Snapshot hyperspectral imaging probe with principal component analysis and confidence ellipse for classification

NASA Astrophysics Data System (ADS)

Lim, Hoong-Ta; Murukeshan, Vadakke Matham

2017-06-01

Hyperspectral imaging combines imaging and spectroscopy to provide detailed spectral information for each spatial point in the image. This gives a three-dimensional spatial-spatial-spectral datacube with hundreds of spectral images. Probe-based hyperspectral imaging systems have been developed so that they can be used in regions where conventional table-top platforms would find it difficult to access. A fiber bundle, which is made up of specially-arranged optical fibers, has recently been developed and integrated with a spectrograph-based hyperspectral imager. This forms a snapshot hyperspectral imaging probe, which is able to form a datacube using the information from each scan. Compared to the other configurations, which require sequential scanning to form a datacube, the snapshot configuration is preferred in real-time applications where motion artifacts and pixel misregistration can be minimized. Principal component analysis is a dimension-reducing technique that can be applied in hyperspectral imaging to convert the spectral information into uncorrelated variables known as principal components. A confidence ellipse can be used to define the region of each class in the principal component feature space and for classification. This paper demonstrates the use of the snapshot hyperspectral imaging probe to acquire data from samples of different colors. The spectral library of each sample was acquired and then analyzed using principal component analysis. Confidence ellipse was then applied to the principal components of each sample and used as the classification criteria. The results show that the applied analysis can be used to perform classification of the spectral data acquired using the snapshot hyperspectral imaging probe.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

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

USDA-ARS?s Scientific Manuscript database

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

Secure and Efficient Transmission of Hyperspectral Images for Geosciences Applications

NASA Astrophysics Data System (ADS)

Carpentieri, Bruno; Pizzolante, Raffaele

2017-12-01

Hyperspectral images are acquired through air-borne or space-borne special cameras (sensors) that collect information coming from the electromagnetic spectrum of the observed terrains. Hyperspectral remote sensing and hyperspectral images are used for a wide range of purposes: originally, they were developed for mining applications and for geology because of the capability of this kind of images to correctly identify various types of underground minerals by analysing the reflected spectrums, but their usage has spread in other application fields, such as ecology, military and surveillance, historical research and even archaeology. The large amount of data obtained by the hyperspectral sensors, the fact that these images are acquired at a high cost by air-borne sensors and that they are generally transmitted to a base, makes it necessary to provide an efficient and secure transmission protocol. In this paper, we propose a novel framework that allows secure and efficient transmission of hyperspectral images, by combining a reversible invisible watermarking scheme, used in conjunction with digital signature techniques, and a state-of-art predictive-based lossless compression algorithm.

3D surface scan of biological samples with a Push-broom Imaging Spectrometer

NASA Astrophysics Data System (ADS)

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

2013-08-01

The food industry is always on the lookout for sensing technologies for rapid and nondestructive inspection of food products. Hyperspectral imaging technology integrates both imaging and spectroscopy into unique imaging sensors. Its application for food safety and quality inspection has made significant progress in recent years. Specifically, hyperspectral imaging has shown its potential for surface contamination detection in many food related applications. Most existing hyperspectral imaging systems use pushbroom scanning which is generally used for flat surface inspection. In some applications it is desirable to be able to acquire hyperspectral images on circular objects such as corn ears, apples, and cucumbers. Past research describes inspection systems that examine all surfaces of individual objects. Most of these systems did not employ hyperspectral imaging. These systems typically utilized a roller to rotate an object, such as an apple. During apple rotation, the camera took multiple images in order to cover the complete surface of the apple. The acquired image data lacked the spectral component present in a hyperspectral image. This paper discusses the development of a hyperspectral imaging system for a 3-D surface scan of biological samples. The new instrument is based on a pushbroom hyperspectral line scanner using a rotational stage to turn the sample. The system is suitable for whole surface hyperspectral imaging of circular objects. In addition to its value to the food industry, the system could be useful for other applications involving 3-D surface inspection.

Thin-film tunable filters for hyperspectral fluorescence microscopy

PubMed Central

Favreau, Peter; Hernandez, Clarissa; Lindsey, Ashley Stringfellow; Alvarez, Diego F.; Rich, Thomas; Prabhat, Prashant

2013-01-01

Abstract. Hyperspectral imaging is a powerful tool that acquires data from many spectral bands, forming a contiguous spectrum. Hyperspectral imaging was originally developed for remote sensing applications; however, hyperspectral techniques have since been applied to biological fluorescence imaging applications, such as fluorescence microscopy and small animal fluorescence imaging. The spectral filtering method largely determines the sensitivity and specificity of any hyperspectral imaging system. There are several types of spectral filtering hardware available for microscopy systems, most commonly acousto-optic tunable filters (AOTFs) and liquid crystal tunable filters (LCTFs). These filtering technologies have advantages and disadvantages. Here, we present a novel tunable filter for hyperspectral imaging—the thin-film tunable filter (TFTF). The TFTF presents several advantages over AOTFs and LCTFs, most notably, a high percentage transmission and a high out-of-band optical density (OD). We present a comparison of a TFTF-based hyperspectral microscopy system and a commercially available AOTF-based system. We have characterized the light transmission, wavelength calibration, and OD of both systems, and have then evaluated the capability of each system for discriminating between green fluorescent protein and highly autofluorescent lung tissue. Our results suggest that TFTFs are an alternative approach for hyperspectral filtering that offers improved transmission and out-of-band blocking. These characteristics make TFTFs well suited for other biomedical imaging devices, such as ophthalmoscopes or endoscopes. PMID:24077519

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.

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng

2017-10-01

Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

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.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models.

PubMed

Cho, Woon; Jang, Jinbeum; Koschan, Andreas; Abidi, Mongi A; Paik, Joonki

2016-11-28

A fundamental limitation of hyperspectral imaging is the inter-band misalignment correlated with subject motion during data acquisition. One way of resolving this problem is to assess the alignment quality of hyperspectral image cubes derived from the state-of-the-art alignment methods. In this paper, we present an automatic selection framework for the optimal alignment method to improve the performance of face recognition. Specifically, we develop two qualitative prediction models based on: 1) a principal curvature map for evaluating the similarity index between sequential target bands and a reference band in the hyperspectral image cube as a full-reference metric; and 2) the cumulative probability of target colors in the HSV color space for evaluating the alignment index of a single sRGB image rendered using all of the bands of the hyperspectral image cube as a no-reference metric. We verify the efficacy of the proposed metrics on a new large-scale database, demonstrating a higher prediction accuracy in determining improved alignment compared to two full-reference and five no-reference image quality metrics. We also validate the ability of the proposed framework to improve hyperspectral face recognition.

Noise properties of a corner-cube Michelson interferometer LWIR hyperspectral imager

NASA Astrophysics Data System (ADS)

Bergstrom, D.; Renhorn, I.; Svensson, T.; Persson, R.; Hallberg, T.; Lindell, R.; Boreman, G.

2010-04-01

Interferometric hyperspectral imagers using infrared focal plane array (FPA) sensors have received increasing interest within the field of security and defence. Setups are commonly based upon either the Sagnac or the Michelson configuration, where the former is usually preferred due to its mechanical robustness. However, the Michelson configuration shows advantages in larger FOV due to better vignetting performance and improved signal-to-noise ratio and cost reduction due to relaxation of beamsplitter specifications. Recently, a laboratory prototype of a more robust and easy-to-align corner-cube Michelson hyperspectral imager has been demonstrated. The prototype is based upon an uncooled bolometric FPA in the LWIR (8-14 μm) spectral band and in this paper the noise properties of this hyperspectral imager are discussed.

Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery

DOE PAGES

Habib, Ayman; Han, Youkyung; Xiong, Weifeng; ...

2016-09-24

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging ismore » based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Lastly, experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.« less

Automated ortho-rectification of UAV-based hyperspectral data over an agricultural field using frame RGB imagery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Habib, Ayman; Han, Youkyung; Xiong, Weifeng

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging ismore » based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Lastly, experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.« less

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.

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.

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.

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

a Band Selection Method for High Precision Registration of Hyperspectral Image

NASA Astrophysics Data System (ADS)

Yang, H.; Li, X.

2018-04-01

During the registration of hyperspectral images and high spatial resolution images, too much bands in a hyperspectral image make it difficult to select bands with good registration performance. Terrible bands are possible to reduce matching speed and accuracy. To solve this problem, an algorithm based on Cram'er-Rao lower bound theory is proposed to select good matching bands in this paper. The algorithm applies the Cram'er-Rao lower bound theory to the study of registration accuracy, and selects good matching bands by CRLB parameters. Experiments show that the algorithm in this paper can choose good matching bands and provide better data for the registration of hyperspectral image and high spatial resolution image.

Research on hyperspectral dynamic scene and image sequence simulation

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Research on the shortwave infrared hyperspectral imaging technology based on Integrated Stepwise filter

NASA Astrophysics Data System (ADS)

Wei, Liqing; Xiao, Xizhong; Wang, Yueming; Zhuang, Xiaoqiong; Wang, Jianyu

2017-11-01

Space-borne hyperspectral imagery is an important tool for earth sciences and industrial applications. Higher spatial and spectral resolutions have been sought persistently, although this results in more power, larger volume and weight during a space-borne spectral imager design. For miniaturization of hyperspectral imager and optimization of spectral splitting methods, several methods are compared in this paper. Spectral time delay integration (TDI) method with high transmittance Integrated Stepwise Filter (ISF) is proposed.With the method, an ISF imaging spectrometer with TDI could achieve higher system sensitivity than the traditional prism/grating imaging spectrometer. In addition, the ISF imaging spectrometer performs well in suppressing infrared background radiation produced by instrument. A compact shortwave infrared (SWIR) hyperspectral imager prototype based on HgCdTe covering the spectral range of 2.0-2.5 μm with 6 TDI stages was designed and integrated. To investigate the performance of ISF spectrometer, a method to derive the optimal blocking band curve of the ISF is introduced, along with known error characteristics. To assess spectral performance of the ISF system, a new spectral calibration based on blackbody radiation with temperature scanning is proposed. The results of the imaging experiment showed the merits of ISF. ISF has great application prospects in the field of high sensitivity and high resolution space-borne hyperspectral imagery.

Sparsely-sampled hyperspectral stimulated Raman scattering microscopy: a theoretical investigation

NASA Astrophysics Data System (ADS)

Lin, Haonan; Liao, Chien-Sheng; Wang, Pu; Huang, Kai-Chih; Bouman, Charles A.; Kong, Nan; Cheng, Ji-Xin

2017-02-01

A hyperspectral image corresponds to a data cube with two spatial dimensions and one spectral dimension. Through linear un-mixing, hyperspectral images can be decomposed into spectral signatures of pure components as well as their concentration maps. Due to this distinct advantage on component identification, hyperspectral imaging becomes a rapidly emerging platform for engineering better medicine and expediting scientific discovery. Among various hyperspectral imaging techniques, hyperspectral stimulated Raman scattering (HSRS) microscopy acquires data in a pixel-by-pixel scanning manner. Nevertheless, current image acquisition speed for HSRS is insufficient to capture the dynamics of freely moving subjects. Instead of reducing the pixel dwell time to achieve speed-up, which would inevitably decrease signal-to-noise ratio (SNR), we propose to reduce the total number of sampled pixels. Location of sampled pixels are carefully engineered with triangular wave Lissajous trajectory. Followed by a model-based image in-painting algorithm, the complete data is recovered for linear unmixing. Simulation results show that by careful selection of trajectory, a fill rate as low as 10% is sufficient to generate accurate linear unmixing results. The proposed framework applies to any hyperspectral beam-scanning imaging platform which demands high acquisition speed.

Data-driven imaging of tissue inflammation using RGB-based hyperspectral reconstruction toward personal monitoring of dermatologic health

PubMed Central

Kim, Taehoon; Visbal-Onufrak, Michelle A.; Konger, Raymond L.; Kim, Young L.

2017-01-01

Sensitive and accurate assessment of dermatologic inflammatory hyperemia in otherwise grossly normal-appearing skin conditions is beneficial to laypeople for monitoring their own skin health on a regular basis, to patients for looking for timely clinical examination, and to primary care physicians or dermatologists for delivering effective treatments. We propose that mathematical hyperspectral reconstruction from RGB images in a simple imaging setup can provide reliable visualization of hemoglobin content in a large skin area. Without relying on a complicated, expensive, and slow hyperspectral imaging system, we demonstrate the feasibility of determining heterogeneous or multifocal areas of inflammatory hyperemia associated with experimental photocarcinogenesis in mice. We envision that RGB-based reconstructed hyperspectral imaging of subclinical inflammatory hyperemic foci could potentially be integrated with the built-in camera (RGB sensor) of a smartphone to develop a simple imaging device that could offer affordable monitoring of dermatologic health. PMID:29188120

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.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

A bench-top hyperspectral imaging system to classify beef from Nellore cattle based on tenderness

NASA Astrophysics Data System (ADS)

Nubiato, Keni Eduardo Zanoni; Mazon, Madeline Rezende; Antonelo, Daniel Silva; Calkins, Chris R.; Naganathan, Govindarajan Konda; Subbiah, Jeyamkondan; da Luz e Silva, Saulo

2018-03-01

The aim of this study was to evaluate the accuracy of classification of Nellore beef aged for 0, 7, 14, or 21 days and classification based on tenderness and aging period using a bench-top hyperspectral imaging system. A hyperspectral imaging system (λ = 928-2524 nm) was used to collect hyperspectral images of the Longissimus thoracis et lumborum (aging n = 376 and tenderness n = 345) of Nellore cattle. The image processing steps included selection of region of interest, extraction of spectra, and indentification and evalution of selected wavelengths for classification. Six linear discriminant models were developed to classify samples based on tenderness and aging period. The model using the first derivative of partial absorbance spectra (give wavelength range spectra) was able to classify steaks based on the tenderness with an overall accuracy of 89.8%. The model using the first derivative of full absorbance spectra was able to classify steaks based on aging period with an overall accuracy of 84.8%. The results demonstrate that the HIS may be a viable technology for classifying beef based on tenderness and aging period.

Beyond the Sparsity-Based Target Detector: A Hybrid Sparsity and Statistics Based Detector for Hyperspectral Images.

PubMed

Du, Bo; Zhang, Yuxiang; Zhang, Liangpei; Tao, Dacheng

2016-08-18

Hyperspectral images provide great potential for target detection, however, new challenges are also introduced for hyperspectral target detection, resulting that hyperspectral target detection should be treated as a new problem and modeled differently. Many classical detectors are proposed based on the linear mixing model and the sparsity model. However, the former type of model cannot deal well with spectral variability in limited endmembers, and the latter type of model usually treats the target detection as a simple classification problem and pays less attention to the low target probability. In this case, can we find an efficient way to utilize both the high-dimension features behind hyperspectral images and the limited target information to extract small targets? This paper proposes a novel sparsitybased detector named the hybrid sparsity and statistics detector (HSSD) for target detection in hyperspectral imagery, which can effectively deal with the above two problems. The proposed algorithm designs a hypothesis-specific dictionary based on the prior hypotheses for the test pixel, which can avoid the imbalanced number of training samples for a class-specific dictionary. Then, a purification process is employed for the background training samples in order to construct an effective competition between the two hypotheses. Next, a sparse representation based binary hypothesis model merged with additive Gaussian noise is proposed to represent the image. Finally, a generalized likelihood ratio test is performed to obtain a more robust detection decision than the reconstruction residual based detection methods. Extensive experimental results with three hyperspectral datasets confirm that the proposed HSSD algorithm clearly outperforms the stateof- the-art target detectors.

Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes

NASA Astrophysics Data System (ADS)

Honkavaara, Eija; Rosnell, Tomi; Oliveira, Raquel; Tommaselli, Antonio

2017-12-01

A recent revolution in miniaturised sensor technology has provided markets with novel hyperspectral imagers operating in the frame format principle. In the case of unmanned aerial vehicle (UAV) based remote sensing, the frame format technology is highly attractive in comparison to the commonly utilised pushbroom scanning technology, because it offers better stability and the possibility to capture stereoscopic data sets, bringing an opportunity for 3D hyperspectral object reconstruction. Tuneable filters are one of the approaches for capturing multi- or hyperspectral frame images. The individual bands are not aligned when operating a sensor based on tuneable filters from a mobile platform, such as UAV, because the full spectrum recording is carried out in the time-sequential principle. The objective of this investigation was to study the aspects of band registration of an imager based on tuneable filters and to develop a rigorous and efficient approach for band registration in complex 3D scenes, such as forests. The method first determines the orientations of selected reference bands and reconstructs the 3D scene using structure-from-motion and dense image matching technologies. The bands, without orientation, are then matched to the oriented bands accounting the 3D scene to provide exterior orientations, and afterwards, hyperspectral orthomosaics, or hyperspectral point clouds, are calculated. The uncertainty aspects of the novel approach were studied. An empirical assessment was carried out in a forested environment using hyperspectral images captured with a hyperspectral 2D frame format camera, based on a tuneable Fabry-Pérot interferometer (FPI) on board a multicopter and supported by a high spatial resolution consumer colour camera. A theoretical assessment showed that the method was capable of providing band registration accuracy better than 0.5-pixel size. The empirical assessment proved the performance and showed that, with the novel method, most parts of the band misalignments were less than the pixel size. Furthermore, it was shown that the performance of the band alignment was dependent on the spatial distance from the reference band.

Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach

PubMed Central

Li, Zhao-Liang

2018-01-01

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval numbers and the spectral uncertainty. The classification results based on a hyperspectral dataset using the FCM, IT2FCM, and the proposed improved IT2FCM* algorithms show that the IT2FCM* method plays the best performance according to the clustering accuracy. In this paper, in order to validate and demonstrate the separability of the IT2FCM*, four type-I fuzzy validity indexes are employed, and a comparative analysis of these fuzzy validity indexes also applied in FCM and IT2FCM methods are made. These four indexes are also applied into different spatial and spectral resolution datasets to analyze the effects of spectral and spatial scaling factors on the separability of FCM, IT2FCM, and IT2FCM* methods. The results of these validity indexes from the hyperspectral datasets show that the improved IT2FCM* algorithm have the best values among these three algorithms in general. The results demonstrate that the IT2FCM* exhibits good performance in hyperspectral remote-sensing image classification because of its ability to handle hyperspectral uncertainty. PMID:29373548

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

NASA Astrophysics Data System (ADS)

Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

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.

Research on hyperspectral dynamic scene and image sequence simulation

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

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.

Geometrical calibration of an AOTF hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

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

2010-02-01

Optical aberrations present an important problem in optical measurements. Geometrical calibration of an imaging system is therefore of the utmost importance for achieving accurate optical measurements. In hyper-spectral imaging systems, the problem of optical aberrations is even more pronounced because optical aberrations are wavelength dependent. Geometrical calibration must therefore be performed over the entire spectral range of the hyper-spectral imaging system, which is usually far greater than that of the visible light spectrum. This problem is especially adverse in AOTF (Acousto- Optic Tunable Filter) hyper-spectral imaging systems, as the diffraction of light in AOTF filters is dependent on both wavelength and angle of incidence. Geometrical calibration of hyper-spectral imaging system was performed by stable caliber of known dimensions, which was imaged at different wavelengths over the entire spectral range. The acquired images were then automatically registered to the caliber model by both parametric and nonparametric transformation based on B-splines and by minimizing normalized correlation coefficient. The calibration method was tested on an AOTF hyper-spectral imaging system in the near infrared spectral range. The results indicated substantial wavelength dependent optical aberration that is especially pronounced in the spectral range closer to the infrared part of the spectrum. The calibration method was able to accurately characterize the aberrations and produce transformations for efficient sub-pixel geometrical calibration over the entire spectral range, finally yielding better spatial resolution of hyperspectral imaging system.

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.

Spatial Mutual Information Based Hyperspectral Band Selection for Classification

PubMed Central

2015-01-01

The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results. PMID:25918742

A practical approach to spectral calibration of short wavelength infrared hyper-spectral imaging systems

NASA Astrophysics Data System (ADS)

Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2010-02-01

Near-infrared spectroscopy is a promising, rapidly developing, reliable and noninvasive technique, used extensively in the biomedicine and in pharmaceutical industry. With the introduction of acousto-optic tunable filters (AOTF) and highly sensitive InGaAs focal plane sensor arrays, real-time high resolution hyper-spectral imaging has become feasible for a number of new biomedical in vivo applications. However, due to the specificity of the AOTF technology and lack of spectral calibration standardization, maintaining long-term stability and compatibility of the acquired hyper-spectral images across different systems is still a challenging problem. Efficiently solving both is essential as the majority of methods for analysis of hyper-spectral images relay on a priori knowledge extracted from large spectral databases, serving as the basis for reliable qualitative or quantitative analysis of various biological samples. In this study, we propose and evaluate fast and reliable spectral calibration of hyper-spectral imaging systems in the short wavelength infrared spectral region. The proposed spectral calibration method is based on light sources or materials, exhibiting distinct spectral features, which enable robust non-rigid registration of the acquired spectra. The calibration accounts for all of the components of a typical hyper-spectral imaging system such as AOTF, light source, lens and optical fibers. The obtained results indicated that practical, fast and reliable spectral calibration of hyper-spectral imaging systems is possible, thereby assuring long-term stability and inter-system compatibility of the acquired hyper-spectral images.

Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants

PubMed Central

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

2016-01-01

Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we “wordify” the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases. PMID:26957018

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2017-04-01

This paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches.

