Science.gov

Sample records for active hyperspectral imaging

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  2. 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...

  3. A hyperspectral image projector for hyperspectral imagers

    NASA Astrophysics Data System (ADS)

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

    2007-04-01

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

  4. Multipurpose hyperspectral imaging system

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  5. Miniaturized handheld hyperspectral imager

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  6. Hyperspectral light field imaging

    NASA Astrophysics Data System (ADS)

    Leitner, Raimund; Kenda, Andreas; Tortschanoff, Andreas

    2015-05-01

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

  7. 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.

  8. Multipurpose Hyperspectral Imaging System

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  9. 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.

  10. Quantum cascade laser based active hyperspectral imaging for standoff detection of chemicals on surfaces

    NASA Astrophysics Data System (ADS)

    Hugger, S.; Fuchs, F.; Jarvis, J.; Yang, Q. K.; Rattunde, M.; Ostendorf, R.; Schilling, C.; Driad, R.; Bronner, W.; Aidam, R.; Wagner, J.; Tybussek, T.; Rieblinger, K.

    2016-02-01

    We employ active hyperspectral imaging using tunable mid-infrared (MIR) quantum cascade lasers for contactless identification of solid and liquid contaminations on surfaces. By collecting the backscattered laser radiation with a camera, a hyperspectral data cube, containing the spatially resolved spectral information of the scene is obtained. Data is analyzed using appropriate algorithms to find the target substances even on substrates with a priori unknown spectra. Eye-save standoff detection of residues of explosives and precursors over extended distances is demonstrated and the main purpose of our system. However, it can be applied to any substance with characteristic reflectance / absorbance spectrum. As an example, we present first results of monitoring food quality by distinguishing fresh and mold contaminated peanuts by their MIR backscattering spectrum.

  11. 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.

  12. Hyperspectral image projector applications

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  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 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.

  15. 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

  16. Development of hyperspectral image projectors

    NASA Astrophysics Data System (ADS)

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

    2006-08-01

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

  17. Active hyperspectral imaging using a quantum cascade laser (QCL) array and digital-pixel focal plane array (DFPA) camera.

    PubMed

    Goyal, Anish; Myers, Travis; Wang, Christine A; Kelly, Michael; Tyrrell, Brian; Gokden, B; Sanchez, Antonio; Turner, George; Capasso, Federico

    2014-06-16

    We demonstrate active hyperspectral imaging using a quantum-cascade laser (QCL) array as the illumination source and a digital-pixel focal-plane-array (DFPA) camera as the receiver. The multi-wavelength QCL array used in this work comprises 15 individually addressable QCLs in which the beams from all lasers are spatially overlapped using wavelength beam combining (WBC). The DFPA camera was configured to integrate the laser light reflected from the sample and to perform on-chip subtraction of the passive thermal background. A 27-frame hyperspectral image was acquired of a liquid contaminant on a diffuse gold surface at a range of 5 meters. The measured spectral reflectance closely matches the calculated reflectance. Furthermore, the high-speed capabilities of the system were demonstrated by capturing differential reflectance images of sand and KClO3 particles that were moving at speeds of up to 10 m/s.

  18. Region-based geometric active contour for classification using hyperspectral remote sensing images

    NASA Astrophysics Data System (ADS)

    Yan, Lin

    2011-12-01

    The high spectral resolution of hyperspectral imaging (HSI) systems greatly enhances the capabilities of discrimination, identification and quantification of objects of different materials from remote sensing images, but they also bring challenges to the processing and analysis of HSI data. One issue is the high computation cost and the curse of dimensionality associated with the high dimensions of HSI data. A second issue is how to effectively utilize the information including spectral and spatial information embedded in HSI data. Geometric Active Contour (GAC) is a widely used image segmentation method that utilizes the geometric information of objects within images. One category of GAC models, the region-based GAC models (RGAC), have good potential for remote sensing image processing because they use both spectral and geometry information in images are robust to initial contour placement. These models have been introduced to target extractions and classifications on remote sensing images. However, there are some restrictions on the applications of the RGAC models on remote sensing. First, the heavy involvement of iterative contour evolutions makes GAC applications time-consuming and inconvenient to use. Second, the current RGAC models must be based on a certain distance metric and the performance of RGAC classifiers are restricted by the performance of the employed distance metrics. According to the key features of the RGAC models analyzed in this dissertation, a classification framework is developed for remote sensing image classifications using the RGAC models. This framework allows the RGAC models to be combined with conventional pixel-based classifiers to promote them to spectral-spatial classifiers and also greatly reduces the iterations of contour evolutions. An extended Chan-Vese (ECV) model is proposed that is able to incorporate the widely used distance metrics in remote sensing image processing. A new type of RGAC model, the edge-oriented RGAC model

  19. Hyperspectral image compressive projection algorithm

    NASA Astrophysics Data System (ADS)

    Rice, Joseph P.; Allen, David W.

    2009-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  1. Bayesian segmentation of hyperspectral images

    NASA Astrophysics Data System (ADS)

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

    2004-11-01

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

  2. Skin detection in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Sanchez, Stephanie M.; Velez-Reyes, Miguel

    2015-05-01

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

  3. 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.

  4. Hyperspectral imaging system for UAV

    NASA Astrophysics Data System (ADS)

    Zhang, Da; Zheng, Yuquan

    2015-10-01

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

  5. 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.

  6. Longwave infrared compressive hyperspectral imager

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  7. Quality assessment for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  8. 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.

  9. Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps.

    PubMed

    Gaston, Edurne; Frías, Jesús M; Cullen, Patrick J; O'Donnell, Colm P; Gowen, Aoife A

    2010-05-26

    Physical stress (i.e., bruising) during harvesting, handling, and transportation triggers enzymatic discoloration of mushrooms, a common and detrimental phenomenon largely mediated by polyphenol oxidase (PPO) enzymes. Hyperspectral imaging (HSI) is a nondestructive technique that combines imaging and spectroscopy to obtain information from a sample. The objective of this study was to assess the ability of HSI to predict the activity of PPO on mushroom caps. Hyperspectral images of mushrooms subjected to various damage treatments were taken, followed by enzyme extraction and PPO activity measurement. Principal component regression (PCR) models (each with three PCs) built on raw reflectance and multiple scatter-corrected (MSC) reflectance data were found to be the best modeling approach. Prediction maps showed that the MSC model allowed for compensation of spectral differences due to sample curvature and surface irregularities. Results reveal the possibility of developing a sensor that could rapidly identify mushrooms with a higher likelihood to develop enzymatic browning, hence aiding produce management decision makers in the industry.

  10. Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Kong, Wenwen; Liu, Fei; Zhang, Chu; Bao, Yidan; Yu, Jiajia; He, Yong

    2014-01-01

    Tomatoes are cultivated around the world and gray mold is one of its most prominent and destructive diseases. An early disease detection method can decrease losses caused by plant diseases and prevent the spread of diseases. The activity of peroxidase (POD) is very important indicator of disease stress for plants. The objective of this study is to examine the possibility of fast detection of POD activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging data. Five pre-treatment methods were investigated. Genetic algorithm-partial least squares (GA-PLS) was applied to select optimal wavelengths. A new fast learning neural algorithm named extreme learning machine (ELM) was employed as multivariate analytical tool in this study. 21 optimal wavelengths were selected by GA-PLS and used as inputs of three calibration models. The optimal prediction result was achieved by ELM model with selected wavelengths, and the r and RMSEP in validation were 0.8647 and 465.9880 respectively. The results indicated that hyperspectral imaging could be considered as a valuable tool for POD activity prediction. The selected wavelengths could be potential resources for instrument development.

  11. 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...

  12. 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.

  13. Hyperspectral imaging camera using wavefront division interference.

    PubMed

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

    2016-03-01

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

  14. 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.

  15. 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.

  16. Hyperspectral imaging of atherosclerotic plaques in vitro

    NASA Astrophysics Data System (ADS)

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

    2011-02-01

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

  17. Hyperspectral imaging utility for transportation systems

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  18. Novel hyperspectral imager for lightweight UAVs

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

  19. 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.

  20. Uncooled long-wave infrared hyperspectral imaging

    NASA Technical Reports Server (NTRS)

    Lucey, Paul G. (Inventor)

    2006-01-01

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

  1. Real-time snapshot hyperspectral imaging endoscope.

    PubMed

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

    2011-05-01

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

  2. MEMS FPI-based smartphone hyperspectral imager

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  3. Hyperspectral imager development at Army Research Laboratory

    NASA Astrophysics Data System (ADS)

    Gupta, Neelam

    2008-04-01

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

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

    SciTech Connect

    Bernacki, Bruce E.; Phillips, Mark C.

    2010-05-01

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

  5. Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Henrot, Simon; Chanussot, Jocelyn; Jutten, Christian

    2016-07-01

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

  6. Landmine detection using passive hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2007-04-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  8. 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.

  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. Quality evaluation of fruit by hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. Mapping Soil Organic Matter with Hyperspectral Imaging

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  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 study on the application of near infrared hyperspectral chemical imaging for monitoring moisture content and water activity in low moisture systems.

    PubMed

    Achata, Eva; Esquerre, Carlos; O'Donnell, Colm; Gowen, Aoife

    2015-01-01

    Moisture content and water activity are key parameters in predicting the stability of low moisture content products. However, conventional methods for moisture content and water activity determination (e.g., loss on drying method, ‎Karl Fischer titration, dew point method) are time consuming, demand specialized equipment and are not amenable to online processing. For this reason they are typically applied at-line on a limited number of samples. Near infrared hyperspectral chemical imaging is an emerging technique for spatially characterising the spectral properties of samples. Due to the fast acquisition of chemical images, many samples can be evaluated simultaneously, thus providing the potential for online evaluation of samples during processing. In this study, the potential of NIR chemical imaging for predicting the moisture content and water activity of a selection of low moisture content food systems is evaluated. PMID:25654530

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

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

    PubMed

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

    2014-03-12

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

  20. A global shutter CMOS image sensor for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

  1. Synergetics Framework for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Driver, Richard D.

    2009-05-01

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

  3. Excitation-scanning hyperspectral imaging microscope.

    PubMed

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

    2014-04-01

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

  4. 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.

  5. 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

  6. Information efficiency in hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

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

    2002-07-01

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

  7. Morphology-based fusion method of hyperspectral image

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

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

    NASA Astrophysics Data System (ADS)

    Tien Hoang, Nguyen; Koike, Katsuaki

    2016-06-01

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

  9. Unsupervised data fusion for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2002-01-01

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

  10. Hyperspectral imaging of polymer/fullerene blends

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  11. Hyperspectral all-sky imaging of auroras.

    PubMed

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

    2012-12-01

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

  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

    2012-12-01

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

  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. Onboard Image Processing System for Hyperspectral Sensor.

    PubMed

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

    2015-01-01

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

  16. A parallel unmixing algorithm for hyperspectral images

    NASA Astrophysics Data System (ADS)

    Robila, Stefan A.; Maciak, Lukasz G.

    2006-10-01

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

  17. Hyperspectral confocal fluorescence imaging of cells

    NASA Astrophysics Data System (ADS)

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

    2007-09-01

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

  18. Pork grade evaluation using hyperspectral imaging techniques

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

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

  19. Hyperspectral Imaging for Defect Detection of Pickling Cucumber

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  20. 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.

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

    NASA Astrophysics Data System (ADS)

    Dombrowski, Mark S.; Willson, Paul

    1999-10-01

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

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

    PubMed

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

    2016-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  4. On-orbit characterization of hyperspectral imagers

    NASA Astrophysics Data System (ADS)

    McCorkel, Joel

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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Hyperspectral imaging for melanoma screening

    NASA Astrophysics Data System (ADS)

    Martin, Justin; Krueger, James; Gareau, Daniel

    2014-03-01

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

  10. Dimensionality reduction of hyperspectral images using kernel ICA

    NASA Astrophysics Data System (ADS)

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

    2009-05-01

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

  11. 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.

  12. 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.

  13. 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.

  14. Detection of explosives by differential hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2014-02-01

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

  15. Performance and application of real-time hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    1998-10-01

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

  16. 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

  17. Hyperspectral Image Turbulence Measurements of the Atmosphere

    NASA Technical Reports Server (NTRS)

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

    2012-01-01

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

  18. 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.

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

    NASA Technical Reports Server (NTRS)

    Fisher, Kevin (Inventor)

    2013-01-01

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

  20. High spectral resolution airborne short wave infrared hyperspectral imager

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  1. 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

  2. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2005-06-01

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

  4. Development of image mappers for hyperspectral biomedical imaging applications

    PubMed Central

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

    2010-01-01

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

  5. 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.

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

    NASA Astrophysics Data System (ADS)

    Lu, Renfu; Chen, Yud-Ren

    1999-01-01

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

  7. Hyperspectral image projector for advanced sensor characterization

    NASA Astrophysics Data System (ADS)

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

    2006-08-01

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

  8. 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.

  9. 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.

  10. Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

    PubMed

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

    2015-12-01

    Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

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

    PubMed

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

    2016-07-01

    Pharmaceutical quality control is important for improving the effectiveness, purity and safety of drugs, as well as for the prevention or control of drug degradation. In the present work, near infrared hyperspectral images (HSI-NIR) of tablets with different expiration dates were employed to evaluate the degradation of captopril into captopril disulfide in different layers, on the top and on the bottom surfaces of the tablets. Multivariate curve resolution (MCR) models were used to extract the concentration distribution maps from the hyperspectral images. Afterward, multivariate image techniques were applied to the concentration distribution maps (CDMs), to extract features and build models relating the main characteristics of the images to their corresponding manufacturing dates. Resolution methods followed by extracting features were able to estimate the tablet manufacture date with a prediction error of 120days. The model developed could be useful to evaluate whether a sample shows a degradation pattern consistent with the date of manufacturing or to detect abnormal behaviors in the natural degradation process of the sample. The information provided by the HIS-NIR is important for the development of the process (QbD), looking inside the formulation, revealing the behavior of the active pharmaceutical ingredient (API) during the product's shelf life. PMID:27163244

  13. 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.

  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. Development of practical thermal infrared hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

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

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yu; Yamada, Yoshiro; Ishii, Juntaro

    2013-10-01

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

  17. NIR DLP hyperspectral imaging system for medical applications

    NASA Astrophysics Data System (ADS)

    Wehner, Eleanor; Thapa, Abhas; Livingston, Edward; Zuzak, Karel

    2011-03-01

    DLP® hyperspectral reflectance imaging in the visible range has been previously shown to quantify hemoglobin oxygenation in subsurface tissues, 1 mm to 2 mm deep. Extending the spectral range into the near infrared reflects biochemical information from deeper subsurface tissues. Unlike any other illumination method, the digital micro-mirror device, DMD, chip is programmable, allowing the user to actively illuminate with precisely predetermined spectra of illumination with a minimum bandpass of approximately 10 nm. It is possible to construct active spectral-based illumination that includes but is not limited to containing sharp cutoffs to act as filters or forming complex spectra, varying the intensity of light at discrete wavelengths. We have characterized and tested a pure NIR, 760 nm to 1600 nm, DLP hyperspectral reflectance imaging system. In its simplest application, the NIR system can be used to quantify the percentage of water in a subject, enabling edema visualization. It can also be used to map vein structure in a patient in real time. During gall bladder surgery, this system could be invaluable in imaging bile through fatty tissue, aiding surgeons in locating the common bile duct in real time without injecting any contrast agents.

  18. 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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  20. 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

  1. 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.

  2. Infrared hyperspectral imaging polarimeter using birefringent prisms.

    PubMed

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

    2011-03-10

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

  3. 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.

  4. Bayesian fusion of hyperspectral astronomical images

    NASA Astrophysics Data System (ADS)

    Jalobeanu, André; Petremand, Matthieu; Collet, Christophe

    2011-03-01

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

  5. 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.

  6. Hyperspectral image analysis using artificial color

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  7. 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

  8. Visible-Infrared Hyperspectral Image Projector

    NASA Technical Reports Server (NTRS)

    Bolcar, Matthew

    2013-01-01

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

  9. 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.

  10. 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.

