetic spectrum for each pixel. However, since the same image is taken multiple times using distinct electromagnetic bands, the size of such images tend to be significant, which leads to greater processing requirements. The aim of this paper is to present a proposed framework for image compression and to study the possible effects of spatial compression on quality of unmixing results. Image compression allows usmore » to reduce the dimensionality of an image while still preserving most of the original information, which could lead to faster image processing. Lastly, this paper presents preliminary results of different training techniques used in Artificial Neural Network (ANN) based compression algorithm.« less

  20. 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.

  1. Parallel hyperspectral image reconstruction using random projections

    NASA Astrophysics Data System (ADS)

    Sevilla, Jorge; Martín, Gabriel; Nascimento, José M. P.

    2016-10-01

    Spaceborne sensors systems are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. Random projections techniques have been demonstrated as an effective and very light way to reduce the number of measurements in hyperspectral data, thus, the data to be transmitted to the Earth station is reduced. However, the reconstruction of the original data from the random projections may be computationally expensive. SpeCA is a blind hyperspectral reconstruction technique that exploits the fact that hyperspectral vectors often belong to a low dimensional subspace. SpeCA has shown promising results in the task of recovering hyperspectral data from a reduced number of random measurements. In this manuscript we focus on the implementation of the SpeCA algorithm for graphics processing units (GPU) using the compute unified device architecture (CUDA). Experimental results conducted using synthetic and real hyperspectral datasets on the GPU architecture by NVIDIA: GeForce GTX 980, reveal that the use of GPUs can provide real-time reconstruction. The achieved speedup is up to 22 times when compared with the processing time of SpeCA running on one core of the Intel i7-4790K CPU (3.4GHz), with 32 Gbyte memory.

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

    NASA Astrophysics Data System (ADS)

    Serranti, Silvia; Cesare, Daniela; Bonifazi, Giuseppe

    2012-05-01

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

  3. Hyperspectral Image Super-resolution via Non-negative Structured Sparse Representation.

    PubMed

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

    2016-03-22

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

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

    PubMed

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

    2016-05-01

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

  5. Sparse Superpixel Unmixing for Exploratory Analysis of CRISM Hyperspectral Images

    NASA Technical Reports Server (NTRS)

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

    2009-01-01

    Fast automated analysis of hyperspectral imagery can inform observation planning and tactical decisions during planetary exploration. Products such as mineralogical maps can focus analysts' attention on areas of interest and assist data mining in large hyperspectral catalogs. In this work, sparse spectral unmixing drafts mineral abundance maps with Compact Reconnaissance Imaging Spectrometer (CRISM) images from the Mars Reconnaissance Orbiter. We demonstrate a novel "superpixel" segmentation strategy enabling efficient unmixing in an interactive session. Tests correlate automatic unmixing results based on redundant spectral libraries against hand-tuned summary products currently in use by CRISM researchers.

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

    PubMed

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

    2015-03-17

    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.

  7. 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.

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

    PubMed Central

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

    2015-01-01

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

  9. Classification of bee pollen grains using hyperspectral microscopy imaging and Fisher linear classifier

    NASA Astrophysics Data System (ADS)

    Su, Kang; Zhu, Siqi; Wei, Lin; Li, Zhen; Yin, Hao; Ye, Pingping; Li, Anming; Chen, Zhenqiang; Li, Migao

    2016-05-01

    The rapid and accurate classification of bee pollen grains is still a challenge. The purpose of this paper is to develop a method which could directly classify bee pollen grains based on fluorescence spectra. Bee pollen grain samples of six species were excited by a 409-nm laser diode source, and their fluorescence images were acquired by a hyperspectral microscopy imaging (HMI) system. One hundred pixels in the region of interest were randomly selected from each single bee pollen species. The fluorescence spectral information in all the selected pixels was stored in an n-dimensional hyperspectral data set, where n=37 for a total of 37 hyperspectral bands (465 to 645 nm). The hyperspectral data set was classified using a Fisher linear classifier. The performance of the Fisher linear classifier was measured by the leave-one-out cross-validation method, which yielded an overall accuracy of 89.2%. Finally, additional blinded samples were used to evaluate the established classification model, which demonstrated that bee pollen mixtures could be classified efficiently with the HMI system.

  10. 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.

  11. Application of hyperspectral imaging spectrometer systems to industrial inspection

    NASA Astrophysics Data System (ADS)

    Willoughby, Charles T.; Folkman, Mark A.; Figueroa, Miguel A.

    1996-01-01

    The past decade has seen the development of multispectral and hyperspectral imaging spectrometers for use in remote sensing applications in the aerospace business. Correspondingly, advanced electronic imaging techniques have been exploited for use in industrial inspection and manufacturing process control. TRW has been involved in hyperspectral imaging since 1989 for use in remote sensing of earth resources and has developed many instruments and related technologies which can easily be re-applied to unique industrial inspection applications. These instruments operate in the visible, near-infrared and short-wave infrared wavebands covering the range from 0.4 microns to 2.5 microns depending on the application. The exploitation of hyperspectral imagers for remote sensing has shown the power of spectral imaging for typing and discrimination tasks, which can be readily applied to industrial applications. In this paper we explain the relevant fundamentals of hyperspectral imaging and how it can be exploited for industrial inspection and process control tasks, particularly those that require color or spectral typing and discrimination. The associated technologies used to perform measurements and reduce the data also are described.

  12. a Review of Hyperspectral Imaging in Close Range Applications

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  13. Airborne Hyperspectral Imaging of Supraglacial Lakes in Greenland's Ablation Zone

    NASA Astrophysics Data System (ADS)

    Adler, J.; Behar, A. E.; Jacobson, N. T.

    2010-12-01

    In 2010 an airborne instrument was assembled to image supraglacial lakes near the Jakobshavn Isbrae of the Greenland Ice Sheet. The instrument was designed to fly on a helicopter, and consists of a hyperspectral imager, a GPS/inertial measurement unit (GPS/IMU), and a data-logging computer. A series of narrow visible optical channels ~13nm wide, such as found in a hyperspectral imager, are theorized to be useful in determining the depths of supraglacial lakes using techniques based on the Beer-Lambert-Bouguer Law. During June, several supraglacial lakes were selected for study each day, based upon MODIS imagery taken during the previous week. Flying over a given lake, several track lines were flown to image both shallow and deep sections of the lake, imaging the full range of depth for future algorithm development. The telescoping instrument mount was constructed to allow the sensor package to be deployed from a helicopter in-flight, with an unobstructed downward-facing field of view. The GPS/IMU records the pointing orientation, altitude, and geographical coordinates of the imager to the data-logger, in order to allow post-flight geo-referencing of the raw hyperspectral imagery. With this geo-referenced spectrum data, a depth map for a given lake can be calculated through reference to a water absorptivity model. This risk-reduction expedition to fly a helicopter-borne hyperspectral imager over the supraglacial lakes of Greenland was a success. The instrument mount for the imager worked as designed, and no vibration issues were encountered. As a result, we have confidence in the instrument platform's performance during future surveys of Greenland's supraglacial lakes. The hyperspectral imager, data acquisition computer, and geo-referencing services are provided by Resonon, Inc. of Bozeman, MT, and the GPS/IMU is manufactured by Cloudcap Technology of Hood River, OR.

  14. Combining hyperspectral imaging and Raman spectroscopy for remote chemical sensing

    NASA Astrophysics Data System (ADS)

    Ingram, John M.; Lo, Edsanter

    2008-04-01

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

  15. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  17. Seeing the Invisible: Revealing Atrial Ablation Lesions Using Hyperspectral Imaging Approach

    PubMed Central

    Muselimyan, Narine; Swift, Luther M.; Asfour, Huda; Chahbazian, Tigran; Mazhari, Ramesh; Mercader, Marco A.; Sarvazyan, Narine A.

    2016-01-01

    Background Currently, there are limited means for high-resolution monitoring of tissue injury during radiofrequency ablation procedures. Objective To develop the next generation of visualization catheters that can reveal irreversible atrial muscle damage caused by ablation and identify viability gaps between the lesions. Methods Radiofrequency lesions were placed on the endocardial surfaces of excised human and bovine atria and left ventricles of blood perfused rat hearts. Tissue was illuminated with 365nm light and a series of images were acquired from individual spectral bands within 420-720nm range. By extracting spectral profiles of individual pixels and spectral unmixing, the relative contribution of ablated and unablated spectra to each pixel was then displayed. Results of spectral unmixing were compared to lesion pathology. Results RF ablation caused significant changes in the tissue autofluorescence profile. The magnitude of these spectral changes in human left atrium was relatively small (< 10% of peak fluorescence value), yet highly significant. Spectral unmixing of hyperspectral datasets enabled high spatial resolution, in-situ delineation of radiofrequency lesion boundaries without the need for exogenous markers. Lesion dimensions derived from hyperspectral imaging approach strongly correlated with histological outcomes. Presence of blood within the myocardium decreased the amplitude of the autofluorescence spectra while having minimal effect on their overall shapes. As a result, the ability of hyperspectral imaging to delineate ablation lesions in vivo was not affected. Conclusions Hyperspectral imaging greatly increases the contrast between ablated and unablated tissue enabling visualization of viability gaps at clinically relevant locations. Data supports the possibility for developing percutaneous hyperspectral catheters for high-resolution ablation guidance. PMID:27930718

  18. 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.

  19. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Zeng, Shan; Bai, Jun; Wang, Haibin

    2015-12-01

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

  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. 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 ...

  4. Context Modeler for Wavelet Compression of Spectral Hyperspectral Images

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  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. 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.

  7. A multidimensional approach for striping noise compensation in hyperspectral imaging devices

    NASA Astrophysics Data System (ADS)

    Meza, Pablo; Parra, Francisca; Torres, Sergio N.; Pezoa, Jorge E.; Coelho, Pablo

    2011-10-01

    Algorithms for striping noise compensation (SNC) for push-broom hyperspectral cameras (PBHCs) are primarily based on image processing techniques. These algorithms rely on the spatial and temporal information available at the readout data; however, they disregard the large amount of spectral information also available at the data. In this paper such flaw has been tackled and a multidimensional approach for SNC is proposed. The main assumption of the proposed approach is the short-term stationary behavior of the spatial, spectral, and temporal input information. This assumption is justified after analyzing the optoelectronic sampling mechanism carried out by PBHCs. Namely, when the wavelength-resolution of hyperspectral cameras is high enough with respect to the target application, the spectral information at neighboring photodetectors in adjacent spectral bands can be regarded as a stationary input. Moreover, when the temporal scanning of hyperspectral information is fast enough, consecutive temporal and spectral data samples can also be regarded as a stationary input at a single photodetector. The strength and applicability of the multidimensional approach presented here is illustrated by compensating for stripping noise real hyperspectral images. To this end, a laboratory prototype, based on a Photonfocus Hurricane hyperspectral camera, has been implemented to acquire data in the range of 400-1000 [nm], at a wavelength resolution of 1.04 [nm]. A mobile platform has been also constructed to simulate and synchronize the scanning procedure of the camera. Finally, an image-processing-based SNC algorithm has been extended yielding an approach that employs all the multidimensional information collected by the camera.

  8. [Study on the Color Determination of Tomato Leaves Stressed by the High Temperature Based on Hyperspectral Imaging].

    PubMed

    Xie, Chuan-qi; Saho, Yong-ni; Gao, Jun-feng; He, Yong

    2015-12-01

    Determination of color values on tomato leaves stressed by the high temperature using hyperspectral imaging technique was studied in this paper. Hyperspectral images of sixty healthy and sixty unhealthy tomato leaves in the wavelengths of 380-1023 nm were acquired by the hyperspectral imaging system. Simultaneously, three color parameters (L*, a* and b*) were measured by a colorimeter. Reflectance of all pixels in the region of interest (ROI) was extracted from the corrected hyperspectral image. Partial Least Squares (PLS) models were established based on different preprocessing methods. Successive Projections Algorithm (SPA) was identified to select effective wavelengths. Finally, Partial Least Squares-Discriminant Analysis (PLS-DA) models were built to classify different types of samples. The results showed that the determination coefficient (R²) were 0. 818, 0. 109 and 0. 896 in the prediction sets of PLS modes; 0.591, 0.244 and 0.673 in the prediction sets of SPA-PLS models. The overall classification accuracy in the prediction sets of PLS-DA models were over 77.50%. It demonstrated that it is feasible to measure color values on tomato leaves and identify different types of samples using hyperspectral imaging technique.

  9. Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

    NASA Astrophysics Data System (ADS)

    Cui, Binge; Ma, Xiudan; Xie, Xiaoyun; Ren, Guangbo; Ma, Yi

    2017-03-01

    The classification of hyperspectral images with a few labeled samples is a major challenge which is difficult to meet unless some spatial characteristics can be exploited. In this study, we proposed a novel spectral-spatial hyperspectral image classification method that exploited spatial autocorrelation of hyperspectral images. First, image segmentation is performed on the hyperspectral image to assign each pixel to a homogeneous region. Second, the visible and infrared bands of hyperspectral image are partitioned into multiple subsets of adjacent bands, and each subset is merged into one band. Recursive edge-preserving filtering is performed on each merged band which utilizes the spectral information of neighborhood pixels. Third, the resulting spectral and spatial feature band set is classified using the SVM classifier. Finally, bilateral filtering is performed to remove "salt-and-pepper" noise in the classification result. To preserve the spatial structure of hyperspectral image, edge-preserving filtering is applied independently before and after the classification process. Experimental results on different hyperspectral images prove that the proposed spectral-spatial classification approach is robust and offers more classification accuracy than state-of-the-art methods when the number of labeled samples is small.

  10. 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 (...

  11. 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.

  12. Detection of lettuce discoloration using hyperspectral reflectance imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  13. Recent Advances in Techniques for Hyperspectral Image Processing

    NASA Technical Reports Server (NTRS)

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

    2009-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2005-04-01

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

  15. Emissivity retrieval from indoor hyperspectral imaging of mineral grains

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  16. Methodology for hyperspectral image classification using novel neural network

    SciTech Connect

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

    1997-04-01

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

  17. 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.

  18. Hyperspectral imaging with stimulated Raman scattering by chirped femtosecond lasers.

    PubMed

    Fu, Dan; Holtom, Gary; Freudiger, Christian; Zhang, Xu; Xie, Xiaoliang Sunney

    2013-04-25

    Raman microscopy is a quantitative, label-free, and noninvasive optical imaging technique for studying inhomogeneous systems. However, the feebleness of Raman scattering significantly limits the use of Raman microscopy to low time resolutions and primarily static samples. Recent developments in narrowband stimulated Raman scattering (SRS) microscopy have significantly increased the acquisition speed of Raman based label-free imaging by a few orders of magnitude, at the expense of reduced spectroscopic information. On the basis of a spectral focusing approach, we present a fast SRS hyperspectral imaging system using chirped femtosecond lasers to achieve rapid Raman spectra acquisition while retaining the full speed and image quality of narrowband SRS imaging. We demonstrate that quantitative concentration determination of cholesterol in the presence of interfering chemical species can be achieved with sensitivity down to 4 mM. For imaging purposes, hyperspectral imaging data in the C-H stretching region is obtained within a minute. We show that mammalian cell SRS hyperspectral imaging reveals the spatially inhomogeneous distribution of saturated lipids, unsaturated lipids, cholesterol, and protein. The combination of fast spectroscopy and label-free chemical imaging will enable new applications in studying biological systems and material systems.

  19. Tongue fissure extraction and classification using hyperspectral imaging technology.

    PubMed

    Li, Qingli; Wang, Yiting; Liu, Hongying; Sun, Zhen; Liu, Zhi

    2010-04-10

    Tongue fissures, an important feature on the tongue surface, may be pathologically related to some diseases. Most existing tongue fissure extraction methods use tongue images captured by traditional charge coupled device cameras. However, these conventional methods cannot be used for an accurate analysis of the tongue surface due to limited information from the images. To solve this, a hyperspectral tongue imager is used to capture tongue images instead of a digital camera. New algorithms for automatic tongue fissure extraction and classification, based on hyperspectral images, are presented. Both spectral and spatial information of the tongue surface is used to segment the tongue body and extract tongue fissures. Then a classification algorithm based on a hidden Markov model is used to classify tongue fissures into 12 typical categories. Results of the experiment show that the new method has good performance in terms of the classification rates of correctness.

  20. "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.