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

NASA Technical Reports Server (NTRS)

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

2001-01-01

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

Line-scan hyperspectral imaging platform for agro-food safety and quality evaluation: System enhancement and characterization

USDA-ARS?s Scientific Manuscript database

Line-scan-based hyperspectral imaging techniques have often served as a research tool to develop rapid multispectral methods based on only a few spectral bands for rapid online applications. With continuing technological advances and greater accessibility to and availability of optoelectronic imagin...

Validation and transferability study of a method based on near-infrared hyperspectral imaging for the detection and quantification of ergot bodies in cereals.

PubMed

Vermeulen, Ph; Fernández Pierna, J A; van Egmond, H P; Zegers, J; Dardenne, P; Baeten, V

2013-09-01

In recent years, near-infrared (NIR) hyperspectral imaging has proved its suitability for quality and safety control in the cereal sector by allowing spectroscopic images to be collected at single-kernel level, which is of great interest to cereal control laboratories. Contaminants in cereals include, inter alia, impurities such as straw, grains from other crops, and insects, as well as undesirable substances such as ergot (sclerotium of Claviceps purpurea). For the cereal sector, the presence of ergot creates a high toxicity risk for animals and humans because of its alkaloid content. A study was undertaken, in which a complete procedure for detecting ergot bodies in cereals was developed, based on their NIR spectral characteristics. These were used to build relevant decision rules based on chemometric tools and on the morphological information obtained from the NIR images. The study sought to transfer this procedure from a pilot online NIR hyperspectral imaging system at laboratory level to a NIR hyperspectral imaging system at industrial level and to validate the latter. All the analyses performed showed that the results obtained using both NIR hyperspectral imaging cameras were quite stable and repeatable. In addition, a correlation higher than 0.94 was obtained between the predicted values obtained by NIR hyperspectral imaging and those supplied by the stereo-microscopic method which is the reference method. The validation of the transferred protocol on blind samples showed that the method could identify and quantify ergot contamination, demonstrating the transferability of the method. These results were obtained on samples with an ergot concentration of 0.02% which is less than the EC limit for cereals (intervention grains) destined for humans fixed at 0.05%.

An advanced scanning method for space-borne hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

Wang, Yue-ming; Lang, Jun-Wei; Wang, Jian-Yu; Jiang, Zi-Qing

2011-08-01

Space-borne hyper-spectral imagery is an important means for the studies and applications of earth science. High cost efficiency could be acquired by optimized system design. In this paper, an advanced scanning method is proposed, which contributes to implement both high temporal and spatial resolution imaging system. Revisit frequency and effective working time of space-borne hyper-spectral imagers could be greatly improved by adopting two-axis scanning system if spatial resolution and radiometric accuracy are not harshly demanded. In order to avoid the quality degradation caused by image rotation, an idea of two-axis rotation has been presented based on the analysis and simulation of two-dimensional scanning motion path and features. Further improvement of the imagers' detection ability under the conditions of small solar altitude angle and low surface reflectance can be realized by the Ground Motion Compensation on pitch axis. The structure and control performance are also described. An intelligent integration technology of two-dimensional scanning and image motion compensation is elaborated in this paper. With this technology, sun-synchronous hyper-spectral imagers are able to pay quick visit to hot spots, acquiring both high spatial and temporal resolution hyper-spectral images, which enables rapid response of emergencies. The result has reference value for developing operational space-borne hyper-spectral imagers.

Compression of hyper-spectral images using an accelerated nonnegative tensor decomposition

NASA Astrophysics Data System (ADS)

Li, Jin; Liu, Zilong

2017-12-01

Nonnegative tensor Tucker decomposition (NTD) in a transform domain (e.g., 2D-DWT, etc) has been used in the compression of hyper-spectral images because it can remove redundancies between spectrum bands and also exploit spatial correlations of each band. However, the use of a NTD has a very high computational cost. In this paper, we propose a low complexity NTD-based compression method of hyper-spectral images. This method is based on a pair-wise multilevel grouping approach for the NTD to overcome its high computational cost. The proposed method has a low complexity under a slight decrease of the coding performance compared to conventional NTD. We experimentally confirm this method, which indicates that this method has the less processing time and keeps a better coding performance than the case that the NTD is not used. The proposed approach has a potential application in the loss compression of hyper-spectral or multi-spectral images

Hyperspectral feature mapping classification based on mathematical morphology

NASA Astrophysics Data System (ADS)

Liu, Chang; Li, Junwei; Wang, Guangping; Wu, Jingli

2016-03-01

This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm's performance is better than that of the other algorithms under the same condition and has higher classification accuracy.

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement in practice.

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.

Development of Hyperspectral Remote Sensing Capability For the Early Detection and Monitoring of Harmful Algal Blooms (HABs) in the Great Lakes

NASA Technical Reports Server (NTRS)

Lekki, John; Anderson, Robert; Nguyen, Quang-Viet; Demers, James; Leshkevich, George; Flatico, Joseph; Kojima, Jun

2013-01-01

Hyperspectral imagers have significant capability for detecting and classifying waterborne constituents. One particularly appropriate application of such instruments in the Great Lakes is to detect and monitor the development of potentially Harmful Algal Blooms (HABs). Two generations of small hyperspectral imagers have been built and tested for aircraft based monitoring of harmful algal blooms. In this paper a discussion of the two instruments as well as field studies conducted using these instruments will be presented. During the second field study, in situ reflectance data was obtained from the Research Vessel Lake Guardian in conjunction with reflectance data obtained with the hyperspectral imager from overflights of the same locations. A comparison of these two data sets shows that the airborne hyperspectral imager closely matches measurements obtained from instruments on the lake surface and thus positively supports its utilization for detecting and monitoring HABs.

Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data

NASA Astrophysics Data System (ADS)

Dementev, A. O.; Dmitriev, E. V.; Kozoderov, V. V.; Egorov, V. D.

2017-10-01

Hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

Multispectral fluorescence imaging technique for discrimination of cucumber (Cucumis Sativus) seed viability

USDA-ARS?s Scientific Manuscript database

In this study, we developed a nondestructive method for discriminating viable cucumber (Cucumis sativus) seeds based on hyperspectral fluorescence imaging. The fluorescence spectra of cucumber seeds in the 420–700 nm range were extracted from hyperspectral fluorescence images obtained using 365 nm u...

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

USDA-ARS?s Scientific Manuscript database

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...

Filtering high resolution hyperspectral imagery and analyzing it for quantification of water quality parameters and aquatic vegetation

NASA Astrophysics Data System (ADS)

Pande-Chhetri, Roshan

High resolution hyperspectral imagery (airborne or ground-based) is gaining momentum as a useful analytical tool in various fields including agriculture and aquatic systems. These images are often contaminated with stripes and noise resulting in lower signal-to-noise ratio, especially in aquatic regions where signal is naturally low. This research investigates effective methods for filtering high spatial resolution hyperspectral imagery and use of the imagery in water quality parameter estimation and aquatic vegetation classification. The striping pattern of the hyperspectral imagery is non-parametric and difficult to filter. In this research, a de-striping algorithm based on wavelet analysis and adaptive Fourier domain normalization was examined. The result of this algorithm was found superior to other available algorithms and yielded highest Peak Signal to Noise Ratio improvement. The algorithm was implemented on individual image bands and on selected bands of the Maximum Noise Fraction (MNF) transformed images. The results showed that image filtering in the MNF domain was efficient and produced best results. The study investigated methods of analyzing hyperspectral imagery to estimate water quality parameters and to map aquatic vegetation in case-2 waters. Ground-based hyperspectral imagery was analyzed to determine chlorophyll-a (Chl-a) concentrations in aquaculture ponds. Two-band and three-band indices were implemented and the effect of using submerged reflectance targets was evaluated. Laboratory measured values were found to be in strong correlation with two-band and three-band spectral indices computed from the hyperspectral image. Coefficients of determination (R2) values were found to be 0.833 and 0.862 without submerged targets and stronger values of 0.975 and 0.982 were obtained using submerged targets. Airborne hyperspectral images were used to detect and classify aquatic vegetation in a black river estuarine system. Image normalization for water surface reflectance and water depths was conducted and non-parametric classifiers such as ANN, SVM and SAM were tested and compared. Quality assessment indicated better classification and detection when non-parametric classifiers were applied to normalized or depth invariant transform images. Best classification accuracy of 73% was achieved when ANN is applied on normalized image and best detection accuracy of around 92% was obtained when SVM or SAM was applied on depth invariant images.

Tunable thin-film optical filters for hyperspectral microscopy

NASA Astrophysics Data System (ADS)

Favreau, Peter F.; Rich, Thomas C.; Prabhat, Prashant; Leavesley, Silas J.

2013-02-01

Hyperspectral imaging was originally developed for use in remote sensing applications. More recently, it has been applied to biological imaging systems, such as fluorescence microscopes. The ability to distinguish molecules based on spectral differences has been especially advantageous for identifying fluorophores in highly autofluorescent tissues. A key component of hyperspectral imaging systems is wavelength filtering. Each filtering technology used for hyperspectral imaging has corresponding advantages and disadvantages. Recently, a new optical filtering technology has been developed that uses multi-layered thin-film optical filters that can be rotated, with respect to incident light, to control the center wavelength of the pass-band. Compared to the majority of tunable filter technologies, these filters have superior optical performance including greater than 90% transmission, steep spectral edges and high out-of-band blocking. Hence, tunable thin-film optical filters present optical characteristics that may make them well-suited for many biological spectral imaging applications. An array of tunable thin-film filters was implemented on an inverted fluorescence microscope (TE 2000, Nikon Instruments) to cover the full visible wavelength range. Images of a previously published model, GFP-expressing endothelial cells in the lung, were acquired using a charge-coupled device camera (Rolera EM-C2, Q-Imaging). This model sample presents fluorescently-labeled cells in a highly autofluorescent environment. Linear unmixing of hyperspectral images indicates that thin-film tunable filters provide equivalent spectral discrimination to our previous acousto-optic tunable filter-based approach, with increased signal-to-noise characteristics. Hence, tunable multi-layered thin film optical filters may provide greatly improved spectral filtering characteristics and therefore enable wider acceptance of hyperspectral widefield microscopy.

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.

Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging

PubMed Central

Mo, Changyeun; Kim, Giyoung; Lee, Kangjin; Kim, Moon S.; Cho, Byoung-Kwan; Lim, Jongguk; Kang, Sukwon

2014-01-01

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400–700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600–700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting. PMID:24763251

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

PubMed

Mo, Changyeun; Kim, Giyoung; Lee, Kangjin; Kim, Moon S; Cho, Byoung-Kwan; Lim, Jongguk; Kang, Sukwon

2014-04-24

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400-700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares-discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400-700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600-700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting.

Hyperspectral imaging based on compressive sensing to determine cancer margins in human pancreatic tissue ex vivo

NASA Astrophysics Data System (ADS)

Peller, Joseph; Thompson, Kyle J.; Siddiqui, Imran; Martinie, John; Iannitti, David A.; Trammell, Susan R.

2017-02-01

Pancreatic cancer is the fourth leading cause of cancer death in the US. Currently, surgery is the only treatment that offers a chance of cure, however, accurately identifying tumor margins in real-time is difficult. Research has demonstrated that optical spectroscopy can be used to distinguish between healthy and diseased tissue. The design of a single-pixel imaging system for cancer detection is discussed. The system differentiates between healthy and diseased tissue based on differences in the optical reflectance spectra of these regions. In this study, pancreatic tissue samples from 6 patients undergoing Whipple procedures are imaged with the system (total number of tissue sample imaged was N=11). Regions of healthy and unhealthy tissue are determined based on SAM analysis of these spectral images. Hyperspectral imaging results are then compared to white light imaging and histological analysis. Cancerous regions were clearly visible in the hyperspectral images. Margins determined via spectral imaging were in good agreement with margins identified by histology, indicating that hyperspectral imaging system can differentiate between healthy and diseased tissue. After imaging the system was able to detect cancerous regions with a sensitivity of 74.50±5.89% and a specificity of 75.53±10.81%. Possible applications of this imaging system include determination of tumor margins during surgery/biopsy and assistance with cancer diagnosis and staging.

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.

Discrimination methods of biological contamination on fresh-cut lettuce based on VNIR and NIR hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms on fresh-cut lettuce. The optimal wavebands that detect worm on fresh-cut lettuce for each type of HSI were investigated using the one-way...

Hyperspectral reflectance and fluorescence line-scan imaging system for online detection of fecal contamination on apples

NASA Astrophysics Data System (ADS)

Kim, Moon S.; Cho, Byoung-Kwan; Yang, Chun-Chieh; Chao, Kaunglin; Lefcourt, Alan M.; Chen, Yud-Ren

2006-10-01

We have developed nondestructive opto-electronic imaging techniques for rapid assessment of safety and wholesomeness of foods. A recently developed fast hyperspectral line-scan imaging system integrated with a commercial apple-sorting machine was evaluated for rapid detection of animal feces matter on apples. Apples obtained from a local orchard were artificially contaminated with cow feces. For the online trial, hyperspectral images with 60 spectral channels, reflectance in the visible to near infrared regions and fluorescence emissions with UV-A excitation, were acquired from apples moving at a processing sorting-line speed of three apples per second. Reflectance and fluorescence imaging required a passive light source, and each method used independent continuous wave (CW) light sources. In this paper, integration of the hyperspectral imaging system with the commercial applesorting machine and preliminary results for detection of fecal contamination on apples, mainly based on the fluorescence method, are presented.

Compressive hyperspectral time-resolved wide-field fluorescence lifetime imaging

NASA Astrophysics Data System (ADS)

Pian, Qi; Yao, Ruoyang; Sinsuebphon, Nattawut; Intes, Xavier

2017-07-01

Spectrally resolved fluorescence lifetime imaging and spatial multiplexing have offered information content and collection-efficiency boosts in microscopy, but efficient implementations for macroscopic applications are still lacking. An imaging platform based on time-resolved structured light and hyperspectral single-pixel detection has been developed to perform quantitative macroscopic fluorescence lifetime imaging (MFLI) over a large field of view (FOV) and multiple spectral bands simultaneously. The system makes use of three digital micromirror device (DMD)-based spatial light modulators (SLMs) to generate spatial optical bases and reconstruct N by N images over 16 spectral channels with a time-resolved capability (∼40 ps temporal resolution) using fewer than N2 optical measurements. We demonstrate the potential of this new imaging platform by quantitatively imaging near-infrared (NIR) Förster resonance energy transfer (FRET) both in vitro and in vivo. The technique is well suited for quantitative hyperspectral lifetime imaging with a high sensitivity and paves the way for many important biomedical applications.

Spectral characterization of near-infrared acousto-optic tunable filter (AOTF) hyperspectral imaging systems using standard calibration materials.

PubMed

Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2011-04-01

In this study, we propose and evaluate a method for spectral characterization of acousto-optic tunable filter (AOTF) hyperspectral imaging systems in the near-infrared (NIR) spectral region from 900 nm to 1700 nm. The proposed spectral characterization method is based on the SRM-2035 standard reference material, exhibiting distinct spectral features, which enables robust non-rigid matching of the acquired and reference spectra. The matching is performed by simultaneously optimizing the parameters of the AOTF tuning curve, spectral resolution, baseline, and multiplicative effects. In this way, the tuning curve (frequency-wavelength characteristics) and the corresponding spectral resolution of the AOTF hyperspectral imaging system can be characterized simultaneously. Also, the method enables simple spectral characterization of the entire imaging plane of hyperspectral imaging systems. The results indicate that the method is accurate and efficient and can easily be integrated with systems operating in diffuse reflection or transmission modes. Therefore, the proposed method is suitable for characterization, calibration, or validation of AOTF hyperspectral imaging systems. © 2011 Society for Applied Spectroscopy

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.

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 spectra in all pixels. We discuss here the performance of a visible prototype HIP. The technology is readily extendable to the ultraviolet and infrared spectral ranges, and the scenes can be static or dynamic.

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Liu, Xi; Zhou, Mei; Qiu, Song; Sun, Li; Liu, Hongying; Li, Qingli; Wang, Yiting

2017-12-01

Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and near-infrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu’s method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

Rapid and visual detection of the main chemical compositions in maize seeds based on Raman hyperspectral imaging

NASA Astrophysics Data System (ADS)

Yang, Guiyan; Wang, Qingyan; Liu, Chen; Wang, Xiaobin; Fan, Shuxiang; Huang, Wenqian

2018-07-01

Rapid and visual detection of the chemical compositions of plant seeds is important but difficult for a traditional seed quality analysis system. In this study, a custom-designed line-scan Raman hyperspectral imaging system was applied for detecting and displaying the main chemical compositions in a heterogeneous maize seed. Raman hyperspectral images collected from the endosperm and embryo of maize seed were acquired and preprocessed by Savitzky-Golay (SG) filter and adaptive iteratively reweighted Penalized Least Squares (airPLS). Three varieties of maize seeds were analyzed, and the characteristics of the spectral and spatial information were extracted from each hyperspectral image. The Raman characteristic peaks, identified at 477, 1443, 1522, 1596 and 1654 cm-1 from 380 to 1800 cm-1 Raman spectra, were related to corn starch, mixture of oil and starch, zeaxanthin, lignin and oil in maize seeds, respectively. Each single-band image corresponding to the characteristic band characterized the spatial distribution of the chemical composition in a seed successfully. The embryo was distinguished from the endosperm by band operation of the single-band images at 477, 1443, and 1596 cm-1 for each variety. Results showed that Raman hyperspectral imaging system could be used for on-line quality control of maize seeds based on the rapid and visual detection of the chemical compositions in maize seeds.

Hyperspectral imaging flow cytometer

DOEpatents

Sinclair, Michael B.; Jones, Howland D. T.

2017-10-25

A hyperspectral imaging flow cytometer can acquire high-resolution hyperspectral images of particles, such as biological cells, flowing through a microfluidic system. The hyperspectral imaging flow cytometer can provide detailed spatial maps of multiple emitting species, cell morphology information, and state of health. An optimized system can image about 20 cells per second. The hyperspectral imaging flow cytometer enables many thousands of cells to be characterized in a single session.

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.

Information Extraction in Tomb Pit Using Hyperspectral Data

NASA Astrophysics Data System (ADS)

Yang, X.; Hou, M.; Lyu, S.; Ma, S.; Gao, Z.; Bai, S.; Gu, M.; Liu, Y.

2018-04-01

Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

Atmospheric correction of short-wave hyperspectral imagery using a fast, full-scattering 1DVar retrieval scheme

NASA Astrophysics Data System (ADS)

Thelen, J.-C.; Havemann, S.; Taylor, J. P.

2012-06-01

Here, we present a new prototype algorithm for the simultaneous retrieval of the atmospheric profiles (temperature, humidity, ozone and aerosol) and the surface reflectance from hyperspectral radiance measurements obtained from air/space-borne, hyperspectral imagers such as the 'Airborne Visible/Infrared Imager (AVIRIS) or Hyperion on board of the Earth Observatory 1. The new scheme, proposed here, consists of a fast radiative transfer code, based on empirical orthogonal functions (EOFs), in conjunction with a 1D-Var retrieval scheme. The inclusion of an 'exact' scattering code based on spherical harmonics, allows for an accurate treatment of Rayleigh scattering and scattering by aerosols, water droplets and ice-crystals, thus making it possible to also retrieve cloud and aerosol optical properties, although here we will concentrate on non-cloudy scenes. We successfully tested this new approach using two hyperspectral images taken by AVIRIS, a whiskbroom imaging spectrometer operated by the NASA Jet Propulsion Laboratory.

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

PubMed

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

2015-01-01

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

Mapping the distribution of materials in hyperspectral data using the USGS Material Identification and Characterization Algorithm (MICA)

USGS Publications Warehouse

Kokaly, R.F.; King, T.V.V.; Hoefen, T.M.

2011-01-01

Identifying materials by measuring and analyzing their reflectance spectra has been an important method in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow scientists to detect materials and map their distributions across the landscape. With new satellite-borne hyperspectral sensors planned for the future, for example, HYSPIRI (HYPerspectral InfraRed Imager), robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral-feature based analysis of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described in this paper. The core concepts and calculations of MICA are presented. A MICA command file has been developed and applied to map minerals in the full-country coverage of the 2007 Afghanistan HyMap hyperspectral data. ?? 2011 IEEE.

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.

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.

Hyperspectral image processing methods

USDA-ARS?s Scientific Manuscript database

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...

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

NASA Astrophysics Data System (ADS)

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

2010-04-01

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

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.

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.

Evaluation of camouflage effectiveness using hyperspectral images

NASA Astrophysics Data System (ADS)

Zavvartorbati, Ahmad; Dehghani, Hamid; Rashidi, Ali Jabar

2017-10-01

Recent advances in camouflage engineering have made it more difficult to detect targets. Assessing the effectiveness of camouflage against different target detection methods leads to identifying the strengths and weaknesses of camouflage designs. One of the target detection methods is to analyze the content of the scene using remote sensing hyperspectral images. In the process of evaluating camouflage designs, there must be comprehensive and efficient evaluation criteria. Three parameters were considered as the main factors affecting the target detection and based on these factors, camouflage effectiveness assessment criteria were proposed. To combine the criteria in the form of a single equation, the equation used in target visual search models was employed and for determining the criteria, a model was presented based on the structure of the computational visual attention systems. Also, in software implementations on the HyMap hyperspectral image, a variety of camouflage levels were created for the real targets in the image. Assessing the camouflage levels using the proposed criteria, comparing and analyzing the results can show that the provided criteria and model are effective for the evaluation of camouflage designs using hyperspectral images.