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

    SciTech Connect

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

    1995-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-05-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  14. Evaluating the Initialization Methods of Wavelet Networks for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Hsu, Pai-Hui

    2016-06-01

    The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms can be applied to extract useful features for hyperspectral image classification. However, the extracted features with fixed position and dilation parameters of the wavelets provide insufficient characteristics of spectrum. In this study, wavelet networks which integrates the advantages of wavelet-based feature extraction and neural networks classification is proposed for hyperspectral image classification. Wavelet networks is a kind of feed-forward neural networks using wavelets as activation function. Both the position and the dilation parameters of the wavelets are optimized as well as the weights of the network during the training phase. The value of wavelet networks lies in their capabilities of optimizing network weights and extracting essential features simultaneously for hyperspectral images classification. In this study, the influence of the learning rate and momentum term during the network training phase is presented, and several initialization modes of wavelet networks were used to test the performance of wavelet networks.

  15. Land Cover Change Detection Based on Genetically Feature Aelection and Image Algebra Using Hyperion Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Seydi, S. T.; Hasanlou, M.

    2015-12-01

    The Earth has always been under the influence of population growth and human activities. This process causes the changes in land use. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Satellite remote sensing has several advantages for monitoring land use/cover resources, especially for large geographic areas. Change detection and attribution of cultivation area over time present additional challenges for correctly analyzing remote sensing imagery. In this regards, for better identifying change in multi temporal images we use hyperspectral images. Hyperspectral images due to high spectral resolution created special placed in many of field. Nevertheless, selecting suitable and adequate features/bands from this data is crucial for any analysis and especially for the change detection algorithms. This research aims to automatically feature selection for detect land use changes are introduced. In this study, the optimal band images using hyperspectral sensor using Hyperion hyperspectral images by using genetic algorithms and Ratio bands, we select the optimal band. In addition, the results reveal the superiority of the implemented method to extract change map with overall accuracy by a margin of nearly 79% using multi temporal hyperspectral imagery.

  16. Pulse tube cooler for flight hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

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

  17. 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.

  18. 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.

  19. 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.

  20. a Diversified Deep Belief Network for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

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

  2. Hyperspectral retinal imaging with a spectrally tunable light source

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  3. [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.

  4. Hyperspectral Imaging Sensors and the Marine Coastal Zone

    NASA Technical Reports Server (NTRS)

    Richardson, Laurie L.

    2000-01-01

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

  5. Hybrid tenso-vectorial compressive sensing for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Li, Qun; Bernal, Edgar A.

    2016-05-01

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

  6. 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...

  7. Phase correction algorithms for a snapshot hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

  8. Identification of seedling cabbages and weeds using hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  9. 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

  10. Fast, electrically tunable filters for hyperspectral imaging

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

  11. Hyperspectral imaging with a liquid crystal polarization interferometer.

    PubMed

    Hegyi, Alex; Martini, Joerg

    2015-11-01

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

  12. Extended SWIR imaging sensors for hyperspectral imaging applications

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

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

    PubMed

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

    2016-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  16. 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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

  19. Hyperspectral imaging and quantitative analysis for prostate cancer detection

    PubMed Central

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

    2012-01-01

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

  20. 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.

  1. 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.

  2. Fault tolerance of SVM algorithm for hyperspectral image

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

  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. 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.

  5. Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

    PubMed

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

    2015-12-01

    Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells. PMID:26554882

  6. 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.

  7. Study on full-polarization hyperspectral imaging technology

    NASA Astrophysics Data System (ADS)

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

    2014-02-01

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

  8. 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.

  9. 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.

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

    SciTech Connect

    Phillips, Mark C.; Ho, Nicolas

    2008-04-23

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

  11. 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.

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

    PubMed

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

    2012-01-01

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

  13. Hyperspectral Imaging of Forest Resources: The Malaysian Experience

    NASA Astrophysics Data System (ADS)

    Mohd Hasmadi, I.; Kamaruzaman, J.

    2008-08-01

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

  14. 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. 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

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

    PubMed

    Valero, Silvia; Salembier, Philippe; Chanussot, Jocelyn

    2013-04-01

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

  17. 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.

  18. 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...

  19. 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.

  20. Embedded GPU implementation of anomaly detection for hyperspectral images

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

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

    PubMed

    Koprowski, Robert

    2015-11-01

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

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

    PubMed

    Koprowski, Robert

    2015-11-01

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

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

    PubMed

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

    2008-10-01

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

  4. Automatic Denoising and Unmixing in Hyperspectral Image Processing

    NASA Astrophysics Data System (ADS)

    Peng, Honghong

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-07-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2008-04-01

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

  9. 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...

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

    NASA Astrophysics Data System (ADS)

    Su, Lijuan; Zhou, Shubo; Yuan, Yan

    2015-01-01

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

  11. Hyperspectral scanning white light interferometry based on compressive imaging

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

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

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

    PubMed

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

    2011-10-01

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

  13. 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.

  14. 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.

  15. A hyperspectral image analysis workbench for environmental science applications

    SciTech Connect

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

    1992-01-01

    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.

  16. Target detection in hyperspectral Imaging using logistic regression

    NASA Astrophysics Data System (ADS)

    Lo, Edisanter; Ientilucci, Emmett

    2016-05-01

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

  17. 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.

  18. 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.

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

    NASA Astrophysics Data System (ADS)

    Tian, Youwen; Zhang, Lin

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

  20. Hyperspectral laser-induced autofluorescence imaging of dental caries

    NASA Astrophysics Data System (ADS)

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

    2012-01-01

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

  1. Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

    PubMed

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

    2015-01-01

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

  2. Hyperspectral image-based methods for spectral diversity

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  3. Hyperspectral imaging of bruises in the SWIR spectral region

    NASA Astrophysics Data System (ADS)

    Randeberg, Lise L.; Hernandez-Palacios, Julio

    2012-02-01

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

  4. Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging

    PubMed Central

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

    2015-01-01

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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  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. 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

  12. [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

  13. 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.

  14. 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.

  15. Infrared hyperspectral upconversion imaging using spatial object translation.

    PubMed

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

    2015-12-28

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

  16. 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.

  17. Mid-infrared hyperspectral imaging for the detection of explosive compounds

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

    Active hyperspectral imaging is a valuable tool in a wide range of applications. A developing market is the detection and identification of energetic compounds through analysis of the resulting absorption spectrum. This work presents a selection of results from a prototype mid-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 over a range of wavelengths. While there are a number of illumination methods, this work 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 directed 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 a MWIR optical parametric oscillator (OPO) source with broad tunability, operating at 2.6 μm to 3.7 μm. Due to material handling procedures associated with explosive compounds, experimental work was undertaken initially using simulant compounds. A second set of compounds that was tested alongside the simulant compounds is a range of confusion compounds. By having the broad wavelength tunability of the OPO, extended absorption spectra of the compounds could be obtained to aid in compound identification. The prototype imager instrument has successfully been used to record the absorption spectra for a range of compounds from the simulant and confusion sets and current work is now investigating actual explosive compounds. The authors see a very promising outlook for the MWIR hyperspectral imager. From an applications point of view this format of imaging instrument could be used for a range of standoff, improvised explosive device (IED) detection applications and potential incident scene forensic investigation.

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

    PubMed

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

    2012-01-01

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

  19. 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.

  20. 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.

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

    PubMed

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

    2011-04-01

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

  2. ACTIVE-EYES: an adaptive pixel-by-pixel image-segmentation sensor architecture for high-dynamic-range hyperspectral imaging.

    PubMed

    Christensen, Marc P; Euliss, Gary W; McFadden, Michael J; Coyle, Kevin M; Milojkovic, Predrag; Haney, Michael W; van der Gracht, Joeseph; Athale, Ravindra A

    2002-10-10

    The ACTIVE-EYES (adaptive control for thermal imagers via electro-optic elements to yield an enhanced sensor) architecture, an adaptive image-segmentation and processing architecture, based on digital micromirror (DMD) array technology, is described. The concept provides efficient front-end processing of multispectral image data by adaptively segmenting and routing portions of the scene data concurrently to an imager and a spectrometer. The goal is to provide a large reduction in the amount of data required to be sensed in a multispectral imager by means of preprocessing the data to extract the most useful spatial and spectral information during detection. The DMD array provides the flexibility to perform a wide range of spatial and spectral analyses on the scene data. The spatial and spectral processing for different portions of the input scene can be tailored in real time to achieve a variety of preprocessing functions. Since the detected intensity of individual pixels may be controlled, the spatial image can be analyzed with gain varied on a pixel-by-pixel basis to enhance dynamic range. Coarse or fine spectral resolution can be achieved in the spectrometer by use of dynamically controllable or addressable dispersion elements. An experimental prototype, which demonstrated the segmentation between an imager and a grating spectrometer, was demonstrated and shown to achieve programmable pixelated intensity control. An information theoretic analysis of the dynamic-range control aspect was conducted to predict the performance enhancements that might be achieved with this architecture. The results indicate that, with a properly configured algorithm, the concept achieves the greatest relative information recovery from a detected image when the scene is made up of a relatively large area of moderate-dynamic-range pixels and a relatively smaller area of strong pixels that would tend to saturate a conventional sensor. PMID:12389978

  3. 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.

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

    PubMed

    Edelman, Gerda; Lopatka, Martin; Aalders, Maurice

    2013-07-01

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

  5. Hyperspectral imaging: a useful technology for transportation analysis

    NASA Astrophysics Data System (ADS)

    Gomez, Richard B.

    2002-09-01

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

  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. 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.

  8. 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.

  9. [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

  10. [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

  11. 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.

  12. 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.

  13. Space-based hyperspectral imaging spectroradiometer for coastal studies

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

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

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

    PubMed

    Bostick, Randall L; Perram, Glen P

    2012-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Bostick, Randall L.; Perram, Glen P.

    2012-03-01

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

  16. Hyperspectral image analysis for standoff trace detection using IR laser spectroscopy

    NASA Astrophysics Data System (ADS)

    Jarvis, J.; Fuchs, F.; Hugger, S.; Ostendorf, R.; Butschek, L.; Yang, Q.; Dreyhaupt, A.; Grahmann, J.; Wagner, J.

    2016-05-01

    In the recent past infrared laser backscattering spectroscopy using Quantum Cascade Lasers (QCL) emitting in the molecular fingerprint region between 7.5 μm and 10 μm proved a highly promising approach for stand-off detection of dangerous substances. In this work we present an active illumination hyperspectral image sensor, utilizing QCLs as spectral selective illumination sources. A high performance Mercury Cadmium Telluride (MCT) imager is used for collection of the diffusely backscattered light. Well known target detection algorithms like the Adaptive Matched Subspace Detector and the Adaptive Coherent Estimator are used to detect pixel vectors in the recorded hyperspectral image that contain traces of explosive substances like PETN, RDX or TNT. In addition we present an extension of the backscattering spectroscopy technique towards real-time detection using a MOEMS EC-QCL.

  17. Infrared hyperspectral imaging results from vapor plume experiments

    SciTech Connect

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

    1995-04-17

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

  18. Hyperspectral image segmentation using a cooperative nonparametric approach

    NASA Astrophysics Data System (ADS)

    Taher, Akar; Chehdi, Kacem; Cariou, Claude

    2013-10-01

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

  19. 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...

  20. 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

  1. Reconstruction of hyperspectral image using matting model for classification

    NASA Astrophysics Data System (ADS)

    Xie, Weiying; Li, Yunsong; Ge, Chiru

    2016-05-01

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

  2. 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).

  3. 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.

  4. 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.

  5. Spatial regularization for the unmixing of hyperspectral images

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  6. Evaluation of a hyperspectral image database for demosaicking purposes

    NASA Astrophysics Data System (ADS)

    Larabi, Mohamed-Chaker; Süsstrunk, Sabine

    2011-01-01

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

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

    PubMed

    Li, Qingli; Liu, Zhi

    2009-04-01

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

  8. Transillumination hyperspectral imaging for histopathological examination of excised tissue

    NASA Astrophysics Data System (ADS)

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

    2011-08-01

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

  9. Hyperspectral image segmentation using spatial-spectral graphs

    NASA Astrophysics Data System (ADS)

    Gillis, David B.; Bowles, Jeffrey H.

    2012-06-01

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

  10. 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.

  11. 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.

  12. Next generation miniature simultaneous multi-hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

    Hinnrichs, Michele; Gupta, Neelam

    2014-03-01

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

  13. 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.

  14. Hyperspectral imaging in the infrared using LIFTIRS

    SciTech Connect

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

    1995-07-01

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

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

    PubMed

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

    2016-10-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  18. 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 ...

  19. Multimodal confocal hyperspectral imaging microscopy with wavelength sweeping source

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

  20. 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.

  1. 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...

  2. 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 ...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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.

  9. 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

  10. Object detection in rural areas using hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Ozturk, Safak; Emre Esin, Yunus; Artan, Yusuf

    2015-10-01

    Object detection has gained considerable interest in remote sensing community with a broad range of applications including the remote monitoring of building development in rural areas. Many earlier studies on this task performed their analysis using either multispectral satellite imagery or color images obtained via an aerial vehicle. In recent years, hyperspectral imaging (HSI) has emerged as an alternative technique for remote monitoring of building developments. Unlike other imaging techniques, HSI provides a continuous spectral signature of the objects in the field of view (FOV) which facilitates the separation among different objects. In general, spectral signature similarity between objects often causes a significant amount of false alarm (FA) rate that adversely effects the overall accuracy of these systems. In order to reduce the high rate of FA posed by the pixel-wise classification, we propose a novel rural building detection method that utilizes both spatial information and spectral signature of the pixels. Proposed technique consists of three parts; a spectral signature classifier, watershed based superpixel map and an oriented-gradient filters based object detector. In our analysis, we have evaluated the performance of proposed approach using hyperspectral image dataset obtained at various elevation levels, namely 500 meters and 3000 meters. NEO HySpex VNIR-1800 camera is used for 182 band hyperspectral data acquisition. First 155 band is used due to the atmospheric effects on the last bands. Performance comparison between the proposed technique and the pixel-wise spectral classifier indicates a reduction in sensitivity rate but a notable increase in specificity and overall accuracy rates. Proposed method yields sensitivity, specificity, accuracy rate of 0.690, 0.997 and 0.992, respectively, whereas pixel-wise classification yields sensitivity, specificity, and accuracy rate of 0.758, 0.983, 0.977, respectively. Note that the sensitivity reduction is

  11. 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.

  12. 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.

  13. Rapid hyperspectral imaging in the mid-infrared

    NASA Astrophysics Data System (ADS)

    Kröger, N.; Egl, A.; Engel, M.; Gretz, N.; Haase, K.; Herpich, I.; Neudecker, S.; Pucci, A.; Schönhals, A.; Petrich, W.

    2014-03-01

    Despite the successes of mid-infrared hyperspectral imaging in a research environment, progress in the migration of technology into the day-to-day clinical application is slow. Clinical acceptance may be improved if the spectroscopy would be faster and the infrared microscopes available at lower cost. Here we present first results of a fast, multi-scale mid-infrared microscopy setup which allows for the investigation of 10.6×11.7 mm2 and 2.8×3.1mm2 fields of view with a resolution of 23.0+/-3.5 μm and 9.4+/-1.8 μm, respectively. Tunable quantum cascade lasers in the wavenumber ranges of 1030-1090 cm-1 and 1160-1320 cm-1 serve as light sources. A vapor cell is used as a frequency reference during the rapid scanning. As far as the imaging is concerned, it is the high spectral power density of the quantum cascade laser which enables the use of a microbolometer array while still obtaining reasonable signal-to-noise ratios on each pixel. Hyperspectral images are taken in times which can be as low as 52s for the overall image acquisition including referencing.