  1. Optical hyperspectral imaging in microscopy and spectroscopy – a review of data acquisition

    PubMed Central

    Gao, Liang; Smith, R. Theodore

    2014-01-01

    Rather than simply acting as a photographic camera capturing two-dimensional (x, y) intensity images or a spectrometer acquiring spectra (λ), a hyperspectral imager measures entire three-dimensional (x, y, λ) 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. PMID:25186815

  2. 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

  3. Hyperspectral instrumentation to image and characterize the fluorescence of materials

    NASA Astrophysics Data System (ADS)

    Bourcier, Frédéric; Walter, Philippe; Pedetti, Silvia; Faye, Delphine; Spezzigu, Piero; Infante, Fulvio; Le Nouy, Patrice; Zedda, Edoardo

    2016-09-01

    Optical instruments for space applications with improved performances (smaller pixels and spectral range extension) are becoming more and more sensitive to chemical contamination and particle sedimentation. Outgassing under vacuum conditions causes dramatic flux losses, especially in the UV bandwidth. Furthermore, it is difficult to perform physicochemical analyses of contaminated surfaces on flight models, in a clean room. Conventional analytical techniques such as FTIR (Fourier Transform Infrared interferometer) need the tool to be in contact with the studied area, which is forbidden when working on satellites. In addition, it does not give any information about the distribution of the contaminants in the field of view. The probed area is large, mono-pixel, and the sensitivity of the instrument is too low for hundred nanometer thin film deposits. A first study has shown that we could benefit from using the UV/visible fluorescence spectra to partially identify contaminants and polymer materials. The shape of the fluorescence spectra of adhesives, paints and varnishes have specific signatures that could be recorded into a designated reference database. The location of the presence of these contaminants on such sensitive optics is also relevant. To acquire both these parameters, we designed a specific compact hyperspectral instrument to remotely acquire cube images (500x500 pixels) in a 5 degree field of view, and on a wide range of continuous wavelengths from UV at 320 nm up to the near infrared at 1000 nm. This paper will present the chosen trade-off between different critical optics for a new portable version of this instrument. It is dedicated to space and cultural heritage applications and the first results on an engineering prototype will be shown.

  4. 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

  5. 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...

  6. Contrast based band selection for optimized weathered oil detection in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Levaux, Florian; Bostater, Charles R., Jr.; Neyt, Xavier

    2012-09-01

    Hyperspectral imagery offers unique benefits for detection of land and water features due to the information contained in reflectance signatures such as the bi-directional reflectance distribution function or BRDF. The reflectance signature directly shows the relative absorption and backscattering features of targets. These features can be very useful in shoreline monitoring or surveillance applications, for example to detect weathered oil. In real-time detection applications, processing of hyperspectral data can be an important tool and Optimal band selection is thus important in real time applications in order to select the essential bands using the absorption and backscatter information. In the present paper, band selection is based upon the optimization of target detection using contrast algorithms. The common definition of the contrast (using only one band out of all possible combinations available within a hyperspectral image) is generalized in order to consider all the possible combinations of wavelength dependent contrasts using hyperspectral images. The inflection (defined here as an approximation of the second derivative) is also used in order to enhance the variations in the reflectance spectra as well as in the contrast spectrua in order to assist in optimal band selection. The results of the selection in term of target detection (false alarms and missed detection) are also compared with a previous method to perform feature detection, namely the matched filter. In this paper, imagery is acquired using a pushbroom hyperspectral sensor mounted at the bow of a small vessel. The sensor is mechanically rotated using an optical rotation stage. This opto-mechanical scanning system produces hyperspectral images with pixel sizes on the order of mm to cm scales, depending upon the distance between the sensor and the shoreline being monitored. The motion of the platform during the acquisition induces distortions in the collected HSI imagery. It is therefore

  7. 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.

  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. 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.

  10. 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.

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

    PubMed

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

    2012-11-30

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

  12. Multispectral and hyperspectral imaging with AOTF for object recognition

    NASA Astrophysics Data System (ADS)

    Gupta, Neelam; Dahmani, Rachid

    1999-01-01

    Acousto-optic tunable-filter (AOTF) technology has been used in the design of a no-moving parts, compact, lightweight, field portable, automated, adaptive spectral imaging system when combined with a high sensitivity imaging detector array. Such a system could detect spectral signatures of targets and/or background, which contain polarization information and can be digitally processed by a variety of algorithms. At the Army Research Laboratory, we have developed and used a number of AOTF imaging systems and are also carrying out the development of such imagers at longer wavelengths. We have carried out hyperspectral and multispectral imaging using AOTF systems covering the spectral range from the visible to mid-IR. One of the imager uses a two-cascaded collinear-architecture AOTF cell in the visible-to-near-IR range with a digital Si charge-coupled device camera as the detector. The images obtained with this system showed no color blurring or image shift due to the angular deviation of different colors as a result of diffraction, and the digital images are stored and processed with great ease. The spatial resolution of the filter was evaluated by means of the lines of a target chart. We have also obtained and processed images from another noncollinear visible-to-near-IR AOTF imager with a digital camera, and used hyperspectral image processing software to enhance object recognition in cluttered background. We are presently working on a mid-IR AOTF imaging system that uses a high- performance InSb focal plane array and image acquisition and processing software. We describe our hyperspectral imaging program and present results from our imaging experiments.

  13. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle

    DOE PAGES

    Hruska, Ryan; Mitchell, Jessica; Anderson, Matthew; ...

    2012-09-17

    During the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral in-flight calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the U.S. Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis.more » The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 meters (based on RMSE).« less

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

    NASA Astrophysics Data System (ADS)

    Zhang, Biyin; Yu, Xin

    2015-12-01

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

  15. Recent Advances in Compressed Sensing: Discrete Uncertainty Principles and Fast Hyperspectral Imaging

    DTIC Science & Technology

    2015-03-26

    medical imaging , e.g., magnetic resonance imaging (MRI). Since the early 1980s, MRI has granted doctors the ability to distinguish between healthy tissue...chemical composition of a star. Conventional hyperspectral cameras are slow. Different methods of hyperspectral imaging either require time to process ...Recent Advances in Compressed Sensing: Discrete Uncertainty Principles and Fast Hyperspectral Imaging THESIS MARCH 2015 Megan E. Lewis, Second

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

    PubMed

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

    2012-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  18. 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.

  19. Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

    PubMed

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

    2015-11-20

    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.

  20. Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Senthilkumar, T.; Jayas, D. S.; White, N. D. G.; Fields, P. G.; Gräfenhan, T.

    2017-03-01

    Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  2. 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.

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

    PubMed Central

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

    2014-01-01

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

  4. Hyperspectral imaging for detection of cholesterol in human skin

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed

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

    2016-01-01

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

  6. Standoff hyperspectral imaging of explosives residues using broadly tunable external cavity quantum cascade laser illumination

    NASA Astrophysics Data System (ADS)

    Bernacki, Bruce E.; Phillips, Mark C.

    2010-04-01

    We describe experimental results on the detection of explosives residues with active hyperspectral imaging by illumination of the target surface using an external cavity quantum cascade laser (ECQCL) and imaging using an uncooled microbolometer camera. Explosives have rich absorption features in the molecular fingerprint region that spans 1500 to 500 wavenumbers and is easily probed by the wavelength range of quantum cascade lasers (QCL), which can be fabricated to emit from 3300 to 400 wavenumbers. Our laboratory-built ECQCL consists of a Fabry-Pérot laser with anti-reflection coated front facet that is arranged in a Littman-Metcalf configuration. The ECQCL was operated quasi-CW with a 100 kHz repetition rate, 50% duty cycle drive signal and tuning range from 1102.95 to 983.8 wavenumbers. The active hyperspectral imaging technique forms an image hypercube by recording one image for each tuning step of the ECQCL. For the experiments reported here, each wavelength band was 2 wavenumbers wide and 60 bands of image data were acquired in 2 seconds. 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 the rich library of spectral identification approaches developed initially in the remote sensing community. These techniques include spectral feature fitting, matched filtering, and mixture tuned matched filtering. Mixtures of materials can be evaluated using linear spectral unmixing approaches and matched filtering or mixture tuned matched filtering. We provide examples of these methods using ENVI, a commercial spectral image processing software package.

  7. Hyperspectral image classification based on NMF Features Selection Method

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  8. 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.

  9. 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.

  10. Real-time airborne hyperspectral imaging of land mines

    NASA Astrophysics Data System (ADS)

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

    2007-04-01

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

  11. Preprocessing and compression of Hyperspectral images captured onboard UAVs

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

  12. Flight and Ground Results from Long-Wave and Mid-wave Airborne Hyperspectral Spectrographic Images

    DTIC Science & Technology

    2009-10-01

    hyperspectral imager for landmine detection ,” in Detection and Remediation Technologies for Mines and Mine-like Targets X, R.S.Harmon, J.T.Broach... hyperspectral imaging of land mines,” in Detection and Remediation Technologies for Mines and Mine-Like Targets XII, R.S.Harmon, J.T.Broach, and... hyperspectral pushbroom imagers which are ideally suited for landmine detection , but which also have numerous applications outside the defence community

  13. Hyperspectral imaging for detecting pathogens grown on agar plates

    NASA Astrophysics Data System (ADS)

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

    2007-09-01

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

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

    PubMed

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

    2011-04-01

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

  15. 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.

  16. Hyperspectral reflectance imaging for detecting citrus canker based on dual-band ratio image classification method

    NASA Astrophysics Data System (ADS)

    Li, Jiangbo; Rao, Xiuqin; Guo, Junxian; Ying, Yibin

    2010-10-01

    Citrus are one of the major fruit produced in China. Most of this production is exported to Europe for fresh consumption, where consumers increasingly demand best quality. Citrus canker is one of the most devastating diseases that threaten peel of most commercial citrus varieties. The aim of this research was to investigate the potential of using hyperspectral imaging technique for detecting canker lesions on citrus fruit. Navel oranges with cankerous, normal and various common diseased skin conditions including wind scar, thrips scarring, scale insect, dehiscent fruit, phytotoxicity, heterochromatic stripe, and insect damage were studied. The imaging system (400-1000 nm) was established to acquire reflectance images from samples. Region of interest (ROI) spectral feature of various diseased peel areas was analyzed and characteristic wavebands (630, 685, and 720 nm) were extracted. The dual-band reflectance ratio (such as Q720/685) algorithm was performed on the hyperspectral images of navel oranges for differentiating canker from normal fruit skin and other surface diseases. The overall classification success rate was 96.84% regardless of the presence of other confounding diseases. The presented processing approach overcame the presence of stem/navel on navel oranges that typically has been a problematic source for false positives in the detection of defects. Because of the limited sample size, delineation of an optimal detection scheme is beyond the scope of the current study. However, the results showed that two-band ratio (Q685/630) along with the use of a simple threshold value segmentation method for discriminating canker on navel oranges from other peel diseases may be feasible.

  17. 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.

  18. A DMD-based hyperspectral imaging system using compressive sensing method

    NASA Astrophysics Data System (ADS)

    Sun, Zhongqiu; Chen, Bo; Cheng, Chengqi

    2014-11-01

    Hyperspectral Imaging Systems (HIS) are widely applied in many fields. However, in the traditional design of HIS, it is much time-consuming to acquire an integrated hyperspectral image. Compressive sensing is an efficient method to process sparse data, and a single-pixel camera which used the digital micromirror device (DMD) for accomplishing the CS algorithms had been developed. Nowadays, DMD achieved great development. The size of mirror array is increasing while switch speed of a single mirror becomes very fast. Consequently, researchers make efforts to design a HIS using CS method. CS method is a method to scale down the spatial information but the hyperspectral datacubes are still huge because of the thousands of bands. In this paper, we design a DMD-based spectrometer architecture using the method of compressed sensing principle, combined with DMD's spectral filter characteristics. In the new architecture, there are two DMDs. One is used for implementing the CS pattern, the other for filtering the bands. It has spectral simply adjustable advantages. With this new technology, we can reduce the amount of information which needs to be transmitted and processed in both spatial and spectral domain. We also present some simulation results of implementation procedures.

  19. Detection of Built-Up Areas Using Polarimetric Synthetic Aperture Radar Data and Hyperspectral Image

    NASA Astrophysics Data System (ADS)

    Bordbari, R.; Maghsoudi, Y.; Salehi, M.

    2015-12-01

    Polarimetric synthetic aperture radar (POLSAR) is an advantageous data for information extraction about objects and structures by using the wave scattering and polarization properties. Hyperspectral remote sensing exploits the fact that all materials reflect, absorb, and emit electromagnetic energy, at specific wavelengths, in distinctive patterns related to their molecular composition. As a result of their fine spectral resolution, Hyperspectral image (HIS) sensors provide a significant amount of information about the physical and chemical composition of the materials occupying the pixel surface. In target detection applications, the main objective is to search the pixels of an HSI data cube for the presence of a specific material (target). In this research, a hierarchical constrained energy minimization (hCEM) method using 5 different adjusting parameters has been used for target detection from hyperspectral data. Furthermore, to detect the built-up areas from POLSAR data, building objects discriminated from surrounding natural media presented on the scene using Freeman polarimetric target decomposition (PTD) and the correlation coefficient between co-pol and cross-pol channels. Also, target detection method has been implemented based on the different polarization basis for using the more information. Finally a majority voting method has been used for fusing the target maps. The polarimetric image C-band SAR data acquired by Radarsat-2, over San Francisco Bay area was used for the evaluation of the proposed method.

  20. 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.

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

    PubMed

    Edelman, Gerda; Lopatka, Martin; Aalders, Maurice

    2013-07-01

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

  2. Hyperspectral optical imaging of two different species of lepidoptera

    NASA Astrophysics Data System (ADS)

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

    2011-05-01

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

  3. Mineral identification in hyperspectral imaging using Sparse-PCA

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  4. An algorithm of remotely sensed hyperspectral image fusion based on spectral unmixing and feature reconstruction

    NASA Astrophysics Data System (ADS)

    Sun, Xuejian; Zhang, Lifu; Cen, Yi; Zhang, Mingyue

    2016-05-01

    In order to get high spatial resolution hyperspectral data, many studies have examined methods to combine spectral information contained in hyperspectral image with spatial information contained in multispectral/panchromatic image. This paper developed a new hyperspectral image fusion method base on the non-negative matrix factorization (NMF) theory. Data sets obtained by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) was used to evaluate the performance of the method. Experimental results show that the proposed algorithm can provide a good way to solve the problem of high spatial resolution hyperspectral data shortage.

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

    PubMed

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

    2015-09-01

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

  6. 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.

  7. Design Analysis of a Space Based Chromotomographic Hyperspectral Imaging Experiment

    DTIC Science & Technology

    2010-03-01

    Tilt Platforms S-340 Platform Recommended Models Mirror Aluminum Aluminum S-340.Ax Invar Zerodur glass S-340.ix Titanium BK7 glass S-340.Tx Steel S-340...composed of a telescope, two grating spectrometers, calibration lamps, and focal plane electronics and cooling system. The telescope is a three mirror ...advanced hyperspectral imager for coastal bathymetry is that the experiment will closely mirror that of the proposed space-based chromotomographic hy

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

    SciTech Connect

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

    1994-08-01

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

  9. M-estimation for robust sparse unmixing of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Toomik, Maria; Lu, Shijian; Nelson, James D. B.

    2016-10-01

    Hyperspectral unmixing methods often use a conventional least squares based lasso which assumes that the data follows the Gaussian distribution. The normality assumption is an approximation which is generally invalid for real imagery data. We consider a robust (non-Gaussian) approach to sparse spectral unmixing of remotely sensed imagery which reduces the sensitivity of the estimator to outliers and relaxes the linearity assumption. The method consists of several appropriate penalties. We propose to use an lp norm with 0 < p < 1 in the sparse regression problem, which induces more sparsity in the results, but makes the problem non-convex. On the other hand, the problem, though non-convex, can be solved quite straightforwardly with an extensible algorithm based on iteratively reweighted least squares. To deal with the huge size of modern spectral libraries we introduce a library reduction step, similar to the multiple signal classification (MUSIC) array processing algorithm, which not only speeds up unmixing but also yields superior results. In the hyperspectral setting we extend the traditional least squares method to the robust heavy-tailed case and propose a generalised M-lasso solution. M-estimation replaces the Gaussian likelihood with a fixed function ρ(e) that restrains outliers. The M-estimate function reduces the effect of errors with large amplitudes or even assigns the outliers zero weights. Our experimental results on real hyperspectral data show that noise with large amplitudes (outliers) often exists in the data. This ability to mitigate the influence of such outliers can therefore offer greater robustness. Qualitative hyperspectral unmixing results on real hyperspectral image data corroborate the efficacy of the proposed method.

  10. Software for hyperspectral, joint photographic experts group (.JPG), portable network graphics (.PNG) and tagged image file format (.TIFF) segmentation

    NASA Astrophysics Data System (ADS)

    Bruno, L. S.; Rodrigo, B. P.; Lucio, A. de C. Jorge

    2016-10-01

    This paper presents a system developed by an application of a neural network Multilayer Perceptron for drone acquired agricultural image segmentation. This application allows a supervised user training the classes that will posteriorly be interpreted by neural network. These classes will be generated manually with pre-selected attributes in the application. After the attribute selection a segmentation process is made to allow the relevant information extraction for different types of images, RGB or Hyperspectral. The application allows extracting the geographical coordinates from the image metadata, geo referencing all pixels on the image. In spite of excessive memory consume on hyperspectral images regions of interest, is possible to perform segmentation, using bands chosen by user that can be combined in different ways to obtain different results.

  11. Spatial and temporal point tracking in real hyperspectral images

    NASA Astrophysics Data System (ADS)

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

    2008-10-01

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

  12. 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. ...

  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. 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...

  15. SPECTRAL SMILE CORRECTION IN CRISM HYPERSPECTRAL IMAGES

    NASA Astrophysics Data System (ADS)

    Ceamanos, X.; Doute, S.