Endmember extraction from hyperspectral image based on discrete firefly algorithm (EE-DFA)

NASA Astrophysics Data System (ADS)

Zhang, Chengye; Qin, Qiming; Zhang, Tianyuan; Sun, Yuanheng; Chen, Chao

2017-04-01

This study proposed a novel method to extract endmembers from hyperspectral image based on discrete firefly algorithm (EE-DFA). Endmembers are the input of many spectral unmixing algorithms. Hence, in this paper, endmember extraction from hyperspectral image is regarded as a combinational optimization problem to get best spectral unmixing results, which can be solved by the discrete firefly algorithm. Two series of experiments were conducted on the synthetic hyperspectral datasets with different SNR and the AVIRIS Cuprite dataset, respectively. The experimental results were compared with the endmembers extracted by four popular methods: the sequential maximum angle convex cone (SMACC), N-FINDR, Vertex Component Analysis (VCA), and Minimum Volume Constrained Nonnegative Matrix Factorization (MVC-NMF). What's more, the effect of the parameters in the proposed method was tested on both synthetic hyperspectral datasets and AVIRIS Cuprite dataset, and the recommended parameters setting was proposed. The results in this study demonstrated that the proposed EE-DFA method showed better performance than the existing popular methods. Moreover, EE-DFA is robust under different SNR conditions.

Forest tree species clssification based on airborne hyper-spectral imagery

NASA Astrophysics Data System (ADS)

Dian, Yuanyong; Li, Zengyuan; Pang, Yong

2013-10-01

Forest precision classification products were the basic data for surveying of forest resource, updating forest subplot information, logging and design of forest. However, due to the diversity of stand structure, complexity of the forest growth environment, it's difficult to discriminate forest tree species using multi-spectral image. The airborne hyperspectral images can achieve the high spatial and spectral resolution imagery of forest canopy, so it will good for tree species level classification. The aim of this paper was to test the effective of combining spatial and spectral features in airborne hyper-spectral image classification. The CASI hyper spectral image data were acquired from Liangshui natural reserves area. Firstly, we use the MNF (minimum noise fraction) transform method for to reduce the hyperspectral image dimensionality and highlighting variation. And secondly, we use the grey level co-occurrence matrix (GLCM) to extract the texture features of forest tree canopy from the hyper-spectral image, and thirdly we fused the texture and the spectral features of forest canopy to classify the trees species using support vector machine (SVM) with different kernel functions. The results showed that when using the SVM classifier, MNF and texture-based features combined with linear kernel function can achieve the best overall accuracy which was 85.92%. It was also confirm that combine the spatial and spectral information can improve the accuracy of tree species classification.

SVM-based feature extraction and classification of aflatoxin contaminated corn using fluorescence hyperspectral data

USDA-ARS?s Scientific Manuscript database

Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...

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.

Hyperspectral imaging using the single-pixel Fourier transform technique

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Multiview hyperspectral topography of tissue structural and functional characteristics

NASA Astrophysics Data System (ADS)

Zhang, Shiwu; Liu, Peng; Huang, Jiwei; Xu, Ronald

2012-12-01

Accurate and in vivo characterization of structural, functional, and molecular characteristics of biological tissue will facilitate quantitative diagnosis, therapeutic guidance, and outcome assessment in many clinical applications, such as wound healing, cancer surgery, and organ transplantation. However, many clinical imaging systems have limitations and fail to provide noninvasive, real time, and quantitative assessment of biological tissue in an operation room. To overcome these limitations, we developed and tested a multiview hyperspectral imaging system. The multiview hyperspectral imaging system integrated the multiview and the hyperspectral imaging techniques in a single portable unit. Four plane mirrors are cohered together as a multiview reflective mirror set with a rectangular cross section. The multiview reflective mirror set was placed between a hyperspectral camera and the measured biological tissue. For a single image acquisition task, a hyperspectral data cube with five views was obtained. The five-view hyperspectral image consisted of a main objective image and four reflective images. Three-dimensional topography of the scene was achieved by correlating the matching pixels between the objective image and the reflective images. Three-dimensional mapping of tissue oxygenation was achieved using a hyperspectral oxygenation algorithm. The multiview hyperspectral imaging technique is currently under quantitative validation in a wound model, a tissue-simulating blood phantom, and an in vivo biological tissue model. The preliminary results have demonstrated the technical feasibility of using multiview hyperspectral imaging for three-dimensional topography of tissue functional properties.

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

USDA-ARS?s Scientific Manuscript database

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

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

EPA Science Inventory

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

Fluorescence hyperspectral imaging technique for the foreign substance detection on fresh-cut lettuce

USDA-ARS?s Scientific Manuscript database

Nondestructive methods based on fluorescence hyperspectral imaging (HSI) techniques were developed in order to detect worms on fresh-cut lettuce. The optimal wavebands for detecting worms on fresh-cut lettuce were investigated using the one-way ANOVA analysis and correlation analysis. The worm detec...

Multiview hyperspectral topography of tissue structural and functional characteristics

NASA Astrophysics Data System (ADS)

Liu, Peng; Huang, Jiwei; Zhang, Shiwu; Xu, Ronald X.

2016-01-01

Accurate and in vivo characterization of structural, functional, and molecular characteristics of biological tissue will facilitate quantitative diagnosis, therapeutic guidance, and outcome assessment in many clinical applications, such as wound healing, cancer surgery, and organ transplantation. We introduced and tested a multiview hyperspectral imaging technique for noninvasive topographic imaging of cutaneous wound oxygenation. The technique integrated a multiview module and a hyperspectral module in a single portable unit. Four plane mirrors were cohered to form a multiview reflective mirror set with a rectangular cross section. The mirror set was placed between a hyperspectral camera and the target biological tissue. For a single image acquisition task, a hyperspectral data cube with five views was obtained. The five-view hyperspectral image consisted of a main objective image and four reflective images. Three-dimensional (3-D) topography of the scene was achieved by correlating the matching pixels between the objective image and the reflective images. 3-D mapping of tissue oxygenation was achieved using a hyperspectral oxygenation algorithm. The multiview hyperspectral imaging technique was validated in a wound model, a tissue-simulating blood phantom, and in vivo biological tissue. The experimental results demonstrated the technical feasibility of using multiview hyperspectral imaging for 3-D topography of tissue functional properties.

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 infrared seen in geothermal fields. Hyperspectral analysis results indicated that kaolinite, smectite, illite, montmorillonite, and sepiolite minerals were distributed in a wide area, which covered the hot spring outlet. Rectorite, lizardite, richterite, dumortierite, nontronite, erionite, and clinoptilolite were observed occasionally.

Programmable spectral engine design of hyperspectral image projectors based on digital micro-mirror device (DMD)

NASA Astrophysics Data System (ADS)

Wang, Xicheng; Gao, Jiaobo; Wu, Jianghui; Li, Jianjun; Cheng, Hongliang

2017-02-01

Recently, hyperspectral image projectors (HIP) have been developed in the field of remote sensing. For the advanced performance of system-level validation, target detection and hyperspectral image calibration, HIP has great possibility of development in military, medicine, commercial and so on. HIP is based on the digital micro-mirror device (DMD) and projection technology, which is capable to project arbitrary programmable spectra (controlled by PC) into the each pixel of the IUT1 (instrument under test), such that the projected image could simulate realistic scenes that hyperspectral image could be measured during its use and enable system-level performance testing and validation. In this paper, we built a visible hyperspectral image projector also called the visible target simulator with double DMDs, which the first DMD is used to product the selected monochromatic light from the wavelength of 410 to 720 um, and the light come to the other one. Then we use computer to load image of realistic scenes to the second DMD, so that the target condition and background could be project by the second DMD with the selected monochromatic light. The target condition can be simulated and the experiment could be controlled and repeated in the lab, making the detector instrument could be tested in the lab. For the moment, we make the focus on the spectral engine design include the optical system, research of DMD programmable spectrum and the spectral resolution of the selected spectrum. The detail is shown.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

NASA Astrophysics Data System (ADS)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

The Effectiveness of Hydrothermal Alteration Mapping based on Hyperspectral Data in Tropical Region

NASA Astrophysics Data System (ADS)

Muhammad, R. R. D.; Saepuloh, A.

2016-09-01

Hyperspectral remote sensing could be used to characterize targets at earth's surface based on their spectra. This capability is useful for mapping and characterizing the distribution of host rocks, alteration assemblages, and minerals. Contrary to the multispectral sensors, the hyperspectral identifies targets with high spectral resolution. The Wayang Windu Geothermal field in West Java, Indonesia was selected as the study area due to the existence of surface manifestation and dense vegetation environment. Therefore, the effectiveness of hyperspectral remote sensing in tropical region was targeted as the study objective. The Spectral Angle Mapper (SAM) method was used to detect the occurrence of clay minerals spatially from Hyperion data. The SAM references of reflectance spectra were obtained from field observation at altered materials. To calculate the effectiveness of hyperspectral data, we used multispectral data from Landsat-8. The comparison method was conducted by comparing the SAM's rule images from Hyperion and Landsat-8, resulting that hyperspectral was more accurate than multispectral data. Hyperion SAM's rule images showed lower value compared to Landsat-8, the significant number derived from using Hyperion was about 24% better. This inferred that the hyperspectral remote sensing is preferable for mineral mapping even though vegetation covered study area.

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Pahlavani, Parham; Bigdeli, Behnaz

2017-12-01

Hyperspectral images contain extremely rich spectral information that offer great potential to discriminate between various land cover classes. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral classification. Furthermore, in the presence of mixed coverage pixels, crisp classifiers produced errors, omission and commission. This paper presents a mutual information-Dempster-Shafer system through an ensemble classification approach for classification of hyperspectral data. First, mutual information is applied to split data into a few independent partitions to overcome high dimensionality. Then, a fuzzy maximum likelihood classifies each band subset. Finally, Dempster-Shafer is applied to fuse the results of the fuzzy classifiers. In order to assess the proposed method, a crisp ensemble system based on a support vector machine as the crisp classifier and weighted majority voting as the crisp fusion method are applied on hyperspectral data. Furthermore, a dimension reduction system is utilized to assess the effectiveness of mutual information band splitting of the proposed method. The proposed methodology provides interesting conclusions on the effectiveness and potentiality of mutual information-Dempster-Shafer based classification of hyperspectral data.

EnGeoMAP - geological applications within the EnMAP hyperspectral satellite science program

NASA Astrophysics Data System (ADS)

Boesche, N. K.; Mielke, C.; Rogass, C.; Guanter, L.

2016-12-01

Hyperspectral investigations from near field to space substantially contribute to geological exploration and mining monitoring of raw material and mineral deposits. Due to their spectral characteristics, large mineral occurrences and minefields can be identified from space and the spatial distribution of distinct proxy minerals be mapped. In the frame of the EnMAP hyperspectral satellite science program a mineral and elemental mapping tool was developed - the EnGeoMAP. It contains a basic mineral mapping and a rare earth element mapping approach. This study shows the performance of EnGeoMAP based on simulated EnMAP data of the rare earth element bearing Mountain Pass Carbonatite Complex, USA, and the Rodalquilar and Lomilla Calderas, Spain, which host the economically relevant gold-silver, lead-zinc-silver-gold and alunite deposits. The mountain pass image data was simulated on the basis of AVIRIS Next Generation images, while the Rodalquilar data is based on HyMap images. The EnGeoMAP - Base approach was applied to both images, while the mountain pass image data were additionally analysed using the EnGeoMAP - REE software tool. The results are mineral and elemental maps that serve as proxies for the regional lithology and deposit types. The validation of the maps is based on chemical analyses of field samples. Current airborne sensors meet the spatial and spectral requirements for detailed mineral mapping and future hyperspectral space borne missions will additionally provide a large coverage. For those hyperspectral missions, EnGeoMAP is a rapid data analysis tool that is provided to spectral geologists working in mineral exploration.

Alteration mineral mapping and metallogenic prediction using CASI/SASI airborne hyperspectral data in Mingshujing area of Gansu Province, NW China

NASA Astrophysics Data System (ADS)

Sun, Yu; Zhao, Yingjun; Qin, Kai; Tian, Feng

2016-04-01

Hyperspectral remote sensing is a frontier of remote sensing. Due to its advantage of integrated image with spectrum, it can realize objects identification, superior to objects classification of multispectral remote sensing. Taken the Mingshujing area in Gansu Province of China as an example, this study extracted the alteration minerals and thus to do metallogenic prediction using CASI/SASI airborne hyperspectral data. The Mingshujing area, located in Liuyuan region of Gansu Province, is dominated by middle Variscan granites and Indosinian granites, with well developed EW- and NE-trending faults. In July 2012, our project team obtained the CASI/SASI hyperspectral data of Liuyuan region by aerial flight. The CASI hyperspectral data have 32 bands and the SASI hyperspectral data have 88 bands, with spectral resolution of 15nm for both. The hyperspectral raw data were first preprocessed, including radiometric correction and geometric correction. We then conducted atmospheric correction using empirical line method based on synchronously measured ground spectra to obtain hyperspectral reflectance data. Spectral dimension of hyperspectral data was reduced by the minimum noise fraction transformation method, and then purity pixels were selected. After these steps, image endmember spectra were obtained. We used the endmember spectrum election method based on expert knowledge to analyze the image endmember spectra. Then, the mixture tuned matched filter (MTMF) mapping method was used to extract mineral information, including limonite, Al-rich sericite, Al-poor sericite and chlorite. Finally, the distribution of minerals in the Mingshujing area was mapped. According to the distribution of limonite and Al-rich sericite mapped by CASI/SASI hyperspectral data, we delineated five gold prospecting areas, and further conducted field verification in these areas. It is shown that there are significant gold mineralized anomalies in surface in the Baixianishan and Xitan prospecting areas. The application of CASI/SASI airborne hyperspectral remote sensing data in the metallogenic prediction of the Mingshujing area has achieved ideal results, indicative of their wide application potential in geological research.

Covariance descriptor fusion for target detection

NASA Astrophysics Data System (ADS)

Cukur, Huseyin; Binol, Hamidullah; Bal, Abdullah; Yavuz, Fatih

2016-05-01

Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

A polarization sensitive hyperspectral imaging system for detection of differences in tissue properties

NASA Astrophysics Data System (ADS)

Peller, Joseph A.; Ceja, Nancy K.; Wawak, Amanda J.; Trammell, Susan R.

2018-02-01

Polarized light imaging and optical spectroscopy can be used to distinguish between healthy and diseased tissue. In this study, the design and testing of a single-pixel hyperspectral imaging system that uses differences in the polarization of light reflected from tissue to differentiate between healthy and thermally damaged tissue is discussed. Thermal lesions were created in porcine skin (n = 8) samples using an IR laser. The damaged regions were clearly visible in the polarized light hyperspectral images. Reflectance hyperspectral and white light imaging was also obtained for all tissue samples. Sizes of the thermally damaged regions as measured via polarized light hyperspectral imaging are compared to sizes of these regions as measured in the reflectance hyperspectral images and white light images. Good agreement between the sizes measured by all three imaging modalities was found. Hyperspectral polarized light imaging can differentiate between healthy and damaged tissue. Possible applications of this imaging system include determination of tumor margins during cancer surgery or pre-surgical biopsy.

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

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

NASA Astrophysics Data System (ADS)

Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

2018-04-01

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Analytical design of a hyper-spectral imaging spectrometer utilizing a convex grating

NASA Astrophysics Data System (ADS)

Kim, Seo H.; Kong, Hong J.; Ku, Hana; Lee, Jun H.

2012-09-01

This paper describes about the new design method for hyper-spectral Imaging spectrometers utilizing convex grating. Hyper-spectral imaging systems are power tools in the field of remote sensing. HSI systems collect at least 100 spectral bands of 10~20 nm width. Because the spectral signature is different and induced unique for each material, it should be possible to discriminate between one material and another based on difference in spectral signature of material. I mathematically analyzed parameters for the intellectual initial design. Main concept of this is the derivative of "ring of minimum aberration without vignetting". This work is a kind of analytical design of an Offner imaging spectrometer. Also, several experiment methods will be contrived to evaluate the performance of imaging spectrometer.

Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: a review

USDA-ARS?s Scientific Manuscript database

New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of...

FIVQ algorithm for interference hyper-spectral image compression

NASA Astrophysics Data System (ADS)

Wen, Jia; Ma, Caiwen; Zhao, Junsuo

2014-07-01

Based on the improved vector quantization (IVQ) algorithm [1] which was proposed in 2012, this paper proposes a further improved vector quantization (FIVQ) algorithm for LASIS (Large Aperture Static Imaging Spectrometer) interference hyper-spectral image compression. To get better image quality, IVQ algorithm takes both the mean values and the VQ indices as the encoding rules. Although IVQ algorithm can improve both the bit rate and the image quality, it still can be further improved in order to get much lower bit rate for the LASIS interference pattern with the special optical characteristics based on the pushing and sweeping in LASIS imaging principle. In the proposed algorithm FIVQ, the neighborhood of the encoding blocks of the interference pattern image, which are using the mean value rules, will be checked whether they have the same mean value as the current processing block. Experiments show the proposed algorithm FIVQ can get lower bit rate compared to that of the IVQ algorithm for the LASIS interference hyper-spectral sequences.

Design and simulation of the circuit of SWIR hyper-spectral imaging spectrometer

NASA Astrophysics Data System (ADS)

Ren, Bin; Li, Zi-tian; Meng, Nan

2009-07-01

With the requirement of the SWIR Hyper-spectral Imaging Spectrometer, this article describes a project of SWIR image circuit based on IRFPA detector. First, the structure of the SWIR Hyper-spectral Imaging Spectrometer is introduced in this paper, and then the infrared imaging circuit design is proposed, which is based on MCT SWIR FPA with 500*256 pixels, the detector NEPTURN, in Safradir Company. According to the scheme, several key technologies have been studied in particular, such as driving circuit, time control circuit, high-speed A/D converter, LVDS (Low Voltage Differential Signaling) transmission circuit. At last, An improved two-point Correction Method was chosen to correct the Non-uniformity of image. The simulation results demonstrate that the proposed method can effectively suppress noises and work with low power consumption. The electric system not only has the advantages of simplicity and compactness but also can work stably, providing 500×256 image at the frame frequency of 200 Hz in good quality.

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

PubMed Central

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

2012-01-01

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

Demonstration of a Corner-cube-interferometer LWIR Hyperspectral Imager

NASA Astrophysics Data System (ADS)

Renhorn, Ingmar G. E.; Svensson, Thomas; Cronström, Staffan; Hallberg, Tomas; Persson, Rolf; Lindell, Roland; Boreman, Glenn D.

2010-01-01

An interferometric long-wavelength infrared (LWIR) hyperspectral imager is demonstrated, based on a Michelson corner-cube interferometer. This class of system is inherently mechanically robust, and should have advantages over Sagnac-interferometer systems in terms of relaxed beamsplitter-coating specifications, and wider unvignetted field of view. Preliminary performance data from the laboratory prototype system are provided regarding imaging, spectral resolution, and fidelity of acquired spectra.

Advanced pushbroom hyperspectral LWIR imagers

NASA Astrophysics Data System (ADS)

Holma, Hannu; Hyvärinen, Timo; Lehtomaa, Jarmo; Karjalainen, Harri; Jaskari, Risto

2009-05-01

Performance studies and instrument designs for hyperspectral pushbroom imagers in thermal wavelength region are introduced. The studies involve imaging systems based on both MCT and microbolometer detector. All the systems employ pushbroom imaging spectrograph with transmission grating and on-axis optics. The aim of the work was to design high performance instruments with good image quality and compact size for various application requirements. A big challenge in realizing these goals without considerable cooling of the whole instrument is to control the instrument radiation from all the surfaces of the instrument itself. This challenge is even bigger in hyperspectral instruments, where the optical power from the target is spread spectrally over tens of pixels, but the instrument radiation is not dispersed. Without any suppression, the instrument radiation can overwhelm the radiation from the target by 1000 times. In the first imager design, BMC-technique (background monitoring on-chip), background suppression and temperature stabilization have been combined with cryo-cooled MCT-detector. The performance of a very compact hyperspectral imager with 84 spectral bands and 384 spatial samples has been studied and NESR of 18 mW/(m2srμm) at 10 μm wavelength for 300 K target has been achieved. This leads to SNR of 580. These results are based on a simulation model. The second version of the imager with an uncooled microbolometer detector and optics in ambient temperature aims at imaging targets at higher temperatures or with illumination. Heater rods with ellipsoidal reflectors can be used to illuminate the swath line of the hyperspectral imager on a target or sample, like drill core in mineralogical analysis. Performance characteristics for microbolometer version have been experimentally verified.

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

Quality evaluation of pansharpened hyperspectral images generated using multispectral images

NASA Astrophysics Data System (ADS)

Matsuoka, Masayuki; Yoshioka, Hiroki

2012-11-01

Hyperspectral remote sensing can provide a smooth spectral curve of a target by using a set of higher spectral resolution detectors. The spatial resolution of the hyperspectral images, however, is generally much lower than that of multispectral images due to the lower energy of incident radiation. Pansharpening is an image-fusion technique that generates higher spatial resolution multispectral images by combining lower resolution multispectral images with higher resolution panchromatic images. In this study, higher resolution hyperspectral images were generated by pansharpening of simulated lower hyperspectral and higher multispectral data. Spectral and spatial qualities of pansharpened images, then, were accessed in relation to the spectral bands of multispectral images. Airborne hyperspectral data of AVIRIS was used in this study, and it was pansharpened using six methods. Quantitative evaluations of pansharpened image are achieved using two frequently used indices, ERGAS, and the Q index.

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.