  14. DLP hyperspectral imaging for surgical and clinical utility

    NASA Astrophysics Data System (ADS)

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

    2009-02-01

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

  15. Static hyperspectral imaging polarimeter for full linear Stokes parameters.

    PubMed

    Mu, Tingkui; Zhang, Chunmin; Jia, Chenling; Ren, Wenyi

    2012-07-30

    A compact, static hyperspectral imaging linear polarimeter (HILP) based on a Savart interferometer (SI) is conceptually described. It improves the existing SI by replacing front polarizer with two Wollaston prisms, and can simultaneously acquire four interferograms corresponding to four linearly polarized lights on a single CCD. The spectral dependence of linear Stokes parameters can be recovered with Fourier transformation. Since there is no rotating or moving parts, the system is relatively robust. The interference model of the HILP is proved. The performance of the system is demonstrated through a numerical simulation, and the methods for compensating the imperfection of the polarization elements are described. PMID:23038368

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

  17. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  19. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  1. On the response function separability of hyperspectral imaging systems

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

    Hyperspectral imaging systems effectively collect information across the spectral and two spatial dimensions by employing three main components: the front lens, the light-diffraction element and a camera. Imperfections in these components introduce spectral and spatial dependent distortions in the recorded hyperspectral image. These can be characterized by a 3D response function that is subsequently used to remove distortions and enhance the resolution of the recorded images by deconvolution. The majority of existing characterization methods assume spatial and spectral separability of the 3D response function. In this way, the complex problem of 3D response function characterization is reduced to independent characterizations of the three orthogonal response function components. However, if the 3D response function is non-separable, such characterization can lead to poor response function estimates, and hence inaccurate and distorted results of the subsequent deconvolution-based calibration and image enhancement. In this paper, we evaluate the influence of the spatial response function non-separability on the results of the calibration by deconvolution. For this purpose, a novel procedure for direct measurement of the 2D spatial response function is proposed along with a quantitative measure of the spatial response function non-separability. The quality of deconvolved images is assessed in terms of full width at half maximum (FWHM) and step edge overshoot magnitude observed in the deconvolved images of slanted edges, images of biological slides, and 1951 USAF resolution test chart. Results show that there are cases, when nonseparability of the system response function is significant and should be considered by the deconvolution-based calibration and image enhancement methods.

  2. 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.

  3. Calibration methodology and performance characterization of a polarimetric hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Holder, Joel G.; Martin, Jacob A.; Pitz, Jeremey; Pezzaniti, Joseph L.; Gross, Kevin C.

    2014-05-01

    Polarimetric hyperspectral imaging (P-HSI) has the potential to improve target detection, material identification, and background characterization over conventional hyperspectral imaging and polarimetric imaging. To fully exploit the spectro-polarimetric signatures captured by such an instrument, a careful calibration process is required to remove the spectrally- and polarimetrically-dependent system response (gain). Calibration of instruments operating in the long-wave infrared (LWIR, 8μm to 12 μm) is further complicated by the polarized spectral radiation generated within the instrument (offset). This paper presents a calibration methodology developed for a LWIR Telops Hyper-Cam modified for polarimetry by replacing the entrance window with a rotatable holographic wire-grid polarizer (4000 line/mm, ZnSe substrate, 350:1 extinction ratio). A standard Fourier-transform spectrometer (FTS) spectro-radiometric calibration is modified to include a Mueller-matrix approach to account for polarized transmission through and polarized selfemission from each optical interface. It is demonstrated that under the ideal polarizer assumption, two distinct blackbody measurements at polarizer angles of 0°, 45°, 90°, and 135° are sufficient to calibrate the system for apparent degree-of-linear-polarization (DoLP) measurements. Noise-equivalent s1, s2, and DoLP are quantified using a wide-area blackbody. A polarization-state generator is used to determine the Mueller deviation matrix. Finally, a realistic scene involving buildings, cars, sky radiance, and natural vegetation is presented.

  4. Identifying volcanic endmembers in hyperspectral images using spectral unmixing

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  5. 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

  6. Construction of a small and lightweight hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

    Vogel, Britta; Hünniger, Dirk; Bastian, Georg

    2014-05-01

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

  7. Hyperspectral Image Target Detection Improvement Based on Total Variation.

    PubMed

    Yang, Shuo; Shi, Zhenwei

    2016-05-01

    For the hyperspectral target detection, the neighbors of a target pixel are very likely to be target pixels, and those of a background pixel are very likely to be background pixels. In order to utilize this spatial homogeneity or smoothness, based on total variation (TV), we propose a novel supervised target detection algorithm which uses a single target spectrum as the prior knowledge. TV can make the image smooth, and has been widely used in image denoising and restoration. The proposed algorithm uses TV to keep the spatial homogeneity or smoothness of the detection output. Meanwhile, a constraint is used to guarantee the spectral signature of the target unsuppressed. The final formulated detection model is an ℓ1-norm convex optimization problem. The split Bregman algorithm is used to solve our optimization problem, as it can solve the ℓ1-norm optimization problem efficiently. Two synthetic and two real hyperspectral images are used to do experiments. The experimental results demonstrate that the proposed algorithm outperforms the other algorithms for the experimental data sets. The experimental results also show that even when the target occupies only one pixel, the proposed algorithm can still obtain good results. This is because in such a case, the background is kept smooth, but at the same time, the algorithm allows for sharp edges in the detection output. PMID:27019489

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

    PubMed

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

    2016-01-01

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

  9. 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.

  10. 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

  11. Illumination-invariant face recognition in hyperspectral images

    NASA Astrophysics Data System (ADS)

    Pan, Zhihong; Healey, Glenn E.; Prasad, Manish; Tromberg, Bruce J.

    2003-09-01

    We examine the performance of illumination-invariant face recognition in hyperspectral images on a database of 200 subjects. The images are acquired over the near-infrared spectral range of 0.7-1.0 microns. Each subject is imaged over a range of facial orientations and expressions. Faces are represented by local spectral information for several tissue types. Illumination variation is modeled by low-dimensional linear subspaces of reflected radiance spectra. One hundred outdoor illumination spectra measured at Boulder, Colorado are used to synthesize the radiance spectra for the face tissue types. Weighted invariant subspace projection over multiple tissue types is used for recognition. Illumination-invariant face recognition is tested for various face rotations as well as different facial expressions.

  12. Hyperspectral imaging applied to medical diagnoses and food safety

    NASA Astrophysics Data System (ADS)

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

    2003-08-01

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

  13. Pathological leucocyte segmentation algorithm based on hyperspectral imaging technique

    NASA Astrophysics Data System (ADS)

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

    2012-05-01

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

  14. New Method for Calibration for Hyperspectral Pushbroom Imaging Systems

    NASA Technical Reports Server (NTRS)

    Ryan, Robert; Olive, Dan; ONeal, Duane; Schere, Chris; Nixon, Thomas; May, Chengye; Ryan, Jim; Stanley, Tom; Witcher, Kern

    1999-01-01

    A new, easy-to-implement approach for achieving highly accurate spectral and radiometric calibration of array-based, hyperspectral pushbroom imagers is presented in this paper. The equivalence of the plane of the exit port of an integrating sphere to a Lambertian surface is utilized to provide a field-filling radiance source for the imager. Several different continuous wave lasers of various wavelengths and a quartz-tungsten-halogen lamp internally illuminate the sphere. The imager is positioned to "stare" into the port, and the resultant data cube is analyzed to determine wavelength calibrations, spectral widths of channels, radiometric characteristics, and signal-to-noise ratio, as well as an estimate of signal-to-noise performance in the field. The "smile" (geometric distortion of spectra) of the system can be quickly ascertained using this method. As the price and availability of solid state laser sources improve, this technique could gain wide acceptance.

  15. Methods for gas detection using stationary hyperspectral imaging sensors

    SciTech Connect

    Conger, James L.; Henderson, John R.

    2012-04-24

    According to one embodiment, a method comprises producing a first hyperspectral imaging (HSI) data cube of a location at a first time using data from a HSI sensor; producing a second HSI data cube of the same location at a second time using data from the HSI sensor; subtracting on a pixel-by-pixel basis the second HSI data cube from the first HSI data cube to produce a raw difference cube; calibrating the raw difference cube to produce a calibrated raw difference cube; selecting at least one desired spectral band based on a gas of interest; producing a detection image based on the at least one selected spectral band and the calibrated raw difference cube; examining the detection image to determine presence of the gas of interest; and outputting a result of the examination. Other methods, systems, and computer program products for detecting the presence of a gas are also described.

  16. 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.

  17. Exploration of virtual dimensionality in hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Chang, Chein-I.

    2006-05-01

    Virtual dimensionality (VD) is a new concept which was developed to estimate the number of spectrally distinct signatures present in hyperspectral image data. Unlike intrinsic dimensionality which is mainly of theoretical interest, the VD is a very useful and practical notion. It is derived from the Neyman-Pearson detection theory. Unfortunately, its utility in hyperspectral data exploitation has yet to be explored. This paper presents several applications to which the VD is applied successfully. Since the VD is derived from a binary hypothesis testing problem for each spectral band, it can be used for band selection. When the test fails for a band, it indicates that there is a signal source in that particular band which must be selected. By the same token it can be further used for dimensionality reduction. For principal components analysis (PCA) or independent component analysis (ICA), the VD helps to determine the number of principal components or independent components are required for exploitation such as detection, classification, compression, etc. For unsupervised target detection and classification, the VD can be used to determine how many unwanted signal sources present in the image data so that they can be eliminated prior to detection and classification. For endmember extraction, the VD provides a good estimate of the number of endmembers needed to be extracted. All these applications are justified by experiments.

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

  19. 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.

  20. 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.

  1. High-performance hyperspectral imaging using virtual slit optics

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  2. 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...

  3. 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.

  4. ASTRAL, a hyperspectral imaging DNA sequencer

    NASA Astrophysics Data System (ADS)

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

    1998-05-01

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

  5. 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

  6. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

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

  8. [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

  9. [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

  10. 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

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

  12. Compressed hyperspectral image sensing with joint sparsity reconstruction

    NASA Astrophysics Data System (ADS)

    Liu, Haiying; Li, Yunsong; Zhang, Jing; Song, Juan; Lv, Pei

    2011-10-01

    Recent compressed sensing (CS) results show that it is possible to accurately reconstruct images from a small number of linear measurements via convex optimization techniques. In this paper, according to the correlation analysis of linear measurements for hyperspectral images, a joint sparsity reconstruction algorithm based on interband prediction and joint optimization is proposed. In the method, linear prediction is first applied to remove the correlations among successive spectral band measurement vectors. The obtained residual measurement vectors are then recovered using the proposed joint optimization based POCS (projections onto convex sets) algorithm with the steepest descent method. In addition, a pixel-guided stopping criterion is introduced to stop the iteration. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed.

  13. Learning Hierarchical Spectral-Spatial Features for Hyperspectral Image Classification.

    PubMed

    Zhou, Yicong; Wei, Yantao

    2016-07-01

    This paper proposes a spectral-spatial feature learning (SSFL) method to obtain robust features of hyperspectral images (HSIs). It combines the spectral feature learning and spatial feature learning in a hierarchical fashion. Stacking a set of SSFL units, a deep hierarchical model called the spectral-spatial networks (SSN) is further proposed for HSI classification. SSN can exploit both discriminative spectral and spatial information simultaneously. Specifically, SSN learns useful high-level features by alternating between spectral and spatial feature learning operations. Then, kernel-based extreme learning machine (KELM), a shallow neural network, is embedded in SSN to classify image pixels. Extensive experiments are performed on two benchmark HSI datasets to verify the effectiveness of SSN. Compared with state-of-the-art methods, SSN with a deep hierarchical architecture obtains higher classification accuracy in terms of the overall accuracy, average accuracy, and kappa ( κ ) coefficient of agreement, especially when the number of the training samples is small.

  14. Application of the MAP estimation model to hyperspectral resolution image enhancement

    NASA Astrophysics Data System (ADS)

    Dong, Guangjun; Zhou, Haifang; Ji, Song; Shu, Rong

    2009-10-01

    This paper makes a study of maximum a posteriori (MAP) estimation method for enhancing the spatial resolution of a hyperspectral image using a higher resolution coincident panchromatic image. Here, the mathematical formulation of the proposed MAP method is described and the detail process step is introduced. Then, enhancement results using PHI hyperspectral image datasets are provided. In general, it is found that the MAP method is able to obtain high-resolution hyperspectral data. Experiment shows that the method is effective while the enhancement for conventional methods, like average estimation, is limited primarily to fuse spectral information.

  15. 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.

  16. Spectral homogenization techniques for the hyperspectral image projector

    NASA Astrophysics Data System (ADS)

    Hillberry, Logan E.; Rice, Joseph P.

    2015-05-01

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

  17. 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.

  18. Spectral Regression Discriminant Analysis for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Pan, Y.; Wu, J.; Huang, H.; Liu, J.

    2012-08-01

    Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.

  19. Detecting liquid contamination on surfaces using hyperspectral imaging data

    NASA Astrophysics Data System (ADS)

    Warren, Russell E.; Cohn, David B.; Gagnon, Marc-André; Farley, Vincent

    2015-05-01

    Over the past two years we have developed a new approach for detecting and identifying the presence of liquid chemical contamination on surfaces using hyperspectral imaging data. This work requires an algorithm for unmixing the data to separate the liquid contamination component of the data from all other possible spectral effects, such as the illumination and reflectance spectra of the pure background. The contamination components from S and P polarized reflectance data are then used to estimate the complex refractive index. We retain the index estimates within spectral windows chosen for each of a set of candidate contaminant materials based on their optical extinction. Spectral estimates within those windows are characteristic of the liquid material, and can be passed on to an algorithm for chemical detection and identification. The resulting algorithm is insensitive to the composition of the surface material, and requires no prior measurements of the uncontaminated surface. In a series of field tests, data from the Telops Hyper-Cam sensor were used to develop and validate our approach. We discuss our hyperspectral unmixing and index estimation approaches, and show results from tests conducted at the Telops facility in Québec under a contract with the U.S. Army Edgewood Chemical Biological Center.

  20. A generalized representation-based approach for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Li, Jiaojiao; Li, Wei; Du, Qian; Li, Yunsong

    2016-05-01

    Sparse representation-based classifier (SRC) is of great interest recently for hyperspectral image classification. It is assumed that a testing pixel is linearly combined with atoms of a dictionary. Under this circumstance, the dictionary includes all the training samples. The objective is to find a weight vector that yields a minimum L2 representation error with the constraint that the weight vector is sparse with a minimum L1 norm. The pixel is assigned to the class whose training samples yield the minimum error. In addition, collaborative representation-based classifier (CRC) is also proposed, where the weight vector has a minimum L2 norm. The CRC has a closed-form solution; when using class-specific representation it can yield even better performance than the SRC. Compared to traditional classifiers such as support vector machine (SVM), SRC and CRC do not have a traditional training-testing fashion as in supervised learning, while their performance is similar to or even better than SVM. In this paper, we investigate a generalized representation-based classifier which uses Lq representation error, Lp weight norm, and adaptive regularization. The classification performance of Lq and Lp combinations is evaluated with several real hyperspectral datasets. Based on these experiments, recommendation is provide for practical implementation.

  1. Framelet-Based Sparse Unmixing of Hyperspectral Images.

    PubMed

    Zhang, Guixu; Xu, Yingying; Fang, Faming

    2016-04-01

    Spectral unmixing aims at estimating the proportions (abundances) of pure spectrums (endmembers) in each mixed pixel of hyperspectral data. Recently, a semi-supervised approach, which takes the spectral library as prior knowledge, has been attracting much attention in unmixing. In this paper, we propose a new semi-supervised unmixing model, termed framelet-based sparse unmixing (FSU), which promotes the abundance sparsity in framelet domain and discriminates the approximation and detail components of hyperspectral data after framelet decomposition. Due to the advantages of the framelet representations, e.g., images have good sparse approximations in framelet domain, and most of the additive noises are included in the detail coefficients, the FSU model has a better antinoise capability, and accordingly leads to more desirable unmixing performance. The existence and uniqueness of the minimizer of the FSU model are then discussed, and the split Bregman algorithm and its convergence property are presented to obtain the minimal solution. Experimental results on both simulated data and real data demonstrate that the FSU model generally performs better than the compared methods. PMID:26849863

  2. 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.