    2009-12-01

    The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is affected by a common artifact in "push-broom" sensors, the so-called "spectral smile". As a consequence, both central wavelength and spectral width of the spectral response vary along the across-track dimension, thus giving rise to a shifting and smoothing of spectra (see Fig. 1 (left)). In fact, both effects are greater for spectra on the edges, while they are minimum for data acquired by central detectors, the so-called "sweet spot". The prior artifacts become particularly critical for Martian observations which contain steep spectra such as CO2 ice-rich polar images. Fig. 1 (right) shows the horizontal brightness gradient which appears in every band corresponding to a steep portion of spectra. The correction of CRISM spectral smile is addressed using a two-step method which aims at modifying data sensibly in order to mimic the optimal CRISM response. First, all spectra, which are previously interpolated by cubic splines, are resampled to the "sweet spot" wavelengths in order to overcome the spectra shift. Secondly, the non-uniform spectral width is overcome by mimicking an increase of spectral resolution thanks to a spectral sharpening. In order to minimize noise, only bands particularly suffering from smile are selected. First, bands corresponding to the outliers of the Minimum Noise Transformation (MNF) eigenvector, which corresponds to the MNF band related to smile (MNF-smile), are selected. Then, a spectral neighborhood Θi, which takes into account the local spectral convexity or concavity, is defined for every selected band in order to maximize spectral shape preservation. The proposed sharpening technique takes into account both the instrument parameters and the observed spectra. First, every reflectance value belonging to a Θi is reevaluated by a sharpening which depends on a ratio of the spectral width of the current detector and the "sweet spot" one. Then, the optimal degree of

  16. 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.

  17. 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.

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

    NASA Astrophysics Data System (ADS)

    Barducci, Alessandro; Marcoionni, Paolo; Pippi, Ivan; Poggesi, Marco

    2003-07-01

    A remote-sensing campaign was performed in September 2001 at nighttime under clear-sky conditions before moonrise to assess the level of light pollution of urban and industrial origin. Two hyperspectral sensors, namely, the Multispectral Infrared and Visible Imaging Spectrometer and the Visible Infrared Scanner-200, which provide spectral coverage from the visible to the thermal infrared, were flown over the Tuscany coast (Italy) on board a Casa 212 airplane. The acquired images were processed to produce radiometrically calibrated data, which were then analyzed and compared with ground-based spectral measurements. Calibrated data acquired at high spectral resolution (~2.5 nm) showed a maximum scene brightness almost of the same order of magnitude as that observed during similar daytime measurements, whereas their average luminosity was 3 orders of magnitude lower. The measurement analysis confirmed that artificial illumination hinders astronomical observations and produces noticeable effects even at great distances from the sources of the illumination.

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

    PubMed

    Barducci, Alessandro; Marcoionni, Paolo; Pippi, Ivan; Poggesi, Marco

    2003-07-20

    A remote-sensing campaign was performed in September 2001 at nighttime under clear-sky conditions before moonrise to assess the level of light pollution of urban and industrial origin. Two hyperspectral sensors, namely, the Multispectral Infrared and Visible Imaging Spectrometer and the Visible Infrared Scanner-200, which provide spectral coverage from the visible to the thermal infrared, were flown over the Tuscany coast (Italy) on board a Casa 212 airplane. The acquired images were processed to produce radiometrically calibrated data, which were then analyzed and compared with ground-based spectral measurements. Calibrated data acquired at high spectral resolution (approximately 2.5 nm) showed a maximum scene brightness almost of the same order of magnitude as that observed during similar daytime measurements, whereas their average luminosity was 3 orders of magnitude lower. The measurement analysis confirmed that artificial illumination hinders astronomical observations and produces noticeable effects even at great distances from the sources of the illumination.

  20. 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).

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

    PubMed Central

    Karuna, Arnica; Borri, Paola; Langbein, Wolfgang

    2015-01-01

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

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

    PubMed

    Masia, Francesco; Karuna, Arnica; Borri, Paola; Langbein, Wolfgang

    2015-08-01

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

  3. Detection of aircraft exhaust in hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Lane, Sarah E.; West, Leanne L.; Gimmestad, Gary G.; Smith, William L., Sr.; Burdette, Edward M.

    2011-10-01

    The use of a hyperspectral imaging system for the detection of gases has been investigated, and algorithms have been developed for various applications. Of particular interest here is the ability to use these algorithms in the detection of the wake disturbances trailing an aircraft. A dataset of long wave infrared (LWIR) hyperspectral datacubes taken with a Telops Hyper-Cam at Hartsfield-Jackson International Airport in Atlanta, Georgia is investigated. The methodology presented here assumes that the aircraft engine exhaust gases will become entrained in wake vortices that develop; therefore, if the exhaust can be detected upon exiting the engines, it can be followed through subsequent datacubes until the vortex disturbance is detected. Gases known to exist in aircraft exhaust are modeled, and the Adaptive Coherence/Cosine Estimator (ACE) is used to search for these gases. Although wake vortices have not been found in the data, an unknown disturbance following the passage of the aircraft has been discovered.

  4. Semi-supervised feature learning for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  5. 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 μm) and Mid-IR (2.3 - 4.6 μm) 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 μl-and gases with sensitivity as high as 10ppm.m-at detection standoff distances > 10 m.

  6. HSI mapping of marine and coastal environments using the advanced airborne hyperspectral imaging system (AAHIS)

    NASA Astrophysics Data System (ADS)

    Holasek, Rick E.; Portigal, Frederick P.; Mooradian, Gregory C.; Voelker, Mark A.; Even, Detlev M.; Fene, Michael W.; Owensby, Pamela D.; Breitwieser, David S.

    1997-08-01

    The advanced airborne hyperspectral imaging system (AAHIS) is an operational, high signal-to-noise ratio, high resolution, integrated hyperspectral imaging spectrometer. The compact, lightweight and portable AAHIS system is normally flown in Piper Aztec aircraft. AAHIS collect 'push- broom' data with 385 spatial channels and 288 simultaneous spectral channels from 433 nm to 832 nm, recording at 12 bits up to 55 frames/second. Typical operation incorporates on-chip pixel binning of four pixels spectrally and two pixels spatially, increasing the signal-to-noise ratio and reducing data rate. When binned, the spectral resolution is 5.5 nm and the instantaneous field-of-view is 1 mrad, resulting in a ground sample distance of 0.5 m from 500 m altitude. The sensor is optimized for littoral region remote sensing for a variety of civilian and defense applications including ecosystem surveying and inventory, detection and monitoring of environmental pollution, infrastructure mapping, and surveillance. Since August 1994, AAHIS has acquired over 120 GB of hyperspectral image data of littoral, urban, desert and tropical scenes. System upgrades include real-time spectral image processing, integrated flight navigation and 3-axis image stabilization. A description of the sensor system, its performance characteristics, and several processed images demonstrating material discrimination are presented. The remote assessment, characterization, and mapping of coral reef health and species identification and floral species at Nu'upia Ponds, are shown and compared to extensive ground truthing in and around Kaneohe Bay, Oahu, Hawaii. SETS emphasizes providing georegistered, GIS-integrated, value- added data products for customers to help them solve real- world problems.

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

    PubMed

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

    2013-03-05

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  10. 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.

  11. Hyperspectral Imaging Coupled with Random Frog and Calibration Models for Assessment of Total Soluble Solids in Mulberries

    PubMed Central

    Zhao, Yan-Ru; Yu, Ke-Qiang; He, Yong

    2015-01-01

    Chemometrics methods coupled with hyperspectral imaging technology in visible and near infrared (Vis/NIR) region (380–1030 nm) were introduced to assess total soluble solids (TSS) in mulberries. Hyperspectral images of 310 mulberries were acquired by hyperspectral reflectance imaging system (512 bands) and their corresponding TSS contents were measured by a Brix meter. Random frog (RF) method was used to select important wavelengths from the full wavelengths. TSS values in mulberry fruits were predicted by partial least squares regression (PLSR) and least-square support vector machine (LS-SVM) models based on full wavelengths and the selected important wavelengths. The optimal PLSR model with 23 important wavelengths was employed to visualise the spatial distribution of TSS in tested samples, and TSS concentrations in mulberries were revealed through the TSS spatial distribution. The results declared that hyperspectral imaging is promising for determining the spatial distribution of TSS content in mulberry fruits, which provides a reference for detecting the internal quality of fruits. PMID:26451273

  12. Diffused Matrix Format: a new storage and processing format for airborne hyperspectral sensor images.

    PubMed

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

    2010-01-01

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

  13. 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.

  14. Software for Acquiring Image Data for PIV

    NASA Technical Reports Server (NTRS)

    Wernet, Mark P.; Cheung, H. M.; Kressler, Brian

    2003-01-01

    PIV Acquisition (PIVACQ) is a computer program for acquisition of data for particle-image velocimetry (PIV). In the PIV system for which PIVACQ was developed, small particles entrained in a flow are illuminated with a sheet of light from a pulsed laser. The illuminated region is monitored by a charge-coupled-device camera that operates in conjunction with a data-acquisition system that includes a frame grabber and a counter-timer board, both installed in a single computer. The camera operates in "frame-straddle" mode where a pair of images can be obtained closely spaced in time (on the order of microseconds). The frame grabber acquires image data from the camera and stores the data in the computer memory. The counter/timer board triggers the camera and synchronizes the pulsing of the laser with acquisition of data from the camera. PIVPROC coordinates all of these functions and provides a graphical user interface, through which the user can control the PIV data-acquisition system. PIVACQ enables the user to acquire a sequence of single-exposure images, display the images, process the images, and then save the images to the computer hard drive. PIVACQ works in conjunction with the PIVPROC program which processes the images of particles into the velocity field in the illuminated plane.

  15. 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.

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

    NASA Astrophysics Data System (ADS)

    He, Zhiping; Shu, Rong; Wang, Jianyu

    2012-11-01

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

  17. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  19. Time-resolved hyperspectral single-pixel camera implementation for compressive wide-field fluorescence lifetime imaging

    NASA Astrophysics Data System (ADS)

    Pian, Qi; Yao, Ruoyang; Intes, Xavier

    2016-03-01

    Single-pixel imaging based on compressive sensing theory has been a highlighted technique in the biomedical imaging field for many years. This interest has been driven by the possibility of performing microscopic or macroscopic imaging based on low-cost detector arrays, increased SNR (signal-to-noise ratio) in the acquired data sets and the ability to perform high quality image reconstruction with compressed data sets by exploiting signal sparsity. In this work, we present our recent work in implementing this technique to perform time domain fluorescence-labeled investigations in preclinical settings. More precisely, we report on our time-resolved hyperspectral single-pixel camera for fast, wide-field mapping of molecular labels and lifetime-based quantification. The hyperspectral single-pixel camera implements a DMD (Digital micro-mirror device) to generate optical masks for modulating the illumination field before it is delivered onto the sample and focuses the emission light signals into a multi-anode hyperspectral time-resolved PMT (Photomultiplier tube) to acquire spatial, temporal and spectral information enriched 4-D data sets. Fluorescence dyes with lifetime and spectral contrast are embedded in well plates and thin tissues. L-1 norm based regularization or the least square method, is applied to solve the underdetermined inverse problem during image reconstruction. These experimental results prove the possibility of fast, wide-field mapping of fluorescent labels with lifetime and spectral contrast in thin media.

  20. 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 ...

  1. Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

    NASA Astrophysics Data System (ADS)

    Damodaran, Bharath Bhushan; Nidamanuri, Rama Rao

    2014-06-01

    Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.

  2. Automatic detection and classification of EOL-concrete and resulting recovered products by hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Palmieri, Roberta; Bonifazi, Giuseppe; Serranti, Silvia

    2014-05-01

    The recovery of materials from Demolition Waste (DW) represents one of the main target of the recycling industry and the its characterization is important in order to set up efficient sorting and/or quality control systems. End-Of-Life (EOL) concrete materials identification is necessary to maximize DW conversion into useful secondary raw materials, so it is fundamental to develop strategies for the implementation of an automatic recognition system of the recovered products. In this paper, HyperSpectral Imaging (HSI) technique was applied in order to detect DW composition. Hyperspectral images were acquired by a laboratory device equipped with a HSI sensing device working in the near infrared range (1000-1700 nm): NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Acquired spectral data were analyzed adopting the PLS_Toolbox (Version 7.5, Eigenvector Research, Inc.) under Matlab® environment (Version 7.11.1, The Mathworks, Inc.), applying different chemometric methods: Principal Component Analysis (PCA) for exploratory data approach and Partial Least Square- Discriminant Analysis (PLS-DA) to build classification models. Results showed that it is possible to recognize DW materials, distinguishing recycled aggregates from contaminants (e.g. bricks, gypsum, plastics, wood, foam, etc.). The developed procedure is cheap, fast and non-destructive: it could be used to make some steps of the recycling process more efficient and less expensive.

  3. Hyperspectral imaging system for disease scanning on banana plants

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  4. 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.

  5. Differentiation of deciduous-calyx Korla fragrant pears using NIR hyperspectral imaging analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Near-infrared hyperspectral imaging was investigated as a potential method for automatic sorting of pears according to their calyx type. The hyperspectral images were analyzed and wavebands at 1190 nm and 1199 nm were selected for differentiating deciduous-calyx fruits from persistent-calyx ones. A ...

  6. POULTRY SKIN TUMOR DETECTION IN HYPERSPECTRAL REFLECTANCE IMAGES BY COMBINING CLASSIFIERS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper presents a new method for detecting poultry skin tumors in hyperspectral reflectance images. We employ the principal component analysis (PCA), discrete wavelet transform (DWT), and kernel discriminant analysis (KDA) to extract the independent feature sets in hyperspectral reflectance imag...

  7. MCT-based SWIR hyperspectral imaging system for evaluation of biological samples

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging has been shown to be a powerful tool for nondestructive evaluation of biological samples. We recently developed a new line-scan-based shortwave infrared (SWIR) hyperspectral imaging system. Critical sensing components of the system include a SWIR spectrograph, an MCT (HgCdTe) a...

  8. Penetration depth measurement of near-infrared hyperspectral imaging light for milk powder

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The increasingly common application of near-infrared (NIR) hyperspectral imaging technique to the analysis of food powders has led to the need for optical characterization of samples. This study was aimed at exploring the feasibility of quantifying penetration depth of NIR hyperspectral imaging ligh...

  9. 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...

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  11. 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.

  12. Detection and discrimination of cotton foreign matter using push-broom based hyperspectral imaging: system design and capability.

    PubMed

    Jiang, Yu; Li, Changying

    2015-01-01

    Cotton quality, a major factor determining both cotton profitability and marketability, is affected by not only the overall quantity of but also the type of the foreign matter. Although current commercial instruments can measure the overall amount of the foreign matter, no instrument can differentiate various types of foreign matter. The goal of this study was to develop a hyperspectral imaging system to discriminate major types of foreign matter in cotton lint. A push-broom based hyperspectral imaging system with a custom-built multi-thread software was developed to acquire hyperspectral images of cotton fiber with 15 types of foreign matter commonly found in the U.S. cotton lint. A total of 450 (30 replicates for each foreign matter) foreign matter samples were cut into 1 by 1 cm2 pieces and imaged on the lint surface using reflectance mode in the spectral range from 400-1000 nm. The mean spectra of the foreign matter and lint were extracted from the user-defined region-of-interests in the hyperspectral images. The principal component analysis was performed on the mean spectra to reduce the feature dimension from the original 256 bands to the top 3 principal components. The score plots of the 3 principal components were used to examine clusterization patterns for classifying the foreign matter. These patterns were further validated by statistical tests. The experimental results showed that the mean spectra of all 15 types of cotton foreign matter were different from that of the lint. Nine types of cotton foreign matter formed distinct clusters in the score plots. Additionally, all of them were significantly different from each other at the significance level of 0.05 except brown leaf and bract. The developed hyperspectral imaging system is effective to detect and classify cotton foreign matter on the lint surface and has the potential to be implemented in commercial cotton classing offices.

  13. 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.

  14. Application of hyperspectral imaging for characterization of intramuscular fat distribution in beef

    NASA Astrophysics Data System (ADS)

    Lohumi, Santosh; Lee, Sangdae; Lee, Hoonsoo; Kim, Moon S.; Lee, Wang-Hee; Cho, Byoung-Kwan

    2016-01-01

    In this study, a hyperspectral imaging system in the spectral region of 400-1000 nm was used for visualization and determination of intramuscular fat concentration in beef samples. Hyperspectral images were acquired for beef samples, and spectral information was then extracted from each single sample from the fat and non-fat regions. The intramuscular fat content was chemically extracted and quantified for the same samples. Chemometrics including analysis of variance (ANOVA) and spectral similarity measures involving spectral angle measure (SAM), and Euclidian distance measure (EDM) were then used to analyze the data. An ANOVA analysis indicates that the two selected spectral variables (e.g., 650.4-736.4 nm) are effective to generate ratio image for visualization of the intramuscular fat distribution in beef. The spectral similarity analysis methods, which is based on the quantifying the spectral similarities by using predetermined endmember spectrum vector, provided comparable results for characterization and detection of intramuscular fat in beef. In term of overall classification accuracy, spectral similarity measure methods outperformed the ratio image of selected bands based on the result of ANOVA analysis. The results demonstrate that proposed technique has a potential for fast and nondestructive determination of intramuscular fat in beef.