Spatial-spectral preprocessing for endmember extraction on GPU's

NASA Astrophysics Data System (ADS)

Jimenez, Luis I.; Plaza, Javier; Plaza, Antonio; Li, Jun

2016-10-01

Spectral unmixing is focused in the identification of spectrally pure signatures, called endmembers, and their corresponding abundances in each pixel of a hyperspectral image. Mainly focused on the spectral information contained in the hyperspectral images, endmember extraction techniques have recently included spatial information to achieve more accurate results. Several algorithms have been developed for automatic or semi-automatic identification of endmembers using spatial and spectral information, including the spectral-spatial endmember extraction (SSEE) where, within a preprocessing step in the technique, both sources of information are extracted from the hyperspectral image and equally used for this purpose. Previous works have implemented the SSEE technique in four main steps: 1) local eigenvectors calculation in each sub-region in which the original hyperspectral image is divided; 2) computation of the maxima and minima projection of all eigenvectors over the entire hyperspectral image in order to obtain a candidates pixels set; 3) expansion and averaging of the signatures of the candidate set; 4) ranking based on the spectral angle distance (SAD). The result of this method is a list of candidate signatures from which the endmembers can be extracted using various spectral-based techniques, such as orthogonal subspace projection (OSP), vertex component analysis (VCA) or N-FINDR. Considering the large volume of data and the complexity of the calculations, there is a need for efficient implementations. Latest- generation hardware accelerators such as commodity graphics processing units (GPUs) offer a good chance for improving the computational performance in this context. In this paper, we develop two different implementations of the SSEE algorithm using GPUs. Both are based on the eigenvectors computation within each sub-region of the first step, one using the singular value decomposition (SVD) and another one using principal component analysis (PCA). Based on our experiments with hyperspectral data sets, high computational performance is observed in both cases.

A Framework of Hyperspectral Image Compression using Neural Networks

DOE PAGES

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

2015-01-01

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

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

NASA Technical Reports Server (NTRS)

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

2003-01-01

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

Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

NASA Astrophysics Data System (ADS)

Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

2018-05-01

Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

Real-Time Noise Removal for Line-Scanning Hyperspectral Devices Using a Minimum Noise Fraction-Based Approach

PubMed Central

Bjorgan, Asgeir; Randeberg, Lise Lyngsnes

2015-01-01

Processing line-by-line and in real-time can be convenient for some applications of line-scanning hyperspectral imaging technology. Some types of processing, like inverse modeling and spectral analysis, can be sensitive to noise. The MNF (minimum noise fraction) transform provides suitable denoising performance, but requires full image availability for the estimation of image and noise statistics. In this work, a modified algorithm is proposed. Incrementally-updated statistics enables the algorithm to denoise the image line-by-line. The denoising performance has been compared to conventional MNF and found to be equal. With a satisfying denoising performance and real-time implementation, the developed algorithm can denoise line-scanned hyperspectral images in real-time. The elimination of waiting time before denoised data are available is an important step towards real-time visualization of processed hyperspectral data. The source code can be found at http://www.github.com/ntnu-bioopt/mnf. This includes an implementation of conventional MNF denoising. PMID:25654717

Post-processing for improving hyperspectral anomaly detection accuracy

NASA Astrophysics Data System (ADS)

Wu, Jee-Cheng; Jiang, Chi-Ming; Huang, Chen-Liang

2015-10-01

Anomaly detection is an important topic in the exploitation of hyperspectral data. Based on the Reed-Xiaoli (RX) detector and a morphology operator, this research proposes a novel technique for improving the accuracy of hyperspectral anomaly detection. Firstly, the RX-based detector is used to process a given input scene. Then, a post-processing scheme using morphology operator is employed to detect those pixels around high-scoring anomaly pixels. Tests were conducted using two real hyperspectral images with ground truth information and the results based on receiver operating characteristic curves, illustrated that the proposed method reduced the false alarm rates of the RXbased detector.

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.

Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

NASA Astrophysics Data System (ADS)

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

2011-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

Analysis of the Radiometric Response of Orange Tree Crown in Hyperspectral Uav Images

NASA Astrophysics Data System (ADS)

Imai, N. N.; Moriya, E. A. S.; Honkavaara, E.; Miyoshi, G. T.; de Moraes, M. V. A.; Tommaselli, A. M. G.; Näsi, R.

2017-10-01

High spatial resolution remote sensing images acquired by drones are highly relevant data source in many applications. However, strong variations of radiometric values are difficult to correct in hyperspectral images. Honkavaara et al. (2013) presented a radiometric block adjustment method in which hyperspectral images taken from remotely piloted aerial systems - RPAS were processed both geometrically and radiometrically to produce a georeferenced mosaic in which the standard Reflectance Factor for the nadir is represented. The plants crowns in permanent cultivation show complex variations since the density of shadows and the irradiance of the surface vary due to the geometry of illumination and the geometry of the arrangement of branches and leaves. An evaluation of the radiometric quality of the mosaic of an orange plantation produced using images captured by a hyperspectral imager based on a tunable Fabry-Pérot interferometer and applying the radiometric block adjustment method, was performed. A high-resolution UAV based hyperspectral survey was carried out in an orange-producing farm located in Santa Cruz do Rio Pardo, state of São Paulo, Brazil. A set of 25 narrow spectral bands with 2.5 cm of GSD images were acquired. Trend analysis was applied to the values of a sample of transects extracted from plants appearing in the mosaic. The results of these trend analysis on the pixels distributed along transects on orange tree crown showed the reflectance factor presented a slightly trend, but the coefficients of the polynomials are very small, so the quality of mosaic is good enough for many applications.

[Different wavelengths selection methods for identification of early blight on tomato leaves by using hyperspectral imaging technique].

PubMed

Cheng, Shu-Xi; Xie, Chuan-Qi; Wang, Qiao-Nan; He, Yong; Shao, Yong-Ni

2014-05-01

Identification of early blight on tomato leaves by using hyperspectral imaging technique based on different effective wavelengths selection methods (successive projections algorithm, SPA; x-loading weights, x-LW; gram-schmidt orthogonaliza-tion, GSO) was studied in the present paper. Hyperspectral images of seventy healthy and seventy infected tomato leaves were obtained by hyperspectral imaging system across the wavelength range of 380-1023 nm. Reflectance of all pixels in region of interest (ROI) was extracted by ENVI 4. 7 software. Least squares-support vector machine (LS-SVM) model was established based on the full spectral wavelengths. It obtained an excellent result with the highest identification accuracy (100%) in both calibration and prediction sets. Then, EW-LS-SVM and EW-LDA models were established based on the selected wavelengths suggested by SPA, x-LW and GSO, respectively. The results showed that all of the EW-LS-SVM and EW-LDA models performed well with the identification accuracy of 100% in EW-LS-SVM model and 100%, 100% and 97. 83% in EW-LDA model, respectively. Moreover, the number of input wavelengths of SPA-LS-SVM, x-LW-LS-SVM and GSO-LS-SVM models were four (492, 550, 633 and 680 nm), three (631, 719 and 747 nm) and two (533 and 657 nm), respectively. Fewer input variables were beneficial for the development of identification instrument. It demonstrated that it is feasible to identify early blight on tomato leaves by using hyperspectral imaging, and SPA, x-LW and GSO were effective wavelengths selection methods.

Enhancing hyperspectral spatial resolution using multispectral image fusion: A wavelet approach

NASA Astrophysics Data System (ADS)

Jazaeri, Amin

High spectral and spatial resolution images have a significant impact in remote sensing applications. Because both spatial and spectral resolutions of spaceborne sensors are fixed by design and it is not possible to further increase the spatial or spectral resolution, techniques such as image fusion must be applied to achieve such goals. This dissertation introduces the concept of wavelet fusion between hyperspectral and multispectral sensors in order to enhance the spectral and spatial resolution of a hyperspectral image. To test the robustness of this concept, images from Hyperion (hyperspectral sensor) and Advanced Land Imager (multispectral sensor) were first co-registered and then fused using different wavelet algorithms. A regression-based fusion algorithm was also implemented for comparison purposes. The results show that the fused images using a combined bi-linear wavelet-regression algorithm have less error than other methods when compared to the ground truth. In addition, a combined regression-wavelet algorithm shows more immunity to misalignment of the pixels due to the lack of proper registration. The quantitative measures of average mean square error show that the performance of wavelet-based methods degrades when the spatial resolution of hyperspectral images becomes eight times less than its corresponding multispectral image. Regardless of what method of fusion is utilized, the main challenge in image fusion is image registration, which is also a very time intensive process. Because the combined regression wavelet technique is computationally expensive, a hybrid technique based on regression and wavelet methods was also implemented to decrease computational overhead. However, the gain in faster computation was offset by the introduction of more error in the outcome. The secondary objective of this dissertation is to examine the feasibility and sensor requirements for image fusion for future NASA missions in order to be able to perform onboard image fusion. In this process, the main challenge of image registration was resolved by registering the input images using transformation matrices of previously acquired data. The composite image resulted from the fusion process remarkably matched the ground truth, indicating the possibility of real time onboard fusion processing.

Using hyperspectral imaging technology to identify diseased tomato leaves

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Naval Research Laboratory Fact Book 2012

DTIC Science & Technology

2012-11-01

Distributed network-based battle management High performance computing supporting uniform and nonuniform memory access with single and multithreaded...hyperspectral systems VNIR, MWIR, and LWIR high-resolution systems Wideband SAR systems RF and laser data links High-speed, high-power...hyperspectral imaging system Long-wave infrared ( LWIR ) quantum well IR photodetector (QWIP) imaging system Research and Development Services Divi- sion

Deep feature representation with stacked sparse auto-encoder and convolutional neural network for hyperspectral imaging-based detection of cucumber defects

USDA-ARS?s Scientific Manuscript database

It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neur...

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

EPA Science Inventory

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

ISS Hyperspectral Imager for the Coastal Ocean (HICO): Application of Space-based Hyperspectral Imagery for the Protection of the Nation’s Coastal Resources

EPA Science Inventory

The International Space Station (ISS) is a "global observation and diagnosis station” that offers a unique vantage for observing the Earth's coastal ecosystems. From its position in low-Earth orbit, the station’s optical sensors provide images which help us under...

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.

Fluorescent marker-based and marker-free discrimination between healthy and cancerous human tissues using hyper-spectral imaging

NASA Astrophysics Data System (ADS)

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

2015-06-01

Two problems are addressed in this paper (i) the fluorescent marker-based and the (ii) marker-free discrimination between healthy and cancerous human tissues. For both applications the performance of hyper-spectral methods are quantified. Fluorescent marker-based tissue classification uses a number of fluorescent markers to dye specific parts of a human cell. The challenge is that the emission spectra of the fluorescent dyes overlap considerably. They are, furthermore disturbed by the inherent auto-fluorescence of human tissue. This results in ambiguities and decreased image contrast causing difficulties for the treatment decision. The higher spectral resolution introduced by tunable-filter-based spectral imaging in combination with spectral unmixing techniques results in an improvement of the image contrast and therefore more reliable information for the physician to choose the treatment decision. Marker-free tissue classification is based solely on the subtle spectral features of human tissue without the use of artificial markers. The challenge in this case is that the spectral differences between healthy and cancerous tissues are subtle and embedded in intra- and inter-patient variations of these features. The contributions of this paper are (i) the evaluation of hyper-spectral imaging in combination with spectral unmixing techniques for fluorescence marker-based tissue classification, (ii) the evaluation of spectral imaging for marker-free intra surgery tissue classification. Within this paper, we consider real hyper-spectral fluorescence and endoscopy data sets to emphasize the practical capability of the proposed methods. It is shown that the combination of spectral imaging with multivariate statistical methods can improve the sensitivity and specificity of the detection and the staging of cancerous tissues compared to standard procedures.

Lensless Photoluminescence Hyperspectral Camera Employing Random Speckle Patterns.

PubMed

Žídek, Karel; Denk, Ondřej; Hlubuček, Jiří

2017-11-10

We propose and demonstrate a spectrally-resolved photoluminescence imaging setup based on the so-called single pixel camera - a technique of compressive sensing, which enables imaging by using a single-pixel photodetector. The method relies on encoding an image by a series of random patterns. In our approach, the image encoding was maintained via laser speckle patterns generated by an excitation laser beam scattered on a diffusor. By using a spectrometer as the single-pixel detector we attained a realization of a spectrally-resolved photoluminescence camera with unmatched simplicity. We present reconstructed hyperspectral images of several model scenes. We also discuss parameters affecting the imaging quality, such as the correlation degree of speckle patterns, pattern fineness, and number of datapoints. Finally, we compare the presented technique to hyperspectral imaging using sample scanning. The presented method enables photoluminescence imaging for a broad range of coherent excitation sources and detection spectral areas.

Quantitative comparison between full-spectrum and filter-based imaging in hyperspectral fluorescence microscopy

PubMed Central

GAO, L.; HAGEN, N.; TKACZYK, T.S.

2012-01-01

Summary We implement a filterless illumination scheme on a hyperspectral fluorescence microscope to achieve full-range spectral imaging. The microscope employs polarisation filtering, spatial filtering and spectral unmixing filtering to replace the role of traditional filters. Quantitative comparisons between full-spectrum and filter-based microscopy are provided in the context of signal dynamic range and accuracy of measured fluorophores’ emission spectra. To show potential applications, a five-colour cell immunofluorescence imaging experiment is theoretically simulated. Simulation results indicate that the use of proposed full-spectrum imaging technique may result in three times improvement in signal dynamic range compared to that can be achieved in the filter-based imaging. PMID:22356127

False alarm recognition in hyperspectral gas plume identification

DOEpatents

Conger, James L [San Ramon, CA; Lawson, Janice K [Tracy, CA; Aimonetti, William D [Livermore, CA

2011-03-29

According to one embodiment, a method for analyzing hyperspectral data includes collecting first hyperspectral data of a scene using a hyperspectral imager during a no-gas period and analyzing the first hyperspectral data using one or more gas plume detection logics. The gas plume detection logic is executed using a low detection threshold, and detects each occurrence of an observed hyperspectral signature. The method also includes generating a histogram for all occurrences of each observed hyperspectral signature which is detected using the gas plume detection logic, and determining a probability of false alarm (PFA) for all occurrences of each observed hyperspectral signature based on the histogram. Possibly at some other time, the method includes collecting second hyperspectral data, and analyzing the second hyperspectral data using the one or more gas plume detection logics and the PFA to determine if any gas is present. Other systems and methods are also included.

Unsupervised Feature Selection Based on the Morisita Index for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Golay, Jean; Kanevski, Mikhail

2017-04-01

Hyperspectral sensors are capable of acquiring images with hundreds of narrow and contiguous spectral bands. Compared with traditional multispectral imagery, the use of hyperspectral images allows better performance in discriminating between land-cover classes, but it also results in large redundancy and high computational data processing. To alleviate such issues, unsupervised feature selection techniques for redundancy minimization can be implemented. Their goal is to select the smallest subset of features (or bands) in such a way that all the information content of a data set is preserved as much as possible. The present research deals with the application to hyperspectral images of a recently introduced technique of unsupervised feature selection: the Morisita-Based filter for Redundancy Minimization (MBRM). MBRM is based on the (multipoint) Morisita index of clustering and on the Morisita estimator of Intrinsic Dimension (ID). The fundamental idea of the technique is to retain only the bands which contribute to increasing the ID of an image. In this way, redundant bands are disregarded, since they have no impact on the ID. Besides, MBRM has several advantages over benchmark techniques: in addition to its ability to deal with large data sets, it can capture highly-nonlinear dependences and its implementation is straightforward in any programming environment. Experimental results on freely available hyperspectral images show the good effectiveness of MBRM in remote sensing data processing. Comparisons with benchmark techniques are carried out and random forests are used to assess the performance of MBRM in reducing the data dimensionality without loss of relevant information. References [1] C. Traina Jr., A.J.M. Traina, L. Wu, C. Faloutsos, Fast feature selection using fractal dimension, in: Proceedings of the XV Brazilian Symposium on Databases, SBBD, pp. 158-171, 2000. [2] J. Golay, M. Kanevski, A new estimator of intrinsic dimension based on the multipoint Morisita index, Pattern Recognition 48(12), pp. 4070-4081, 2015. [3] J. Golay, M. Kanevski, Unsupervised feature selection based on the Morisita estimator of intrinsic dimension, arXiv:1608.05581, 2016.

Design Analysis of a Space Based Chromotomographic Hyperspectral Imaging Experiment

DTIC Science & Technology

2010-03-01

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

Surface retrievals from Hyperion EO1 using a new, fast, 1D-Var based retrieval code

NASA Astrophysics Data System (ADS)

Thelen, Jean-Claude; Havemann, Stephan; Wong, Gerald

2015-05-01

We have developed a new algorithm for the simultaneous retrieval of the atmospheric profiles (temperature, humidity, ozone and aerosol) and the surface reflectance from hyperspectral radiance measurements obtained from air/space-borne, hyperspectral imagers such as Hyperion EO-1. The new scheme, proposed here, consists of a fast radiative transfer code, based on empirical orthogonal functions (EOFs), in conjunction with a 1D-Var retrieval scheme. The inclusion of an 'exact' scattering code based on spherical harmonics, allows for an accurate treatment of Rayleigh scattering and scattering by aerosols, water droplets and ice-crystals, thus making it possible to also retrieve cloud and aerosol optical properties, although here we will concentrate on non-cloudy scenes. We successfully tested this new approach using hyperspectral images taken by Hyperion EO-1, an experimental pushbroom imaging spectrometer operated by NASA.

Super resolution terahertz imaging by subpixel estimation: application to hyperspectral beam profiling

NASA Astrophysics Data System (ADS)

Logofătu, Petre C.; Damian, Victor

2018-05-01

A super-resolution terahertz imaging technique based on subpixel estimation was applied to hyperspectral beam profiling. The topic of hyperspectral beam profiling was chosen because the beam profile and its dependence on wavelength are not well known and are important for imaging applications. Super-resolution is required here to avoid diffraction effects and to provide a stronger signal. Super-resolution usually adds supplementary information to the measurement, but in this case, it is a prerequisite for it. We report that the beam profile is almost Gaussian for many frequencies; the waist of the Gaussian profile increases with frequency while the center wobbles slightly. Knowledge of the beam profile may subsequently be used as reference for imaging.

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

USDA-ARS?s Scientific Manuscript database

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

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.

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

PubMed

Jiang, Yu; Li, Changying

2015-01-01

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

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing.

PubMed

William, David J; Rybicki, Nancy B; Lombana, Alfonso V; O'Brien, Tim M; Gomez, Richard B

2003-01-01

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.

[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 introduced to eliminate the impact of nonlinear hyperspectral data to some extent through mapping the original nonlinear hyperspectral data to the high dimensional linear feature space, so the relationship between the nonlinear hyperspectral data and the encapsulation temperatures of EVA films was fully disclosed finally. Compared with the prediction results of three proposed models, the prediction performance of LMNN was superior to the other two, whose final recognition accuracy achieved 100%. The results indicated that the methods of combination of LMNN model with the hyperspectral imaging techniques was the best one for accurately and rapidly determining the encapsulation temperatures of EVA films of photovoltaic cells. In addition, this paper had created the ideal conditions for automatically monitoring and effectively controlling the encapsulation temperatures of EVA films in the photovoltaic cells production process.

Reflectance Hyperspectral Imaging for Investigation of Works of Art: Old Master Paintings and Illuminated Manuscripts.

PubMed

Cucci, Costanza; Delaney, John K; Picollo, Marcello

2016-10-18

Diffuse reflectance hyperspectral imaging, or reflectance imaging spectroscopy, is a sophisticated technique that enables the capture of hundreds of images in contiguous narrow spectral bands (bandwidth < 10 nm), typically in the visible (Vis, 400-750 nm) and the near-infrared (NIR, 750-2500 nm) regions. This sequence of images provides a data set that is called an image-cube or file-cube. Two dimensions of the image-cube are the spatial dimensions of the scene, and the third dimension is the wavelength. In this way, each spatial pixel in the image has an associated reflectance spectrum. This "big data" image-cube allows for the mining of artists' materials and mapping their distribution across the surface of a work of art. Reflectance hyperspectral imaging, introduced in the 1980s by Goetz and co-workers, led to a revolution in the field of remote sensing of the earth and near planets ( Goetz, F. H.; Vane, G.; Solomon, B. N.; Rock, N. Imaging Spectrometry for Earth Remote Sensing . Science , 1985 , 228 , 1147 - 1152 ). In the subsequent decades, thanks to rapid advances in solid-state sensor technology, reflectance hyperspectral imaging, once only available to large government laboratories, was extended to new fields of application, such as monitoring agri-foods, pharmaceutical products, the environment, and cultural heritage. In the 2000s, the potential of this noninvasive technology for the study of artworks became evident and, consequently, the methodology is becoming more widely used in the art conservation science field. Typically hyperspectral reflectance image-cubes contain millions of spectra. Many of these spectra are similar, making the reduction of the data set size an important first step. Thus, image-processing tools based on multivariate techniques, such as principal component analysis (PCA), automated classification methods, or expert knowledge systems, that search for known spectral features are often applied. These algorithms seek to reduce the large number of high-quality spectra to a common subset, which allow identifying and mapping artists' materials and alteration products. Hence, reflectance hyperspectral imaging is finding its place as the starting point to find sites on polychrome surfaces for spot analytical techniques, such as X-ray fluorescence, Raman spectroscopy, and Fourier transform infrared spectroscopy. Reflectance hyperspectral imaging can also provide image products that are a mainstay in the art conservation field, such as color-accurate images, broadband near-infrared images, and false-color products. This Account reports on the research activity carried out by two research groups, one at the "Nello Carrara" Institute of Applied Physics of the Italian National Research Council (IFAC-CNR) in Florence and the other at the National Gallery of Art (NGA) in Washington, D.C. Both groups have conducted parallel research, with frequent interchanges, to develop multispectral and hyperspectral imaging systems to study works of art. In the past decade, they have designed and experimented with some of the earliest spectral imaging prototypes for museum applications. In this Account, a brief presentation of the hyperspectral sensor systems is given with case studies showing how reflectance hyperspectral imaging is answering key questions in cultural heritage.