  3. Maximum margin metric learning based target detection for hyperspectral images

    NASA Astrophysics Data System (ADS)

    Dong, Yanni; Zhang, Liangpei; Zhang, Lefei; Du, Bo

    2015-10-01

    Target detection is one of the most important problems in hyperspectral image (HSI) processing. However, the classical algorithms depend on the specific statistical hypothesis test, and the algorithms may only perform well under certain conditions, e.g., the adaptive matched subspace detector algorithm assumes that the background covariance matrices do not include the target signatures, which seldom happens in the real world. How to develop a proper metric for measuring the separability between targets and backgrounds becomes the key in target detection. This paper proposes an efficient maximum margin metric learning (MMML) based target detection algorithm, which aims at exploring the limited samples in metric learning and transfers the metric learning problem for hyperspectral target detection into a maximum margin problem which can be optimized via a cutting plane method, and maximally separates the target samples from the background ones. The extensive experimental results with different HSIs demonstrate that the proposed method outperforms both the state-of-the-art target detection algorithms and the other classical metric learning methods.

  4. Snapshot Hyperspectral Light-Sheet Imaging of Signal Transduction in Live Pancreatic Islets.

    PubMed

    Lavagnino, Zeno; Dwight, Jason; Ustione, Alessandro; Nguyen, Thuc-Uyen; Tkaczyk, Tomasz S; Piston, David W

    2016-07-26

    The observation of ionic signaling dynamics in intact pancreatic islets has contributed greatly to our understanding of both α- and β-cell function. Insulin secretion from β-cells depends on the firing of action potentials and consequent rises of intracellular calcium activity ([Ca(2+)]i). Zinc (Zn(2+)) is cosecreted with insulin, and has been postulated to play a role in cell-to-cell cross talk within an islet, in particular inhibiting glucagon secretion from α-cells. Thus, measuring [Ca(2+)]i and Zn(2+) dynamics from both α- and β-cells will elucidate mechanisms underlying islet hormone secretion. [Ca(2+)]i and intracellular Zn(2+) can be measured using fluorescent biosensors, but the most efficient sensors have overlapping spectra that complicate their discrimination. Hyperspectral imaging can be used to distinguish signals from multiple fluorophores, but available hyperspectral implementations are either too slow to measure the dynamics of ionic signals or not suitable for thick samples. We have developed a five-dimensional (x,y,z,t,λ) imaging system that leverages a snapshot hyperspectral imaging method, image mapping spectrometry, and light-sheet microscopy. This system provides subsecond temporal resolution from deep within multicellular structures. Using a single excitation wavelength (488 nm) we acquired images from triply labeled samples with two biosensors and a genetically expressing fluorescent protein (spectrally overlapping with one of the biosensors) with high temporal resolution. Measurements of [Ca(2+)]i and Zn(2+) within both α- and β-cells as a function of glucose concentration show heterogeneous uptake of Zn(2+) into α-cells that correlates to the known heterogeneities in [Ca(2+)]i. These differences in intracellular Zn(2+) among α-cells may contribute to the inhibition in glucagon secretion observed at elevated glucose levels.

  5. Snapshot Hyperspectral Light-Sheet Imaging of Signal Transduction in Live Pancreatic Islets.

    PubMed

    Lavagnino, Zeno; Dwight, Jason; Ustione, Alessandro; Nguyen, Thuc-Uyen; Tkaczyk, Tomasz S; Piston, David W

    2016-07-26

    The observation of ionic signaling dynamics in intact pancreatic islets has contributed greatly to our understanding of both α- and β-cell function. Insulin secretion from β-cells depends on the firing of action potentials and consequent rises of intracellular calcium activity ([Ca(2+)]i). Zinc (Zn(2+)) is cosecreted with insulin, and has been postulated to play a role in cell-to-cell cross talk within an islet, in particular inhibiting glucagon secretion from α-cells. Thus, measuring [Ca(2+)]i and Zn(2+) dynamics from both α- and β-cells will elucidate mechanisms underlying islet hormone secretion. [Ca(2+)]i and intracellular Zn(2+) can be measured using fluorescent biosensors, but the most efficient sensors have overlapping spectra that complicate their discrimination. Hyperspectral imaging can be used to distinguish signals from multiple fluorophores, but available hyperspectral implementations are either too slow to measure the dynamics of ionic signals or not suitable for thick samples. We have developed a five-dimensional (x,y,z,t,λ) imaging system that leverages a snapshot hyperspectral imaging method, image mapping spectrometry, and light-sheet microscopy. This system provides subsecond temporal resolution from deep within multicellular structures. Using a single excitation wavelength (488 nm) we acquired images from triply labeled samples with two biosensors and a genetically expressing fluorescent protein (spectrally overlapping with one of the biosensors) with high temporal resolution. Measurements of [Ca(2+)]i and Zn(2+) within both α- and β-cells as a function of glucose concentration show heterogeneous uptake of Zn(2+) into α-cells that correlates to the known heterogeneities in [Ca(2+)]i. These differences in intracellular Zn(2+) among α-cells may contribute to the inhibition in glucagon secretion observed at elevated glucose levels. PMID:27463142

  6. 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.

  7. 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

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

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

  9. A prior knowledge model for multidimensional striping noise compensation in hyperspectral imaging devices

    NASA Astrophysics Data System (ADS)

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

    2012-09-01

    In this paper, a prior knowledge model is proposed in order to increase the effectiveness of a multidimensional striping noise compensation (SNC) algorithm. This is accomplished by considering an optoelectronic approach, thereby generating a more accurate mathematical representation of the hyperspectral acquisition process. The proposed model includes knowledge on the system spectral response, which can be obtained by means of an input with known spectral radiation. Further, the model also considers the dependence of the noise structure on the analog-digital conversion process, that is, schemes such as active-pixel sensor (APS) and passive-pixel sensor (PPS) have been considered. Finally, the model takes advantage of the degree of crosstalk between consecutive bands in order to determinate how much of this spectral information is contributing to the read out data obtained in a particular band. All prior knowledge is obtained by a series of experimental analysis, and then integrated into the model. After estimating the required parameters, the applicability of the multidimensional SNC is illustrated by compensating for stripping noise in hyperspectral images acquired using an experimental setup. A laboratory prototype, based on both a Photonfocus Hurricane hyperspectral camera and a Xeva Xenics NIR hyperspectral camera, has been implemented to acquire data in the range of 400-1000 [nm] and 900-1700 [nm], respectively. Also, a mobile platform has been used to simulate and synchronize the scanning procedure of the cameras and an uniform tungsten lamp has been installed to ensure an equal spectral radiance between the different bands for calibration purpose.

  10. 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.

  11. Aliasing removing of hyperspectral image based on fractal structure matching

    NASA Astrophysics Data System (ADS)

    Wei, Ran; Zhang, Ye; Zhang, Junping

    2015-05-01

    Due to the richness on high frequency components, hyperspectral image (HSI) is more sensitive to distortion like aliasing. Many methods aiming at removing such distortion have been proposed. However, seldom of them are suitable to HSI, due to low spatial resolution characteristic of HSI. Fortunately, HSI contains plentiful spectral information, which can be exploited to overcome such difficulties. Motivated by this, we proposed an aliasing removing method for HSI. The major differences between proposed and current methods is that proposed algorithm is able to utilize fractal structure information, thus the dilemma originated from low-resolution of HSI is solved. Experiments on real HSI data demonstrated subjectively and objectively that proposed method can not only remove annoying visual effect brought by aliasing, but also recover more high frequency component.

  12. 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

  13. 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.

  14. Infrared adaptive spectral imagers for direct detection of spectral signatures and hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Goldstein, Neil; Fox, Marsha; Adler-Golden, Steven; Gregor, Brian

    2013-03-01

    Field test results are presented for a prototype long-wave adaptive imager that provides both hyperspectral imagery and contrast imagery based on the direct application of hyperspectral detection algorithms in hardware. Programmable spatial light modulators are used to provide both spectral and spatial resolution using a single element detector. Programmable spectral and spatial detection filters can be used to superimpose any possible analog spectral detection filter on the image. In this work, we demonstrate three modes of operation, including hyperspectral imagery, and one and two-dimensional imagery using a generalized matched filter for detection of a specific target gas within the scene.

  15. 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.

  16. 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 ...

  17. 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

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

    PubMed

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

    2015-01-01

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

  19. 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.

  20. 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.

  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.

    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.

  2. Detection of single graves by airborne hyperspectral imaging.

    PubMed

    Leblanc, G; Kalacska, M; Soffer, R

    2014-12-01

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

  3. Detection of single graves by airborne hyperspectral imaging.

    PubMed

    Leblanc, G; Kalacska, M; Soffer, R

    2014-12-01

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

  4. Assessing pasture quality and degradation status using hyperspectral imaging: a case study from western Tibet

    NASA Astrophysics Data System (ADS)

    Lehnert, Lukas W.; Meyer, Hanna; Meyer, Nele; Reudenbach, Christoph; Bendix, Jörg

    2013-10-01

    Alpine grasslands on the Tibetan Plateau (TP) are suffering from pasture degradation induced by over-grazing, climate change and improper livestock management. Meanwhile, the status of pastures is largely unknown especially in poor accessible western parts on the TP. The aim of this case study was to assess the suitability of hyperspectral imaging to predict quality and amount of forage on the western TP. Therefore, 18 ground- based hyperspectral images taken along two transects on a winter pasture were used to estimate leaf chlorophyll content, photosynthetic-active vegetation cover (PV) and proportion of grasses. For calibration and validation purposes, chlorophyll content of 20 grass plants was measured in situ. From the images reference spectra of grass and non-grass species were collected. PV was assessed from similarity of images to mean vegetation spectra using spectral angle mapper and threshold classifications. A set of 48 previously published hyperspectral vegetation indices (VI) was used as predictors to estimate chlorophyll content and to discriminate grass and non-grass pixels. Separation into grass and non-grass species was performed using partial least squares (PLS) discriminant analysis and chlorophyll content was estimated with PLS regression. The accuracy of the models was assessed with leave-one-out cross validation and normalised root mean square errors (nRMSE) for chlorophyll and contingency matrices for grass classification and total PV separation. Highest error rates were observed for discrimination between vegetated and non-vegetated parts (Overall accuracy = 0.85), whilst accuracies of grass and non grass separation (Overall accuracy = 0.98) and chlorophyll estimation were higher (nRMSE = 10.7).

  5. 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.

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

    PubMed Central

    Moroni, Monica; Dacquino, Carlo; Cenedese, Antonio

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

  8. 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

  9. Growth characteristics of three Fusarium species evaluated by near-infrared hyperspectral imaging and multivariate image analysis.

    PubMed

    Williams, Paul J; Geladi, Paul; Britz, Trevor J; Manley, Marena

    2012-11-01

    Colony growth of three Fusarium spp. on potato dextrose agar was followed by collecting near-infrared (NIR) hyperspectral images of the colonies at regular intervals after inoculation up to 55 h. After principal component analysis (PCA), two clusters were apparent in the score plot along principal component 1. Using the brushing technique, these clusters were divided into four groups of pixels with similar score values. These could be visualised as growth zones within the colonies in the corresponding score image. Three spectral bands, i.e. 1,166, 1,380 and 1,918 nm, were prominent in the multiplicative scatter corrected and Savitzky-Golay second derivative spectra. These indicated chemical changes, associated with carbohydrates (1,166 and 1,380 nm) and protein (1,918 nm), that occurred as the mycelium grew and matured. The protein band was more prominent in the mature fungal material while the carbohydrate band was less pronounced. The younger material and the agar were characterised by the carbohydrate spectral band. Integrating whole mycelium colonies as the sum of pixels over time made it possible to construct curves that resembled growth curves; this included the lag phase, active growth phase, deceleration phase and phase of constant growth. Growth profiles constructed from individual growth zones indicated more detailed growth characteristics. The use of NIR hyperspectral imaging and multivariate image analysis (MIA) allowed one to visualise radial growth rings in the PCA score images. This would not have been possible with bulk spectroscopy. Interpreting spectral data enabled better understanding of microbial growth characteristics on agar medium. NIR hyperspectral imaging combined with MIA is a powerful tool for the evaluation of growth characteristics of fungi.

  10. Assessment of effects of lossy compression of hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Su, Jonathan K.; Hsu, Su May; Orloff, Seth

    2004-08-01

    Hyperspectral imaging (HSI) sensors provide imagery with hundreds of spectral bands, typically covering VNIR and/or SWIR wavelengths. This high spectral resolution aids applications such as terrain classification and material identification, but it can also produce imagery that occupies well over 100 MB, which creates problems for storage and transmission. This paper investigates the effects of lossy compression on a representative HSI cube, with background classification serving as an example application. The compression scheme first performs principal components analysis spectrally, then discards many of the lower-importance principal-component (PC) images, and then applies JPEG2000 spatial compression to each of the individual retained PC images. The assessment of compression effects considers both general-purpose distortion measures, such as root mean square difference, and statistical tests for deciding whether compression causes significant degradations in classification. Experimental results demonstrate the effectiveness of proper PC-image rate allocation, which enabled compression at ratios of 100-340 without producing significant classification differences. Results also indicate that distortion might serve as a predictor of compression-induced changes in application performance.

  11. 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.

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

    NASA Astrophysics Data System (ADS)

    Hua, Wenshen; Liu, Xun; Yang, Jia

    2013-12-01

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

  13. 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

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

    PubMed

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

    2016-01-01

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

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

    PubMed

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

    2015-11-01

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

  16. [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

  17. Lossless compression of hyperspectral images based on the prediction error block

    NASA Astrophysics Data System (ADS)

    Li, Yongjun; Li, Yunsong; Song, Juan; Liu, Weijia; Li, Jiaojiao

    2016-05-01

    A lossless compression algorithm of hyperspectral image based on distributed source coding is proposed, which is used to compress the spaceborne hyperspectral data effectively. In order to make full use of the intra-frame correlation and inter-frame correlation, the prediction error block scheme are introduced. Compared with the scalar coset based distributed compression method (s-DSC) proposed by E.Magli et al., that is , the bitrate of the whole block is determined by its maximum prediction error, and the s-DSC-classify scheme proposed by Song Juan that is based on classification and coset coding, the prediction error block scheme could reduce the bitrate efficiently. Experimental results on hyperspectral images show that the proposed scheme can offer both high compression performance and low encoder complexity and decoder complexity, which is available for on-board compression of hyperspectral images.

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  19. 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.

  20. Development of a hyperspectral fluorescence lifetime imaging microscope and its application to tissue imaging

    NASA Astrophysics Data System (ADS)

    Owen, Dylan M.; Manning, Hugh B.; de Beule, Pieter; Talbot, Clifford; Requejo-Isidro, Jose; Dunsby, Chris; McGinty, James; Benninger, Richard K. P.; Elson, Dan S.; Munro, Ian; Galletly, Neil P.; Lever, M. Jon; Stamp, Gordon W.; Anand, Praveen; Neil, Mark A. A.; French, Paul M. W.

    2007-02-01

    We present the design, characterization and application of a novel, rapid, optically sectioned hyperspectral fluorescence lifetime imaging (FLIM) microscope. The system is based on a line scanning confocal configuration and uses a highspeed time-gated detector to extract lifetime information from many pixels in parallel. This allows the full spectraltemporal profiles of a fluorescence decay to be obtained from every pixel in an image. Line illumination and slit detection also gives the microscope a confocal optical sectioning ability. The system is applied to test samples and unstained biological tissue. In future, this microscope will be combined with recently-developed continuously electronically tunable, pulsed light sources based on tapered, micro-structured optical fibers. This will allow hyperspectral FLIM to be combined with the advantages of excitation spectroscopy to gain further insight into complex biological specimens including tissue and live cell imaging.