  15. 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

  16. Parallel optimization of pixel purity index algorithm for massive hyperspectral images in cloud computing environment

    NASA Astrophysics Data System (ADS)

    Chen, Yufeng; Wu, Zebin; Sun, Le; Wei, Zhihui; Li, Yonglong

    2016-04-01

    With the gradual increase in the spatial and spectral resolution of hyperspectral images, the size of image data becomes larger and larger, and the complexity of processing algorithms is growing, which poses a big challenge to efficient massive hyperspectral image processing. Cloud computing technologies distribute computing tasks to a large number of computing resources for handling large data sets without the limitation of memory and computing resource of a single machine. This paper proposes a parallel pixel purity index (PPI) algorithm for unmixing massive hyperspectral images based on a MapReduce programming model for the first time in the literature. According to the characteristics of hyperspectral images, we describe the design principle of the algorithm, illustrate the main cloud unmixing processes of PPI, and analyze the time complexity of serial and parallel algorithms. Experimental results demonstrate that the parallel implementation of the PPI algorithm on the cloud can effectively process big hyperspectral data and accelerate the algorithm.

  17. Hyperspectral Raman imaging of bone growth and regrowth chemistry

    NASA Astrophysics Data System (ADS)

    Pezzuti, Jerilyn A.; Morris, Michael D.; Bonadio, Jeffrey F.; Goldstein, Steven A.

    1998-06-01

    Hyperspectral Raman microscopic imaging of carbonated hydroxyapatite (HAP) is used to follow the chemistry of bone growth and regrowth. Deep red excitation is employed to minimize protein fluorescence interference. A passive line generator based on Powell lens optics and a motorized translation stage provide the imaging capabilities. Raman image contrast is generated from several lines of the HAP Raman spectrum, primarily the PO4-3. Factor analysis is used to minimize the integration time needed for acceptable contrast and to explore the chemical species within the bone. Bone age is visualized as variations in image intensity. High definition, high resolution images of newly formed bone and mature bone are compared qualitatively. The technique is currently under evaluation for study of experimental therapies for fracture repair.

  18. [Study on Visual Identification of Corn Seeds Based on Hyperspectral Imaging Technology].

    PubMed

    Wu, Xiang; Zhang, Wei-zheng; Lu, Jiang-feng; Qiu, Zheng-jun; He, Yong

    2016-02-01

    The seed purity is an important indicator of seed quality. The paper proposes a visual identification method of corn seed based on the near-infrared (874~1,734 nm) hyperspectral image technology. Hyperspectral image data of 4 cultivars of a total of 384 corn seed samples will be acquired. Then 288 of samples are to be selected randomly as the calibration set, and the remaining 96 samples will be used for the prediction set. After inspection of the near-infrared spectral curves, 7 effective wavelengths (EWs) are to be selected by successive projection algorithm (SPA). And then 7 EWs of the calibration set will be used as input to build a partial least squares (PLS) model. Good results are to be obtained with Rc = 0.917 7, RMSECV = 0.444 2; Rcv = 0.911 5, RMSECV = 0.459 9. And the total identification rate of the developed PLS model will be 78.5% for the calibration set and 70.8% for the prediction set. Finally, average spectral data of each corn seed in a hyperspectral image will be extracted by image process technology, and used as input of the developed SPA-PLS model. In the produced identification map, different colors are to be used to represent different predicted cultivars. 3 mixture samples of corn seeds will be identified, and help to achieve satisfied visual effects. The result indicates that, by means of the visual identification technology we could intuitively observe the distribution of corn seeds of different cultivars in mixture samples. The research provides help for the identification and screening of seeds in agricultural production.

  19. Acquired portosystemic collaterals: anatomy and imaging.

    PubMed

    Leite, Andréa Farias de Melo; Mota, Américo; Chagas-Neto, Francisco Abaeté; Teixeira, Sara Reis; Elias Junior, Jorge; Muglia, Valdair Francisco

    2016-01-01

    Portosystemic shunts are enlarged vessels that form collateral pathological pathways between the splanchnic circulation and the systemic circulation. Although their causes are multifactorial, portosystemic shunts all have one mechanism in common-increased portal venous pressure, which diverts the blood flow from the gastrointestinal tract to the systemic circulation. Congenital and acquired collateral pathways have both been described in the literature. The aim of this pictorial essay was to discuss the distinct anatomic and imaging features of portosystemic shunts, as well as to provide a robust method of differentiating between acquired portosystemic shunts and similar pathologies, through the use of illustrations and schematic drawings. Imaging of portosystemic shunts provides subclinical markers of increased portal venous pressure. Therefore, radiologists play a crucial role in the identification of portosystemic shunts. Early detection of portosystemic shunts can allow ample time to perform endovascular shunt operations, which can relieve portal hypertension and prevent acute or chronic complications in at-risk patient populations.

  20. Acquired portosystemic collaterals: anatomy and imaging*

    PubMed Central

    Leite, Andréa Farias de Melo; Mota Jr., Américo; Chagas-Neto, Francisco Abaeté; Teixeira, Sara Reis; Elias Junior, Jorge; Muglia, Valdair Francisco

    2016-01-01

    Portosystemic shunts are enlarged vessels that form collateral pathological pathways between the splanchnic circulation and the systemic circulation. Although their causes are multifactorial, portosystemic shunts all have one mechanism in common-increased portal venous pressure, which diverts the blood flow from the gastrointestinal tract to the systemic circulation. Congenital and acquired collateral pathways have both been described in the literature. The aim of this pictorial essay was to discuss the distinct anatomic and imaging features of portosystemic shunts, as well as to provide a robust method of differentiating between acquired portosystemic shunts and similar pathologies, through the use of illustrations and schematic drawings. Imaging of portosystemic shunts provides subclinical markers of increased portal venous pressure. Therefore, radiologists play a crucial role in the identification of portosystemic shunts. Early detection of portosystemic shunts can allow ample time to perform endovascular shunt operations, which can relieve portal hypertension and prevent acute or chronic complications in at-risk patient populations. PMID:27777479

  1. Performance evaluation of Normalized Difference Chlorophyll Index in northern Gulf of Mexico estuaries using the Hyperspectral Imager for the Coastal Ocean

    EPA Science Inventory

    The Hyperspectral Imager for the Coastal Ocean (HICO) was used to derive chlorophyll-a (chl-a) based on the Normalized Difference Chlorophyll Index (NDCI) in two Gulf of Mexico coastal estuaries. Chl-a data were acquired from discrete in-situ water sample analysis and above-water...

  2. 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.

  3. NIR hyperspectral imaging to evaluate degradation in captopril commercial tablets.

    PubMed

    França, Leandro de Moura; Pimentel, Maria Fernanda; Simões, Simone da Silva; Grangeiro, Severino; Prats-Montalbán, José M; Ferrer, Alberto

    2016-07-01

    Pharmaceutical quality control is important for improving the effectiveness, purity and safety of drugs, as well as for the prevention or control of drug degradation. In the present work, near infrared hyperspectral images (HSI-NIR) of tablets with different expiration dates were employed to evaluate the degradation of captopril into captopril disulfide in different layers, on the top and on the bottom surfaces of the tablets. Multivariate curve resolution (MCR) models were used to extract the concentration distribution maps from the hyperspectral images. Afterward, multivariate image techniques were applied to the concentration distribution maps (CDMs), to extract features and build models relating the main characteristics of the images to their corresponding manufacturing dates. Resolution methods followed by extracting features were able to estimate the tablet manufacture date with a prediction error of 120days. The model developed could be useful to evaluate whether a sample shows a degradation pattern consistent with the date of manufacturing or to detect abnormal behaviors in the natural degradation process of the sample. The information provided by the HIS-NIR is important for the development of the process (QbD), looking inside the formulation, revealing the behavior of the active pharmaceutical ingredient (API) during the product's shelf life.

  4. Hyperspectral imaging and characterization of live cells by broadband coherent anti-Stokes Raman scattering (CARS) microscopy with singular value decomposition (SVD) analysis.

    PubMed

    Khmaladze, Alexander; Jasensky, Joshua; Price, Erika; Zhang, Chi; Boughton, Andrew; Han, Xiaofeng; Seeley, Emily; Liu, Xinran; Banaszak Holl, Mark M; Chen, Zhan

    2014-01-01

    Coherent anti-Stokes Raman scattering (CARS) microscopy can be used as a powerful imaging technique to identify chemical compositions of complex samples in biology, biophysics, medicine, and materials science. In this work we developed a CARS microscopic system capable of hyperspectral imaging. By employing an ultrafast laser source, a photonic crystal fiber, and a scanning laser microscope together with spectral detection by a highly sensitive back-illuminated cooled charge-coupled device (CCD) camera, we were able to rapidly acquire and process hyperspectral images of live cells with chemical selectivity. We discuss various aspects of hyperspectral CARS image analysis and demonstrate the use of singular value decomposition methods to characterize the cellular lipid content.

  5. MCT-based SWIR hyperspectral imaging system for evaluation of biological samples

    NASA Astrophysics Data System (ADS)

    Hong, Seok Min; Lee, Hoonsoo; Baek, Insuck; Kim, Moon S.

    2016-05-01

    Hyperspectral imaging has been shown to be a powerful tool for nondestructive evaluation of biological samples. We recently developed a new line-scan-based shortwave infrared (SWIR) hyperspectral imaging system. Critical sensing components of the system include a SWIR spectrograph, an MCT (HgCdTe) array detector, and a custom-designed illumination source. The system has an effective imaging range from 900 nm to 2500 nm. In this paper, we present SWIR hyperspectral images of plant leaves and fruits, and preliminary SWIR image analysis results.

  6. Surgical and clinical needs for DLP hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Livingston, Edward H.

    2010-02-01

    Surgical technology advances slowly and only when there is overwhelming need for change. Change is resisted by surgeons and is made hard by FDA rules that inhibit innovation. There is a pressing need to improve surgeon's visualization of the operative field during laparoscopic surgery to minimize the risk for significant injury that can occur when surgeons are operating around delicate, hidden structures. We propose to use a Digital Light Processor-based hyperspectral imaging system to assist an operating surgeon's ability to see through tissues and identify otherwise hidden structures such as bile ducts during laparoscopic cholecystectomy.

  7. 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

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

    NASA Astrophysics Data System (ADS)

    Cancio, Leopoldo C.

    2010-02-01

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

  9. 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

  10. 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.

  11. Melanoma detection using smartphone and multimode hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    MacKinnon, Nicholas; Vasefi, Fartash; Booth, Nicholas; Farkas, Daniel L.

    2016-04-01

    This project's goal is to determine how to effectively implement a technology continuum from a low cost, remotely deployable imaging device to a more sophisticated multimode imaging system within a standard clinical practice. In this work a smartphone is used in conjunction with an optical attachment to capture cross-polarized and collinear color images of a nevus that are analyzed to quantify chromophore distribution. The nevus is also imaged by a multimode hyperspectral system, our proprietary SkinSpect™ device. Relative accuracy and biological plausibility of the two systems algorithms are compared to assess aspects of feasibility of in-home or primary care practitioner smartphone screening prior to rigorous clinical analysis via the SkinSpect.

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

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

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

  13. Advanced Airborne Hyperspectral Imaging System (AAHIS): an imaging spectrometer for maritime applications

    NASA Astrophysics Data System (ADS)

    Voelker, Mark A.; Resmini, Ronald G.; Mooradian, Gregory C.; McCord, Thomas B.; Warren, Christopher P.; Fene, Michael W.; Coyle, Christopher C.; Anderson, Richard

    1995-06-01

    The Advanced Airborne Hyperspectral Imaging System (AAHIS) is a compact, lightweight visible and near IR pushbroom hyperspectral imaging spectrometer flown on a Piper Aztec aircraft. AAHIS is optimized for use in shallow water, littoral, and vegetation remote sensing. Data are collected at up to 55 frames/second and may be displayed and analyzed inflight or recorded for post-flight processing. Swath width is 200 meters at a flight altitude of 1 km. Each image pixel contains hyperspectral data simultaneously recorded in up to 288 contiguous spectral channels covering the 432 to 832 nm spectral region. Pixel binning typically yields pixels 1.0 meter square with a spectral channel width of 5.5 nm. Design and performance of the AAHIS is presented, including processed imagery demonstrating feature detection and materials discrimination on land and underwater at depths up to 27 meters.

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

    NASA Astrophysics Data System (ADS)

    Padma, S.; Sanjeevi, S.

    2014-10-01

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

  15. Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging.

    PubMed

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

    2012-03-16

    Many subjective assessment methods for fresh meat quality are still widely used in the meat industry, making the development of an objective and non-destructive technique for assessing meat quality traits a vital need. In this study, a hyperspectral imaging technique was investigated for objective determination of pork quality attributes. Hyperspectral images in the near infrared region (900-1700 nm) were acquired for pork samples from the longissimus dorsi muscle, and the representative spectral information was extracted from the loin eye area. Several mathematical pre-treatments including first and second derivatives, standard normal variate (SNV) and multiplicative scatter correction (MSC) were applied to examine the influence of spectral variations in predicting pork quality characteristics. Spectral information was used for predicting color features (L, a, b, chroma and hue angle), drip loss, pH and sensory characteristics by partial least-squares regression (PLS-R) models. Independent sets of feature-related wavelengths were selected for predicting each quality attribute. The results showed that color reflectance (L), pH and drip loss of pork meat could be predicted with determination coefficients (R(CV)(2)) of 0.93, 0.87 and 0.83, respectively. The regression coefficients from the PLS-R models at the selected optimal wavelengths were applied in a pixel-wise manner to convert spectral images to prediction maps that display the distribution of attributes within the sample. Results indicated that this technique is a potential tool for rapid assessment of pork quality.

  16. [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.

  17. Hyperspectral imaging of UVR effects on fungal spectrum

    NASA Astrophysics Data System (ADS)

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

    2005-08-01

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

  18. Oil Adulteration Identification by Hyperspectral Imaging Using QHM and ICA

    PubMed Central

    Han, Zhongzhi; Wan, Jianhua; Deng, Limiao; Liu, Kangwei

    2016-01-01

    To investigate the feasibility of identification of qualified and adulterated oil product using hyperspectral imaging(HIS) technique, a novel feature set based on quantized histogram matrix (QHM) and feature selection method using improved kernel independent component analysis (iKICA) is proposed for HSI. We use UV and Halogen excitations in this study. Region of interest(ROI) of hyperspectral images of 256 oil samples from four varieties are obtained within the spectral region of 400–720nm. Radiation indexes extracted from each ROI are used as feature vectors. These indexes are individual band radiation index (RI), difference of consecutive spectral band radiation index (DRI), ratio of consecutive spectral band radiation index (RRI) and normalized DRI (NDRI). Another set of features called quantized histogram matrix (QHM) are extracted by applying quantization on the image histogram from these features. Based on these feature sets, improved kernel independent component analysis (iKICA) is used to select significant features. For comparison, algorithms such as plus L reduce R (plusLrR), Fisher, multidimensional scaling (MDS), independent component analysis (ICA), and principle component analysis (PCA) are also used to select the most significant wavelengths or features. Support vector machine (SVM) is used as the classifier. Experimental results show that the proposed methods are able to obtain robust and better classification performance with fewer number of spectral bands and simplify the design of computer vision systems. PMID:26820311

  19. A novel highly parallel algorithm for linearly unmixing hyperspectral images

    NASA Astrophysics Data System (ADS)

    Guerra, Raúl; López, Sebastián.; Callico, Gustavo M.; López, Jose F.; Sarmiento, Roberto

    2014-10-01

    Endmember extraction and abundances calculation represent critical steps within the process of linearly unmixing a given hyperspectral image because of two main reasons. The first one is due to the need of computing a set of accurate endmembers in order to further obtain confident abundance maps. The second one refers to the huge amount of operations involved in these time-consuming processes. This work proposes an algorithm to estimate the endmembers of a hyperspectral image under analysis and its abundances at the same time. The main advantage of this algorithm is its high parallelization degree and the mathematical simplicity of the operations implemented. This algorithm estimates the endmembers as virtual pixels. In particular, the proposed algorithm performs the descent gradient method to iteratively refine the endmembers and the abundances, reducing the mean square error, according with the linear unmixing model. Some mathematical restrictions must be added so the method converges in a unique and realistic solution. According with the algorithm nature, these restrictions can be easily implemented. The results obtained with synthetic images demonstrate the well behavior of the algorithm proposed. Moreover, the results obtained with the well-known Cuprite dataset also corroborate the benefits of our proposal.

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

    NASA Technical Reports Server (NTRS)

    Thome, Kurtis

    2009-01-01

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

  1. 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.

  2. [Hyperspectral image classification based on 3-D gabor filter and support vector machines].

    PubMed

    Feng, Xiao; Xiao, Peng-feng; Li, Qi; Liu, Xiao-xi; Wu, Xiao-cui

    2014-08-01

    A three-dimensional Gabor filter was developed for classification of hyperspectral remote sensing image. This method is based on the characteristics of hyperspectral image and the principle of texture extraction with 2-D Gabor filters. Three-dimensional Gabor filter is able to filter all the bands of hyperspectral image simultaneously, capturing the specific responses in different scales, orientations, and spectral-dependent properties from enormous image information, which greatly reduces the time consumption in hyperspectral image texture extraction, and solve the overlay difficulties of filtered spectrums. Using the designed three-dimensional Gabor filters in different scales and orientations, Hyperion image which covers the typical area of Qi Lian Mountain was processed with full bands to get 26 Gabor texture features and the spatial differences of Gabor feature textures corresponding to each land types were analyzed. On the basis of automatic subspace separation, the dimensions of the hyperspectral image were reduced by band index (BI) method which provides different band combinations for classification in order to search for the optimal magnitude of dimension reduction. Adding three-dimensional Gabor texture features successively according to its discrimination to the given land types, supervised classification was carried out with the classifier support vector machines (SVM). It is shown that the method using three-dimensional Gabor texture features and BI band selection based on automatic subspace separation for hyperspectral image classification can not only reduce dimensions; but also improve the classification accuracy and efficiency of hyperspectral image.