A hyperspectral imaging system for the evaluation of the human iris spectral reflectance

NASA Astrophysics Data System (ADS)

Di Cecilia, Luca; Marazzi, Francesco; Rovati, Luigi

2017-02-01

According to previous studies, the measurement of the human iris pigmentation can be exploited to detect certain eye pathological conditions in their early stage. In this paper, we propose an instrument and a method to perform hyperspectral quantitative measurements of the iris spectral reflectance. The system is based on a simple imaging setup, which includes a monochrome camera mounted on a standard ophthalmic microscope movement controller, a monochromator, and a flashing LED-based slit lamp. To assure quantitative measurements, the system is properly calibrated against a NIST reflectance standard. Iris reflectance images can be obtained in the spectral range 495-795 nm with a resolution of 25 nm. Each image consists of 1280 x 1024 pixels having a spatial resolution of 18 μm. Reflectance spectra can be calculated both from discrete areas of the iris and as the average of the whole iris surface. Preliminary results suggest that hyperspectral imaging of the iris can provide much more morphological and spectral information with respect to conventional qualitative colorimetric methods.

Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

PubMed

Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

2015-01-01

There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.

Tower testing of a 64W shortwave infrared supercontinuum laser for use as a hyperspectral imaging illuminator

NASA Astrophysics Data System (ADS)

Meola, Joseph; Absi, Anthony; Islam, Mohammed N.; Peterson, Lauren M.; Ke, Kevin; Freeman, Michael J.; Ifaraguerri, Agustin I.

2014-06-01

Hyperspectral imaging systems are currently used for numerous activities related to spectral identification of materials. These passive imaging systems rely on naturally reflected/emitted radiation as the source of the signal. Thermal infrared systems measure radiation emitted from objects in the scene. As such, they can operate at both day and night. However, visible through shortwave infrared systems measure solar illumination reflected from objects. As a result, their use is limited to daytime applications. Omni Sciences has produced high powered broadband shortwave infrared super-continuum laser illuminators. A 64-watt breadboard system was recently packaged and tested at Wright-Patterson Air Force Base to gauge beam quality and to serve as a proof-of-concept for potential use as an illuminator for a hyperspectral receiver. The laser illuminator was placed in a tower and directed along a 1.4km slant path to various target materials with reflected radiation measured with both a broadband camera and a hyperspectral imaging system to gauge performance.

Snapshot hyperspectral retinal imaging using compact spectral resolving detector array.

PubMed

Li, Hao; Liu, Wenzhong; Dong, Biqin; Kaluzny, Joel V; Fawzi, Amani A; Zhang, Hao F

2017-06-01

Hyperspectral retinal imaging captures the light spectrum from each imaging pixel. It provides spectrally encoded retinal physiological and morphological information, which could potentially benefit diagnosis and therapeutic monitoring of retinal diseases. The key challenges in hyperspectral retinal imaging are how to achieve snapshot imaging to avoid motions between the images from multiple spectral bands, and how to design a compact snapshot imager suitable for clinical use. Here, we developed a compact, snapshot hyperspectral fundus camera for rodents using a novel spectral resolving detector array (SRDA), on which a thin-film Fabry-Perot cavity filter was monolithically fabricated on each imaging pixel. We achieved hyperspectral retinal imaging with 16 wavelength bands (460 to 630 nm) at 20 fps. We also demonstrated false-color vessel contrast enhancement and retinal oxygen saturation (sO 2 ) measurement through spectral analysis. This work could potentially bring hyperspectral retinal imaging from bench to bedside. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Efficiency of the spectral-spatial classification of hyperspectral imaging data

NASA Astrophysics Data System (ADS)

Borzov, S. M.; Potaturkin, O. I.

2017-01-01

The efficiency of methods of the spectral-spatial classification of similarly looking types of vegetation on the basis of hyperspectral data of remote sensing of the Earth, which take into account local neighborhoods of analyzed image pixels, is experimentally studied. Algorithms that involve spatial pre-processing of the raw data and post-processing of pixel-based spectral classification maps are considered. Results obtained both for a large-size hyperspectral image and for its test fragment with different methods of training set construction are reported. The classification accuracy in all cases is estimated through comparisons of ground-truth data and classification maps formed by using the compared methods. The reasons for the differences in these estimates are discussed.

Hyperspectral imaging polarimeter in the infrared

NASA Astrophysics Data System (ADS)

Jensen, Gary L.; Peterson, James Q.

1998-11-01

The Space Dynamics Laboratory at Utah State University is building an infrared Hyperspectral Imaging Polarimeter (HIP). Designed for high spatial and spectral resolution polarimetry of backscattered sunlight from cloud tops in the 2.7 micrometer water band, it will fly aboard the Flying Infrared Signatures Technology Aircraft (FISTA), an Air Force KC-135. It is a proof-of-concept sensor, combining hyperspectral pushbroom imaging with high speed, solid state polarimetry, using as many off-the-shelf components as possible, and utilizing an optical breadboard design for rapid prototyping. It is based around a 256 X 320 window selectable InSb camera, a solid-state Ferro-electric Liquid Crystal (FLC) polarimeter, and a transmissive diffraction grating.

Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging

NASA Astrophysics Data System (ADS)

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

2016-12-01

Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.

High dynamic range hyperspectral imaging for camouflage performance test and evaluation

NASA Astrophysics Data System (ADS)

Pearce, D.; Feenan, J.

2016-10-01

This paper demonstrates the use of high dynamic range processing applied to the specific technique of hyper-spectral imaging with linescan spectrometers. The technique provides an improvement in signal to noise for reflectance estimation. This is demonstrated for field measurements of rural imagery collected from a ground-based linescan spectrometer of rural scenes. Once fully developed, the specific application is expected to improve the colour estimation approaches and consequently the test and evaluation accuracy of camouflage performance tests. Data are presented on both field and laboratory experiments that have been used to evaluate the improvements granted by the adoption of high dynamic range data acquisition in the field of hyperspectral imaging. High dynamic ranging imaging is well suited to the hyperspectral domain due to the large variation in solar irradiance across the visible and short wave infra-red (SWIR) spectrum coupled with the wavelength dependence of the nominal silicon detector response. Under field measurement conditions it is generally impractical to provide artificial illumination; consequently, an adaptation of the hyperspectral imaging and re ectance estimation process has been developed to accommodate the solar spectrum. This is shown to improve the signal to noise ratio for the re ectance estimation process of scene materials in the 400-500 nm and 700-900 nm regions.

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions.

PubMed

Feng, Chao-Hui; Makino, Yoshio; Yoshimura, Masatoshi; Thuyet, Dang Quoc; García-Martín, Juan Francisco

2018-02-01

The potential of hyperspectral imaging with wavelengths of 380 to 1000 nm was used to determine the pH of cooked sausages after different storage conditions (4 °C for 1 d, 35 °C for 1, 3, and 5 d). The mean spectra of the sausages were extracted from the hyperspectral images and partial least squares regression (PLSR) model was developed to relate spectral profiles with the pH of the cooked sausages. Eleven important wavelengths were selected based on the regression coefficient values. The PLSR model established using the optimal wavelengths showed good precision being the prediction coefficient of determination (R p 2 ) 0.909 and the root mean square error of prediction 0.035. The prediction map for illustrating pH indices in sausages was for the first time developed by R statistics. The overall results suggested that hyperspectral imaging combined with PLSR and R statistics are capable to quantify and visualize the sausages pH evolution under different storage conditions. In this paper, hyperspectral imaging is for the first time used to detect pH in cooked sausages using R statistics, which provides another useful information for the researchers who do not have the access to Matlab. Eleven optimal wavelengths were successfully selected, which were used for simplifying the PLSR model established based on the full wavelengths. This simplified model achieved a high R p 2 (0.909) and a low root mean square error of prediction (0.035), which can be useful for the design of multispectral imaging systems. © 2017 Institute of Food Technologists®.

Identification of spectral phenotypes in age-related macular degeneration patients

NASA Astrophysics Data System (ADS)

Davis, Bert; Russell, Steven; Abramoff, Michael; Nemeth, Sheila C.; Barriga, E. Simon; Soliz, Peter

2007-02-01

The purpose of this study is to show that there exists a spectral characteristic that differentiates normal macular tissue from various types of genetic-based macular diseases. This paper demonstrates statistically that hyperspectral images of macular and other retinal tissue can be used to spectrally differentiate different forms of age-related macular degeneration. A hyperspectral fundus imaging device has been developed and tested for the purpose of collecting hyperspectral images of the human retina. A methodology based on partial least squares and ANOVA has been applied to determine the hyperspectral representation of individual spectral characteristics of retinal features. Each discrete tissue type in the retina has an identifiable spectral shape or signature which, when combined with spatial context, aids in detection of pathological features. Variations in the amount and distribution of various ocular pigments or the inclusion of additional biochemical substances will allow detection of pathological conditions prior to traditional histological presentation. Fundus imaging cameras are ubiquitous and are one of the most common imaging modalities used in documenting a patient's retinal state for diagnosis, e.g. remotely, or for monitoring the progression of an ocular disease. The added diagnostic information obtained with only a minor retro-fit of a specialized spectral camera will lead to new diagnostic information to the clinical ophthalmologist or eye-care specialist.

GPU Lossless Hyperspectral Data Compression System

NASA Technical Reports Server (NTRS)

Aranki, Nazeeh I.; Keymeulen, Didier; Kiely, Aaron B.; Klimesh, Matthew A.

2014-01-01

Hyperspectral imaging systems onboard aircraft or spacecraft can acquire large amounts of data, putting a strain on limited downlink and storage resources. Onboard data compression can mitigate this problem but may require a system capable of a high throughput. In order to achieve a high throughput with a software compressor, a graphics processing unit (GPU) implementation of a compressor was developed targeting the current state-of-the-art GPUs from NVIDIA(R). The implementation is based on the fast lossless (FL) compression algorithm reported in "Fast Lossless Compression of Multispectral-Image Data" (NPO- 42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which operates on hyperspectral data and achieves excellent compression performance while having low complexity. The FL compressor uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. The new Consultative Committee for Space Data Systems (CCSDS) Standard for Lossless Multispectral & Hyperspectral image compression (CCSDS 123) is based on the FL compressor. The software makes use of the highly-parallel processing capability of GPUs to achieve a throughput at least six times higher than that of a software implementation running on a single-core CPU. This implementation provides a practical real-time solution for compression of data from airborne hyperspectral instruments.

Naval EarthMap Observer (NEMO) science and naval products

NASA Astrophysics Data System (ADS)

Davis, Curtiss O.; Kappus, Mary E.; Gao, Bo-Cai; Bissett, W. Paul; Snyder, William A.

1998-11-01

A wide variety of applications of imaging spectrometry have been demonstrated using data from aircraft systems. Based on this experience the Navy is pursuing the Hyperspectral Remote Sensing Technology (HRST) Program to use hyperspectral imagery to characterize the littoral environment, for scientific and environmental studies and to meet Naval needs. To obtain the required space based hyperspectral imagery the Navy has joined in a partnership with industry to build and fly the Naval EarthMap Observer (NEMO). The NEMO spacecraft has the Coastal Ocean Imaging Spectrometer (COIS) a hyperspectral imager with adequate spectral and spatial resolution and a high signal-to- noise ratio to provide long term monitoring and real-time characterization of the coastal environment. It includes on- board processing for rapid data analysis and data compression, a large volume recorder, and high speed downlink to handle the required large volumes of data. This paper describes the algorithms for processing the COIS data to provide at-launch ocean data products and the research and modeling that are planned to use COIS data to advance our understanding of the dynamics of the coastal ocean.

Detecting brain tumor in pathological slides using hyperspectral imaging

PubMed Central

Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M.; Sarmiento, Roberto

2018-01-01

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides. PMID:29552415

Detecting brain tumor in pathological slides using hyperspectral imaging.

PubMed

Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M; Sarmiento, Roberto

2018-02-01

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.

Comparing methods for analysis of biomedical hyperspectral image data

NASA Astrophysics Data System (ADS)

Leavesley, Silas J.; Sweat, Brenner; Abbott, Caitlyn; Favreau, Peter F.; Annamdevula, Naga S.; Rich, Thomas C.

2017-02-01

Over the past 2 decades, hyperspectral imaging technologies have been adapted to address the need for molecule-specific identification in the biomedical imaging field. Applications have ranged from single-cell microscopy to whole-animal in vivo imaging and from basic research to clinical systems. Enabling this growth has been the availability of faster, more effective hyperspectral filtering technologies and more sensitive detectors. Hence, the potential for growth of biomedical hyperspectral imaging is high, and many hyperspectral imaging options are already commercially available. However, despite the growth in hyperspectral technologies for biomedical imaging, little work has been done to aid users of hyperspectral imaging instruments in selecting appropriate analysis algorithms. Here, we present an approach for comparing the effectiveness of spectral analysis algorithms by combining experimental image data with a theoretical "what if" scenario. This approach allows us to quantify several key outcomes that characterize a hyperspectral imaging study: linearity of sensitivity, positive detection cut-off slope, dynamic range, and false positive events. We present results of using this approach for comparing the effectiveness of several common spectral analysis algorithms for detecting weak fluorescent protein emission in the midst of strong tissue autofluorescence. Results indicate that this approach should be applicable to a very wide range of applications, allowing a quantitative assessment of the effectiveness of the combined biology, hardware, and computational analysis for detecting a specific molecular signature.

Visible, Very Near IR and Short Wave IR Hyperspectral Drone Imaging System for Agriculture and Natural Water Applications

NASA Astrophysics Data System (ADS)

Saari, H.; Akujärvi, A.; Holmlund, C.; Ojanen, H.; Kaivosoja, J.; Nissinen, A.; Niemeläinen, O.

2017-10-01

The accurate determination of the quality parameters of crops requires a spectral range from 400 nm to 2500 nm (Kawamura et al., 2010, Thenkabail et al., 2002). Presently the hyperspectral imaging systems that cover this wavelength range consist of several separate hyperspectral imagers and the system weight is from 5 to 15 kg. In addition the cost of the Short Wave Infrared (SWIR) cameras is high (  50 k€). VTT has previously developed compact hyperspectral imagers for drones and Cubesats for Visible and Very near Infrared (VNIR) spectral ranges (Saari et al., 2013, Mannila et al., 2013, Näsilä et al., 2016). Recently VTT has started to develop a hyperspectral imaging system that will enable imaging simultaneously in the Visible, VNIR, and SWIR spectral bands. The system can be operated from a drone, on a camera stand, or attached to a tractor. The targeted main applications of the DroneKnowledge hyperspectral system are grass, peas, and cereals. In this paper the characteristics of the built system are shortly described. The system was used for spectral measurements of wheat, several grass species and pea plants fixed to the camera mount in the test fields in Southern Finland and in the green house. The wheat, grass and pea field measurements were also carried out using the system mounted on the tractor. The work is part of the Finnish nationally funded DroneKnowledge - Towards knowledge based export of small UAS remote sensing technology project.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2012-05-01

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

Using hyperspectral imaging to determine germination of native Australian plant seeds.

PubMed

Nansen, Christian; Zhao, Genpin; Dakin, Nicole; Zhao, Chunhui; Turner, Shane R

2015-04-01

We investigated the ability to accurately and non-destructively determine the germination of three native Australian tree species, Acacia cowleana Tate (Fabaceae), Banksia prionotes L.F. (Proteaceae), and Corymbia calophylla (Lindl.) K.D. Hill & L.A.S. Johnson (Myrtaceae) based on hyperspectral imaging data. While similar studies have been conducted on agricultural and horticultural seeds, we are unaware of any published studies involving reflectance-based assessments of the germination of tree seeds. Hyperspectral imaging data (110 narrow spectral bands from 423.6nm to 878.9nm) were acquired of individual seeds after 0, 1, 2, 5, 10, 20, 30, and 50days of standardized rapid ageing. At each time point, seeds were subjected to hyperspectral imaging to obtain reflectance profiles from individual seeds. A standard germination test was performed, and we predicted that loss of germination was associated with a significant change in seed coat reflectance profiles. Forward linear discriminant analysis (LDA) was used to select the 10 spectral bands with the highest contribution to classifications of the three species. In all species, germination decreased from over 90% to below 20% in about 10-30days of experimental ageing. P50 values (equal to 50% germination) for each species were 19.3 (A. cowleana), 7.0 (B. prionotes) and 22.9 (C. calophylla) days. Based on independent validation of classifications of hyperspectral imaging data, we found that germination of Acacia and Corymbia seeds could be classified with over 85% accuracy, while it was about 80% for Banksia seeds. The selected spectral bands in each LDA-based classification were located near known pigment peaks involved in photosynthesis and/or near spectral bands used in published indices to predict chlorophyll or nitrogen content in leaves. The results suggested that seed germination may be successfully classified (predicted) based on reflectance in narrow spectral bands associated with the primary metabolism function and performance of plants. Copyright © 2015 Elsevier B.V. All rights reserved.

Mapping Geology and Vegetation using Hyperspectral Data in Antarctica: Current Challenges, New Solutions and Looking to the Future

NASA Astrophysics Data System (ADS)

Black, M.; Riley, T. R.; Fleming, A. H.; Ferrier, G.; Fretwell, P.; Casanovas, P.

2015-12-01

Antarctica is a unique and geographically remote environment. Traditional field campaigns investigating geology and vegetation in the region encounter numerous challenges including the harsh polar climate, the invasive nature of the work, steep topography and high infrastructure costs. Additionally, such field campaigns are often limited in terms of spatial and temporal resolution, and particularly, the topographical challenges presented in the Antarctic mean that many areas remain inaccessible. Remote Sensing, particularly hyperspectral imaging, may provide a solution to overcome the difficulties associated with field based mapping in the Antarctic. Planned satellite launches, such as EnMAP and HyspIRI, if successful, will yield large-scale, repeated hyperspectral imagery of Antarctica. Hyperspectral imagery has proven mapping capabilities and can yield greater information than can be attained using multispectral data. As a precursor to future satellite imagery, we utilise hyperspectral imagery from the first known airborne hyperspectral survey carried out in the Antarctic by the British Antarctic Survey and partners in 2011. Multiple imaging spectrometers were simultaneously deployed covering the visible, shortwave and thermal infrared regions of the electromagnetic spectrum. Additional data was generated during a field campaign deploying multiple ground spectrometers covering the same wavelengths as the airborne imagers. We utilise this imagery to assess the current challenges and propose some new solutions for mapping vegetation and geology, which may be directly applicable to future satellite hyperspectral imagery in the Antarctic.

Global Learning Spectral Archive- A new Way to deal with Unknown Urban Spectra -

NASA Astrophysics Data System (ADS)

Jilge, M.; Heiden, U.; Habermeyer, M.; Jürgens, C.

2015-12-01

Rapid urbanization processes and the need of identifying urban materials demand urban planners and the remote sensing community since years. Urban planners cannot overcome the issue of up-to-date information of urban materials due to time-intensive fieldwork. Hyperspectral remote sensing can facilitate this issue by interpreting spectral signals to provide information of occurring materials. However, the complexity of urban areas and the occurrence of diverse urban materials vary due to regional and cultural aspects as well as the size of a city, which makes identification of surface materials a challenging analysis task. For the various surface material identification approaches, spectral libraries containing pure material spectra are commonly used, which are derived from field, laboratory or the hyperspectral image itself. One of the requirements for successful image analysis is that all spectrally different surface materials are represented by the library. Currently, a universal library, applicable in every urban area worldwide and taking each spectral variability into account, is and will not be existent. In this study, the issue of unknown surface material spectra and the demand of an urban site-specific spectral library is tackled by the development of a learning spectral archive tool. Starting with an incomplete library of labelled image spectra from several German cities, surface materials of pure image pixels will be identified in a hyperspectral image based on a similarity measure (e.g. SID-SAM). Additionally, unknown image spectra of urban objects are identified based on an object- and spectral-based-rule set. The detected unknown surface material spectra are entered with additional metadata, such as regional occurrence into the existing spectral library and thus, are reusable for further studies. Our approach is suitable for pure surface material detection of urban hyperspectral images that is globally applicable by taking incompleteness into account. The generically development enables the implementation of different hyperspectral sensors.

Classification of Salmonella serotypes with hyperspectral microscope imagery

USDA-ARS?s Scientific Manuscript database

Previous research has demonstrated an optical method with acousto-optic tunable filter (AOTF) based hyperspectral microscope imaging (HMI) had potential for classifying gram-negative from gram-positive foodborne pathogenic bacteria rapidly and nondestructively with a minimum sample preparation. In t...

Hyperspectral imaging of endogenous fluorescent metabolic molecules to identify pain states in central nervous system tissue

NASA Astrophysics Data System (ADS)

Staikopoulos, Vasiliki; Gosnell, Martin E.; Anwer, Ayad G.; Mustafa, Sanam; Hutchinson, Mark R.; Goldys, Ewa M.