  1. Hyperspectral image analysis for the determination of alteration minerals in geothermal fields: Çürüksu (Denizli) Graben, Turkey

    NASA Astrophysics Data System (ADS)

    Uygur, Merve; Karaman, Muhittin; Kumral, Mustafa

    2016-04-01

    Çürüksu (Denizli) Graben hosts various geothermal fields such as Kızıldere, Yenice, Gerali, Karahayıt, and Tekkehamam. Neotectonic activities, which are caused by extensional tectonism, and deep circulation in sub-volcanic intrusions are heat sources of hydrothermal solutions. The temperature of hydrothermal solutions is between 53 and 260 degree Celsius. Phyllic, argillic, silicic, and carbonatization alterations and various hydrothermal minerals have been identified in various research studies of these areas. Surfaced hydrothermal alteration minerals are one set of potential indicators of geothermal resources. Developing the exploration tools to define the surface indicators of geothermal fields can assist in the recognition of geothermal resources. Thermal and hyperspectral imaging and analysis can be used for defining the surface indicators of geothermal fields. This study tests the hypothesis that hyperspectral image analysis based on EO-1 Hyperion images can be used for the delineation and definition of surfaced hydrothermal alteration in geothermal fields. Hyperspectral image analyses were applied to images covering the geothermal fields whose alteration characteristic are known. To reduce data dimensionality and identify spectral endmembers, Kruse's multi-step process was applied to atmospherically and geometrically-corrected hyperspectral images. Minimum Noise Fraction was used to reduce the spectral dimensions and isolate noise in the images. Extreme pixels were identified from high order MNF bands using the Pixel Purity Index. n-Dimensional Visualization was utilized for unique pixel identification. Spectral similarities between pixel spectral signatures and known endmember spectrum (USGS Spectral Library) were compared with Spectral Angle Mapper Classification. EO-1 Hyperion hyperspectral images and hyperspectral analysis are sensitive to hydrothermal alteration minerals, as their diagnostic spectral signatures span the visible and shortwave

  2. 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

    2016-01-01

    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

  3. Nearest feature line embedding approach to hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Chang, Yang-Lang; Liu, Jin-Nan; Han, Chin-Chuan; Chen, Ying-Nong; Hsieh, Tung-Ju; Huang, Bormin

    2012-10-01

    In this paper, a nearest feature line (NFL) embedding transformation is proposed for dimension reduction of hyperspectral image (HSI). Eigenspace projection approaches are generally used for feature extraction of HSI in remote sensing image classification. In order to improve the classification accuracy, the feature vectors of high dimensions are reduced to the low dimensionalities by the effective projection transformation. Similarly, the proposed NFL measurement is embedded into the transformation during the discriminant analysis stage instead of the matching stage. The class separability, neighborhood structure preservation, and NFL measurement are also simultaneously considered to find the effective and discriminating transformation in eigenspaces for image classification. The nearest neighbor classifier is used to show the discriminative performance. The proposed NFL embedding transformation is compared with several conventional state-of-the-art algorithms. It was evaluated by the AVIRIS data sets of Northwest Tippecanoe County. Experimental results have demonstrated that NFL embedding method is an effective transformation for dimension reduction in land cover classification of earth remote sensing.

  4. Compressive fluorescence microscopy for biological and hyperspectral imaging.

    PubMed

    Studer, Vincent; Bobin, Jérome; Chahid, Makhlad; Mousavi, Hamed Shams; Candes, Emmanuel; Dahan, Maxime

    2012-06-26

    The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices--especially in optics--have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells, and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher-dimensional signals, which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point to some remaining challenges for CS fluorescence microscopy. PMID:22689950

  5. 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

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

    NASA Astrophysics Data System (ADS)

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

    2013-06-01

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

  7. 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

  8. 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

  9. In vivo hyperspectral imaging of traumatic skin injuries in a porcine model

    NASA Astrophysics Data System (ADS)

    Randeberg, Lise L.; Winnem, Andreas M.; Larsen, Eivind L. P.; Haaverstad, Rune; Haugen, Olav A.; Svaasand, Lars O.

    2007-02-01

    Studies of immediate skin reactions are important to understand the underlying biological mechanisms involved in traumatic or chemical damage to the skin. In this study the spatial and spectral information provided by hyperspectral images was used to identify and characterize non-penetrating skin injuries in a porcine model. A hyperspectral imaging system (Hyspex, Norsk Elektro Optikk AS) was used to monitor the temporal development of minor skin injuries in an anesthetized Norwegian domestic pig. Hyperspectral data were collected in the wavelength range 400-1000 nm (VNIR), with a spectral sampling interval of 3.7 nm. The measurements were initiated immediately after inflicting the injury, and were repeated at least five times at each site with irregular frequency. The last measurement was performed 4 hours after injury. Punch biopsies (5 mm), were collected from adjacent normal skin, and at the center and the margin of each injury. The study was approved by the national animal research authority. The hyperspectral data were analyzed with respect to oxy- and deoxyhemoglobin, and erythema index. The skin biopsies were examined to determine the extent of skin damage in the bruised zones. Preliminary results show that hyperspectral imaging allows discrimination between traumatized skin and normal skin in an early phase. The extent and location of the hemorrhages can be determined from hyperspectral images. These findings might contribute to a better understanding of immediate skin reactions to minor trauma, and thereby the development of a better diagnostic modality for non-penetrating skin injuries in forensic medicine.

  10. 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.

  11. 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.

  12. Quantitative vibrational imaging by hyperspectral stimulated Raman scattering microscopy and multivariate curve resolution analysis.

    PubMed

    Zhang, Delong; Wang, Ping; Slipchenko, Mikhail N; Ben-Amotz, Dor; Weiner, Andrew M; Cheng, Ji-Xin

    2013-01-01

    Spectroscopic imaging has been an increasingly critical approach for unveiling specific molecules in biological environments. Toward this goal, we demonstrate hyperspectral stimulated Raman loss (SRL) imaging by intrapulse spectral scanning through a femtosecond pulse shaper. The hyperspectral stack of SRL images is further analyzed by a multivariate curve resolution (MCR) method to reconstruct quantitative concentration images for each individual component and retrieve the corresponding vibrational Raman spectra. Using these methods, we demonstrate quantitative mapping of dimethyl sulfoxide concentration in aqueous solutions and in fat tissue. Moreover, MCR is performed on SRL images of breast cancer cells to generate maps of principal chemical components along with their respective vibrational spectra. These results show the great capability and potential of hyperspectral SRL microscopy for quantitative imaging of complicated biomolecule mixtures through resolving overlapped Raman bands.

  13. 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.

  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. 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.

  16. 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.

  17. 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.

  18. Gas plume quantification in downlooking hyperspectral longwave infrared images

    NASA Astrophysics Data System (ADS)

    Turcotte, Caroline S.; Davenport, Michael R.

    2010-10-01

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

  19. 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".

  20. 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.

  1. 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.

  2. 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.

  3. Optimal Segmentation Strategy for Compact Representation of Hyperspectral Image Cubes

    SciTech Connect

    Paglieroni, D; Roberts, R

    2000-02-08

    By producing compact representations of hyperspectral image cubes (hypercubes), image storage requirements and the amount of time it takes to extract essential elements of information can both be dramatically reduced. However, these compact representations must preserve the important spectral features within hypercube pixels and the spatial structure associated with background and objects or phenomena of interest. This paper describes a novel approach for automatically and efficiently generating a particular type of compact hypercube representation, referred to as a supercube. The hypercube is segmented into regions that contain pixels with similar spectral shapes that are spatially connected, and the pixel connectivity constraint can be relaxed. Thresholds of similarity in spectral shape between pairs of pixels are derived directly from the hypercube data. One superpixel is generated for each region as some linear combination of pixels belonging to that region. The superpixels are optimal in the sense that the linear combination coefficients are computed so as to minimize the level of noise. Each hypercube pixel is represented in the supercube by applying a gain and bias to the superpixel assigned to the region containing that pixel. Examples are provided.

  4. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

  7. 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...

  8. 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

  9. 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...

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

    PubMed

    Katrašnik, Jaka; Pernuš, Franjo; Likar, Boštjan

    2013-05-20

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

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

    PubMed

    Katrašnik, Jaka; Pernuš, Franjo; Likar, Boštjan

    2013-05-20

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

  12. Hyperspectral imaging based procedures applied to bottom ash characterization

    NASA Astrophysics Data System (ADS)

    Bonifazi, Giuseppe; Serranti, Silvia

    2007-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-10-01

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

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

    PubMed

    Martin, Jacob A; Gross, Kevin C

    2016-08-01

    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. PMID:27505760

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

    NASA Astrophysics Data System (ADS)

    Serranti, Silvia; Bonifazi, Giuseppe

    2014-03-01

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

  16. 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.

  17. Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking.

    PubMed

    Wang, Qi; Lin, Jianzhe; Yuan, Yuan

    2016-06-01

    Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors. PMID:27008675

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

    PubMed

    Martin, Jacob A; Gross, Kevin C

    2016-08-01

    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.

  19. Hyperspectral molecular imaging of multiple receptors using immunolabeled plasmonic nanoparticles

    NASA Astrophysics Data System (ADS)

    Seekell, Kevin; Crow, Matthew J.; Marinakos, Stella; Ostrander, Julie; Chilkoti, Ashutosh; Wax, Adam

    2011-11-01

    This work presents simultaneous imaging and detection of three different cell receptors using three types of plasmonic nanoparticles (NPs). The size, shape, and composition-dependent scattering profiles of these NPs allow for a system of multiple distinct molecular markers using a single optical source. With this goal in mind, tags consisting of anti-epidermal growth factor receptor gold nanorods, anti-insulin-like growth factor 1-R silver nanospheres, and human epidermal growth factor receptor 2Ab gold nanospheres were developed to monitor the expression of receptors commonly overexpressed by cancer cells. These labels were chosen because they scatter strongly in distinct spectral windows. A hyperspectral darkfield microspectroscopy system was developed to record the scattering spectra of cells labeled with these molecular tags. Simultaneous monitoring of multiple tags may lead to applications such as profiling of cell line immunophenotype and investigation of receptor signaling pathways. Single, dual, and triple tag experiments were performed to analyze NP tag specificity as well as their interactions. Distinct resonance peaks were observed in these studies, showing the ability to characterize cell lines using conjugated NPs. However, interpreting shifts in these peaks due to changes in a cellular dielectric environment may be complicated by plasmon coupling between NPs bound to proximal receptors and other coupling mechanisms due to the receptors themselves.

  20. 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.

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

    SciTech Connect

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

    2004-07-01

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

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

    NASA Astrophysics Data System (ADS)

    Hill, Samuel L.; Clemens, Peter

    2015-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  4. Spectral-spatial classification of hyperspectral images with k-means++ partitional clustering

    NASA Astrophysics Data System (ADS)

    Kazanskiy, Nikolay L.; Serafimovich, Pavel G.; Zimichev, Evgeniy A.

    2015-03-01

    We propose and investigate a complex hyperspectral image classification method with regard to the spatial proximity of pixels. Key feature of the method is that it uses common and relatively simple algorithms to attain high accuracy. The method combines the results of pixel-wise support vector machine classification and a set of contours derived from kmeans++ image clustering. To prevent redundant processing of similar data a principal component analysis is used. The method proposed enables the accuracy and speed of hyperspectral image classification to be enhanced.

  5. 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.

  6. Theoretical Analysis of the Sensitivity and Speed Improvement of ISIS over a Comparable Traditional Hyperspectral Imager

    SciTech Connect

    Brian R. Stallard; Stephen M. Gentry

    1998-09-01

    The analysis presented herein predicts that, under signal-independent noise limited conditions, an Information-efficient Spectral Imaging Sensor (ISIS) style hyperspectral imaging system design can obtain significant signal-to-noise ratio (SNR) and speed increase relative to a comparable traditional hyperspectral imaging (HSI) instrument. Factors of forty are reasonable for a single vector, and factors of eight are reasonable for a five-vector measurement. These advantages can be traded with other system parameters in an overall sensor system design to allow a variety of applications to be done that otherwise would be impossible within the constraints of the traditional HSI style design.

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

    NASA Astrophysics Data System (ADS)

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

    Introduction: In preparation for the upcoming International Mars Sample Return mission (MSR), returning samples containing potential biohazards, we have implemented a hyperspec-tral method of in-situ analysis of grains performed in BSL4 quarantine conditions, by combining several non-destructive imaging diagnostics. This allows sample transportation on optimized experimental setups, while monitoring the sample quarantine conditions. Our hyperspectral methodology was tested during analyses of meteorites [1-2] and cometary and interstellar grains from the recent NASA Stardust mission [3-6]. Synchrotron Radiation protocols: X-ray analysis methods are widely accepted as the least destructive probes of fragile, unique samples. Diffraction, X-ray fluorescence and ab-sorption micro/nano-spectroscopies were performed on chondritic test samples using focused monochromatic beams at the ESRF synchrotron in Grenoble, France. 2D maps of grain com-position down to ppm concentrations and polycrystalline structure have simultaneously been acquired, followed by X-ray absorption performed on elements of Z 26. Ideally, absorption micro-tomography can later be performed in full-beam mode to record the 3D morphology of the grain followed by fluorescence-tomography in focus-beam mode which complements this picture with a 3D elemental image of the grain. Lab-based protocols: Raman and IR-based spectroscopies have been performed in reflection mode for mineralogical imaging of the grains in the laboratory using commercial microscopes. The spatial resolution varied in the 1-10 m range. Laser limited penetration of opaque samples permits only 2D imaging of the few nanometer-thick outer layers of the grains. Mineralogical maps are now routinely acquired using Raman spectroscopy at sub-micron scales through the 3 container walls of the Martian sample holder, followed by IR few-micrometer spot measurements recording C-based and potential aqueous alteration distributions. Sample Holder: A

  8. Research on Ground-Based LWIR Hyperspectral Imaging Remote Gas Detection.

    PubMed

    Zheng, Wei-jian; Lei, Zheng-gang; Yu, Chun-chao; Yang, Zhi-xiong; Wang, Hai-yangi; Fu, Yan-peng; Li, Xun-niu; Liao, Ning-fang; Su, Jun-hong

    2016-02-01

    The new progress of ground-based long-wave infrared remote sensing is presented, which describes the windowing spatial and temporal modulation Fourier spectroscopy imaging in details. The prototype forms the interference fringes based on the corner-cube of spatial modulation of Michelson interferometer, using cooled long-wave infrared photovoltaic staring FPA (focal plane array) detector. The LWIR hyperspectral imaging is achieved by the process of collection, reorganization, correction, apodization, FFT etc. from data cube. Noise equivalent spectral radiance (NESR), which is the sensitivity index of CHIPED-1 LWIR hyperspectral imaging prototype, can reach 5.6 x 10⁻⁸ W · (cm⁻¹ · sr · cm²)⁻¹ at single sampling. The data is the same as commercial temporal modulation hyperspectral imaging spectrometer. It can prove the advantage of this technique. This technique still has space to be improved. For instance, spectral response range of CHIPED-1 LWIR hyperspectral imaging prototype can reach 11. 5 µm by testing the transmission curve of polypropylene film. In this article, choosing the results of outdoor high-rise and diethyl ether gas experiment as an example, the authors research on the detecting method of 2D distribution chemical gas VOC by infrared hyperspectral imaging. There is no observed diethyl ether gas from the infrared spectral slice of the same wave number in complicated background and low concentration. By doing the difference spectrum, the authors can see the space distribution of diethyl ether gas clearly. Hyperspectral imaging is used in the field of organic gas VOC infrared detection. Relative to wide band infrared imaging, it has some advantages. Such as, it has high sensitivity, the strong anti-interference ability, identify the variety, and so on.

  9. Research on Ground-Based LWIR Hyperspectral Imaging Remote Gas Detection.

    PubMed

    Zheng, Wei-jian; Lei, Zheng-gang; Yu, Chun-chao; Yang, Zhi-xiong; Wang, Hai-yangi; Fu, Yan-peng; Li, Xun-niu; Liao, Ning-fang; Su, Jun-hong

    2016-02-01

    The new progress of ground-based long-wave infrared remote sensing is presented, which describes the windowing spatial and temporal modulation Fourier spectroscopy imaging in details. The prototype forms the interference fringes based on the corner-cube of spatial modulation of Michelson interferometer, using cooled long-wave infrared photovoltaic staring FPA (focal plane array) detector. The LWIR hyperspectral imaging is achieved by the process of collection, reorganization, correction, apodization, FFT etc. from data cube. Noise equivalent spectral radiance (NESR), which is the sensitivity index of CHIPED-1 LWIR hyperspectral imaging prototype, can reach 5.6 x 10⁻⁸ W · (cm⁻¹ · sr · cm²)⁻¹ at single sampling. The data is the same as commercial temporal modulation hyperspectral imaging spectrometer. It can prove the advantage of this technique. This technique still has space to be improved. For instance, spectral response range of CHIPED-1 LWIR hyperspectral imaging prototype can reach 11. 5 µm by testing the transmission curve of polypropylene film. In this article, choosing the results of outdoor high-rise and diethyl ether gas experiment as an example, the authors research on the detecting method of 2D distribution chemical gas VOC by infrared hyperspectral imaging. There is no observed diethyl ether gas from the infrared spectral slice of the same wave number in complicated background and low concentration. By doing the difference spectrum, the authors can see the space distribution of diethyl ether gas clearly. Hyperspectral imaging is used in the field of organic gas VOC infrared detection. Relative to wide band infrared imaging, it has some advantages. Such as, it has high sensitivity, the strong anti-interference ability, identify the variety, and so on. PMID:27209776

  10. Determination of Basic Density and Moisture Content of Loblolly Pine Wood Disks using a NIR Hyperspectral Imaging System

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The use of near infrared (NIR) hyperspectral imaging for the estimation of basic density (BD) and moisture content (MC) of loblolly pine (Pinus taeda L.) disks is reported. A total of 125 wood disks ranging in age from 13 to 19 years were analysed. Hyperspectral images were collected using an imagin...