  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

  4. Comparative Evaluation of Hyperspectral Imaging and Bathymetric Lidar for Measuring Channel Morphology Across a Range of River Environments

    NASA Astrophysics Data System (ADS)

    Legleiter, C. J.; Overstreet, B. T.; Glennie, C. L.; Pan, Z.; Fernandez-Diaz, J. C.; Singhania, A.

    2014-12-01

    Reliable topographic information is critical to many applications in the riverine sciences. Quantifying morphologic change, modeling flow and sediment transport, and assessing aquatic habitat all require accurate, spatially distributed measurements of bed elevation. Remote sensing has emerged as a powerful tool for acquiring such data, but the capabilities and limitations associated with various remote sensing techniques must be evaluated systematically. In this study, we assessed the potential of hyperspectral imaging and bathymetric LiDAR for measuring channel morphology across a range of conditions in two distinct field sites: the clear-flowing Snake River in Grand Teton National Park and the confluence of the Blue and Colorado Rivers in north-central Colorado, USA. Field measurements of water column optical properties highlighted differences among these streams, including the highly turbid Muddy Creek also entering the Colorado, and enabled theoretical calculations of bathymetric precision (smallest detectable change in depth) and dynamic range (maximum detectable depth). Hyperspectral imaging can yield more precise depth estimates in shallow, clear water but bathymetric LiDAR could provide more consistent performance across a broader range of depths. Spectrally-based depth retrieval was highly accurate on the Snake River but less reliable in the more complex confluence setting. Stratification of the Blue/Colorado site into clear and turbid subsets did not improve depth retrieval performance. To obtain bed elevations, image-derived depth estimates were subtracted from water surface elevations derived from near-infrared LiDAR acquired at the same time as the hyperspectral images. For the water-penetrating green LiDAR, bed elevations were inferred from laser waveforms. On the Snake River, hyperspectral imaging resulted in smaller mean and root mean square errors than bathymetric LiDAR, but at the Blue/Colorado site the optical approach was subject to a shallow

  5. Compressive Hyperspectral Imaging and Anomaly Detection

    DTIC Science & Technology

    2013-03-01

    simple, yet effective method of using the spatial information to increase the accuracy of target detection. The idea is to apply TV denoising [4] to the...a zero value, and isolated false alarm pixels are usually eliminated by the TV denoising algorithm. 2 2.1.1 TV Denoising Here we briefly describe the...total variation denoising model[4] we use in the above. Given an image I ∈ R2, we solve the following L1 minimization problem to denoise the image

  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. 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.)

  8. In vivo hyperspectral imaging and differentiation of skin cancer

    NASA Astrophysics Data System (ADS)

    Zherdeva, Larisa A.; Bratchenko, Ivan A.; Myakinin, Oleg O.; Moryatov, Alexander A.; Kozlov, Sergey V.; Zakharov, Valery P.

    2016-10-01

    Results of hyperspectral imaging analysis for in vivo visualization of skin neoplasms are presented. 16 melanomas, 19 basal cell carcinomas and 10 benign tumors with different stages of neoplasm growth were tested. The HSI system provide skin tissue images with 5 nm spectral resolution in the range of 450-750 nm with automatic stabilization of each frame compensating displacement of the scanning area due to spontaneous macro-movements of the patient. The integrated optical densities in 530-600 and 600-670 nm ranges are used for real-time hemoglobin and melanin distribution imaging in skin tissue. It was shown that the total accuracy of skin cancer identification exceeds 90% and 70% for differentiation of melanomas from BCC and begihn tumors. It was demonstrated the possibility for HSI classification of melanomas of different stages.

  9. 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.

  10. 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...

  11. 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

  12. Identification of early cancerous lesion of esophagus with endoscopic images by hyperspectral image technique (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Huang, Shih-Wei; Chen, Shih-Hua; Chen, Weichung; Wu, I.-Chen; Wu, Ming Tsang; Kuo, Chie-Tong; Wang, Hsiang-Chen

    2016-03-01

    This study presents a method to identify early esophageal cancer within endoscope using hyperspectral imaging technology. The research samples are three kinds of endoscopic images including white light endoscopic, chromoendoscopic, and narrow-band endoscopic images with different stages of pathological changes (normal, dysplasia, dysplasia - esophageal cancer, and esophageal cancer). Research is divided into two parts: first, we analysis the reflectance spectra of endoscopic images with different stages to know the spectral responses by pathological changes. Second, we identified early cancerous lesion of esophagus by principal component analysis (PCA) of the reflectance spectra of endoscopic images. The results of this study show that the identification of early cancerous lesion is possible achieve from three kinds of images. In which the spectral characteristics of NBI endoscopy images of a gray area than those without the existence of the problem the first two, and the trend is very clear. Therefore, if simply to reflect differences in the degree of spectral identification, chromoendoscopic images are suitable samples. The best identification of early esophageal cancer is using the NBI endoscopic images. Based on the results, the use of hyperspectral imaging technology in the early endoscopic esophageal cancer lesion image recognition helps clinicians quickly diagnose. We hope for the future to have a relatively large amount of endoscopic image by establishing a hyperspectral imaging database system developed in this study, so the clinician can take this repository more efficiently preliminary diagnosis.

  13. Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging

    NASA Astrophysics Data System (ADS)

    Schubert, Till; Wenzel, Susanne; Roscher, Ribana; Stachniss, Cyrill

    2016-06-01

    The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the analysis of latent traces and extends the classical concept to the conservation of the crime scene for retrospective laboratory analysis. We examine specimen of blood, semen and saliva traces in several dilution steps, prepared on cardboard substrate. As our key result we successfully make latent traces visible up to dilution factor of 1:8000. We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the shortwave infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood classifier, assuming normally distributed data. Further, we use Random Forest as a competitive approach. The classifiers retrieve the exact positions of labelled trace preparation up to highest dilution and determine posterior probabilities. By modelling the classification task with a Markov Random Field we are able to integrate prior information about the spatial relation of neighboured pixel labels.

  14. 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.

  15. Online monitoring of red meat color using hyperspectral imaging.

    PubMed

    Kamruzzaman, Mohammed; Makino, Yoshio; Oshita, Seiichi

    2016-06-01

    A hyperspectral imaging system in the spectral range of 400-1000 nm was tested to develop an online monitoring system for red meat (beef, lamb, and pork) color in the meat industry. Instead of selecting different sets of important wavelengths for beef, lamb, and pork, a set of feature wavelengths were selected using the successive projection algorithm for red meat colors (L*, a*, b) for convenient industrial application. Only six wavelengths (450, 460, 600, 620, 820, and 980 nm) were further chosen as predictive feature wavelengths for predicting L*, a*, and b* in red meat. Multiple linear regression models were then developed and predicted L*, a*, and b* with coefficients of determination (R(2)p) of 0.97, 0.84, and 0.82, and root mean square error of prediction of 1.72, 1.73, and 1.35, respectively. Finally, distribution maps of meat surface color were generated. The results indicated that hyperspectral imaging has the potential to be used for rapid assessment of meat color.

  16. Hyperspectral Imager for Coastal Ocean (HICO)

    DTIC Science & Technology

    2008-01-01

    spectral features Signal to Noise Ratio > 200 to 1 for a 5% surface albedo scene Provides adequate residual SNR after atmospheric removal...significantly improve the SNR of the HICO sensor system. NOVASOL engineers designed the instrument with a potential launch and operation from space in...order is in the center. IMPACT/APPLICATIONS The HICO spectrograph was designed for coastal imaging and is optimized for high throughput and SNR in

  17. Hyperspectral imaging for dermal hemoglobin spectroscopy

    NASA Astrophysics Data System (ADS)

    Dwyer, Peter J.; DiMarzio, Charles A.

    1999-10-01

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

  18. Application of hyperspectral imaging in food safety inspection and control: a review.

    PubMed

    Feng, Yao-Ze; Sun, Da-Wen

    2012-01-01

    Food safety is a great public concern, and outbreaks of food-borne illnesses can lead to disturbance to the society. Consequently, fast and nondestructive methods are required for sensing the safety situation of produce. As an emerging technology, hyperspectral imaging has been successfully employed in food safety inspection and control. After presenting the fundamentals of hyperspectral imaging, this paper provides a comprehensive review on its application in determination of physical, chemical, and biological contamination on food products. Additionally, other studies, including detecting meat and meat bone in feedstuffs as well as organic residue on food processing equipment, are also reported due to their close relationship with food safety control. With these applications, it can be demonstrated that miscellaneous hyperspectral imaging techniques including near-infrared hyperspectral imaging, fluorescence hyperspectral imaging, and Raman hyperspectral imaging or their combinations are powerful tools for food safety surveillance. Moreover, it is envisaged that hyperspectral imaging can be considered as an alternative technique for conventional methods in realizing inspection automation, leading to the elimination of the occurrence of food safety problems at the utmost.

  19. An assessment of independent component analysis for detection of military targets from hyperspectral images

    NASA Astrophysics Data System (ADS)

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

    2011-10-01

    Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed-Xiaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed-Xiaoli anomaly detector and Uniform Target Detector (RXD-UTD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms.

  20. Contrast enhancement of subcutaneous blood vessel images by means of visible and near-infrared hyper-spectral imaging

    NASA Astrophysics Data System (ADS)

    Katrašnik, Jaka; Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

    2009-02-01

    Visualization of subcutaneous veins is very difficult with the naked eye, but important for diagnosis of medical conditions and different medical procedures such as catheter insertion and blood withdrawal. Moreover, recent studies showed that the images of subcutaneous veins could be used for biometric identification. The majority of methods used for enhancing the contrast between the subcutaneous veins and surrounding tissue are based on simple imaging systems utilizing CMOS or CCD cameras with LED illumination capable of acquiring images from the near infrared spectral region, usually near 900 nm. However, such simplified imaging methods cannot exploit the full potential of the spectral information. In this paper, a new highly versatile method for enhancing the contrast of subcutaneous veins based on state-of-the-art high-resolution hyper-spectral imaging system utilizing the spectral region from 550 to 1700 nm is presented. First, a detailed analysis of the contrast between the subcutaneous veins and the surrounding tissue as a function of wavelength, for several different positions on the human arm, was performed in order to extract the spectral regions with the highest contrast. The highest contrast images were acquired at 1100 nm, however, combining the individual images from the extracted spectral regions by the proposed contrast enhancement method resulted in a single image with up to ten-fold better contrast. Therefore, the proposed method has proved to be a useful tool for visualization of subcutaneous veins.

  1. 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.

  2. Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process.

    PubMed

    Liu, Dan; Sun, Da-Wen; Qu, Jiahuan; Zeng, Xin-An; Pu, Hongbin; Ma, Ji

    2014-01-01

    The feasibility of using hyperspectral imaging technique (1000-2500 nm) for predicting moisture content (MC) during the salting process of porcine meat was assessed. Different spectral profiles including reflectance spectra (RS), absorbance spectra (AS) and Kubelka-Munk spectra (KMS) were examined to investigate the influence of spectroscopic transformations on predicting moisture content of salted pork slice. The best full-wavelength partial least squares regression (PLSR) models were acquired based on reflectance spectra (Rc(2)=0.969, RMSEC=0.921%; Rc(2)=0.941, RMSEP=1.23%). On the basis of the optimal wavelengths identified using the regression coefficient, two calibration models of PLSR and multiple linear regression (MLR) were compared. The optimal RS-MLR model was considered to be the best for determining the moisture content of salted pork, with a Rc(2) of 0.917 and RMSEP of 1.48%. Visualisation of moisture distribution in each pixel of the hyperspectral image using the prediction model display moisture evolution and migration in pork slices.

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

    NASA Astrophysics Data System (ADS)

    Hemissi, S.; Riadh Farah, I.

    2015-04-01

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

  4. 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

  5. The use of hyperspectral imaging in the VNIR (400-1000nm) and SWIR range (1000-2500nm) for detecting counterfeit drugs with identical API composition.

    PubMed

    Wilczyński, Sławomir; Koprowski, Robert; Marmion, Mathieu; Duda, Piotr; Błońska-Fajfrowska, Barbara

    2016-11-01

    The risk of death from taking counterfeit drugs is now greater than the probability of dying from malaria and AIDS combined (at least half a million deaths each year). At the same time, counterfeit medicines are falsified more and more "skillfully". According to WHO about 10% of counterfeit drugs are copies of original products. The methods of hyperspectral imaging and image analysis and processing were used to detect counterfeit drugs. Original Viagra® (Pfizer) and counterfeit tablets were compared. Hyperspectral imaging was used to acquire hyperspectral data cubes from both original and counterfeit tablets in the spectral range of 400-2500nm. Spectral parameters for both the original Viagra® and counterfeit drugs were compared. Grey-Level Co-Occurrence Matrix (GLCM) analysis and Principal Component Analysis (PCA) were performed. Hyperspectral analysis of the surface of the original Viagra® and counterfeit tablets demonstrates significant differences in reflectance (maximum difference for 1619.75nm). The GLCM contrast for the falsified drug is on average higher than for the original one 16±4%. GLCM contrast analysis enables to quantify homogeneity of distribution of tablet ingredients and enables to distinguish tablets with identical chemical composition. SWIR (1000-2500nm) hyperspectral imaging has a definite advantage over imaging in VNIR (400-1000nm) - higher wavelength is less sensitive to non-uniform illumination.

  6. 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.

  7. Male body image following acquired brain injury.

    PubMed

    Howes, Hannah; Edwards, Stephen; Benton, David

    2005-02-01

    The purpose of this study was to investigate body image concerns and psycho-emotional health in males with acquired brain injury (ABI). Using a between subjects study of 25 males with ABI and 25 matched controls, variables were analysed using correlations and 2 x 2 analyses of variance (ANOVAs) with head injury and injury type as independent variables. Body image and psycho-emotional health were evaluated using self-report questionnaires. Disability and cognitive impairment were measured using a mixture of self-report, cognitive testing and clinical notes. Results indicated that males with ABI had significantly lower self-esteem and body dissatisfaction on a number of items relating to physical and sexual functioning. There were significant differences in body image between stroke and TBI, but there was no corresponding relationship with psycho-emotional health. These body image differences might be explained by age. The finding that ABI has a negative effect on body image and that this relates to psycho-emotional health should be investigated further, perhaps being included in future rehabilitation strategies.

  8. Multiscale target extraction using a spectral saliency map for a hyperspectral image.

    PubMed

    Zhang, Jing; Geng, Wenhao; Zhuo, Li; Tian, Qi; Cao, Yan

    2016-10-01

    With the rapid growth of the capabilities for hyperspectral imagery acquisition, how to efficiently find the significant target in hyperspectral imagery has become a fundamental task for remote-sensing applications. Existing target extraction methods mainly separate targets from background with a threshold based on pixels and single-scale image information extraction. However, due to the high dimensional characteristics and the complex background of hyperspectral imagery, it is difficult to obtain good extraction results with existing methods. Saliency detection has been a promising topic because saliency features can quickly locate saliency regions from complex backgrounds. Considering the spatial and spectral characteristics of a hyperspectral image, a multiscale target extraction method using a spectral saliency map is proposed for a hyperspectral image, which includes: (1) a spectral saliency model is constructed for detecting spectral saliency map in a hyperspectral image; (2) focus of attention (FOA) as the seed point is competed in the spectral saliency map by the winner-take-all (WTA) network; (3) the multiscale image is segmented by region growing based on the minimum-heterogeneity rule after calculating the heterogeneity of the seed point with its surrounding pixels; (4) the salient target is detected and segmented under the constraint of the spectral saliency map. The experimental results show that the proposed method can effectively improve the accuracy of target extraction for hyperspectral images.

  9. An improved hyperspectral image classification approach based on ISODATA and SKR method

    NASA Astrophysics Data System (ADS)

    Hong, Pu; Ye, Xiao-feng; Yu, Hui; Zhang, Zhi-jie; Cai, Yu-fei; Tang, Xin; Tang, Wei; Wang, Chensheng

    2016-11-01

    Hyper-spectral images can not only provide spatial information but also a wealth of spectral information. A short list of applications includes environmental mapping, global change research, geological research, wetlands mapping, assessment of trafficability, plant and mineral identification and abundance estimation, crop analysis, and bathymetry. A crucial aspect of hyperspectral image analysis is the identification of materials present in an object or scene being imaged. Classification of a hyperspectral image sequence amounts to identifying which pixels contain various spectrally distinct materials that have been specified by the user. Several techniques for classification of multi-hyperspectral pixels have been used from minimum distance and maximum likelihood classifiers to correlation matched filter-based approaches such as spectral signature matching and the spectral angle mapper. In this paper, an improved hyperspectral images classification algorithm is proposed. In the proposed method, an improved similarity measurement method is applied, in which both the spectrum similarity and space similarity are considered. We use two different weighted matrix to estimate the spectrum similarity and space similarity between two pixels, respectively. And then whether these two pixels represent the same material can be determined. In order to reduce the computational cost the wavelet transform is also applied prior to extract the spectral and space features. The proposed method is tested using hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory. Experimental results the efficiency of this new method on hyperspectral images associated with space object material identification.