2016-12-01

Fluorescence-based bio-imaging methods have been extensively used to identify molecular changes occurring in biological samples in various pathological adaptations. Auto-fluorescence generated by endogenous fluorescent molecules within these samples can interfere with signal to background noise making positive antibody based fluorescent staining difficult to resolve. Hyperspectral imaging uses spectral and spatial imaging information for target detection and classification, and can be used to resolve changes in endogenous fluorescent molecules such as flavins, bound and free NADH and retinoids that are involved in cell metabolism. Hyperspectral auto-fluorescence imaging of spinal cord slices was used in this study to detect metabolic differences within pain processing regions of non-pain versus sciatic chronic constriction injury (CCI) animals, an established animal model of peripheral neuropathy. By using an endogenous source of contrast, subtle metabolic variations were detected between tissue samples, making it possible to distinguish between animals from non-injured and injured groups. Tissue maps of native fluorophores, flavins, bound and free NADH and retinoids unveiled subtle metabolic signatures and helped uncover significant tissue regions with compromised mitochondrial function. Taken together, our results demonstrate that hyperspectral imaging provides a new non-invasive method to investigate central changes of peripheral neuropathic injury and other neurodegenerative disease models, and paves the way for novel cellular characterisation in health, disease and during treatment, with proper account of intrinsic cellular heterogeneity.

Characterization and modelling of the spatially- and spectrally-varying point-spread function in hyperspectral imaging systems for computational correction of axial optical aberrations

NASA Astrophysics Data System (ADS)

Špiclin, Žiga; Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2012-03-01

Spatial resolution of hyperspectral imaging systems can vary significantly due to axial optical aberrations that originate from wavelength-induced index-of-refraction variations of the imaging optics. For systems that have a broad spectral range, the spatial resolution will vary significantly both with respect to the acquisition wavelength and with respect to the spatial position within each spectral image. Variations of the spatial resolution can be effectively characterized as part of the calibration procedure by a local image-based estimation of the pointspread function (PSF) of the hyperspectral imaging system. The estimated PSF can then be used in the image deconvolution methods to improve the spatial resolution of the spectral images. We estimated the PSFs from the spectral images of a line grid geometric caliber. From individual line segments of the line grid, the PSF was obtained by a non-parametric estimation procedure that used an orthogonal series representation of the PSF. By using the non-parametric estimation procedure, the PSFs were estimated at different spatial positions and at different wavelengths. The variations of the spatial resolution were characterized by the radius and the fullwidth half-maximum of each PSF and by the modulation transfer function, computed from images of USAF1951 resolution target. The estimation and characterization of the PSFs and the image deconvolution based spatial resolution enhancement were tested on images obtained by a hyperspectral imaging system with an acousto-optic tunable filter in the visible spectral range. The results demonstrate that the spatial resolution of the acquired spectral images can be significantly improved using the estimated PSFs and image deconvolution methods.

[Lithology feature extraction of CASI hyperspectral data based on fractal signal algorithm].

PubMed

Tang, Chao; Chen, Jian-Ping; Cui, Jing; Wen, Bo-Tao

2014-05-01

Hyperspectral data is characterized by combination of image and spectrum and large data volume dimension reduction is the main research direction. Band selection and feature extraction is the primary method used for this objective. In the present article, the authors tested methods applied for the lithology feature extraction from hyperspectral data. Based on the self-similarity of hyperspectral data, the authors explored the application of fractal algorithm to lithology feature extraction from CASI hyperspectral data. The "carpet method" was corrected and then applied to calculate the fractal value of every pixel in the hyperspectral data. The results show that fractal information highlights the exposed bedrock lithology better than the original hyperspectral data The fractal signal and characterized scale are influenced by the spectral curve shape, the initial scale selection and iteration step. At present, research on the fractal signal of spectral curve is rare, implying the necessity of further quantitative analysis and investigation of its physical implications.

Identification of staphylococcus species with hyperspectral microscope imaging and classification algrorithms

USDA-ARS?s Scientific Manuscript database

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...

[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 98.44% of identification accuracy was achieved in the calibration set for the PCA-LS-SVM model and the SPA-LS-SVM model, respectively, and a 95.83% of identification accuracy was achieved in the validation set for both the PCA-LS-SVM and the SPA- LS-SVM models, which were built based on the hyperspectral data of adaxial surface. Comparatively, the results of the PCA-LS-SVM and the SPA-LS-SVM models built based on the hyperspectral data of abaxial surface both achieved identification accuracies of 100% for both calibration set and validation set. The overall results demonstrated that use of hyperspectral data of adaxial and abaxial leaf surfaces coupled with the use of PCA-LS-SVM and the SPA-LS-SVM could achieve an accurate identification of pummelo cultivars. It was feasible to use hyperspectral imaging technology to identify different pummelo cultivars, and hyperspectral imaging technology provided an alternate way of rapid identification of pummelo cultivars. Moreover, the results in this paper demonstrated that the data from the abaxial surface of leaf was more sensitive in identifying pummelo cultivars. This study provided a new method for to the fast discrimination of pummelo cultivars.

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.

Early detection of toxigenic fungi on maize by hyperspectral imaging analysis.

PubMed

Del Fiore, A; Reverberi, M; Ricelli, A; Pinzari, F; Serranti, S; Fabbri, A A; Bonifazi, G; Fanelli, C

2010-11-15

Fungi can grow on many food commodities. Some fungal species, such as Aspergillus flavus, Aspergillus parasiticus, Aspergillus niger and Fusarium spp., can produce, under suitable conditions, mycotoxins, secondary metabolites which are toxic for humans and animals. Toxigenic fungi are a real issue, especially for the cereal industry. The aim of this work is to carry out a non destructive, hyperspectral imaging-based method to detect toxigenic fungi on maize kernels, and to discriminate between healthy and diseased kernels. A desktop spectral scanner equipped with an imaging based spectrometer ImSpector- Specim V10, working in the visible-near infrared spectral range (400-1000 nm) was used. The results show that the hyperspectral imaging is able to rapidly discriminate commercial maize kernels infected with toxigenic fungi from uninfected controls when traditional methods are not yet effective: i.e. from 48 h after inoculation with A. niger or A. flavus. Copyright © 2010 Elsevier B.V. All rights reserved.

ICER-3D Hyperspectral Image Compression Software

NASA Technical Reports Server (NTRS)

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

2010-01-01

Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.

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

NASA Astrophysics Data System (ADS)

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

2010-10-01

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

Hyperspectral data acquisition and analysis in imaging and real-time active MIR backscattering spectroscopy

NASA Astrophysics Data System (ADS)

Jarvis, Jan; Haertelt, Marko; Hugger, Stefan; Butschek, Lorenz; Fuchs, Frank; Ostendorf, Ralf; Wagner, Joachim; Beyerer, Juergen

2017-04-01

In this work we present data analysis algorithms for detection of hazardous substances in hyperspectral observations acquired using active mid-infrared (MIR) backscattering spectroscopy. We present a novel background extraction algorithm based on the adaptive target generation process proposed by Ren and Chang called the adaptive background generation process (ABGP) that generates a robust and physically meaningful set of background spectra for operation of the well-known adaptive matched subspace detection (AMSD) algorithm. It is shown that the resulting AMSD-ABGP detection algorithm competes well with other widely used detection algorithms. The method is demonstrated in measurement data obtained by two fundamentally different active MIR hyperspectral data acquisition devices. A hyperspectral image sensor applicable in static scenes takes a wavelength sequential approach to hyperspectral data acquisition, whereas a rapid wavelength-scanning single-element detector variant of the same principle uses spatial scanning to generate the hyperspectral observation. It is shown that the measurement timescale of the latter is sufficient for the application of the data analysis algorithms even in dynamic scenarios.

Feature selection from hyperspectral imaging for guava fruit defects detection

NASA Astrophysics Data System (ADS)

Mat Jafri, Mohd. Zubir; Tan, Sou Ching

2017-06-01

Development of technology makes hyperspectral imaging commonly used for defect detection. In this research, a hyperspectral imaging system was setup in lab to target for guava fruits defect detection. Guava fruit was selected as the object as to our knowledge, there is fewer attempts were made for guava defect detection based on hyperspectral imaging. The common fluorescent light source was used to represent the uncontrolled lighting condition in lab and analysis was carried out in a specific wavelength range due to inefficiency of this particular light source. Based on the data, the reflectance intensity of this specific setup could be categorized in two groups. Sequential feature selection with linear discriminant (LD) and quadratic discriminant (QD) function were used to select features that could potentially be used in defects detection. Besides the ordinary training method, training dataset in discriminant was separated in two to cater for the uncontrolled lighting condition. These two parts were separated based on the brighter and dimmer area. Four evaluation matrixes were evaluated which are LD with common training method, QD with common training method, LD with two part training method and QD with two part training method. These evaluation matrixes were evaluated using F1-score with total 48 defected areas. Experiment shown that F1-score of linear discriminant with the compensated method hitting 0.8 score, which is the highest score among all.

Classification of high-resolution multi-swath hyperspectral data using Landsat 8 surface reflectance data as a calibration target and a novel histogram based unsupervised classification technique to determine natural classes from biophysically relevant fit parameters

NASA Astrophysics Data System (ADS)

McCann, C.; Repasky, K. S.; Morin, M.; Lawrence, R. L.; Powell, S. L.

2016-12-01

Compact, cost-effective, flight-based hyperspectral imaging systems can provide scientifically relevant data over large areas for a variety of applications such as ecosystem studies, precision agriculture, and land management. To fully realize this capability, unsupervised classification techniques based on radiometrically-calibrated data that cluster based on biophysical similarity rather than simply spectral similarity are needed. An automated technique to produce high-resolution, large-area, radiometrically-calibrated hyperspectral data sets based on the Landsat surface reflectance data product as a calibration target was developed and applied to three subsequent years of data covering approximately 1850 hectares. The radiometrically-calibrated data allows inter-comparison of the temporal series. Advantages of the radiometric calibration technique include the need for minimal site access, no ancillary instrumentation, and automated processing. Fitting the reflectance spectra of each pixel using a set of biophysically relevant basis functions reduces the data from 80 spectral bands to 9 parameters providing noise reduction and data compression. Examination of histograms of these parameters allows for determination of natural splitting into biophysical similar clusters. This method creates clusters that are similar in terms of biophysical parameters, not simply spectral proximity. Furthermore, this method can be applied to other data sets, such as urban scenes, by developing other physically meaningful basis functions. The ability to use hyperspectral imaging for a variety of important applications requires the development of data processing techniques that can be automated. The radiometric-calibration combined with the histogram based unsupervised classification technique presented here provide one potential avenue for managing big-data associated with hyperspectral imaging.

Hyperspectral imaging for nondestructive evaluation of tomatoes

USDA-ARS?s Scientific Manuscript database

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...

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

NASA Astrophysics Data System (ADS)

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

2012-09-01

Hyperspectral imagery offers unique benefits for detection of land and water features due to the information contained in reflectance signatures such as the bi-directional reflectance distribution function or BRDF. The reflectance signature directly shows the relative absorption and backscattering features of targets. These features can be very useful in shoreline monitoring or surveillance applications, for example to detect weathered oil. In real-time detection applications, processing of hyperspectral data can be an important tool and Optimal band selection is thus important in real time applications in order to select the essential bands using the absorption and backscatter information. In the present paper, band selection is based upon the optimization of target detection using contrast algorithms. The common definition of the contrast (using only one band out of all possible combinations available within a hyperspectral image) is generalized in order to consider all the possible combinations of wavelength dependent contrasts using hyperspectral images. The inflection (defined here as an approximation of the second derivative) is also used in order to enhance the variations in the reflectance spectra as well as in the contrast spectrua in order to assist in optimal band selection. The results of the selection in term of target detection (false alarms and missed detection) are also compared with a previous method to perform feature detection, namely the matched filter. In this paper, imagery is acquired using a pushbroom hyperspectral sensor mounted at the bow of a small vessel. The sensor is mechanically rotated using an optical rotation stage. This opto-mechanical scanning system produces hyperspectral images with pixel sizes on the order of mm to cm scales, depending upon the distance between the sensor and the shoreline being monitored. The motion of the platform during the acquisition induces distortions in the collected HSI imagery. It is therefore necessary to apply a motion correction to the imagery. In this paper, imagery is corrected for the pitching motion of a vessel, which causes most of the deformation when the vessel is anchored at 2 points (bow and stern) during the acquisition of the hyperspectral imagry.

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.

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

Fourier Spectral Filter Array for Optimal Multispectral Imaging.

PubMed

Jia, Jie; Barnard, Kenneth J; Hirakawa, Keigo

2016-04-01

Limitations to existing multispectral imaging modalities include speed, cost, range, spatial resolution, and application-specific system designs that lack versatility of the hyperspectral imaging modalities. In this paper, we propose a novel general-purpose single-shot passive multispectral imaging modality. Central to this design is a new type of spectral filter array (SFA) based not on the notion of spatially multiplexing narrowband filters, but instead aimed at enabling single-shot Fourier transform spectroscopy. We refer to this new SFA pattern as Fourier SFA, and we prove that this design solves the problem of optimally sampling the hyperspectral image data.

Hyperspectral infrared nanoimaging of organic samples based on Fourier transform infrared nanospectroscopy

PubMed Central

Amenabar, Iban; Poly, Simon; Goikoetxea, Monika; Nuansing, Wiwat; Lasch, Peter; Hillenbrand, Rainer

2017-01-01

Infrared nanospectroscopy enables novel possibilities for chemical and structural analysis of nanocomposites, biomaterials or optoelectronic devices. Here we introduce hyperspectral infrared nanoimaging based on Fourier transform infrared nanospectroscopy with a tunable bandwidth-limited laser continuum. We describe the technical implementations and present hyperspectral infrared near-field images of about 5,000 pixel, each one covering the spectral range from 1,000 to 1,900 cm−1. To verify the technique and to demonstrate its application potential, we imaged a three-component polymer blend and a melanin granule in a human hair cross-section, and demonstrate that multivariate data analysis can be applied for extracting spatially resolved chemical information. Particularly, we demonstrate that distribution and chemical interaction between the polymer components can be mapped with a spatial resolution of about 30 nm. We foresee wide application potential of hyperspectral infrared nanoimaging for valuable chemical materials characterization and quality control in various fields ranging from materials sciences to biomedicine. PMID:28198384

Hyperspectral infrared nanoimaging of organic samples based on Fourier transform infrared nanospectroscopy

NASA Astrophysics Data System (ADS)

Amenabar, Iban; Poly, Simon; Goikoetxea, Monika; Nuansing, Wiwat; Lasch, Peter; Hillenbrand, Rainer

2017-02-01

Infrared nanospectroscopy enables novel possibilities for chemical and structural analysis of nanocomposites, biomaterials or optoelectronic devices. Here we introduce hyperspectral infrared nanoimaging based on Fourier transform infrared nanospectroscopy with a tunable bandwidth-limited laser continuum. We describe the technical implementations and present hyperspectral infrared near-field images of about 5,000 pixel, each one covering the spectral range from 1,000 to 1,900 cm-1. To verify the technique and to demonstrate its application potential, we imaged a three-component polymer blend and a melanin granule in a human hair cross-section, and demonstrate that multivariate data analysis can be applied for extracting spatially resolved chemical information. Particularly, we demonstrate that distribution and chemical interaction between the polymer components can be mapped with a spatial resolution of about 30 nm. We foresee wide application potential of hyperspectral infrared nanoimaging for valuable chemical materials characterization and quality control in various fields ranging from materials sciences to biomedicine.

A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork.

PubMed

Xu, Yi; Chen, Quansheng; Liu, Yan; Sun, Xin; Huang, Qiping; Ouyang, Qin; Zhao, Jiewen

2018-04-01

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork

PubMed Central

Xu, Yi; Chen, Quansheng; Liu, Yan; Sun, Xin; Huang, Qiping; Ouyang, Qin; Zhao, Jiewen

2018-01-01

Abstract This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control. PMID:29805285

Utility of hyperspectral imagers in the mining industry: Italy's gypsum reserves

NASA Astrophysics Data System (ADS)

Wilson, Janette H.; Greenberger, Rebecca N.

2014-05-01

The mining industry is plagued with socioeconomic and safety roadblocks with not many solutions in the midst of a demanding market. As more and more geologic research using hyperspectral technology has been performed, along with an affordable price point for commercial use of hyperspectral technology, the benefits of hyperspectral imaging to the mining industry has become apparent. This study identifies the key areas of use for hyperspectral imaging in the mining industry through a case study of gypsum mine samples obtained from a mine in central Tuscany.

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.

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.

Hyperspectral Image Denoising Using a Nonlocal Spectral Spatial Principal Component Analysis

NASA Astrophysics Data System (ADS)

Li, D.; Xu, L.; Peng, J.; Ma, J.

2018-04-01

Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on. In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting the leading principal components. The main contribution of paper is to introduce the spectral spatial structure and nonlocal similarity of the HSIs into the PCA denoising model. PCA with spectral spatial structure can exploit spectral correlation and spatial correlation of HSI by using 3D blocks instead of 2D patches. Nonlocal similarity means the similarity between the referenced pixel and other pixels in nonlocal area, where Mahalanobis distance algorithm is used to estimate the spatial spectral similarity by calculating the distance in 3D blocks. The proposed method is tested on both simulated and real hyperspectral images, the results demonstrate that the proposed method is superior to several other popular methods in HSI denoising.

Analysis of hyperspectral scattering images using a moment method for apple firmness prediction

USDA-ARS?s Scientific Manuscript database

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...

A method of minimum volume simplex analysis constrained unmixing for hyperspectral image

NASA Astrophysics Data System (ADS)

Zou, Jinlin; Lan, Jinhui; Zeng, Yiliang; Wu, Hongtao

2017-07-01

The signal recorded by a low resolution hyperspectral remote sensor from a given pixel, letting alone the effects of the complex terrain, is a mixture of substances. To improve the accuracy of classification and sub-pixel object detection, hyperspectral unmixing(HU) is a frontier-line in remote sensing area. Unmixing algorithm based on geometric has become popular since the hyperspectral image possesses abundant spectral information and the mixed model is easy to understand. However, most of the algorithms are based on pure pixel assumption, and since the non-linear mixed model is complex, it is hard to obtain the optimal endmembers especially under a highly mixed spectral data. To provide a simple but accurate method, we propose a minimum volume simplex analysis constrained (MVSAC) unmixing algorithm. The proposed approach combines the algebraic constraints that are inherent to the convex minimum volume with abundance soft constraint. While considering abundance fraction, we can obtain the pure endmember set and abundance fraction correspondingly, and the final unmixing result is closer to reality and has better accuracy. We illustrate the performance of the proposed algorithm in unmixing simulated data and real hyperspectral data, and the result indicates that the proposed method can obtain the distinct signatures correctly without redundant endmember and yields much better performance than the pure pixel based algorithm.

Quantum cascade lasers (QCL) for active hyperspectral imaging

NASA Astrophysics Data System (ADS)

Yang, Quankui; Fuchs, Frank; Wagner, Joachim

2014-04-01

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

LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas

NASA Astrophysics Data System (ADS)

Dekker, Rob J.; Schwering, Piet B. W.; Benoist, Koen W.; Pignatti, Stefano; Santini, Federico; Friman, Ola

2013-05-01

This paper studies change detection of LWIR (Long Wave Infrared) hyperspectral imagery. Goal is to improve target acquisition and situation awareness in urban areas with respect to conventional techniques. Hyperspectral and conventional broadband high-spatial-resolution data were collected during the DUCAS trials in Zeebrugge, Belgium, in June 2011. LWIR data were acquired using the ITRES Thermal Airborne Spectrographic Imager TASI-600 that operates in the spectral range of 8.0-11.5 μm (32 band configuration). Broadband data were acquired using two aeroplanemounted FLIR SC7000 MWIR cameras. Acquisition of the images was around noon. To limit the number of false alarms due to atmospheric changes, the time interval between the images is less than 2 hours. Local co-registration adjustment was applied to compensate for misregistration errors in the order of a few pixels. The targets in the data that will be analysed in this paper are different kinds of vehicles. Change detection algorithms that were applied and evaluated are Euclidean distance, Mahalanobis distance, Chronochrome (CC), Covariance Equalisation (CE), and Hyperbolic Anomalous Change Detection (HACD). Based on Receiver Operating Characteristics (ROC) we conclude that LWIR hyperspectral has an advantage over MWIR broadband change detection. The best hyperspectral detector is HACD because it is most robust to noise. MWIR high spatial-resolution broadband results show that it helps to apply a false alarm reduction strategy based on spatial processing.

Sub-pixel mapping of hyperspectral imagery using super-resolution

NASA Astrophysics Data System (ADS)

Sharma, Shreya; Sharma, Shakti; Buddhiraju, Krishna M.

2016-04-01

With the development of remote sensing technologies, it has become possible to obtain an overview of landscape elements which helps in studying the changes on earth's surface due to climate, geological, geomorphological and human activities. Remote sensing measures the electromagnetic radiations from the earth's surface and match the spectral similarity between the observed signature and the known standard signatures of the various targets. However, problem lies when image classification techniques assume pixels to be pure. In hyperspectral imagery, images have high spectral resolution but poor spatial resolution. Therefore, the spectra obtained is often contaminated due to the presence of mixed pixels and causes misclassification. To utilise this high spectral information, spatial resolution has to be enhanced. Many factors make the spatial resolution one of the most expensive and hardest to improve in imaging systems. To solve this problem, post-processing of hyperspectral images is done to retrieve more information from the already acquired images. The algorithm to enhance spatial resolution of the images by dividing them into sub-pixels is known as super-resolution and several researches have been done in this domain.In this paper, we propose a new method for super-resolution based on ant colony optimization and review the popular methods of sub-pixel mapping of hyperspectral images along with their comparative analysis.