  11. Identification of unknown waste sites using MIVIS hyperspectral images

    SciTech Connect

    Gomarasca, M.A.; Strobelt, S.

    1996-11-01

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

  12. Hyperspectral diffuse reflectance imaging for rapid, noncontact measurement of the optical properties of turbid materials

    NASA Astrophysics Data System (ADS)

    Qin, Jianwei; Lu, Renfu

    2006-11-01

    We present a method and technique of using hyperspectral diffuse reflectance for rapid determination of the optical properties of turbid media. A hyperspectral imaging system in line scanning mode was used to acquire spatial diffuse reflectance profiles from liquid phantoms made up of absorbing dyes and fat emulsion scatterers over the spectral range of 450-1000 nm instantaneously. The hyperspectral reflectance data were analyzed by using a steady-state diffusion approximation model for semi-infinite homogeneous media. A calibration procedure was developed to compensate the nonuniform instrument response of the imaging system, and a curve-fitting algorithm was used to extract absorption and reduced scattering coefficients (μa and μs‧, respectively) for the phantoms in the wavelength range from 530 to 900 nm. The hyperspectral imaging system gave good measures of μa and μs‧ for the phantoms with average fitting errors of 12% and 7%, respectively. The hyperspectral imaging technique is fast, noncontact, and easy to use, which makes it especially suitable for measurement of the optical properties of turbid liquid and solid foods.

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

    PubMed

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

    2015-07-13

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

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

    PubMed

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

    2015-07-13

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

  15. 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

  16. 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.

  17. Accounting for Variance in Hyperspectral Data Coming from Limitations of the Imaging System

    NASA Astrophysics Data System (ADS)

    Shurygin, B.; Shestakova, M.; Nikolenko, A.; Badasen, E.; Strakhov, P.

    2016-06-01

    Over the course of the past few years, a number of methods was developed to incorporate hyperspectral imaging specifics into generic data mining techniques, traditionally used for hyperspectral data processing. Projection pursuit methods embody the largest class of methods empoyed for hyperspectral image data reduction, however, they all have certain drawbacks making them either hard to use or inefficient. It has been shown that hyperspectral image (HSI) statistics tend to display "heavy tails" (Manolakis2003)(Theiler2005), rendering most of the projection pursuit methods hard to use. Taking into consideration the magnitude of described deviations of observed data PDFs from normal distribution, it is apparent that a priori knowledge of variance in data caused by the imaging system is to be employed in order to efficiently classify objects on HSIs (Kerr, 2015), especially in cases of wildly varying SNR. A number of attempts to describe this variance and compensating techniques has been made (Aiazzi2006), however, new data quality standards are not yet set and accounting for the detector response is made under large set of assumptions. Current paper addresses the issue of hyperspectral image classification in the context of different variance sources based on the knowledge of calibration curves (both spectral and radiometric) obtained for each pixel of imaging camera. A camera produced by ZAO NPO Lepton (Russia) was calibrated and used to obtain a test image. A priori known values of SNR and spectral channel cross-correlation were incorporated into calculating test statistics used in dimensionality reduction and feature extraction. Expectation-Maximization classification algorithm modification for non-Gaussian model as described by (Veracini2010) was further employed. The impact of calibration data coarsening by ignoring non-uniformities on false alarm rate was studied. Case study shows both regions of scene-dominated variance and sensor-dominated variance, leading

  18. Along-track calibration of SWIR push-broom hyperspectral imaging system

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

    Push-broom hyperspectral imaging systems are increasingly used for various medical, agricultural and military purposes. The acquired images contain spectral information in every pixel of the imaged scene collecting additional information about the imaged scene compared to the classical RGB color imaging. Due to the misalignment and imperfections in the optical components comprising the push-broom hyperspectral imaging system, variable spectral and spatial misalignments and blur are present in the acquired images. To capture these distortions, a spatially and spectrally variant response function must be identified at each spatial and spectral position. In this study, we propose a procedure to characterize the variant response function of Short-Wavelength Infrared (SWIR) push-broom hyperspectral imaging systems in the across-track and along-track direction and remove its effect from the acquired images. A custom laser-machined spatial calibration targets are used for the characterization. The spatial and spectral variability of the response function in the across-track and along-track direction is modeled by a parametrized basis function. Finally, the characterization results are used to restore the distorted hyperspectral images in the across-track and along-track direction by a Richardson-Lucy deconvolution-based algorithm. The proposed calibration method in the across-track and along-track direction is thoroughly evaluated on images of targets with well-defined geometric properties. The results suggest that the proposed procedure is well suited for fast and accurate spatial calibration of push-broom hyperspectral imaging systems.

  19. 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. PMID:27566254

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

    PubMed Central

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

    2014-01-01

    Abstract. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors. PMID:25277147

  1. Chemometric analysis of multisensor hyperspectral images of precipitated atmospheric particulate matter.

    PubMed

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

    2015-09-15

    The chemometric analysis of multisensor hyperspectral data allows a comprehensive image-based analysis of precipitated atmospheric particles. Atmospheric particulate matter was precipitated on aluminum foils and analyzed by Raman microspectroscopy and subsequently by electron microscopy and energy dispersive X-ray spectroscopy. All obtained images were of the same spot of an area of 100 × 100 μm(2). The two hyperspectral data sets and the high-resolution scanning electron microscope images were fused into a combined multisensor hyperspectral data set. This multisensor data cube was analyzed using principal component analysis, hierarchical cluster analysis, k-means clustering, and vertex component analysis. The detailed chemometric analysis of the multisensor data allowed an extensive chemical interpretation of the precipitated particles, and their structure and composition led to a comprehensive understanding of atmospheric particulate matter.

  2. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  6. 3D optical sectioning with a new hyperspectral confocal fluorescence imaging system.

    SciTech Connect

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

    2007-02-01

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

  7. Biological sample evaluation using a line-scan based SWIR hyperspectral imaging system

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A new line-scan hyperspectral imaging system was developed to enable short wavelength infrared (SWIR) imagery for biological sample evaluation. Critical sensing components include a SWIR imaging spectrograph and an HgCdTe (MCT) focal plane array detector. To date, agricultural applications of infra...

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  9. A four-dimensional snapshot hyperspectral video-endoscope for bio-imaging applications

    PubMed Central

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2016-01-01

    Hyperspectral imaging has proven significance in bio-imaging applications and it has the ability to capture up to several hundred images of different wavelengths offering relevant spectral signatures. To use hyperspectral imaging for in vivo monitoring and diagnosis of the internal body cavities, a snapshot hyperspectral video-endoscope is required. However, such reported systems provide only about 50 wavelengths. We have developed a four-dimensional snapshot hyperspectral video-endoscope with a spectral range of 400–1000 nm, which can detect 756 wavelengths for imaging, significantly more than such systems. Capturing the three-dimensional datacube sequentially gives the fourth dimension. All these are achieved through a flexible two-dimensional to one-dimensional fiber bundle. The potential of this custom designed and fabricated compact biomedical probe is demonstrated by imaging phantom tissue samples in reflectance and fluorescence imaging modalities. It is envisaged that this novel concept and developed probe will contribute significantly towards diagnostic in vivo biomedical imaging in the near future. PMID:27044607

  10. A four-dimensional snapshot hyperspectral video-endoscope for bio-imaging applications.

    PubMed

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2016-01-01

    Hyperspectral imaging has proven significance in bio-imaging applications and it has the ability to capture up to several hundred images of different wavelengths offering relevant spectral signatures. To use hyperspectral imaging for in vivo monitoring and diagnosis of the internal body cavities, a snapshot hyperspectral video-endoscope is required. However, such reported systems provide only about 50 wavelengths. We have developed a four-dimensional snapshot hyperspectral video-endoscope with a spectral range of 400-1000 nm, which can detect 756 wavelengths for imaging, significantly more than such systems. Capturing the three-dimensional datacube sequentially gives the fourth dimension. All these are achieved through a flexible two-dimensional to one-dimensional fiber bundle. The potential of this custom designed and fabricated compact biomedical probe is demonstrated by imaging phantom tissue samples in reflectance and fluorescence imaging modalities. It is envisaged that this novel concept and developed probe will contribute significantly towards diagnostic in vivo biomedical imaging in the near future. PMID:27044607

  11. Hyperspectral Imaging and Spectroscopy of Fluorescently Coupled Acyl-CoA: Cholesterol Acyltransferase in Insect Cells

    NASA Technical Reports Server (NTRS)

    Malak, H.; Mahtani, H.; Herman, P.; Vecer, J.; Lu, X.; Chang, T. Y.; Richmond, Robert C.; Whitaker, Ann F. (Technical Monitor)

    2001-01-01

    A high-performance hyperspectral imaging module with high throughput of light suitable for low-intensity fluorescence microscopic imaging and subsequent analysis, including single-pixel-defined emission spectroscopy, was tested on Sf21 insect cells expressing green fluorescence associated with recombinant green fluorescent protein linked or not with the membrane protein acyl-CoA:cholesterol acyltransferase. The imager utilized the phenomenon of optical activity as a new technique providing information over a spectral range of 220-1400 nm, and was inserted between the microscope and an 8-bit CCD video-rate camera. The resulting fluorescence image did not introduce observable image aberrations. The images provided parallel acquisition of well resolved concurrent spatial and spectral information such that fluorescence associated with green fluorescent protein alone was demonstrated to be diffuse within the Sf21 insect cell, and that green fluorescence associated with the membrane protein was shown to be specifically concentrated within regions of the cell cytoplasm. Emission spectra analyzed from different regions of the fluorescence image showed blue shift specific for the regions of concentration associated with the membrane protein.

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

    NASA Astrophysics Data System (ADS)

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

    2006-05-01

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

  13. [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. PMID:27209760

  14. Cotton crop spectral imaging analysis: a web-based hyperspectral synthetic imagery simulation system

    NASA Astrophysics Data System (ADS)

    Alarcon, Vladimir J.; Sassenrath, Gretchen F.

    2004-11-01

    The development of spectral libraries for specific vegetation species and soils is useful for identifying different physiological or physical-chemical characteristics. Usually, spectral libraries are provided as a data-base add-in of current commercial software used for analyzing hyperspectral imagery. The use of those databases requires installation of the software in the user"s machine for either visualizing or using the spectral libraries. There are also spectral libraries available on the web but the data is static and partitioned by spectrum of vegetation or soil because the size of the files of actual hyperspectral images precludes it"s publication on the web. In this paper, a web-based simulation environment for generating hyperspectral synthetic imagery of cotton plots is presented. The system was developed using Java and is based on a previous synthetic imagery program1. The mathematical and numerical formulation of the model is briefly sketched. The core computing components of the simulation environment were written in C for their computational efficiency. The emerging Java Native Interface (JNI) technique and standard Java techniques were used to design a user-friendly simulator. The simulation system provides interactive user control and real time visualization of the resulting hyperspectral image through standard web browsers. It shows potential for providing web-based hyperspectral libraries, in the form of images, for public use.

  15. Analysis of pregerminated barley using hyperspectral image analysis.

    PubMed

    Arngren, Morten; Hansen, Per Waaben; Eriksen, Birger; Larsen, Jan; Larsen, Rasmus

    2011-11-01

    Pregermination is one of many serious degradations to barley when used for malting. A pregerminated barley kernel can under certain conditions not regerminate and is reduced to animal feed of lower quality. Identifying pregermination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pregerminated barley include visual approaches, e.g. to identify the root sprout, or using an embryo staining method, which use a time-consuming procedure. We present an approach using a near-infrared (NIR) hyperspectral imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel's lack of germination and is unable to identify dormancy, kernel damage etc. The analysis is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised classification framework based on a set of extracted features insensitive to the kernel orientation. An out-of-sample classification error of 32% (CI(95%): 29-35%) is obtained for single kernels when grouped into the three categories, and an error of 3% (CI(95%): 0-15%) is achieved on a bulk kernel level. The model provides class probabilities for each kernel, which can assist in achieving homogeneous germination profiles. This research can further be developed to establish an automated and faster procedure as an alternative to the standard procedures for pregerminated barley.

  16. Hyperspectral Imaging for Burn Depth Assessment in an Animal Model

    PubMed Central

    Chin, Michael S.; Babchenko, Oksana; Lujan-Hernandez, Jorge; Nobel, Lisa; Ignotz, Ronald; Lalikos, Janice F.

    2015-01-01

    Abstract Background: Differentiating between superficial and deep-dermal (DD) burns remains challenging. Superficial-dermal burns heal with conservative treatment; DD burns often require excision and skin grafting. Decision of surgical treatment is often delayed until burn depth is definitively identified. This study’s aim is to assess the ability of hyperspectral imaging (HSI) to differentiate burn depth. Methods: Thermal injury of graded severity was generated on the dorsum of hairless mice with a heated brass rod. Perfusion and oxygenation parameters of injured skin were measured with HSI, a noninvasive method of diffuse reflectance spectroscopy, at 2 minutes, 1, 24, 48 and 72 hours after wounding. Burn depth was measured histologically in 12 mice from each burn group (n = 72) at 72 hours. Results: Three levels of burn depth were verified histologically: intermediate-dermal (ID), DD, and full-thickness. At 24 hours post injury, total hemoglobin (tHb) increased by 67% and 16% in ID and DD burns, respectively. In contrast, tHb decreased to 36% of its original levels in full-thickness burns. Differences in deoxygenated and tHb among all groups were significant (P < 0.001) at 24 hours post injury. Conclusions: HSI was able to differentiate among 3 discrete levels of burn injury. This is likely because of its correlation with skin perfusion: superficial burn injury causes an inflammatory response and increased perfusion to the burn site, whereas deeper burns destroy the dermal microvasculature and a decrease in perfusion follows. This study supports further investigation of HSI in early burn depth assessment. PMID:26894016

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

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

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

  18. [Visualization of the chilling storage time for turbot flesh based on hyperspectral imaging technique].

    PubMed

    Zhu, Feng-Le; Zhang, Hai-Liang; Shao, Yong-Ni; He, Yong

    2014-07-01

    This study proposed a new method using visible and near infrared (Vis/NIR) hyperspectral imaging for the detection and visualization of the chilling storage time for turbot flesh rapid and nondestructively. A total of 160 fish samples with 8 different storage days were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROD inside each image. Partial least squares regression (PLSR) was applied as calibration method to correlate the spectral data and storage time for the 120 samples in calibration set. Then the PLSR model was used to predict the storage time for the 40 prediction samples, which achieved accurate results with determination coefficient (R2) of 0.966 2 and root mean square error of prediction (RMSEP) of 0.679 9 d. Finally, the storage time of each pixel in the hyperspectral images for all prediction samples was predicted and displayed in different colors for visualization based on pseudo-color images with the aid of an IDL program. The results indicated that hyperspectral imaging technique combined with chemometrics and image processing allows the determination and visualization of the chilling storage time for fish, displaying fish freshness status and distribution vividly and laying a foundation for the automatic processing of aquatic products.