  10. Hyperspectral nanoscale imaging on dielectric substrates with coaxial optical antenna scan probes.

    PubMed

    Weber-Bargioni, Alexander; Schwartzberg, Adam; Cornaglia, Matteo; Ismach, Ariel; Urban, Jeffrey J; Pang, Yuanjie; Gordon, Reuven; Bokor, Jeffrey; Salmeron, Miquel B; Ogletree, D Frank; Ashby, Paul; Cabrini, Stefano; Schuck, P James

    2011-03-09

    We have demonstrated hyperspectral tip-enhanced Raman imaging on dielectric substrates using linearly polarized light and nanofabricated coaxial antenna tips. A full Raman spectrum was acquired at each pixel of a 256 by 256 pixel contact-mode atomic force microscope image of carbon nanotubes grown on a fused silica microscope coverslip, allowing D and G mode intensity and D-mode peak shifts to be measured with ∼20 nm spatial resolution. Tip enhancement was sufficient to acquire useful Raman spectra in 50-100 ms. Coaxial scan probes combine the efficiency and enhanced, ultralocalized optical fields of plasmonically coupled antennae with the superior topographical imaging properties of sharp metal tips. The yield of the coaxial tip fabrication process is close to 100%, and the tips are sufficiently durable to support hours of contact-mode force microscope imaging. Our coaxial probes avoid the limitations associated with the "gap-mode" imaging geometry used in most tip-enhanced Raman studies to date, where a sharp metal tip is held ∼1 nm above a metallic substrate with the sample located in the gap.

  11. Excitation-scanning hyperspectral imaging system for microscopic and endoscopic applications

    NASA Astrophysics Data System (ADS)

    Mayes, Sam A.; Leavesley, Silas J.; Rich, Thomas C.

    2016-04-01

    Current microscopic and endoscopic technologies for cancer screening utilize white-light illumination sources. Hyper-spectral imaging has been shown to improve sensitivity while retaining specificity when compared to white-light imaging in both microscopy and in vivo imaging. However, hyperspectral imaging methods have historically suffered from slow acquisition times due to the narrow bandwidth of spectral filters. Often minutes are required to gather a full image stack. We have developed a novel approach called excitation-scanning hyperspectral imaging that provides 2-3 orders of magnitude increased signal strength. This reduces acquisition times significantly, allowing for live video acquisition. Here, we describe a preliminary prototype excitation-scanning hyperspectral imaging system that can be coupled with endoscopes or microscopes for hyperspectral imaging of tissues and cells. Our system is comprised of three subsystems: illumination, transmission, and imaging. The illumination subsystem employs light-emitting diode arrays to illuminate at different wavelengths. The transmission subsystem utilizes a unique geometry of optics and a liquid light guide. Software controls allow us to interface with and control the subsystems and components. Digital and analog signals are used to coordinate wavelength intensity, cycling and camera triggering. Testing of the system shows it can cycle 16 wavelengths at as fast as 1 ms per cycle. Additionally, more than 18% of the light transmits through the system. Our setup should allow for hyperspectral imaging of tissue and cells in real time.

  12. Design and Performance of the Hyper-Cam, an Infrared Hyperspectral Imaging Sensor

    DTIC Science & Technology

    2009-10-01

    UXO) and for the detection of camouflaged targets . Imaging spectrometers have unmatched capabilities to meet the requirements of these applications...Cam, operating in the LWIR band, is well suited for standoff chemical agent detection and can be used as a powerful hyperspectral imager for any...Design and Performance of the Hyper-Cam, an Infrared Hyperspectral Imaging Sensor Philippe Lagueux*a, Vincent Farleya, Martin Chamberlanda

  13. Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging.

    PubMed

    Zhang, Ruoyu; Ying, Yibin; Rao, Xiuqin; Li, Jiangbo

    2012-09-01

    Hyperspectral fluorescence imaging (HSFI) is potentially useful for assessing food and agricultural products, because it combines the merits of both hyperspectral imaging and fluorescence spectroscopy. This paper provides an introduction to HSFI: the principle and components of HSFI, calibration and image processing are described. In addition, recent advances in the application of HSFI to food and agricultural product assessment are reviewed, such as contaminant detection, constituent analysis and quality evaluation. Finally, current limitations and likely future development trends are discussed.

  14. 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.

  15. 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.

  16. Application of hyperspectral fluorescence lifetime imaging to tissue autofluorescence: arthritis

    NASA Astrophysics Data System (ADS)

    Talbot, C. B.; Benninger, R. K. P.; de Beule, P.; Requejo-Isidro, J.; Elson, D. S.; Dunsby, C.; Munro, I.; Neil, M. A.; Sandison, A.; Sofat, N.; Nagase, H.; French, P. M. W.; Lever, M. J.

    2005-08-01

    Tissue contains many natural fluorophores and therefore by exploiting autofluorescence, we can obtain information from tissue with less interference than conventional histological techniques. However, conventional intensity imaging is prone to artifacts since it is an absolute measurement. Fluorescence lifetime and spectral measurements are relative measurements and therefore allow for better measurements. We have applied FLIM and hyperspectral FLIM to the study of articular cartilage and its disease arthritis. We have analyzed normal human articular cartilage and cartilage which was in the early stages of disease. In this case, it was found that FLIM was able to detect changes in the diseased tissue that were not detectable with the conventional diagnosis. Specifically, the fluorescence lifetimes (FL) of the cells were different between the two samples. We have also applied hyperspectral FLIM to degraded cartilage through treatment with interleukin-1. In this case, it was found that there was a shift in the emission spectrum with treatment and that the lifetime had also increased. We also showed that there was greater contrast between the cells and the extracellular matrix (ECM) at longer wavelengths.

  17. [Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].

    PubMed

    Huang, Tao; Li, Xiao-yu; Xu, Meng-ling; Jin, Rui; Ku, Jing; Xu, Sen-miao; Wu, Zhen-zhong

    2015-01-01

    The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA

  18. 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).

  19. High speed measurement of corn seed viability using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Ambrose, Ashabahebwa; Kandpal, Lalit Mohan; Kim, Moon S.; Lee, Wang-Hee; Cho, Byoung-Kwan

    2016-03-01

    Corn is one of the most cultivated crops all over world as food for humans as well as animals. Optimized agronomic practices and improved technological interventions during planting, harvesting and post-harvest handling are critical to improving the quantity and quality of corn production. Seed germination and vigor are the primary determinants of high yield notwithstanding any other factors that may play during the growth period. Seed viability may be lost during storage due to unfavorable conditions e.g. moisture content and temperatures, or physical damage during mechanical processing e.g. shelling, or over heating during drying. It is therefore vital for seed companies and farmers to test and ascertain seed viability to avoid losses of any kind. This study aimed at investigating the possibility of using hyperspectral imaging (HSI) technique to discriminate viable and nonviable corn seeds. A group of corn samples were heat treated by using microwave process while a group of seeds were kept as control group (untreated). The hyperspectral images of corn seeds of both groups were captured between 400 and 2500 nm wave range. Partial least squares discriminant analysis (PLS-DA) was built for the classification of aged (heat treated) and normal (untreated) corn seeds. The model showed highest classification accuracy of 97.6% (calibration) and 95.6% (prediction) in the SWIR region of the HSI. Furthermore, the PLS-DA and binary images were capable to provide the visual information of treated and untreated corn seeds. The overall results suggest that HSI technique is accurate for classification of viable and non-viable seeds with non-destructive manner.

  20. 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.

  1. Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

    NASA Astrophysics Data System (ADS)

    Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

    2017-01-01

    Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

  2. Characterization of a new fertilizer during field trials by hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Bonifazi, Giuseppe; Serranti, Silvia; Trella, Agata; Garcia Izquierdo, Carlos

    2016-05-01

    This work was carried out in the framework of the LIFE RESAFE Project (LIFE12 ENV/IT/000356) "Innovative fertilizer from urban waste, bio-char and farm residues as substitute of chemical fertilizers". The aim of RESAFE project is the production of a new fertilizer from waste for agricultural practices. The new fertilizer was tested on 5 different crops during field trials carried out in Spain: barley, corn, tomato, potato and melon. For each crop six different treatments were applied and compared to verify the quality of RESAFE fertilizer. Soil samples were collected at the beginning and at the end of the experiment. The possibility to apply hyperspectral imaging (HSI) to perform soil evolution monitoring and characterization in respect to the fertilizer utilization and quality of the resulting crops was investigated. Soil samples were acquired by HSI in the near infrared field (1000-1700 nm) and on the same samples classical chemical analyses were carried out with reference to total nitrogen, total organic carbon, C/N ratio, total organic matter. Hyperspectral data were analyzed adopting a chemometric approach through application of Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares Analysis (PLS) for estimation of chemical parameters. The results showed as the proposed hardware and software integrated architecture allows to implement low cost and easy to use analytical procedures able to quantitatively assess soil chemical-physical attributes according to different fertilization strategies, in respect of different environmental conditions and selected crops.

  3. [Use of Near-Infrared Hyperspectral Images to Differentiate Architectural Coatings with Different Qualities].

    PubMed

    Jiang, Jin-bao; Qiao, Xiao-jun; He, Ru-yan; Tian, Fen-min

    2016-02-01

    Architectural coatings sold in market fall into many categories which mean different models and qualities. The research plans to differentiate different kinds of architectural coatings in quality using hyperspectral technology. Near-Infrared hyperspectral images of four kinds of architectural coatings (in a descending quality order of brand A, B, C, and D) in same color were acquired. The optimal wavelengths were selected at 1283 and 2447 nm to differentiate the four kinds of coatings through ANOVA (Analysis of Variance) method. The band ratio index of R₁₂₈₃/R₂₄₄₇ was built and the results were segmented into the corresponding coatings, and the accuracies of segmentation were compared with that from Maximum Likely Classification (MLC). The results indicated all J-M distances are more than 1.8 except between C and D; the lowest accuracy of 87.54% in segmentation and 95.63% in MLC were both from brand D, and others' accuracies all were over 90% in both ratio index and MLC. Therefore, the ratio index R₁₂₈₃/R₂₄₄₇ could be used to distinguish different kinds of architectural coatings. Also, the research could provide support for identification, quality acceptance, as well as conformity assessment of architectural coatings.

  4. Bands selection and classification of hyperspectral images based on hybrid kernels SVM by evolutionary algorithm

    NASA Astrophysics Data System (ADS)

    Hu, Yan-Yan; Li, Dong-Sheng

    2016-01-01

    The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.

  5. 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

  6. 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.

  7. Hyperspectral Imaging of Neoplastic Progression in a Mouse Model of Oral Carcinogenesis.

    PubMed

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

    2016-02-27

    Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.

  8. Development of algorithms for detection of mechanical injury on white mushrooms (Agaricus bisporus) using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Gowen, A. A.; O'Donnell, C. P.

    2009-05-01

    White mushrooms were subjected to mechanical injury by controlled shaking in a plastic box at 400 rpm for different times (0, 60, 120, 300 and 600 s). Immediately after shaking, hyperspectral images were obtained using two pushbroom line-scanning hyperspectral imaging instruments, one operating in the wavelength range of 400 - 1000 nm with spectroscopic resolution of 5 nm, the other operating in the wavelength range of 950 - 1700 nm with spectroscopic resolution of 7 nm. Different spectral and spatial pretreatments were investigated to reduce the effect of sample curvature on hyperspectral data. Algorithms based on Chemometric techniques (Principal Component Analysis and Partial Least Squares Discriminant Analysis) and image processing methods (masking, thresholding, morphological operations) were developed for pixel classification in hyperspectral images. In addition, correlation analysis, spectral angle mapping and scaled difference of sample spectra were investigated and compared with the chemometric approaches.

  9. 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 ...

  10. 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...

  11. Hyperspectral Imaging of Neoplastic Progression in a Mouse Model of Oral Carcinogenesis

    PubMed Central

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

    2016-01-01

    Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection. PMID:27656034

  12. Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

    2016-03-01

    Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.

  13. A novel CMOS-compatible, monolithically integrated line-scan hyperspectral imager covering the VIS-NIR range

    NASA Astrophysics Data System (ADS)

    Gonzalez, Pilar; Tack, Klaas; Geelen, Bert; Masschelein, Bart; Charle, Wouter; Vereecke, Bart; Lambrechts, Andy

    2016-05-01

    Imec has developed a process for the monolithic integration of optical filters on top of CMOS image sensors, leading to compact, cost-efficient and faster hyperspectral cameras. Different prototype sensors are available, most notably a 600- 1000 nm line-scan imager, and two mosaic sensors: a 4x4 VIS (470-620 nm range) and a 5x5 VNIR (600-1000 nm). In response to the users' demand for a single sensor able to cover both the VIS and NIR ranges, further developments have been made to enable more demanding applications. As a result, this paper presents the latest addition to imec's family of monolithically-integrated hyperspectral sensors: a line scan sensor covering the range 470-900 nm. This new prototype sensor can acquire hyperspectral image cubes of 2048 pixels over 192 bands (128 bands for the 600- 900 nm range, and 64 bands for the 470-620 nm range) at 340 cubes per second for normal machine vision illumination levels.

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

    SciTech Connect

    Baba, Justin S; Boudreaux, Philip R

    2012-01-01

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

  15. Probability Density and CFAR Threshold Estimation for Hyperspectral Imaging

    SciTech Connect

    Clark, G A

    2004-09-21

    The work reported here shows the proof of principle (using a small data set) for a suite of algorithms designed to estimate the probability density function of hyperspectral background data and compute the appropriate Constant False Alarm Rate (CFAR) matched filter decision threshold for a chemical plume detector. Future work will provide a thorough demonstration of the algorithms and their performance with a large data set. The LASI (Large Aperture Search Initiative) Project involves instrumentation and image processing for hyperspectral images of chemical plumes in the atmosphere. The work reported here involves research and development on algorithms for reducing the false alarm rate in chemical plume detection and identification algorithms operating on hyperspectral image cubes. The chemical plume detection algorithms to date have used matched filters designed using generalized maximum likelihood ratio hypothesis testing algorithms [1, 2, 5, 6, 7, 12, 10, 11, 13]. One of the key challenges in hyperspectral imaging research is the high false alarm rate that often results from the plume detector [1, 2]. The overall goal of this work is to extend the classical matched filter detector to apply Constant False Alarm Rate (CFAR) methods to reduce the false alarm rate, or Probability of False Alarm P{sub FA} of the matched filter [4, 8, 9, 12]. A detector designer is interested in minimizing the probability of false alarm while simultaneously maximizing the probability of detection P{sub D}. This is summarized by the Receiver Operating Characteristic Curve (ROC) [10, 11], which is actually a family of curves depicting P{sub D} vs. P{sub FA}parameterized by varying levels of signal to noise (or clutter) ratio (SNR or SCR). Often, it is advantageous to be able to specify a desired P{sub FA} and develop a ROC curve (P{sub D} vs. decision threshold r{sub 0}) for that case. That is the purpose of this work. Specifically, this work develops a set of algorithms and MATLAB

  16. 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.

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

    PubMed Central

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

    2015-01-01

    Abstract. 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  19. HIL range performance of notional hyperspectral imaging sensors

    NASA Astrophysics Data System (ADS)

    Hodgkin, Van A.; Howell, Christopher L.

    2016-05-01

    In the use of conventional broadband imaging systems, whether reflective or emissive, scene image contrasts are often so low that target discrimination is difficult or uncertain, and it is contrast that drives human-in-the-loop (HIL) sensor range performance. This situation can occur even when the spectral shapes of the target and background signatures (radiances) across the sensor waveband differ significantly from each other. The fundamental components of broadband image contrast are the spectral integrals of the target and background signatures, and this spectral integration can average away the spectral differences between scene objects. In many low broadband image contrast situations, hyperspectral imaging (HSI) can preserve a greater degree of the intrinsic scene spectral contrast for the display, and more display contrast means greater range performance by a trained observer. This paper documents a study using spectral radiometric signature modeling and the U.S. Army's Night Vision Integrated Performance Model (NV-IPM) to show how waveband selection by a notional HSI sensor using spectral contrast optimization can significantly increase HIL sensor range performance over conventional broadband sensors.

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

    NASA Astrophysics Data System (ADS)

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

    2000-09-01

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

  1. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

    The traditional model for space-based earth observations involves long mission times, high cost, and long development time. Because of the significant time and monetary investment required, riskier instrument development missions or those with very specific scientific goals are unlikely to successfully obtain funding. However, a niche for earth observations exploiting new technologies in focused, short lifetime missions is opening with the growth of the small satellite market and launch opportunities for these satellites. These low-cost, short-lived missions provide an experimental platform for testing new sensor technologies that may transition to larger, more long-lived platforms. The low costs and short lifetimes also increase acceptable risk to sensors, enabling large decreases in cost using commercial off the shelf (COTS) parts and allowing early-career scientists and engineers to gain experience with these projects. We are building a low-cost long-wave infrared spectral sensor, funded by the NASA Experimental Project to Stimulate Competitive Research program (EPSCOR), to demonstrate the ways in which a university's scientific and instrument development programs can fit into this niche. The sensor is a low-mass, power efficient thermal hyperspectral imager with electronics contained in a pressure vessel to enable the use of COTS electronics, and will be compatible with small satellite platforms. The sensor, called Thermal Hyperspectral Imager (THI), is based on a Sagnac interferometer and uses an uncooled 320x256 microbolometer array. The sensor will collect calibrated radiance data at long-wave infrared (LWIR, 8-14 microns) wavelengths in 230-meter pixels with 20 wavenumber spectral resolution from a 400-km orbit.