Advances in hyperspectral LWIR pushbroom imagers

NASA Astrophysics Data System (ADS)

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

2011-06-01

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

Hyperspectral imaging fluorescence excitation scanning for colon cancer detection

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Comparison of hyperspectral transformation accuracies of multispectral Landsat TM, ETM+, OLI and EO-1 ALI images for detecting minerals in a geothermal prospect area

NASA Astrophysics Data System (ADS)

Hoang, Nguyen Tien; Koike, Katsuaki

2018-03-01

Hyperspectral remote sensing generally provides more detailed spectral information and greater accuracy than multispectral remote sensing for identification of surface materials. However, there have been no hyperspectral imagers that cover the entire Earth surface. This lack points to a need for producing pseudo-hyperspectral imagery by hyperspectral transformation from multispectral images. We have recently developed such a method, a Pseudo-Hyperspectral Image Transformation Algorithm (PHITA), which transforms Landsat 7 ETM+ images into pseudo-EO-1 Hyperion images using multiple linear regression models of ETM+ and Hyperion band reflectance data. This study extends the PHITA to transform TM, OLI, and EO-1 ALI sensor images into pseudo-Hyperion images. By choosing a part of the Fish Lake Valley geothermal prospect area in the western United States for study, the pseudo-Hyperion images produced from the TM, ETM+, OLI, and ALI images by PHITA were confirmed to be applicable to mineral mapping. Using a reference map as the truth, three main minerals (muscovite and chlorite mixture, opal, and calcite) were identified with high overall accuracies from the pseudo-images (> 95% and > 42% for excluding and including unclassified pixels, respectively). The highest accuracy was obtained from the ALI image, followed by ETM+, TM, and OLI images in descending order. The TM, OLI, and ALI images can be alternatives to ETM+ imagery for the hyperspectral transformation that aids the production of pseudo-Hyperion images for areas without high-quality ETM+ images because of scan line corrector failure, and for long-term global monitoring of land surfaces.

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%.

Imaging Beyond What Man Can See

NASA Technical Reports Server (NTRS)

May, George; Mitchell, Brian

2004-01-01

Three lightweight, portable hyperspectral sensor systems have been built that capture energy from 200 to 1700 nanometers (ultravio1et to shortwave infrared). The sensors incorporate a line scanning technique that requires no relative movement between the target and the sensor. This unique capability, combined with portability, opens up new uses of hyperspectral imaging for laboratory and field environments. Each system has a GUI-based software package that allows the user to communicate with the imaging device for setting spatial resolution, spectral bands and other parameters. NASA's Space Partnership Development has sponsored these innovative developments and their application to human problems on Earth and in space. Hyperspectral datasets have been captured and analyzed in numerous areas including precision agriculture, food safety, biomedical imaging, and forensics. Discussion on research results will include realtime detection of food contaminants, molds and toxin research on corn, identifying counterfeit documents, non-invasive wound monitoring and aircraft applications. Future research will include development of a thermal infrared hyperspectral sensor that will support natural resource applications on Earth and thermal analyses during long duration space flight. This paper incorporates a variety of disciplines and imaging technologies that have been linked together to allow the expansion of remote sensing across both traditional and non-traditional boundaries.

Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

Katrašnik, Jaka; Pernuš, Franjo; Likar, Boštjan

2010-02-01

The goal of this article is to present a novel method for spectral characterization and calibration of spectrometers and hyper-spectral imaging systems based on non-collinear acousto-optical tunable filters. The method characterizes the spectral tuning curve (frequency-wavelength characteristic) of the AOTF (Acousto-Optic Tunable Filter) filter by matching the acquired and modeled spectra of the HgAr calibration lamp, which emits line spectrum that can be well modeled via AOTF transfer function. In this way, not only tuning curve characterization and corresponding spectral calibration but also spectral resolution assessment is performed. The obtained results indicated that the proposed method is efficient, accurate and feasible for routine calibration of AOTF spectrometers and hyper-spectral imaging systems and thereby a highly competitive alternative to the existing calibration methods.

Software for Simulation of Hyperspectral Images

NASA Technical Reports Server (NTRS)

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

2002-01-01

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

Line-scan hyperspectral imaging techniques for food and agricultural applications

USDA-ARS?s Scientific Manuscript database

Hyperspectral imaging technologies in the food and agricultural area have been evolved rapidly during the past 15 years owing to tremendous interest from both academic and industrial fields. Line-scan hyperspectral imaging is a major method that has been intensively researched and developed in diffe...

Hyperspectral Remote Sensing of Atmospheric Profiles from Satellites and Aircraft

NASA Technical Reports Server (NTRS)

Smith, W. L.; Zhou, D. K.; Harrison, F. W.; Revercomb, H. E.; Larar, A. M.; Huang, H. L.; Huang, B.

2001-01-01

A future hyperspectral resolution remote imaging and sounding system, called the GIFTS (Geostationary Imaging Fourier Transform Spectrometer), is described. An airborne system, which produces the type of hyperspectral resolution sounding data to be achieved with the GIFTS, has been flown on high altitude aircraft. Results from simulations and from the airborne measurements are presented to demonstrate the revolutionary remote sounding capabilities to be realized with future satellite hyperspectral remote imaging/sounding systems.

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.

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.

Testing a high-power LED based light source for hyperspectral imaging microscopy

NASA Astrophysics Data System (ADS)

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

2017-02-01

Our lab has worked to develop high-speed hyperspectral imaging systems that scan the fluorescence excitation spectrum for biomedical imaging applications. Hyperspectral imaging can be used in remote sensing, medical imaging, reaction analysis, and other applications. Here, we describe the development of a hyperspectral imaging system that comprised an inverted Nikon Eclipse microscope, sCMOS camera, and a custom light source that utilized a series of high-power LEDs. LED selection was performed to achieve wavelengths of 350-590 nm. To reduce scattering, LEDs with low viewing angles were selected. LEDs were surface-mount soldered and powered by an RCD. We utilized 3D printed mounting brackets to assemble all circuit components. Spectraradiometric calibration was performed using a spectrometer (QE65000, Ocean Optics) and integrating sphere (FOIS-1, Ocean Optics). Optical output and LED driving current were measured over a range of illumination intensities. A normalization algorithm was used to calibrate and optimize the intensity of the light source. The highest illumination power was at 375 nm (3300 mW/cm2), while the lowest illumination power was at 515, 525, and 590 nm (5200 mW/cm2). Comparing the intensities supplied by each LED to the intensities measured at the microscope stage, we found there was a great loss in power output. Future work will focus on using two of the same LEDs to double the power and finding more LED and/or laser diodes and chips around the range. This custom hyperspectral imaging system could be used for the detection of cancer and the identification of biomolecules.

A survey of landmine detection using hyperspectral imaging

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Hyperspectral Image Classification using a Self-Organizing Map

NASA Technical Reports Server (NTRS)

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

2001-01-01

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

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data.

PubMed

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-05-15

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

PubMed Central

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-01-01

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. PMID:28505135

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

Evaluation of a novel laparoscopic camera for characterization of renal ischemia in a porcine model using digital light processing (DLP) hyperspectral imaging

NASA Astrophysics Data System (ADS)

Olweny, Ephrem O.; Tan, Yung K.; Faddegon, Stephen; Jackson, Neil; Wehner, Eleanor F.; Best, Sara L.; Park, Samuel K.; Thapa, Abhas; Cadeddu, Jeffrey A.; Zuzak, Karel J.

2012-03-01

Digital light processing hyperspectral imaging (DLP® HSI) was adapted for use during laparoscopic surgery by coupling a conventional laparoscopic light guide with a DLP-based Agile Light source (OL 490, Optronic Laboratories, Orlando, FL), incorporating a 0° laparoscope, and a customized digital CCD camera (DVC, Austin, TX). The system was used to characterize renal ischemia in a porcine model.

A FPGA implementation for linearly unmixing a hyperspectral image using OpenCL

NASA Astrophysics Data System (ADS)

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

2017-10-01

Hyperspectral imaging systems provide images in which single pixels have information from across the electromagnetic spectrum of the scene under analysis. These systems divide the spectrum into many contiguos channels, which may be even out of the visible part of the spectra. The main advantage of the hyperspectral imaging technology is that certain objects leave unique fingerprints in the electromagnetic spectrum, known as spectral signatures, which allow to distinguish between different materials that may look like the same in a traditional RGB image. Accordingly, the most important hyperspectral imaging applications are related with distinguishing or identifying materials in a particular scene. In hyperspectral imaging applications under real-time constraints, the huge amount of information provided by the hyperspectral sensors has to be rapidly processed and analysed. For such purpose, parallel hardware devices, such as Field Programmable Gate Arrays (FPGAs) are typically used. However, developing hardware applications typically requires expertise in the specific targeted device, as well as in the tools and methodologies which can be used to perform the implementation of the desired algorithms in the specific device. In this scenario, the Open Computing Language (OpenCL) emerges as a very interesting solution in which a single high-level synthesis design language can be used to efficiently develop applications in multiple and different hardware devices. In this work, the Fast Algorithm for Linearly Unmixing Hyperspectral Images (FUN) has been implemented into a Bitware Stratix V Altera FPGA using OpenCL. The obtained results demonstrate the suitability of OpenCL as a viable design methodology for quickly creating efficient FPGAs designs for real-time hyperspectral imaging applications.

New Thermal Infrared Hyperspectral Imagers

DTIC Science & Technology

2009-10-01

involve imaging systems based on both MCT and microbolometer detector . All the systems base on push-broom imaging spectrograph with transmission grating...application requirements. The studies involve imaging systems based on both MCT and microbolometer detector . All the systems base on push-broom...remote sensing imager utilizes MCT detector combined with BMC-technique (background monitoring on-chip), background suppression and temperature

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

USDA-ARS?s Scientific Manuscript database

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

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

USDA-ARS?s Scientific Manuscript database

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...

Visible and near-infrared hyperspectral imaging for cooking loss classification of fresh broiler breast fillets

USDA-ARS?s Scientific Manuscript database

Cooking loss (CL) is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI) technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast...

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

USDA-ARS?s Scientific Manuscript database

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...

Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

A partial least squares regression (PLSR) model to map internal soluble solids content (SSC) of apples using visible/near-infrared (VNIR) hyperspectral imaging was developed. The reflectance spectra of sliced apples were extracted from hyperspectral absorbance images obtained in the 400e1000 nm rang...

Design and fabrication of a 900-1700 nm hyper-spectral imaging spectrometer

NASA Astrophysics Data System (ADS)

Kim, Tae Hyoung; Kong, Hong Jin; Kim, Tae Hoon; Shin, Jae Sung

2010-02-01

This paper presents a 900-1700 nm hyper-spectral imaging spectrometer which offers low distortions, a low F-number, a compact size, an easily-fabricated design and a low cost (is presented in this paper). The starting point for its optical design is discussed according to the geometrical aberration theory and Rowland circle condition. It is shown that these methods are useful in designing a push-broom hyper-spectral imaging spectrometer that has an aperture of f/2.4, modulation transfer functions of less than 0.8 at 25 cycles/mm, and spot sizes less than 10 μm. A prototype of the optimized hyper-spectral imaging spectrometer has been fabricated using a high precision machine and the experimental demonstration with the fabricated hyper-spectral imaging spectrometer is presented.

Removing cosmic spikes using a hyperspectral upper-bound spectrum method

DOE PAGES

Anthony, Stephen Michael; Timlin, Jerilyn A.

2016-11-04

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in amore » hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. As a result, a comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.« less

Removing cosmic spikes using a hyperspectral upper-bound spectrum method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anthony, Stephen Michael; Timlin, Jerilyn A.

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in amore » hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. As a result, a comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.« less

Removing Cosmic Spikes Using a Hyperspectral Upper-Bound Spectrum Method.

PubMed

Anthony, Stephen M; Timlin, Jerilyn A

2017-03-01

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.

Two-dimensional imaging of gas temperature and concentration based on hyperspectral tomography

NASA Astrophysics Data System (ADS)

Xin, Ming-yuan; Jin, Xing; Wang, Guang-yu; Song, Junling

2016-10-01

Two-dimensional imaging of gas temperature and concentration is realized by hyperspectral tomography, which has the characteristics of using multi-wavelengths absorption spectral information, so that the imaging could be accomplished in a small number of projections and viewing angles. A temperature and concentration model is established to simulate the combustion conditions and a total number of 10 near-infrared absorption spectral information of H2O is used. An improved simulated annealing algorithm by adjusting search step is performed the main search algorithm for the tomography. By adding random errors into the absorption area information, the stability of the algorithm is tested, and the results are compared with the reconstructions provided by algebraic reconstruction technique which takes advantage of 2 spectral information contents in imaging. The results show that the two methods perform equivalent in low-level noise environment, but at high-level, hyperspectral tomography turns out to be more stable.

Hyperspectral imagery for observing spectral signature change in Aspergillus flavus

NASA Astrophysics Data System (ADS)

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

2005-11-01

Aflatoxin contaminated corn is dangerous for domestic animals when used as feed and cause liver cancer when consumed by human beings. Therefore, the ability to detect A. flavus and its toxic metabolite, aflatoxin, is important. The objective of this study is to measure A. flavus growth using hyperspectral technology and develop spectral signatures for A. flavus. Based on the research group's previous experiments using hyperspectral imaging techniques, it has been confirmed that the spectral signature of A. flavus is unique and readily identifiable against any background or surrounding surface and among other fungal strains. This study focused on observing changes in the A. flavus spectral signature over an eight-day growth period. The study used a visible-near-infrared hyperspectral image system for data acquisition. This image system uses focal plane pushbroom scanning for high spatial and high spectral resolution imaging. Procedures previously developed by the research group were used for image calibration and image processing. The results showed that while A. flavus gradually progressed along the experiment timeline, the day-to-day surface reflectance of A. flavus displayed significant difference in discreet regions of the wavelength spectrum. External disturbance due to environmental changes also altered the growth and subsequently changed the reflectance patterns of A. flavus.

Hyperspectral imaging for food processing automation

NASA Astrophysics Data System (ADS)

Park, Bosoon; Lawrence, Kurt C.; Windham, William R.; Smith, Doug P.; Feldner, Peggy W.

2002-11-01

This paper presents the research results that demonstrates hyperspectral imaging could be used effectively for detecting feces (from duodenum, ceca, and colon) and ingesta on the surface of poultry carcasses, and potential application for real-time, on-line processing of poultry for automatic safety inspection. The hyperspectral imaging system included a line scan camera with prism-grating-prism spectrograph, fiber optic line lighting, motorized lens control, and hyperspectral image processing software. Hyperspectral image processing algorithms, specifically band ratio of dual-wavelength (565/517) images and thresholding were effective on the identification of fecal and ingesta contamination of poultry carcasses. A multispectral imaging system including a common aperture camera with three optical trim filters (515.4 nm with 8.6- nm FWHM), 566.4 nm with 8.8-nm FWHM, and 631 nm with 10.2-nm FWHM), which were selected and validated by a hyperspectral imaging system, was developed for a real-time, on-line application. A total image processing time required to perform the current multispectral images captured by a common aperture camera was approximately 251 msec or 3.99 frames/sec. A preliminary test shows that the accuracy of real-time multispectral imaging system to detect feces and ingesta on corn/soybean fed poultry carcasses was 96%. However, many false positive spots that cause system errors were also detected.

Source-to-Sink Methods by Hyperspectral Imaging: a Case Study of the Laminated Sediments of Lake Linné (Svalbard).

NASA Astrophysics Data System (ADS)

Van Exem, A.; Debret, M.; Copard, Y.; Verpoorter, C.; Sorrel, P.; de Wet, G.; Werner, A.; Roof, S.; Laignel, B.; Retelle, M.

2016-12-01

Laminated sediments contained valuable information recorded on a micrometric scale. Information about sediments flux and origins require high-resolution source tracking analysis. Quick and non-destructive, hyperspectral imaging provides contiguous reflectance datasets into 2 dimensions with a spatial resolution of 0.02 mm. Located on the west of the Spitzbergen, Lake Linné is the largest lake in the region. Erosion is mainly driven by glacier fluctuations and three different bedrocks are potential sediment sources. Organic matter (coal) is only found in some carboniferous rocks. Four cores recovered from different parts of the lake contain millimeter scale laminae. Two approaches were compared: (i) measurement of statistical correlations between the sediments and source samples, (ii) extraction of extreme spectral signatures from the VNIR hyperspectral images. Total Organic Carbon (TOC) values of all samples were also given by bulk geochemistry (RE6 ® pyrolyzer). Consequently, the measured similarity between the hyperspectral image and the field samples illustrates the sources contribution within the core. Three sample clusters and three equivalent spectral signatures were found. TOC values from the archive show good correlation (r=0.86, p<0.001, n=73) with the hyperspectral signature relative to TOC content. A least-squares regression (r²=0.74) was used to extrapolate TOC values in order to represent their distribution at 0.02 mm resolution. This is the first source-to-sink study based on imaging spectroscopy. Our results indicate that hyperspectral imagery is a useful tool to (i) identify sediment sources, (ii) perform continuous paleo-environmental reconstruction at high resolution, and (iii) can provide quantitative results (TOC values) validated by destructive analyses.

Hyperspectral imaging fluorescence excitation scanning for colon cancer detection

PubMed Central

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

2016-01-01

Abstract. Optical spectroscopy and hyperspectral imaging have shown the potential to discriminate between cancerous and noncancerous tissue with high sensitivity and specificity. However, to date, these techniques have not been effectively translated to real-time endoscope platforms. Hyperspectral imaging of the fluorescence excitation spectrum represents new technology that may be well suited for endoscopic implementation. However, the feasibility of detecting differences between normal and cancerous mucosa using fluorescence excitation-scanning hyperspectral imaging has not been evaluated. The goal of this study was to evaluate the initial feasibility of using fluorescence excitation-scanning hyperspectral imaging for measuring changes in fluorescence excitation spectrum concurrent with colonic adenocarcinoma using a small pre-pilot-scale sample size. Ex vivo analysis was performed using resected pairs of colorectal adenocarcinoma and normal mucosa. Adenocarcinoma was confirmed by histologic evaluation of hematoxylin and eosin (H&E) permanent sections. Specimens were imaged using a custom hyperspectral imaging fluorescence excitation-scanning microscope system. Results demonstrated consistent spectral differences between normal and cancerous tissues over the fluorescence excitation range of 390 to 450 nm that could be the basis for wavelength-dependent detection of colorectal cancers. Hence, excitation-scanning hyperspectral imaging may offer an alternative approach for discriminating adenocarcinoma from surrounding normal colonic mucosa, but further studies will be required to evaluate the accuracy of this approach using a larger patient cohort. PMID:27792808

Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

NASA Astrophysics Data System (ADS)

Fei, Baowei; Lu, Guolan; Wang, Xu; Zhang, Hongzheng; Little, James V.; Magliocca, Kelly R.; Chen, Amy Y.

2017-02-01

We are developing label-free hyperspectral imaging (HSI) for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on the HSI image has an optical spectrum. We developed preprocessing and classification methods for HSI data. We used spectral features from HSI data for the classification of cancer and benign tissue. We collected surgical tissue specimens from 16 human patients who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90+/-8%, sensitivity of 89+/-9%, and specificity of 91+/-6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94+/-6%, sensitivity of 94+/-6%, and specificity of 95+/-6%. Hyperspectral imaging outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study suggests that label-free hyperspectral imaging has great potential for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the hyperspectral imaging technology is warranted for its application in image-guided surgery.

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.

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.

Ground-based Observation System Development for the Moon Hyper-spectral Imaging

NASA Astrophysics Data System (ADS)

Wang, Yang; Huang, Yu; Wang, Shurong; Li, Zhanfeng; Zhang, Zihui; Hu, Xiuqing; Zhang, Peng

2017-05-01

The Moon provides a suitable radiance source for on-orbit calibration of space-borne optical instruments. A ground-based observation system dedicated to the hyper-spectral radiometry of the Moon has been developed for improving and validating the current lunar model. The observation instrument using a dispersive imaging spectrometer is particularly designed for high-accuracy observations of the lunar radiance. The simulation and analysis of the push-broom mechanism is made in detail for lunar observations, and the automated tracking and scanning is well accomplished in different observational condition. A three-month series of hyper-spectral imaging experiments of the Moon have been performed in the wavelength range from 400 to 1000 nm near Lijiang Observatory (Yunnan, China) at phase angles -83°-87°. Preliminary results and data comparison are presented, and it shows the instrument performance and lunar observation capability of this system are well validated. Beyond previous measurements, this observation system provides the entire lunar disk images of continuous spectral coverage by adopting the push-broom mode with special scanning scheme and leads to the further research of lunar photometric model.

Characterization of microplastic litter from oceans by an innovative approach based on hyperspectral imaging.

PubMed

Serranti, Silvia; Palmieri, Roberta; Bonifazi, Giuseppe; Cózar, Andrés

2018-06-01

An innovative approach, based on HyperSpectral Imaging (HSI), was developed in order to set up an efficient method to analyze marine microplastic litter. HSI was applied to samples collected by surface-trawling plankton nets from several parts of the world (i.e. Arctic, Mediterranean, South Atlantic and North Pacific). Reliable information on abundance, size, shape and polymer type for the whole ensemble of plastic particles in each sample was retrieved from single hyperspectral images. The simultaneous characterization of the polymeric composition of the plastic debris represents an important analytical advantage considering that this information, and even the validation of the plastic nature of the small debris, is a common flaw in the analysis of marine microplastic pollution. HSI was revealed as a rapid, non-invasive, non-destructive and reliable technology for the characterization of the microplastic waste, opening a promising way for improving the plastic pollution monitoring. Copyright © 2018 Elsevier Ltd. All rights reserved.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

Hyperspectral analysis of columbia spotted frog habitat

USGS Publications Warehouse

Shive, J.P.; Pilliod, D.S.; Peterson, C.R.