  19. D Hyperspectral Frame Imager Camera Data in Photogrammetric Mosaicking

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  20. Research on algorithm for infrared hyperspectral imaging Fourier transform spectrometer technology

    NASA Astrophysics Data System (ADS)

    Wan, Lifang; Chen, Yan; Liao, Ningfang; Lv, Hang; He, Shufang; Li, Yasheng

    2015-08-01

    This paper reported the algorithm for Infrared Hyperspectral Imaging Radiometric Spectrometer Technology. Six different apodization functions are been used and compared, and the phase corrected technologies of Forman is researched and improved, fast fourier transform(FFT)is been used in this paper instead of the linear convolution to reduce the quantity of computation.The interferograms is achieved by the Infrared Hyperspectral Imaging Radiometric Spectrometer which are corrected and rebuilded by the improved algorithm, this algorithm reduce the noise and accelerate the computing speed with the higher accuracy of spectrometers.

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

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  2. Combined optical coherence tomography and hyper-spectral imaging using a double clad fiber coupler

    NASA Astrophysics Data System (ADS)

    Guay-Lord, Robin; Lurie, Kristen L.; Attendu, Xavier; Mageau, Lucas; Godbout, Nicolas; Ellerbee Bowden, Audrey K.; Strupler, Mathias; Boudoux, Caroline

    2016-03-01

    This proceedings shows the combination of Optical Coherence Tomography (OCT) and Hyper-Spectral Imaging (HSI) using a double-clad optical fiber. The single mode core of the fiber is used to transmit OCT signals, while the cladding, with its large collection area, provides an efficient way to capture the reflectance spectrum of the sample. The combination of both methods enables three-dimensional acquisition of sample morphology with OCT, enhanced by the molecular information contained in its hyper-spectral image. We believe that the combination of these techniques could result in endoscopes with enhanced tissue identification capability.

  3. Comparisom of Wavelet-Based and Hht-Based Feature Extraction Methods for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Huang, X.-M.; Hsu, P.-H.

    2012-07-01

    Hyperspectral images, which contain rich and fine spectral information, can be used to identify surface objects and improve land use/cover classification accuracy. Due to the property of high dimensionality of hyperspectral data, traditional statistics-based classifiers cannot be directly used on such images with limited training samples. This problem is referred as "curse of dimensionality". The commonly used method to solve this problem is dimensionality reduction, and feature extraction is used to reduce the dimensionality of hyperspectral images more frequently. There are two types of feature extraction methods. The first type is based on statistical property of data. The other type is based on time-frequency analysis. In this study, the time-frequency analysis methods are used to extract the features for hyperspectral image classification. Firstly, it has been proven that wavelet-based feature extraction provide an effective tool for spectral feature extraction. On the other hand, Hilbert-Huang transform (HHT), a relative new time-frequency analysis tool, has been widely used in nonlinear and nonstationary data analysis. In this study, wavelet transform and HHT are implemented on the hyperspectral data for physical spectral analysis. Therefore, we can get a small number of salient features, reduce the dimensionality of hyperspectral images and keep the accuracy of classification results. An AVIRIS data set is used to test the performance of the proposed HHT-based feature extraction methods; then, the results are compared with wavelet-based feature extraction. According to the experiment results, HHT-based feature extraction methods are effective tools and the results are similar with wavelet-based feature extraction methods.

  4. RVC-CAL library for endmember and abundance estimation in hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Lazcano López, R.; Madroñal Quintín, D.; Juárez Martínez, E.; Sanz Álvaro, C.

    2015-10-01

    Hyperspectral imaging (HI) collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. Although this technology was initially developed for remote sensing and earth observation, its multiple advantages - such as high spectral resolution - led to its application in other fields, as cancer detection. However, this new field has shown specific requirements; for instance, it needs to accomplish strong time specifications, since all the potential applications - like surgical guidance or in vivo tumor detection - imply real-time requisites. Achieving this time requirements is a great challenge, as hyperspectral images generate extremely high volumes of data to process. Thus, some new research lines are studying new processing techniques, and the most relevant ones are related to system parallelization. In that line, this paper describes the construction of a new hyperspectral processing library for RVC-CAL language, which is specifically designed for multimedia applications and allows multithreading compilation and system parallelization. This paper presents the development of the required library functions to implement two of the four stages of the hyperspectral imaging processing chain--endmember and abundances estimation. The results obtained show that the library achieves speedups of 30%, approximately, comparing to an existing software of hyperspectral images analysis; concretely, the endmember estimation step reaches an average speedup of 27.6%, which saves almost 8 seconds in the execution time. It also shows the existence of some bottlenecks, as the communication interfaces among the different actors due to the volume of data to transfer. Finally, it is shown that the library considerably simplifies the implementation process. Thus, experimental results show the potential of a RVC-CAL library for analyzing hyperspectral images in real-time, as it provides enough resources to study the system performance.

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

    PubMed Central

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

    2014-01-01

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

  6. 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. The Narrow Band AOTF Based Hyperspectral Microscopic Imaging on the Rat Skin Stratum Configuration

    NASA Astrophysics Data System (ADS)

    Zhang, C.; Wang, H.; Huang, J.; Gao, Q.

    2014-08-01

    A noncollinear acousto-optic tunable filter (AOTF) was designed with a comprehensive treatment of the properties of TeO2 as an acoustooptic (A-O) material. The results of optical testing validated that it predicted the performance of the designed AOTF. The bandwidth of the AOTF was very narrow in the visible light range. The high spectral resolution of AOTF was useful in practical applications to the hyperspectral imaging. The experimentally observed spectral pattern of the diffracted light was nearly the same as the theoretical result. The measured tuning relationship between the diffracted central optical wavelength and acoustic frequency was in accordance with the theoretical prediction. It demonstrates the accuracy of our design theory. Furthermore, by selecting the AOTF as the spectroscopic element, a hyperspectral microscopic imaging system was designed. The hyperspectral microscopic images of the rat skin tissue under the different optical center wavelength were acquired. The experimental results indicated that the imaging performance was satisfactory. The stratums of the rat skin can be clearly distinguished. The inner details of the epidermis and the corium can be shown on the hyperspectral microscopic images. Some differences also can be found by the comparison of the hyperspectal images under the different optical wavelengths. The study indicated the applicability and the advantage of our system on biomedicine area.

  8. Hyperspectral visible-near infrared imaging for the detection of waxed rice

    NASA Astrophysics Data System (ADS)

    Zhao, Mantong

    2014-11-01

    Presently, unscrupulous traders in the market use the industrial wax to wax the rice. The industrial wax is a particularly hazardous substance. Visible-near infrared hyperspectral images (400-1,000 nm) can be used for the detection of the waxed rice and the non-waxed rice. This study was carried out to find effective testing methods based on the visible-near infrared imaging spectrometry to detect whether the rice was waxed or not. An imaging spectroscopy system was assembled to acquire hyperspectral images from 80 grains of waxed rice and 80 grains of non-waxed rice over visible and near infrared spectral region. Spectra of 100 grains of rice were analyzed by principal component analysis (PCA) to extract the information of hyperspectral images. PCA provides an effective compressed representation of the spectral signal of each pixel in the spectral domain. We used PCA to acquire the effective wavelengths from the spectra. Based on the effective wavelengths, the predict models were set up by using partial least squares (PLS) analysis and linear discriminant analysis (LDA). Also, compared with the PLS of 80% for the waxed rice and 86.7% for the non-waxed rice detection rate, LDA gives 93.3% and 96.7% detection rate. The results demonstrated that the LDA could detect the waxed rice better, while illustrating the hyperspectral imaging technique with the visible-near infrared region could be a reliable method for the waxed rice detection.

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

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  11. MEMS scanner enabled real-time depth sensitive hyperspectral imaging of biological tissue

    PubMed Central

    Wang, Youmin; Bish, Sheldon; Tunnell, James W; Zhang, Xiaojing

    2010-01-01

    We demonstrate a hyperspectral and depth sensitive diffuse optical imaging microsystem, where fast scanning is provided by a CMOS compatible 2-axis MEMS mirror. By using lissajous scanning patterns, large field-of-view (FOV) of 1.2cm x 1.2cm images with lateral resolution of 100µm can be taken at 1.3 frames-per-second (fps). Hyperspectral and depth-sensitive images were acquired on tissue simulating phantom samples containing quantum dots (QDs) patterned at various depths in Polydimethylsiloxane (PDMS). Device performance delivers 6 nm spectral resolution and 0.43 wavelengths per second acquisition speed. A sample of porcine epithelium with subcutaneously placed QDs was also imaged. Images of the biological sample were processed by spectral unmixing in order to qualitatively separate chromophores in the final images and demonstrate spectral performance of the imaging system. PMID:21164757

  12. MEMS scanner enabled real-time depth sensitive hyperspectral imaging of biological tissue.

    PubMed

    Wang, Youmin; Bish, Sheldon; Tunnell, James W; Zhang, Xiaojing

    2010-11-01

    We demonstrate a hyperspectral and depth sensitive diffuse optical imaging microsystem, where fast scanning is provided by a CMOS compatible 2-axis MEMS mirror. By using lissajous scanning patterns, large field-of-view (FOV) of 1.2 cmx1.2 cm images with lateral resolution of 100 µm can be taken at 1.3 frames-per-second (fps). Hyperspectral and depth-sensitive images were acquired on tissue simulating phantom samples containing quantum dots (QDs) patterned at various depths in Polydimethylsiloxane (PDMS). Device performance delivers 6 nm spectral resolution and 0.43 wavelengths per second acquisition speed. A sample of porcine epithelium with subcutaneously placed QDs was also imaged. Images of the biological sample were processed by spectral unmixing in order to qualitatively separate chromophores in the final images and demonstrate spectral performance of the imaging system. PMID:21164757

  13. Shortwave infrared hyperspectral Imaging for cotton foreign matter classification

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Various types of cotton foreign matter seriously reduce the commercial value of cotton lint and further degrade the quality of textile products for consumers. This research was aimed to investigate the potential of a non-contact technique, i.e., liquid crystal tunable filter (LCTF) hyperspectral ima...

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

    NASA Astrophysics Data System (ADS)

    Lim, Hoong-Ta; Murukeshan, Vadakke Matham

    2015-04-01

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

  15. [Spectral curve shape feature-based hyperspectral remote sensing image retrieval].

    PubMed

    Li, Fei; Zhou, Cheng-Hu; Chen, Rong-Guo

    2008-11-01

    With the rapid development of technology of sensors and data transmission, using all kinds of airplane sensors and satellite sensors, the authors can get different voluminous remote sensing image data of earth. Those voluminous remote sensing image data bring problems of data storage and management. It is becoming increasingly necessary to retrieve some information the authors need from those voluminous image data. Image retrieval was proposed by CHANG firstly in 1980 and can be regarded as expansion of traditional information retrieval. Oriented to the demands of efficient retrieval for voluminous remote sensing image, and considering that there are many bands in hyperspectral remote sensing image, the authors first analyzed image distance function and similarity measure in image retrieval. The most crucial issues in retrieval are spectral features extraction and similarity measure. In the present paper, the authors used classical Douglas-Peucker algorithm (hereinafter referred to DP algorithm) for curve simplification to extract shape features of spectral curve, in order to speed up hyperspectral remote sensing image retrieval. And the authors proposed a new method of spectral curve and remote sensing image retrieval, called Douglas-Peucker Spectral Retrieval algorithm (hereinafter referred to DPSR algorithm). Spectral shape features were used in image retrieval. DPSR used features of spectral curve, reduced the computation amount, realized match and retrieval efficiently, and is suitable for spectral curve retrieval in hyperspectral remote sensing image. The authors selected four ground features (grass, apple garden, grape garden and pond) in OMISI hyperspectral remote sensing image to compute similarity measure results, in order to test the effect of DPSR algorithm. Compared with traditional analysis method such as spectral angle match (SAM) and spectral information divergence (SID), DPSR can maintain high precision of results with less amount of computation

  16. Method for quantifying image quality in push-broom hyperspectral cameras

    NASA Astrophysics Data System (ADS)

    Høye, Gudrun; Løke, Trond; Fridman, Andrei

    2015-05-01

    We propose a method for measuring and quantifying image quality in push-broom hyperspectral cameras in terms of spatial misregistration caused by keystone and variations in the point spread function (PSF) across spectral channels, and image sharpness. The method is suitable for both traditional push-broom hyperspectral cameras where keystone is corrected in hardware and cameras where keystone is corrected in postprocessing, such as resampling and mixel cameras. We show how the measured camera performance can be presented graphically in an intuitive and easy to understand way, comprising both image sharpness and spatial misregistration in the same figure. For the misregistration, we suggest that both the mean standard deviation and the maximum value for each pixel are shown. We also suggest how the method could be expanded to quantify spectral misregistration caused by the smile effect and corresponding PSF variations. Finally, we have measured the performance of two HySpex SWIR 384 cameras using the suggested method. The method appears well suited for assessing camera quality and for comparing the performance of different hyperspectral imagers and could become the future standard for how to measure and quantify the image quality of push-broom hyperspectral cameras.

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

    PubMed

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

    2012-12-01

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

  18. 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.

  19. Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery.

    PubMed

    Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W; Chen, Zhuo Georgia; Fei, Baowei

    2015-01-01

    Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor. PMID:26720879

  20. Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

    NASA Astrophysics Data System (ADS)

    Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W.; Chen, Zhuo Georgia; Fei, Baowei

    2015-12-01

    Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

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

    SciTech Connect

    Love, Steven P

    2008-01-01

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

  2. [The Classification of Wheat Varieties Based on Near Infrared Hyperspectral Imaging and Information Fusion].

    PubMed

    Dong, Gao; Guo, Jiani; Wang, Cheng; Chen, Zi-long; Zheng, Ling; Zhu, Da-zhou

    2015-12-01

    Hyperspectral imaging technology has great potential in the identification of crop varieties because it contains both image information and spectral information for the object. But so far most studies only used the spectral information, the image information has not been effectively utilized. In this study, hyperspectral images of single seed of three types including strong gluten wheat, medium gluten wheat, and weak gluten wheat were collected by near infrared hyperspectra imager, 12 morphological characteristics such as length, width, rectangularity, circularity and eccentricity were extracted, the average spectra of endosperm and embryo were acquired by the mask which was created by image segmentation. Partial least squares discriminant analysis (PLADA) and least squares support vector machine (LSSVM) were used to construct the classification model with image information, results showed that the binary classification accuracy between strong gluten wheat and weak gluten wheat could achieve 98%, for strong gluten wheat and medium gluten wheat, it was only 74.22%, which indicated that hyperspectral images could reflect the differences of varieties, but the accuracy might be poor when recognizing the varieties just by image information. Soft independent modeling of class analogy (SIMCA), PLSDA and LSSVM were used to established the classification model with spectral information, the classification effect of endosperm is slightly better than the embryo, it demonstrated that the grain shape could influence the classification accuracy. Then, we fused the spectral and image information, SIMCA, PLSDA and LSSVM were used to established the identification model, the fusion model showed better performance than the individual image model and spectral model, the classification accuracy which used the PLSDA raise from 96.67% to 98.89%, it showed that digging the morphological and spectral characteristics of the hyperspectral image could effectively improve the classification

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

    PubMed

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

    2014-08-01

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

  4. [Determination of Soluble Solid Content in Strawberry Using Hyperspectral Imaging Combined with Feature Extraction Methods].

    PubMed

    Ding, Xi-bin; Zhang, Chu; Liu, Fei; Song, Xing-lin; Kong, Wen-wen; He, Yong

    2015-04-01

    Hyperspectral imaging combined with feature extraction methods were applied to determine soluble sugar content (SSC) in mature and scatheless strawberry. Hyperspectral images of 154 strawberries covering the spectral range of 874-1,734 nm were captured and the spectral data were extracted from the hyperspectral images, and the spectra of 941~1,612 nm were preprocessed by moving average (MA). Nineteen samples were defined as outliers by the residual method, and the remaining 135 samples were divided into the calibration set (n = 90) and the prediction set (n = 45). Successive projections algorithm (SPA), genetic algorithm partial least squares (GAPLS) combined with SPA, weighted regression coefficient (Bw) and competitive adaptive reweighted sampling (CARS) were applied to select 14, 17, 24 and 25 effective wavelengths, respectively. Principal component analysis (PCA) and wavelet transform (WT) were applied to extract feature information with 20 and 58 features, respectively. PLS models were built based on the full spectra, the effective wavelengths and the features, respectively. All PLS models obtained good results. PLS models using full-spectra and features extracted by WT obtained the best results with correlation coefficient of calibration (r(c)) and correlation coefficient of prediction (r(p)) over 0.9. The overall results indicated that hyperspectral imaging combined with feature extraction methods could be used for detection of SSC in strawberry. PMID:26197594

  5. Detection of wheat powdery mildew by differentiating background factors using hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Accurate assessment of crop disease severities is the key for precision application of pesticides to prevent disease infestation. In-situ hyperspectral imaging technology can provide high-resolution imagery with spectra for rapid identification of crop disease and determining disease infestation pat...