  3. 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.

  4. Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

    PubMed Central

    Xiao, Rui; Gao, Junbin; Bossomaier, Terry

    2016-01-01

    A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression. PMID:27695102

  5. Hyperspectral Imaging for Determining Pigment Contents in Cucumber Leaves in Response to Angular Leaf Spot Disease

    PubMed Central

    Zhao, Yan-Ru; Li, Xiaoli; Yu, Ke-Qiang; Cheng, Fan; He, Yong

    2016-01-01

    Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll (Chl), and carotenoid (Car) contents in cucumber leaves in response to angular leaf spot (ALS). Altogether, 196 hyperspectral images of cucumber leaves with five infection severities of ALS were captured by a hyperspectral imaging system in the range of 380–1,030 nm covering 512 wavebands. Mean spectrum were extracted from regions of interest (ROIs) in the hyperspectral images. Partial least square regression (PLSR) models were used to develop quantitative analysis between the spectra and the pigment contents measured by biochemical analyses. In addition, regression coefficients (RCs) in PLSR models were employed to select important wavelengths (IWs) for modelling. It was found that the PLSR models developed by the IWs provided the optimal measurement results with correlation coefficient (R) of prediction of 0.871 and 0.876 for Chl and Car contents, respectively. Finally, Chl and Car distributions in cucumber leaves with the ALS infection were mapped by applying the optimal models pixel-wise to the hyperspectral images. The results proved the feasibility of hyperspectral imaging for visualizing the pigment distributions in cucumber leaves in response to ALS. PMID:27283050

  6. Hyperspectral Imaging for Determining Pigment Contents in Cucumber Leaves in Response to Angular Leaf Spot Disease.

    PubMed

    Zhao, Yan-Ru; Li, Xiaoli; Yu, Ke-Qiang; Cheng, Fan; He, Yong

    2016-06-10

    Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll (Chl), and carotenoid (Car) contents in cucumber leaves in response to angular leaf spot (ALS). Altogether, 196 hyperspectral images of cucumber leaves with five infection severities of ALS were captured by a hyperspectral imaging system in the range of 380-1,030 nm covering 512 wavebands. Mean spectrum were extracted from regions of interest (ROIs) in the hyperspectral images. Partial least square regression (PLSR) models were used to develop quantitative analysis between the spectra and the pigment contents measured by biochemical analyses. In addition, regression coefficients (RCs) in PLSR models were employed to select important wavelengths (IWs) for modelling. It was found that the PLSR models developed by the IWs provided the optimal measurement results with correlation coefficient (R) of prediction of 0.871 and 0.876 for Chl and Car contents, respectively. Finally, Chl and Car distributions in cucumber leaves with the ALS infection were mapped by applying the optimal models pixel-wise to the hyperspectral images. The results proved the feasibility of hyperspectral imaging for visualizing the pigment distributions in cucumber leaves in response to ALS.

  7. [Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

    PubMed

    Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei

    2015-11-01

    A novel method of combination of the chemometrics and the hyperspectral imaging techniques was presented to detect the temperatures of Ethylene-Vinyl Acetate copolymer (EVA) films in photovoltaic cells during the thermal encapsulation process. Four varieties of the EVA films which had been heated at the temperatures of 128, 132, 142 and 148 °C during the photovoltaic cells production process were used for investigation in this paper. These copolymer encapsulation films were firstly scanned by the hyperspectral imaging equipment (Spectral Imaging Ltd. Oulu, Finland). The scanning band range of hyperspectral equipemnt was set between 904.58 and 1700.01 nm. The hyperspectral dataset of copolymer films was randomly divided into two parts for the training and test purpose. Each type of the training set and test set contained 90 and 10 instances, respectively. The obtained hyperspectral images of EVA films were dealt with by using the ENVI (Exelis Visual Information Solutions, USA) software. The size of region of interest (ROI) of each obtained hyperspectral image of EVA film was set as 150 x 150 pixels. The average of reflectance hyper spectra of all the pixels in the ROI was used as the characteristic curve to represent the instance. There kinds of chemometrics methods including partial least squares regression (PLSR), multi-class support vector machine (SVM) and large margin nearest neighbor (LMNN) were used to correlate the characteristic hyper spectra with the encapsulation temperatures of of copolymer films. The plot of weighted regression coefficients illustrated that both bands of short- and long-wave near infrared hyperspectral data contributed to enhancing the prediction accuracy of the forecast model. Because the attained reflectance hyperspectral data of EVA materials displayed the strong nonlinearity, the prediction performance of linear modeling method of PLSR declined and the prediction precision only reached to 95%. The kernel-based forecast models were

  8. Digital micromirror device as a spatial illuminator for fluorescence lifetime and hyperspectral imaging.

    PubMed

    Bednarkiewicz, Artur; Bouhifd, Mounir; Whelan, Maurice P

    2008-03-20

    Time-domain fluorescence lifetime imaging (FLIM) and hyper-spectral imaging (HSI) are two advanced microscopy techniques widely used in biological studies. Typically both FLIM and HSI are performed with either a whole-field or raster-scanning approach, which often prove to be technically complex and expensive, requiring the user to accept a compromise among precision, speed, and spatial resolution. We propose the use of a digital micromirror device (DMD) as a spatial illuminator for time-domain FLIM and HSI with a laser diode excitation source. The rather unique features of the DMD allow both random and parallel access to regions of interest (ROIs) on the sample, in a very rapid and repeatable fashion. As a consequence both spectral and lifetime images can be acquired with a precision normally associated with single-point systems but with a high degree of flexibility in their spatial construction. In addition, the DMD system offers a very efficient way of implementing a global analysis approach for FLIM, where average fluorescence decay parameters are first acquired for a ROI and then used as initial estimates in determining their spatial distribution within the ROI. Experimental results obtained on phantoms employing fluorescent dyes clearly show how the DMD method supports both spectral and temporal separation for target identification in HSI and FLIM, respectively.

  9. Determination of pasture quality using airborne hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

  10. Video-rate chemical identification and visualization with snapshot hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Bodkin, Andrew; Sheinis, A.; Norton, A.; Daly, J.; Roberts, C.; Beaven, S.; Weinheimer, J.

    2012-06-01

    Hyperspectral imaging has important benefits in remote sensing and target discrimination applications. This paper describes a class of snapshot-mode hyperspectral imaging systems which utilize a unique optical processor that provides video-rate hyperspectral datacubes. This system consists of numerous parallel optical paths which collect the full threedimensional (two spatial, one spectral) hyperspectral datacube with each video frame and are ideal for recording data from transient events, or on unstable platforms. We will present the results of laboratory and field-tests for several of these imagers operating at visible, near-infrared, MWIR and LWIR wavelengths. Measurement results for nitrate detection and identification as well as additional chemical identification and analysis will be presented.

  11. The Short Wave Aerostat-Mounted Imager (SWAMI): A novel platform for acquiring remotely sensed data from a tethered balloon

    USGS Publications Warehouse

    Vierling, L.A.; Fersdahl, M.; Chen, X.; Li, Z.; Zimmerman, P.

    2006-01-01

    We describe a new remote sensing system called the Short Wave Aerostat-Mounted Imager (SWAMI). The SWAMI is designed to acquire co-located video imagery and hyperspectral data to study basic remote sensing questions and to link landscape level trace gas fluxes with spatially and temporally appropriate spectral observations. The SWAMI can fly at altitudes up to 2 km above ground level to bridge the spatial gap between radiometric measurements collected near the surface and those acquired by other aircraft or satellites. The SWAMI platform consists of a dual channel hyperspectral spectroradiometer, video camera, GPS, thermal infrared sensor, and several meteorological and control sensors. All SWAMI functions (e.g. data acquisition and sensor pointing) can be controlled from the ground via wireless transmission. Sample data from the sampling platform are presented, along with several potential scientific applications of SWAMI data. ?? 2006 Elsevier Inc. All rights reserved.

  12. Regularization of Mars Reconnaissance Orbiter CRISM along-track oversampled hyperspectral imaging observations of Mars

    NASA Astrophysics Data System (ADS)

    Kreisch, C. D.; O'Sullivan, J. A.; Arvidson, R. E.; Politte, D. V.; He, L.; Stein, N. T.; Finkel, J.; Guinness, E. A.; Wolff, M. J.; Lapôtre, M. G. A.

    2017-01-01

    Mars Reconnaissance Orbiter Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) hyperspectral image data have been acquired in an along-track oversampled (ATO) mode with the intent of processing the data to better than the nominal ∼18 m/pixel ground resolution. We have implemented an iterative maximum log-likelihood method (MLM) that utilizes the instrument spectral and spatial transfer functions and includes a penalty function to regularize the data. Products are produced both in sensor space and as projected hyperspectral image cubes at 12 m/pixel. Preprocessing steps include retrieval of surface single scattering albedos (SSA) using the Hapke Function and DISORT-based radiative modeling of atmospheric gases and aerosols. Resultant SSA cubes are despiked to remove extrema and tested to ensure that the remaining data are Poisson-distributed, an underlying assumption for the MLM algorithm implementation. Two examples of processed ATO data sets are presented. ATO0002EC79 covers the route taken by the Curiosity rover during its initial ascent of Mount Sharp in Gale Crater. SSA data are used to model mineral abundances and grain sizes predicted to be present in the Namib barchan sand dune sampled and analyzed by Curiosity. CRISM based results compare favorably to in situ results derived from Curiosity's measurement campaign. ATO0002DDF9 covers Marathon Valley on the Cape Tribulation rim segment of Endeavour Crater. SSA spectra indicate the presence of a minor component of Fe3+ and Mg2+ smectites on the valley floor and walls. Localization to 12 m/pixel provided the detailed spatial information needed for the Opportunity rover to traverse to and characterize those outcrops that have the deepest absorptions. The combination of orbital and rover-based data show that the smectite-bearing outcrops in Marathon Valley are impact breccias that are basaltic in composition and that have been isochemically altered in a low water to rock environment.

  13. Damage and quality assessment in wheat by NIR hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Delwiche, Stephen R.; Kim, Moon S.; Dong, Yanhong

    2010-04-01

    Fusarium head blight is a fungal disease that affects the world's small grains, such as wheat and barley. Attacking the spikelets during development, the fungus causes a reduction of yield and grain of poorer processing quality. It also is a health concern because of the secondary metabolite, deoxynivalenol, which often accompanies the fungus. While chemical methods exist to measure the concentration of the mycotoxin and manual visual inspection is used to ascertain the level of Fusarium damage, research has been active in developing fast, optically based techniques that can assess this form of damage. In the current study a near-infrared (1000-1700 nm) hyperspectral image system was assembled and applied to Fusarium-damaged kernel recognition. With anticipation of an eventual multispectral imaging system design, 5 wavelengths were manually selected from a pool of 146 images as the most promising, such that when combined in pairs or triplets, Fusarium damage could be identified. We present the results of two pairs of wavelengths [(1199, 1474 nm) and (1315, 1474 nm)] whose reflectance values produced adequate separation of kernels of healthy appearance (i.e., asymptomatic condition) from kernels possessing Fusarium damage.

  14. Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection.

    PubMed

    Regeling, Bianca; Thies, Boris; Gerstner, Andreas O H; Westermann, Stephan; Müller, Nina A; Bendix, Jörg; Laffers, Wiebke

    2016-08-13

    Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope's fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.

  15. Hyper-spectral imaging of aircraft exhaust plumes

    NASA Astrophysics Data System (ADS)

    Bowen, Spencer; Bradley, Kenneth; Gross, Kevin; Perram, Glen; Marciniak, Michael

    2008-10-01

    An imaging Fourier-transform spectrometer has been used to determine low spatial resolution temperature and chemical species concentration distributions of aircraft jet engine exhaust plumes. An overview of the imaging Fourier transform spectrometer and the methodology of the project is presented. Results to date are shared and future work is discussed. Exhaust plume data from a Turbine Technologies, LTD, SR-30 turbojet engine at three engine settings was collected using a Telops Field-portable Imaging Radiometric Spectrometer Technology Mid-Wave Extended (FIRST-MWE). Although the plume exhibited high temporal frequency fluctuations, temporal averaging of hyper-spectral data-cubes produced steady-state distributions, which, when co-added and Fourier transformed, produced workable spectra. These spectra were then reduced using a simplified gaseous effluent model to fit forward-modeled spectra obtained from the Line-By-Line Radiative Transfer Model (LBLRTM) and the high-resolution transmission (HITRAN) molecular absorption database to determine approximate temperature and concentration distributions. It is theorized that further development of the physical model will produce better agreement between measured and modeled data.

  16. Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection

    PubMed Central

    Regeling, Bianca; Thies, Boris; Gerstner, Andreas O. H.; Westermann, Stephan; Müller, Nina A.; Bendix, Jörg; Laffers, Wiebke

    2016-01-01

    Hyperspectral imaging (HSI) is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details. PMID:27529255

  17. 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

  18. Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

    NASA Astrophysics Data System (ADS)

    Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing

    2016-10-01

    Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a "distance" factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

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

    SciTech Connect

    Timlin, Jerilyn Ann; Nieman, Linda T.

    2005-11-01

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

  20. [Principles and applications of hyperspectral imaging technique in quality and safety inspection of fruits and vegetables].

    PubMed

    Zhang, Bao-Hua; Li, Jiang-Bo; Fan, Shu-Xiang; Huang, Wen-Qian; Zhang, Chi; Wang Qing-Yan; Xiao, Guang-Dong

    2014-10-01

    The quality and safety of fruits and vegetables are the most concerns of consumers. Chemical analytical methods are traditional inspection methods which are time-consuming and labor intensive destructive inspection techniques. With the rapid development of imaging technique and spectral technique, hyperspectral imaging technique has been widely used in the nondestructive inspection of quality and safety of fruits and vegetables. Hyperspectral imaging integrates the advantages of traditional imaging and spectroscopy. It can obtain both spatial and spectral information of inspected objects. Therefore, it can be used in either external quality inspection as traditional imaging system, or internal quality or safety inspection as spectroscopy. In recent years, many research papers about the nondestructive inspection of quality and safety of fruits and vegetables by using hyperspectral imaging have been published, and in order to introduce the principles of nondestructive inspection and track the latest research development of hyperspectral imaging in the nondestructive inspection of quality and safety of fruits and vegetables, this paper reviews the principles, developments and applications of hyperspectral imaging in the external quality, internal quality and safety inspection of fruits and vegetables. Additionally, the basic components, analytical methods, future trends and challenges are also reported or discussed in this paper.

  1. Thermal infrared hyperspectral imaging from vehicle-carried instrumentation

    NASA Astrophysics Data System (ADS)

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

    2002-09-01

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

  2. 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 (μa) and reduced scattering coefficients (μs') 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. μa and μs' 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 μs' decreased consistently with the increase of wavelength for most of the tested samples. Both μa and μs' 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 μa and μs' (i.e., μa × μs' and μeff) than using μa or μs', where μeff = [3 μa (μa + μs')]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.

  3. [Multi-Target Recognition of Internal and External Defects of Potato by Semi-Transmission Hyperspectral Imaging and Manifold Learning Algorithm].

    PubMed

    Huang, Tao; Li, Xiao-yu; Jin, Rui; Ku, Jing; Xu, Sen-miao; Xu, Meng-ling; Wu, Zhen-zhong; Kong, De-guo

    2015-04-01

    The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and

  4. 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.

  5. Development of a compressive sampling hyperspectral imager prototype

    NASA Astrophysics Data System (ADS)

    Barducci, Alessandro; Guzzi, Donatella; Lastri, Cinzia; Nardino, Vanni; Marcoionni, Paolo; Pippi, Ivan

    2013-10-01

    Compressive sensing (CS) is a new technology that investigates the chance to sample signals at a lower rate than the traditional sampling theory. The main advantage of CS is that compression takes place during the sampling phase, making possible significant savings in terms of the ADC, data storage memory, down-link bandwidth, and electrical power absorption. The CS technology could have primary importance for spaceborne missions and technology, paving the way to noteworthy reductions of payload mass, volume, and cost. On the contrary, the main CS disadvantage is made by the intensive off-line data processing necessary to obtain the desired source estimation. In this paper we summarize the CS architecture and its possible implementations for Earth observation, giving evidence of possible bottlenecks hindering this technology. CS necessarily employs a multiplexing scheme, which should produce some SNR disadvantage. Moreover, this approach would necessitate optical light modulators and 2-dim detector arrays of high frame rate. This paper describes the development of a sensor prototype at laboratory level that will be utilized for the experimental assessment of CS performance and the related reconstruction errors. The experimental test-bed adopts a push-broom imaging spectrometer, a liquid crystal plate, a standard CCD camera and a Silicon PhotoMultiplier (SiPM) matrix. The prototype is being developed within the framework of the ESA ITI-B Project titled "Hyperspectral Passive Satellite Imaging via Compressive Sensing".

  6. Hyperspectral imaging for detection of scab in wheat

    NASA Astrophysics Data System (ADS)

    Delwiche, Stephen R.; Kim, Moon S.