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  12. Development of a Micro-UAV Hyperspectral Imaging Platform for Assessing Hydrogeological Hazards

    NASA Astrophysics Data System (ADS)

    Chen, Z.; Alabsi, M.

    2015-12-01

    The exacerbating global weather changes have cast significant impacts upon the proportion of water supplied to agriculture. Therefore, one of the 21stCentury Grant Challenges faced by global population is securing water for food. However, the soil-water behavior in an agricultural environment is complex; among others, one of the key properties we recognize is water repellence or hydrophobicity, which affects many hydrogeological and hazardous conditions such as excessive water infiltration, runoff, and soil erosion. Under a US-Israel research program funded by USDA and BARD at Israel, we have proposed the development of a novel micro-unmanned aerial vehicle (micro-UAV or drone) based hyperspectral imaging platform for identifying and assessing soil repellence at low altitudes with enhanced flexibility, much reduced cost, and ultimately easy use. This aerial imaging system consists of a generic micro-UAV, hyperspectral sensor aided by GPS/IMU, on-board computing units, and a ground station. The target benefits of this system include: (1) programmable waypoint navigation and robotic control for multi-view imaging; (2) ability of two- or three-dimensional scene reconstruction for complex terrains; and (3) fusion with other sensors to realize real-time diagnosis (e.g., humidity and solar irradiation that may affect soil-water sensing). In this talk we present our methodology and processes in integration of hyperspectral imaging, on-board sensing and computing, hyperspectral data modeling, and preliminary field demonstration and verification of the developed prototype.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  16. 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.

  17. Evaluation of the effectiveness of laser crust removal on granites by means of hyperspectral imaging techniques

    NASA Astrophysics Data System (ADS)

    Pozo-Antonio, J. S.; Fiorucci, M. P.; Ramil, A.; López, A. J.; Rivas, T.

    2015-08-01

    In this paper, we present a study of the application of the hyperspectral imaging technique in order to non-destructively evaluate the laser cleaning of the biogenic patina and the sulphated black crust developed on a fine-grained granite used in the construction of Cultural Heritage in NW Spain. The grained polymineral texture of the granite hinders the adjustment of laser irradiation parameters during the cleaning, and therefore the in situ process control. The cleaning was performed with a nanosecond pulsed Nd:YVO4 laser at 355 nm. A hyperspectral camera was used to in situ assess the effectiveness of cleaning by recording images of the rock surfaces before and during the laser treatment. Different analytical techniques were used to test the ability of the hyperspectral imaging technique to evaluate the cleaning process of the granite samples: optical microscopy, scanning electron microscopy with energy dispersive X-ray spectrometry (SEM - EDX), Fourier transform infrared spectroscopy (FTIR) and spectrophotometer colour measurements. The results indicated that hyperspectral imaging technique is a reliable and more affordable technique to in situ evaluate the process of laser cleaning of the biogenic patina and the sulphated black crust in fine-grained granites.

  18. Quantitative analysis of melamine in milk powders using near-infrared hyperspectral imaging and band ratio

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Since 2008, the detection of the adulterant melamine (2,4,6-triamino-1,3,5-triazine) in food products has become the subject of research due to several food safety scares. Near-infrared (NIR) hyperspectral imaging offers great potential for food safety and quality research because it combines the fe...

  19. Multivariate Curve Resolution Applied to Hyperspectral Imaging Analysis of Chocolate Samples.

    PubMed

    Zhang, Xin; de Juan, Anna; Tauler, Romà

    2015-08-01

    This paper shows the application of Raman and infrared hyperspectral imaging combined with multivariate curve resolution (MCR) to the analysis of the constituents of commercial chocolate samples. The combination of different spectral data pretreatment methods allowed decreasing the high fluorescent Raman signal contribution of whey in the investigated chocolate samples. Using equality constraints during MCR analysis, estimations of the pure spectra of the chocolate sample constituents were improved, as well as their relative contributions and their spatial distribution on the analyzed samples. In addition, unknown constituents could be also resolved. White chocolate constituents resolved from Raman hyperspectral image indicate that, at macro scale, sucrose, lactose, fat, and whey constituents were intermixed in particles. Infrared hyperspectral imaging did not suffer from fluorescence and could be applied for white and milk chocolate. As a conclusion of this study, micro-hyperspectral imaging coupled to the MCR method is confirmed to be an appropriate tool for the direct analysis of the constituents of chocolate samples, and by extension, it is proposed for the analysis of other mixture constituents in commercial food samples.

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

    PubMed

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

    2014-01-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    PubMed

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

    2015-04-01

    Hyperspectral imaging is a technology that is beginning to occupy an important place in medical research with good prospects in future clinical applications. We evaluated the role of hyperspectral imaging in association with a mixture-tuned matched filtering method in the characterization of open wounds. The methodology and the processing steps of the hyperspectral image that have been performed in order to obtain the most useful information about the wound are described in detail. Correlations between the hyperspectral image and clinical examination are described, leading to a pattern that permits relative evaluation of the square area of the wound and its different components in comparison with the surrounding normal skin. Our results showed that the described method can identify different types of tissues that are present in the wounded area and can objectively measure their respective abundance, which proves its value in wound characterization. In conclusion, the method that was described in this preliminary case presentation shows promising results, but needs further evaluation in order to become a reliable and useful tool. PMID:25867619

  3. Hyperspectral imaging with in-line interferometric femtosecond stimulated Raman scattering spectroscopy

    NASA Astrophysics Data System (ADS)

    Dobner, Sven; Fallnich, Carsten

    2014-02-01

    We present the hyperspectral imaging capabilities of in-line interferometric femtosecond stimulated Raman scattering. The beneficial features of this method, namely, the improved signal-to-background ratio compared to other applicable broadband stimulated Raman scattering methods and the simple experimental implementation, allow for a rather fast acquisition of three-dimensional raster-scanned hyperspectral data-sets, which is shown for PMMA beads and a lipid droplet in water as a demonstration. A subsequent application of a principle component analysis displays the chemical selectivity of the method.

  4. Hyperspectral imaging with in-line interferometric femtosecond stimulated Raman scattering spectroscopy.

    PubMed

    Dobner, Sven; Fallnich, Carsten

    2014-02-28

    We present the hyperspectral imaging capabilities of in-line interferometric femtosecond stimulated Raman scattering. The beneficial features of this method, namely, the improved signal-to-background ratio compared to other applicable broadband stimulated Raman scattering methods and the simple experimental implementation, allow for a rather fast acquisition of three-dimensional raster-scanned hyperspectral data-sets, which is shown for PMMA beads and a lipid droplet in water as a demonstration. A subsequent application of a principle component analysis displays the chemical selectivity of the method.

  5. Toward prediction of hyperspectral target detection performance after lossy image compression

    NASA Astrophysics Data System (ADS)

    Kaufman, Jason R.; Vongsy, Karmon M.; Dill, Jeffrey C.

    2016-05-01

    Hyperspectral imagery (HSI) offers numerous advantages over traditional sensing modalities with its high spectral content that allows for classification, anomaly detection, target discrimination, and change detection. However, this imaging modality produces a huge amount of data, which requires transmission, processing, and storage resources; hyperspectral compression is a viable solution to these challenges. It is well known that lossy compression of hyperspectral imagery can impact hyperspectral target detection. Here we examine lossy compressed hyperspectral imagery from data-centric and target-centric perspectives. The compression ratio (CR), root mean square error (RMSE), the signal to noise ratio (SNR), and the correlation coefficient are computed directly from the imagery and provide insight to how the imagery has been affected by the lossy compression process. With targets present in the imagery, we perform target detection with the spectral angle mapper (SAM) and adaptive coherence estimator (ACE) and evaluate the change in target detection performance by examining receiver operating characteristic (ROC) curves and the target signal-to-clutter ratio (SCR). Finally, we observe relationships between the data- and target-centric metrics for selected visible/near-infrared to shortwave infrared (VNIR/SWIR) HSI data, targets, and backgrounds that motivate potential prediction of change in target detection performance as a function of compression ratio.

  6. Toward prediction of hyperspectral target detection performance after lossy image compression

    NASA Astrophysics Data System (ADS)

    Kaufman, Jason R.; Vongsy, Karmon M.; Dill, Jeffrey C.

    2016-05-01

    Hyperspectral imagery (HSI) offers numerous advantages over traditional sensing modalities with its high spectral content that allows for classification, anomaly detection, target discrimination, and change detection. However, this imaging modality produces a huge amount of data, which requires transmission, processing, and storage resources; hyperspectral compression is a viable solution to these challenges. It is well known that lossy compression of hyperspectral imagery can impact hyperspectral target detection. Here we examine lossy compressed hyperspectral imagery from data-centric and target-centric perspectives. The compression ratio (CR), root mean square error (RMSE), the signal to noise ratio (SNR), and the correlation coefficient are computed directly from the imagery and provide insight to how the imagery has been affected by the lossy compression process. With targets present in the imagery, we perform target detection with the spectral angle mapper (SAM) and adaptive coherence estimator (ACE) and evaluate the change in target detection performance by examining receiver operating characteristic (ROC) curves and the target signal-to-clutter ratio (SCR). Finally, we observe relationships between the data- and target-centric metrics for selected visible/near-infrared to shortwave infrared (VNIR/SWIR) HSI data, targets, and backgrounds that motivate potential prediction of change in target detection performance as a function of compression ratio.

  7. Complex patterns in fossilized stromatolites revealed by hyperspectral imaging (400-2496 nm).

    PubMed

    Murphy, R J; Van Kranendonk, M J; Kelloway, S J; Wainwright, I E

    2016-09-01

    Hyperspectral imaging (400-2496 nm) was used to quantitatively map surface textures and compositional variations in stromatolites to determine whether complexity of textures could be used as evidence to support biogenicity in the absence of preserved biomarkers. Four samples of 2.72-2.4 Ga stromatolites from a variety of settings, encompassing marine and lacustrine environments, were selected for hyperspectral imaging. Images of the sawn surfaces of samples were processed to identify reflectance and mineral absorption features and quantify their intensity (as an index of mineral abundance) using automated feature extraction. Amounts of ferrous iron were quantified using a ratio of reflectance at 1650 and 1299 nm. Visible near infrared imagery (400-970 nm) did not reveal additional textural patterns to those obtained from visual inspection. Shortwave infrared imagery (1000-2496 nm), however, revealed complex laminar and convoluted patterns, including a distinctive texture of sharp peaks and broad, low troughs in one sample, similar to living tufted microbial mats. Spectral analysis revealed another sample to be composed of dolomite. Two other samples were dominated by calcite or chlorite ± illite. Large variations in amounts of ferrous iron were found, but ferric iron was exclusively located in the oxidation crust. Hyperspectral imaging revealed large differences between parts of a sample of biogenic and non-biogenic origin. The former was characterized by calcite with varying amounts of ferrous iron, distributed in lenticular, convoluted patterns; the latter by Mg-Fe chlorite with large amounts of aluminium silicate, distributed as fine laminar layers. All minerals identified by hyperspectral imaging were confirmed by thin section petrography and XRD analyses. Spatial statistics generated from quantitative minerals maps showed different patterns between these different parts of the sample. Thus, hyperspectral imaging was shown to be a powerful tool for

  8. Complex patterns in fossilized stromatolites revealed by hyperspectral imaging (400-2496 nm).

    PubMed

    Murphy, R J; Van Kranendonk, M J; Kelloway, S J; Wainwright, I E

    2016-09-01

    Hyperspectral imaging (400-2496 nm) was used to quantitatively map surface textures and compositional variations in stromatolites to determine whether complexity of textures could be used as evidence to support biogenicity in the absence of preserved biomarkers. Four samples of 2.72-2.4 Ga stromatolites from a variety of settings, encompassing marine and lacustrine environments, were selected for hyperspectral imaging. Images of the sawn surfaces of samples were processed to identify reflectance and mineral absorption features and quantify their intensity (as an index of mineral abundance) using automated feature extraction. Amounts of ferrous iron were quantified using a ratio of reflectance at 1650 and 1299 nm. Visible near infrared imagery (400-970 nm) did not reveal additional textural patterns to those obtained from visual inspection. Shortwave infrared imagery (1000-2496 nm), however, revealed complex laminar and convoluted patterns, including a distinctive texture of sharp peaks and broad, low troughs in one sample, similar to living tufted microbial mats. Spectral analysis revealed another sample to be composed of dolomite. Two other samples were dominated by calcite or chlorite ± illite. Large variations in amounts of ferrous iron were found, but ferric iron was exclusively located in the oxidation crust. Hyperspectral imaging revealed large differences between parts of a sample of biogenic and non-biogenic origin. The former was characterized by calcite with varying amounts of ferrous iron, distributed in lenticular, convoluted patterns; the latter by Mg-Fe chlorite with large amounts of aluminium silicate, distributed as fine laminar layers. All minerals identified by hyperspectral imaging were confirmed by thin section petrography and XRD analyses. Spatial statistics generated from quantitative minerals maps showed different patterns between these different parts of the sample. Thus, hyperspectral imaging was shown to be a powerful tool for

  9. Spectral-spatial classification combined with diffusion theory based inverse modeling of hyperspectral images

    NASA Astrophysics Data System (ADS)

    Paluchowski, Lukasz A.; Bjorgan, Asgeir; Nordgaard, Hâvard B.; Randeberg, Lise L.

    2016-02-01

    Hyperspectral imagery opens a new perspective for biomedical diagnostics and tissue characterization. High spectral resolution can give insight into optical properties of the skin tissue. However, at the same time the amount of collected data represents a challenge when it comes to decomposition into clusters and extraction of useful diagnostic information. In this study spectral-spatial classification and inverse diffusion modeling were employed to hyperspectral images obtained from a porcine burn model using a hyperspectral push-broom camera. The implemented method takes advantage of spatial and spectral information simultaneously, and provides information about the average optical properties within each cluster. The implemented algorithm allows mapping spectral and spatial heterogeneity of the burn injury as well as dynamic changes of spectral properties within the burn area. The combination of statistical and physics informed tools allowed for initial separation of different burn wounds and further detailed characterization of the injuries in short post-injury time.

  10. Research on segmentation method of hyperspectral remote sensing images based on probabilistic neural networks

    NASA Astrophysics Data System (ADS)

    Liu, Gang; Liu, Xingjian

    2008-12-01

    Image segmentation is essential for information extraction from remote sensing image, but it remains the lacks of a general mathematical theory, object merging for poor object boundary localization, dealing with object fragmentation and sensitivity of current procedures to noise. This paper focuses on hyper-spectral image segmentation using probabilistic neural networks (PNN). The methodology, implementation and optimization of a PNN are studied, and a constructed PNN is applied to segment hyper-spectral image. The experience demonstrates main advantage of a PNN that it has quick training and learning, gives a measurement of confidence associated with an output, and has the ability to process large data set. It is concluded that the PNN is superior in image segmentation and the obtained results are satisfied.

  11. Hyperspectral imager, from ultraviolet to visible, with a KDP acousto-optic tunable filter.

    PubMed

    Gupta, Neelam; Voloshinov, Vitaly

    2004-05-01

    Hyperspectral imaging in the ultraviolet to visible spectral region has applications in astronomy, biology, chemistry, medical sciences, etc. A novel electronically tunable, random-wavelength access, compact, no-moving-parts, vibration-insensitive, computer-controlled hyperspectral imager operating from 220 to 480 nm with a spectral resolution of 160 cm(-1), e.g., 2 nm at 350 nm, has been developed by use of a KDP acousto-optic tunable filter (AOTF) with an enhanced CCD camera and a pair of crossed calcite Glan-Tayl