    2000-12-01

    Scab (Fusarium head blight) is a disease that causes wheat kernels to be shriveled, underweight, and difficult to mill. Scab is also a health concern because of the possible concomitant production of the mycotoxin deoxynivalenol. Current official inspection procedures entail manual human inspection. A study was undertaken to explore the possibility of detecting scab-damaged wheat kernels by machine vision. A custom-made hyperspectral imaging system, possessing a wavelength range of 425 to 860 nm with neighboring bands 3.7 nm apart, a spatial resolution of 0.022 mm2/pixel, and 16-bit per pixel dynamic range, gathered images of non-touching kernels from three wheat varieties. Each variety was represented by 32 normal and 32 scab-damaged kernels. From a search of wavelengths that could be used to separate the two classes (normal vs. scab), a linear discriminant function was constructed from the best R((lambda) 1)/R((lambda) 2), based on the assumption of a multivariate normal distribution for each class and the pooling of the covariance error that averaged between 2 and 17%, dependent on wheat variety. With expansion to the testing of more varieties, a two-to-four wavelength machine vision system appears to be a feasible alternative to manual inspection.

  7. 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.

  8. LWIR hyperspectral imaging application and detection of chemical precursors

    NASA Astrophysics Data System (ADS)

    Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Dubé, Denis

    2012-10-01

    Detection and identification of Toxic industrial chemicals (TICs) represent a major challenge to protect and sustain first responder and public security. In this context, passive Hyperspectral Imaging (HSI) is a promising technology for the standoff detection and identification of chemical vapors emanating from a distant location. To investigate this method, the Department of National Defense and Public Safety Canada have mandated Defense Research and Development Canada (DRDC) - Valcartier to develop and test Very Long Wave Infrared (VLWIR) HSI sensors for standoff detection. The initial effort was focused to address the standoff detection and identification of toxic industrial chemicals (TICs), surrogates and precursors. Sensors such as the Improved Compact ATmospheric Sounding Interferometer (iCATSI) and the Multi-option Differential Detection and Imaging Fourier Spectrometer (MoDDIFS) were developed for this application. This paper presents the sensor developments and preliminary results of standoff detection and identification of TICs and precursors. The iCATSI and MoDDIFS sensors are based on the optical differential Fourier-transform infrared (FTIR) radiometric technology and are able to detect, spectrally resolve and identify small leak at ranges in excess of 1 km. Results from a series of trials in asymmetric threat type scenarios are reported. These results serve to establish the potential of passive standoff HSI detection of TICs, precursors and surrogates.

  9. Detection of chemical pollutants by passive LWIR hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Dubé, Denis

    2012-09-01

    Toxic industrial chemicals (TICs) represent a major threat to public health and security. Their detection constitutes a real challenge to security and first responder's communities. One promising detection method is based on the passive standoff identification of chemical vapors emanating from the laboratory under surveillance. To investigate this method, the Department of National Defense and Public Safety Canada have mandated Defense Research and Development Canada (DRDC) - Valcartier to develop and test passive Long Wave Infrared (LWIR) hyperspectral imaging (HSI) sensors for standoff detection. The initial effort was focused to address the standoff detection and identification of toxic industrial chemicals (TICs) and precursors. Sensors such as the Multi-option Differential Detection and Imaging Fourier Spectrometer (MoDDIFS) and the Improved Compact ATmospheric Sounding Interferometer (iCATSI) were developed for this application. This paper describes the sensor developments and presents initial results of standoff detection and identification of TICs and precursors. The standoff sensors are based on the differential Fourier-transform infrared (FTIR) radiometric technology and are able to detect, spectrally resolve and identify small leak plumes at ranges in excess of 1 km. Results from a series of trials in asymmetric threat type scenarios will be presented. These results will serve to establish the potential of the method for standoff detection of TICs precursors and surrogates.

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

    PubMed

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

    2014-10-21

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

  11. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

  14. High resolution hyperspectral imaging with a high throughput virtual slit

    NASA Astrophysics Data System (ADS)

    Gooding, Edward A.; Gunn, Thomas; Cenko, Andrew T.; Hajian, Arsen R.

    2016-05-01

    Hyperspectral imaging (HSI) device users often require both high spectral resolution, on the order of 1 nm, and high light-gathering power. A wide entrance slit assures reasonable étendue but degrades spectral resolution. Spectrometers built using High Throughput Virtual Slit™ (HTVS) technology optimize both parameters simultaneously. Two remote sensing use cases that require high spectral resolution are discussed. First, detection of atmospheric gases with intrinsically narrow absorption lines, such as hydrocarbon vapors or combustion exhaust gases such as NOx and CO2. Detecting exhaust gas species with high precision has become increasingly important in the light of recent events in the automobile industry. Second, distinguishing reflected daylight from emission spectra in the visible and NIR (VNIR) regions is most easily accomplished using the Fraunhofer absorption lines in solar spectra. While ground reflectance spectral features in the VNIR are generally quite broad, the Fraunhofer lines are narrow and provide a signature of intrinsic vs. extrinsic illumination. The High Throughput Virtual Slit enables higher spectral resolution than is achievable with conventional spectrometers by manipulating the beam profile in pupil space. By reshaping the instrument pupil with reflective optics, HTVS-equipped instruments create a tall, narrow image profile at the exit focal plane, typically delivering 5X or better the spectral resolution achievable with a conventional design.

  15. Hyperspectral imaging coupled with chemometric analysis for non-invasive differentiation of black pens

    NASA Astrophysics Data System (ADS)

    Chlebda, Damian K.; Majda, Alicja; Łojewski, Tomasz; Łojewska, Joanna

    2016-11-01

    Differentiation of the written text can be performed with a non-invasive and non-contact tool that connects conventional imaging methods with spectroscopy. Hyperspectral imaging (HSI) is a relatively new and rapid analytical technique that can be applied in forensic science disciplines. It allows an image of the sample to be acquired, with full spectral information within every pixel. For this paper, HSI and three statistical methods (hierarchical cluster analysis, principal component analysis, and spectral angle mapper) were used to distinguish between traces of modern black gel pen inks. Non-invasiveness and high efficiency are among the unquestionable advantages of ink differentiation using HSI. It is also less time-consuming than traditional methods such as chromatography. In this study, a set of 45 modern gel pen ink marks deposited on a paper sheet were registered. The spectral characteristics embodied in every pixel were extracted from an image and analysed using statistical methods, externally and directly on the hypercube. As a result, different black gel inks deposited on paper can be distinguished and classified into several groups, in a non-invasive manner.

  16. Potential of near-infrared hyperspectral reflectance imaging for screening of farm feed contamination

    NASA Astrophysics Data System (ADS)

    Wang, Wenbo; Paliwal, Jitendra

    2005-09-01

    With the outbreak of Bovine Spongiform Encephalopathy (BSE) (commonly known as mad cow disease) in 1987 in the United Kingdom and a recent case discovered in Alberta, more and more emphasis is placed on food and farm feed quality and safety issues internationally. The disease is believed to be spread through farm feed contamination by animal byproducts in the form of meat-and-bone-meal (MBM). The paper reviewed the available techniques necessary to the enforcement of legislation concerning the feed safety issues. The standard microscopy method, although highly sensitive, is laborious and costly. A method to routinely screen farm feed contamination certainly helps to reduce the complexity of safety inspection. A hyperspectral imaging system working in the near-infrared wavelength region of 1100-1600 nm was used to study the possibility of detection of ground broiler feed contamination by ground pork. Hyperspectral images of raw broiler feed, ground broiler feed, ground pork, and contaminated feed samples were acquired. Raw broiler feed samples were found to possess comparatively large spectral variations due to light scattering effect. Ground feed adulterated with 1%, 3%, 5%, and 10% of ground pork was tested to identify feed contamination. Discriminant analysis using Mahalanobis distance showed that the model trained using pure ground feed samples and pure ground pork samples resulted in 100% false negative errors for all test replicates of contaminated samples. A discriminant model trained with pure ground feed samples and 10% contamination level samples resulted in 12.5% false positive error and 0% false negative error.

  17. Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique.

    PubMed

    Xie, Anguo; Sun, Da-Wen; Xu, Zhongyue; Zhu, Zhiwei

    2015-07-01

    Quality determination of frozen food is a time-consuming and laborious work as it normally takes a long time to thaw the frozen samples before measurements can be carried out. In this research, a rapid and non-destructive determination technique for frozen pork quality was tested with a hyperspectral imaging (HSI) system. In this study, 120 pieces of pork meat were frozen by four kinds of methods with various freezing temperatures from -20 to -120°C. The hyperspectral images of the samples were acquired at the frozen state. Quality indicators including drip loss, pH value, color, cooking loss and Warner-Bratzler shear force (WBSF) of the samples were measured after thawing. The spectral characteristics of the frozen meat samples were studied and it was revealed that the reflectance at 1100nm had a close relationship with the freezing temperature (R=-0.832, p<0.01). Partial least squares regression (PLSR) was applied to establish the spectral models, and the models were then optimized. Results showed that the improved region of interest (ROI) method could be used to extract effective spectral information to withstand the interference of freezing, and choosing appropriate spectral bands and spectral pretreatment techniques were crucial to develop robust mathematical model. The performances of the models established were diverse based on different quality indicators. The coefficients of determination for prediction (Rp(2)) for L*, cooking loss, b*, drip loss and a* were 0.907, 0.845, 0.814, 0.762, and 0.716, respectively. However there were low correlations (Rp(2)) for pH and WBSF measurements. The current study indicated that HSI had the potential for non-destructive determination of frozen meat quality without thawing.

  18. 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.

  19. Interactive visualization of hyperspectral images on a hyperbolic disk

    NASA Astrophysics Data System (ADS)

    Goodenough, Adam A.; Schlamm, Ariel; Brown, Scott D.; Messinger, David

    2011-06-01

    Visualization of the high-dimensional data set that makes up hyperspectral images necessitates a dimensionality reduction approach to make that data useful to a human analyst. The expression of spectral data as color images, individual pixel spectra plots, principal component images, and 2D/3D scatter plots of a subset of the data are a few examples of common techniques. However, these approaches leave the user with little ability to intuit knowledge of the full N-dimensional spectral data space or to directly or easily interact with that data. In this work, we look at developing an interactive, intuitive visualization and analysis tool based on using a Poincaré disk as a window into that high dimensional space. The Poincaré disk represents an infinite, two-dimensional hyperbolic space such that distances and areas increase exponentially as you move farther from the center of the disk. By projecting N-dimensional data into this space using a non-linear, yet relative distance metric preserving projection (such as the Sammon projection), we can simultaneously view the entire data set while maintaining natural clustering and spacing. The disk also provides a means to interact with the data; the user is presented with a "fish-eye" view of the space which can be navigated and manipulated with a mouse to "zoom" into clusters of data and to select spectral data points. By coupling this interaction with a synchronous view of the data as a spatial RGB image and the ability to examine individual pixel spectra, the user has full control over the data set for classification, analysis, and instructive use.

  20. 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...

  1. A Minimum Spanning Forest Based Method for Noninvasive Cancer Detection with Hyperspectral Imaging

    PubMed Central

    Pike, Robert; Lu, Guolan; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

    2016-01-01

    Goal The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine (SVM) classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection. PMID:26285052

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

    PubMed

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

    2014-10-10

    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.

  3. 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

  4. Intraoperative brain hemodynamic response assessment with real-time hyperspectral optical imaging (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Laurence, Audrey; Pichette, Julien; Angulo-Rodríguez, Leticia M.; Saint Pierre, Catherine; Lesage, Frédéric; Bouthillier, Alain; Nguyen, Dang Khoa; Leblond, Frédéric

    2016-03-01

    Following normal neuronal activity, there is an increase in cerebral blood flow and cerebral blood volume to provide oxygenated hemoglobin to active neurons. For abnormal activity such as epileptiform discharges, this hemodynamic response may be inadequate to meet the high metabolic demands. To verify this hypothesis, we developed a novel hyperspectral imaging system able to monitor real-time cortical hemodynamic changes during brain surgery. The imaging system is directly integrated into a surgical microscope, using the white-light source for illumination. A snapshot hyperspectral camera is used for detection (4x4 mosaic filter array detecting 16 wavelengths simultaneously). We present calibration experiments where phantoms made of intralipid and food dyes were imaged. Relative concentrations of three dyes were recovered at a video rate of 30 frames per second. We also present hyperspectral recordings during brain surgery of epileptic patients with concurrent electrocorticography recordings. Relative concentration maps of oxygenated and deoxygenated hemoglobin were extracted from the data, allowing real-time studies of hemodynamic changes with a good spatial resolution. Finally, we present preliminary results on phantoms obtained with an integrated spatial frequency domain imaging system to recover tissue optical properties. This additional module, used together with the hyperspectral imaging system, will allow quantification of hemoglobin concentrations maps. Our hyperspectral imaging system offers a new tool to analyze hemodynamic changes, especially in the case of epileptiform discharges. It also offers an opportunity to study brain connectivity by analyzing correlations between hemodynamic responses of different tissue regions.

  5. 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...

  6. 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.

  7. Classification of organic beef freshness using VNIR hyperspectral imaging.

    PubMed

    Crichton, Stuart O J; Kirchner, Sascha M; Porley, Victoria; Retz, Stefanie; von Gersdorff, Gardis; Hensel, Oliver; Weygandt, Martin; Sturm, Barbara

    2017-02-08

    Consumer trust in the food industry is heavily reliant upon accurate labelling of meat products. As such, methods, which can verify whether meat is correctly labelled are of great value to producers, retailers, and consumers. This paper illustrates two approaches to classify between, fresh and frozen thawed, and in a novel manner matured and matured frozen-thawed, as well as fresh and matured beef using the 500-1010nm waveband, captured using hyperspectral imaging, and CIELAB measurements. The results show successful classification based upon CIELAB between 1) fresh and frozen-thawed (CCR=0.93), and 2) fresh and matured (CCR=0.92). With successful classification between matured and matured frozen-thawed beef using the entire spectral range (CCR=1.00). The performance of reduced spectral models is also investigated. Overall it was found that CIELAB co-ordinates can be used for successful classification for all comparisons except between matured and matured frozen-thawed. Biochemical and physical changes of the meat are thoroughly discussed for each condition.

  8. Estimating index of refraction from polarimetric hyperspectral imaging measurements.

    PubMed

    Martin, Jacob A; Gross, Kevin C

    2016-08-08

    Current material identification techniques rely on estimating reflectivity or emissivity which vary with viewing angle. As off-nadir remote sensing platforms become increasingly prevalent, techniques robust to changing viewing geometries are desired. A technique leveraging polarimetric hyperspectral imaging (P-HSI), to estimate complex index of refraction, N̂(ν̃), an inherent material property, is presented. The imaginary component of N̂(ν̃) is modeled using a small number of "knot" points and interpolation at in-between frequencies ν̃. The real component is derived via the Kramers-Kronig relationship. P-HSI measurements of blackbody radiation scattered off of a smooth quartz window show that N̂(ν̃) can be retrieved to within 0.08 RMS error between 875 cm-1 ≤ ν̃ ≤ 1250 cm-1. P-HSI emission measurements of a heated smooth Pyrex beaker also enable successful N̂(ν̃) estimates, which are also invariant to object temperature.

  9. Large margin distribution machine for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Zhan, Kun; Wang, Haibo; Huang, He; Xie, Yuange

    2016-11-01

    Support vector machine (SVM) classifiers are widely applied to hyperspectral image (HSI) classification and provide significant advantages in terms of accuracy, simplicity, and robustness. SVM is a well-known learning algorithm that maximizes the minimum margin. However, recent theoretical results pointed out that maximizing the minimum margin leads to a lower generalization performance than optimizing the margin distribution, and proved that the margin distribution is more important. In this paper, a large margin distribution machine (LDM) is applied to HSI classification, and optimizing the margin distribution achieves a better generalization performance than SVM. Since the raw HSI feature space is not the most effective space for representing HSI, we adopt factor analysis to learn an effective HSI feature and the learned features are further filtered by a structure-preserved filter to fully exploit the spatial structure information of HSI. The spatial structure information is integrated in the feature learning process to obtain a better HSI feature. Then we propose a multiclass LDM to classify the filtered HSI feature. Experimental results show that the proposed LDM with feature learning method achieves the classification performance of the state-of-the-art methods in terms of visual quality and three quantitative evaluations and indicates that LDM has a high generalization performance.

  10. Hyperspectral imaging based techniques in ornamental stone characterization

    NASA Astrophysics Data System (ADS)

    Bonifazi, Giuseppe; Serranti, Silvia; Menesatti, Paolo

    2005-11-01

    Ornamental stones are usually utilized for many purposes, ranging from structural to aesthetic ones. In this wide range of utilization, many different industrial sectors are involved. For all of them it is very important, at a different level, that these materials satisfy not only specific physical-chemical-mechanical requirements, but also some attributes that are much more difficult to quantify, that is those attributes strictly related to the final pictorial aspect of the stone manufactured goods. Stone pictorial-aesthetic characteristics are strongly influenced by stone surface status, that is by the surfaces reflectance properties. Such a property depends from stone compositional-textural characteristics and from the working procedures applied. The first set of attributes are related to stone mineral composition and their micro/macro arrangement, the others are related to the tools utilized and the actions applied in terms of operation sequence and workers knowledge-expertise. Each stone and each macro-operation carried