2010-01-01

Wildlife managers increasingly are using remotely sensed imagery to improve habitat delineations and sampling strategies. Advances in remote sensing technology, such as hyperspectral imagery, provide more information than previously was available with multispectral sensors. We evaluated accuracy of high-resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs (Rana luteiventris) and compared the results to multispectral image classification and United States Geological Survey topographic maps. The study area spanned 3 lake basins in the Salmon River Mountains, Idaho, USA. Hyperspectral data were collected with an airborne sensor on 30 June 2002 and on 8 July 2006. A 12-year comprehensive ground survey of the study area for Columbia spotted frog reproduction served as validation for image classifications. Hyperspectral image classification accuracy of wetlands was high, with a producer's accuracy of 96 (44 wetlands) correctly classified with the 2002 data and 89 (41 wetlands) correctly classified with the 2006 data. We applied habitat-based rules to delineate breeding habitat from other wetlands, and successfully predicted 74 (14 wetlands) of known breeding wetlands for the Columbia spotted frog. Emergent sedge microhabitat classification showed promise for directly predicting Columbia spotted frog egg mass locations within a wetland by correctly identifying 72 (23 of 32) of known locations. Our study indicates hyperspectral imagery can be an effective tool for mapping spotted frog breeding habitat in the selected mountain basins. We conclude that this technique has potential for improving site selection for inventory and monitoring programs conducted across similar wetland habitat and can be a useful tool for delineating wildlife habitats. ?? 2010 The Wildlife Society.

New Application of Hyperspectral Imaging for Bacterial Cell Classification

USDA-ARS?s Scientific Manuscript database

Hyperspectral microscopy has shown potential as a method for rapid detection of foodborne pathogenic bacteria with spectral characteristics from bacterial cells. Hyperspectral microscope images (HMIs) are collected from broiler chicken isolates of Salmonella serotypes Enteritidis, Typhimurium, Infa...

Long wave infrared (8 to 14 microns) hyperspectral imager based on an uncooled thermal camera and the traditional CI block interferometer (SI-LWIR-UC)

NASA Astrophysics Data System (ADS)

Cabib, Dario; Lavi, Moshe; Gil, Amir; Milman, Uri

2011-06-01

Since the early '90's CI has been involved in the development of FTIR hyperspectral imagers based on a Sagnac or similar type of interferometer. CI also pioneered the commercialization of such hyperspectral imagers in those years. After having developed a visible version based on a CCD in the early '90's (taken on by a spin-off company for biomedical applications) and a 3 to 5 micron infrared version based on a cooled InSb camera in 2008, it is now developing an LWIR version based on an uncooled camera for the 8 to 14 microns range. In this paper we will present design features and expected performance of the system. The instrument is designed to be rugged for field use, yield a relatively high spectral resolution of 8 cm-1, an IFOV of 0.5 mrad., a 640x480 pixel spectral cube in less than a minute and a noise equivalent spectral radiance of 40 nW/cm2/sr/cm-1 at 10μ. The actually measured performance will be presented in a future paper.

Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

PubMed

Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

2015-03-01

Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

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.

Going Deeper With Contextual CNN for Hyperspectral Image Classification.

PubMed

Lee, Hyungtae; Kwon, Heesung

2017-10-01

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets.

Exploring hyperspectral imaging data sets with topological data analysis.

PubMed

Duponchel, Ludovic

2018-02-13

Analytical chemistry is rapidly changing. Indeed we acquire always more data in order to go ever further in the exploration of complex samples. Hyperspectral imaging has not escaped this trend. It quickly became a tool of choice for molecular characterisation of complex samples in many scientific domains. The main reason is that it simultaneously provides spectral and spatial information. As a result, chemometrics has provided many exploration tools (PCA, clustering, MCR-ALS …) well-suited for such data structure at early stage. However we are today facing a new challenge considering the always increasing number of pixels in the data cubes we have to manage. The idea is therefore to introduce a new paradigm of Topological Data Analysis in order explore hyperspectral imaging data sets highlighting its nice properties and specific features. With this paper, we shall also point out the fact that conventional chemometric methods are often based on variance analysis or simply impose a data model which implicitly defines the geometry of the data set. Thus we will show that it is not always appropriate in the framework of hyperspectral imaging data sets exploration. Copyright © 2017 Elsevier B.V. All rights reserved.

Methyl green and nitrotetrazolium blue chloride co-expression in colon tissue: A hyperspectral microscopic imaging analysis

NASA Astrophysics Data System (ADS)

Li, Qingli; Liu, Hongying; Wang, Yiting; Sun, Zhen; Guo, Fangmin; Zhu, Jianzhong

2014-12-01

Histological observation of dual-stained colon sections is usually performed by visual observation under a light microscope, or by viewing on a computer screen with the assistance of image processing software in both research and clinical settings. These traditional methods are usually not sufficient to reliably differentiate spatially overlapping chromogens generated by different dyes. Hyperspectral microscopic imaging technology offers a solution for these constraints as the hyperspectral microscopic images contain information that allows differentiation between spatially co-located chromogens with similar but different spectra. In this paper, a hyperspectral microscopic imaging (HMI) system is used to identify methyl green and nitrotetrazolium blue chloride in dual-stained colon sections. Hyperspectral microscopic images are captured and the normalized score algorithm is proposed to identify the stains and generate the co-expression results. Experimental results show that the proposed normalized score algorithm can generate more accurate co-localization results than the spectral angle mapper algorithm. The hyperspectral microscopic imaging technology can enhance the visualization of dual-stained colon sections, improve the contrast and legibility of each stain using their spectral signatures, which is helpful for pathologist performing histological analyses.

Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

NASA Astrophysics Data System (ADS)

Jamshidpour, N.; Homayouni, S.; Safari, A.

2017-09-01

Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

Comparison of different detection methods for citrus greening disease based on airborne multispectral and hyperspectral imagery

USDA-ARS?s Scientific Manuscript database

Citrus greening or Huanglongbing (HLB) is a devastating disease spread in many citrus groves since first found in 2005 in Florida. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were taken to detect citrus greening infected trees in 2007 and 2010. Ground truthi...

High spatial resolution LWIR hyperspectral sensor

NASA Astrophysics Data System (ADS)

Roberts, Carson B.; Bodkin, Andrew; Daly, James T.; Meola, Joseph

2015-06-01

Presented is a new hyperspectral imager design based on multiple slit scanning. This represents an innovation in the classic trade-off between speed and resolution. This LWIR design has been able to produce data-cubes at 3 times the rate of conventional single slit scan devices. The instrument has a built-in radiometric and spectral calibrator.

Reweighted mass center based object-oriented sparse subspace clustering for hyperspectral images

NASA Astrophysics Data System (ADS)

Zhai, Han; Zhang, Hongyan; Zhang, Liangpei; Li, Pingxiang

2016-10-01

Considering the inevitable obstacles faced by the pixel-based clustering methods, such as salt-and-pepper noise, high computational complexity, and the lack of spatial information, a reweighted mass center based object-oriented sparse subspace clustering (RMC-OOSSC) algorithm for hyperspectral images (HSIs) is proposed. First, the mean-shift segmentation method is utilized to oversegment the HSI to obtain meaningful objects. Second, a distance reweighted mass center learning model is presented to extract the representative and discriminative features for each object. Third, assuming that all the objects are sampled from a union of subspaces, it is natural to apply the SSC algorithm to the HSI. Faced with the high correlation among the hyperspectral objects, a weighting scheme is adopted to ensure that the highly correlated objects are preferred in the procedure of sparse representation, to reduce the representation errors. Two widely used hyperspectral datasets were utilized to test the performance of the proposed RMC-OOSSC algorithm, obtaining high clustering accuracies (overall accuracy) of 71.98% and 89.57%, respectively. The experimental results show that the proposed method clearly improves the clustering performance with respect to the other state-of-the-art clustering methods, and it significantly reduces the computational time.

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.

Impact of JPEG2000 compression on endmember extraction and unmixing of remotely sensed hyperspectral data

NASA Astrophysics Data System (ADS)

Martin, Gabriel; Gonzalez-Ruiz, Vicente; Plaza, Antonio; Ortiz, Juan P.; Garcia, Inmaculada

2010-07-01

Lossy hyperspectral image compression has received considerable interest in recent years due to the extremely high dimensionality of the data. However, the impact of lossy compression on spectral unmixing techniques has not been widely studied. These techniques characterize mixed pixels (resulting from insufficient spatial resolution) in terms of a suitable combination of spectrally pure substances (called endmembers) weighted by their estimated fractional abundances. This paper focuses on the impact of JPEG2000-based lossy compression of hyperspectral images on the quality of the endmembers extracted by different algorithms. The three considered algorithms are the orthogonal subspace projection (OSP), which uses only spatial information, and the automatic morphological endmember extraction (AMEE) and spatial spectral endmember extraction (SSEE), which integrate both spatial and spectral information in the search for endmembers. The impact of compression on the resulting abundance estimation based on the endmembers derived by different methods is also substantiated. Experimental results are conducted using a hyperspectral data set collected by NASA Jet Propulsion Laboratory over the Cuprite mining district in Nevada. The experimental results are quantitatively analyzed using reference information available from U.S. Geological Survey, resulting in recommendations to specialists interested in applying endmember extraction and unmixing algorithms to compressed hyperspectral data.

Application of airborne hyperspectral remote sensing for the retrieval of forest inventory parameters

NASA Astrophysics Data System (ADS)

Dmitriev, Yegor V.; Kozoderov, Vladimir V.; Sokolov, Anton A.

2016-04-01

Collecting and updating forest inventory data play an important part in the forest management. The data can be obtained directly by using exact enough but low efficient ground based methods as well as from the remote sensing measurements. We present applications of airborne hyperspectral remote sensing for the retrieval of such important inventory parameters as the forest species and age composition. The hyperspectral images of the test region were obtained from the airplane equipped by the produced in Russia light-weight airborne video-spectrometer of visible and near infrared spectral range and high resolution photo-camera on the same gyro-stabilized platform. The quality of the thematic processing depends on many factors such as the atmospheric conditions, characteristics of measuring instruments, corrections and preprocessing methods, etc. An important role plays the construction of the classifier together with methods of the reduction of the feature space. The performance of different spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. For the reduction of the feature space we used the earlier proposed stable feature selection method. The results of the classification of hyperspectral airborne images by using the Multiclass Support Vector Machine method with Gaussian kernel and the parametric Bayesian classifier based on the Gaussian mixture model and their comparative analysis are demonstrated.

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.

Application of VNIR hyperspectral imaging for non-destructive prediction of pH, color, and drip loss of chicken breast fillets

USDA-ARS?s Scientific Manuscript database

Non-destructive and rapid prediction of quality attributes of chicken breast fillets using visible and near-infrared (VNIR) hyperspectral imaging (400-1000 nm) was carried out in this work. All hyperspectral images were acquired for bone (dorsal) side of chicken breast. A forward principal component...

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. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modification of measurement methods for evaluation of tissue-engineered cartilage function and biochemical properties using nanosecond pulsed laser

NASA Astrophysics Data System (ADS)

Ishihara, Miya; Sato, Masato; Kutsuna, Toshiharu; Ishihara, Masayuki; Mochida, Joji; Kikuchi, Makoto

2008-02-01

There is a demand in the field of regenerative medicine for measurement technology that enables determination of functions and components of engineered tissue. To meet this demand, we developed a method for extracellular matrix characterization using time-resolved autofluorescence spectroscopy, which enabled simultaneous measurements with mechanical properties using relaxation of laser-induced stress wave. In this study, in addition to time-resolved fluorescent spectroscopy, hyperspectral sensor, which enables to capture both spectral and spatial information, was used for evaluation of biochemical characterization of tissue-engineered cartilage. Hyperspectral imaging system provides spectral resolution of 1.2 nm and image rate of 100 images/sec. The imaging system consisted of the hyperspectral sensor, a scanner for x-y plane imaging, magnifying optics and Xenon lamp for transmmissive lighting. Cellular imaging using the hyperspectral image system has been achieved by improvement in spatial resolution up to 9 micrometer. The spectroscopic cellular imaging could be observed using cultured chondrocytes as sample. At early stage of culture, the hyperspectral imaging offered information about cellular function associated with endogeneous fluorescent biomolecules.

Fast quantifying collision strength index of ethylene-vinyl acetate copolymer coverings on the fields based on near infrared hyperspectral imaging techniques

PubMed Central

Chen, Y. M.; Lin, P.; He, Y.; He, J. Q.; Zhang, J.; Li, X. L.

2016-01-01

A novel strategy based on the near infrared hyperspectral imaging techniques and chemometrics were explored for fast quantifying the collision strength index of ethylene-vinyl acetate copolymer (EVAC) coverings on the fields. The reflectance spectral data of EVAC coverings was obtained by using the near infrared hyperspectral meter. The collision analysis equipment was employed to measure the collision intensity of EVAC materials. The preprocessing algorithms were firstly performed before the calibration. The algorithms of random frog and successive projection (SP) were applied to extracting the fingerprint wavebands. A correlation model between the significant spectral curves which reflected the cross-linking attributions of the inner organic molecules and the degree of collision strength was set up by taking advantage of the support vector machine regression (SVMR) approach. The SP-SVMR model attained the residual predictive deviation of 3.074, the square of percentage of correlation coefficient of 93.48% and 93.05% and the root mean square error of 1.963 and 2.091 for the calibration and validation sets, respectively, which exhibited the best forecast performance. The results indicated that the approaches of integrating the near infrared hyperspectral imaging techniques with the chemometrics could be utilized to rapidly determine the degree of collision strength of EVAC. PMID:26875544

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.

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

NASA Astrophysics Data System (ADS)

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

2015-01-01

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

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

PubMed

Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo

2017-01-01

Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR. An advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.

Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.

PubMed

Al-Khafaji, Suhad Lateef; Jun Zhou; Zia, Ali; Liew, Alan Wee-Chung

2018-02-01

Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named spectral-spatial scale invariant feature transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.

Near-common-path interferometer for imaging Fourier-transform spectroscopy in wide-field microscopy

PubMed Central

Wadduwage, Dushan N.; Singh, Vijay Raj; Choi, Heejin; Yaqoob, Zahid; Heemskerk, Hans; Matsudaira, Paul; So, Peter T. C.

2017-01-01

Imaging Fourier-transform spectroscopy (IFTS) is a powerful method for biological hyperspectral analysis based on various imaging modalities, such as fluorescence or Raman. Since the measurements are taken in the Fourier space of the spectrum, it can also take advantage of compressed sensing strategies. IFTS has been readily implemented in high-throughput, high-content microscope systems based on wide-field imaging modalities. However, there are limitations in existing wide-field IFTS designs. Non-common-path approaches are less phase-stable. Alternatively, designs based on the common-path Sagnac interferometer are stable, but incompatible with high-throughput imaging. They require exhaustive sequential scanning over large interferometric path delays, making compressive strategic data acquisition impossible. In this paper, we present a novel phase-stable, near-common-path interferometer enabling high-throughput hyperspectral imaging based on strategic data acquisition. Our results suggest that this approach can improve throughput over those of many other wide-field spectral techniques by more than an order of magnitude without compromising phase stability. PMID:29392168

Multipurpose hyperspectral imaging system

USDA-ARS?s Scientific Manuscript database

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...

Locality-preserving sparse representation-based classification in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Gao, Lianru; Yu, Haoyang; Zhang, Bing; Li, Qingting

2016-10-01

This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

Parallel ICA and its hardware implementation in hyperspectral image analysis

NASA Astrophysics Data System (ADS)

Du, Hongtao; Qi, Hairong; Peterson, Gregory D.

2004-04-01

Advances in hyperspectral images have dramatically boosted remote sensing applications by providing abundant information using hundreds of contiguous spectral bands. However, the high volume of information also results in excessive computation burden. Since most materials have specific characteristics only at certain bands, a lot of these information is redundant. This property of hyperspectral images has motivated many researchers to study various dimensionality reduction algorithms, including Projection Pursuit (PP), Principal Component Analysis (PCA), wavelet transform, and Independent Component Analysis (ICA), where ICA is one of the most popular techniques. It searches for a linear or nonlinear transformation which minimizes the statistical dependence between spectral bands. Through this process, ICA can eliminate superfluous but retain practical information given only the observations of hyperspectral images. One hurdle of applying ICA in hyperspectral image (HSI) analysis, however, is its long computation time, especially for high volume hyperspectral data sets. Even the most efficient method, FastICA, is a very time-consuming process. In this paper, we present a parallel ICA (pICA) algorithm derived from FastICA. During the unmixing process, pICA divides the estimation of weight matrix into sub-processes which can be conducted in parallel on multiple processors. The decorrelation process is decomposed into the internal decorrelation and the external decorrelation, which perform weight vector decorrelations within individual processors and between cooperative processors, respectively. In order to further improve the performance of pICA, we seek hardware solutions in the implementation of pICA. Until now, there are very few hardware designs for ICA-related processes due to the complicated and iterant computation. This paper discusses capacity limitation of FPGA implementations for pICA in HSI analysis. A synthesis of Application-Specific Integrated Circuit (ASIC) is designed for pICA-based dimensionality reduction in HSI analysis. The pICA design is implemented using standard-height cells and aimed at TSMC 0.18 micron process. During the synthesis procedure, three ICA-related reconfigurable components are developed for the reuse and retargeting purpose. Preliminary results show that the standard-height cell based ASIC synthesis provide an effective solution for pICA and ICA-related processes in HSI analysis.

Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using the visible/near-infrared hyperspectral imaging technique.

PubMed

Cao, Yang; Zhang, Chaojie; Chen, Quansheng; Li, Yanyu; Qi, Shuai; Tian, Lin; Ren, YongLin

2015-08-01

Identifying stored-product insects is essential for granary management. Automated, computer-based classification methods are rapidly developing in many areas. A hyperspectral imaging technique could potentially be developed to identify stored-product insect species and geographical strains. This study tested and adapted the technique using four geographical strains of each of two insect species, the rice weevil and maize weevil, to collect and analyse the resultant hyperspectral data. Three characteristic images that corresponded to the dominant wavelengths, 505, 659 and 955 nm, were selected by multivariate image analysis. Each image was processed, and 22 morphological and textural features from regions of interest were extracted as the inputs for an identification model. We found the backpropagation neural network model to be the superior method for distinguishing between the insect species and geographical strains. The overall recognition rates of the classification model for insect species were 100 and 98.13% for the calibration and prediction sets respectively, while the rates of the model for geographical strains were 94.17 and 86.88% respectively. This study has demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a potential instrument for identifying insects and could become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects. © 2014 Society of Chemical Industry.

Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models.

PubMed

Zhu, Hongyan; Chu, Bingquan; Fan, Yangyang; Tao, Xiaoya; Yin, Wenxin; He, Yong

2017-08-10

We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm-partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre  = 0.9812, RPD = 5.17) and SSC (R pre  = 0.9523, RPD = 3.26) at 380-1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874-1734 nm for predicting pH (R pre  = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.

Hyperspectral Image Classification via Kernel Sparse Representation

DTIC Science & Technology

2013-01-01

classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model , where...joint sparsity model , where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training...hyperspectral imagery, joint spar- sity model , kernel methods, sparse representation. I. INTRODUCTION HYPERSPECTRAL imaging sensors capture images

Characterization of a compact 6-band multifunctional camera based on patterned spectral filters in the focal plane

NASA Astrophysics Data System (ADS)

Torkildsen, H. E.; Hovland, H.; Opsahl, T.; Haavardsholm, T. V.; Nicolas, S.; Skauli, T.

2014-06-01

In some applications of multi- or hyperspectral imaging, it is important to have a compact sensor. The most compact spectral imaging sensors are based on spectral filtering in the focal plane. For hyperspectral imaging, it has been proposed to use a "linearly variable" bandpass filter in the focal plane, combined with scanning of the field of view. As the image of a given object in the scene moves across the field of view, it is observed through parts of the filter with varying center wavelength, and a complete spectrum can be assembled. However if the radiance received from the object varies with viewing angle, or with time, then the reconstructed spectrum will be distorted. We describe a camera design where this hyperspectral functionality is traded for multispectral imaging with better spectral integrity. Spectral distortion is minimized by using a patterned filter with 6 bands arranged close together, so that a scene object is seen by each spectral band in rapid succession and with minimal change in viewing angle. The set of 6 bands is repeated 4 times so that the spectral data can be checked for internal consistency. Still the total extent of the filter in the scan direction is small. Therefore the remainder of the image sensor can be used for conventional imaging with potential for using motion tracking and 3D reconstruction to support the spectral imaging function. We show detailed characterization of the point spread function of the camera, demonstrating the importance of such characterization as a basis for image reconstruction. A simplified image reconstruction based on feature-based image coregistration is shown to yield reasonable results. Elimination of spectral artifacts due to scene motion is demonstrated.

Hyperspectral retrieval of surface reflectances: A new scheme

NASA Astrophysics Data System (ADS)

Thelen, Jean-Claude; Havemann, Stephan

2013-05-01

Here, we present a new prototype algorithm for the simultaneous retrieval of the atmospheric profiles (temperature, humidity, ozone and aerosol) and the surface reflectance from hyperspectral radiance measurements obtained from air/space borne, hyperspectral imagers. The new scheme, proposed here, consists of a fast radiative transfer code, based on empirical orthogonal functions (EOFs), in conjunction with a 1D-Var retrieval scheme. The inclusion of an 'exact' scattering code based on spherical harmonics, allows for an accurate treatment of Rayleigh scattering and scattering by aerosols, water droplets and ice-crystals, thus making it possible to also retrieve cloud and aerosol optical properties, although here we will concentrate on non-cloudy scenes.

Hyper-spectral imager of the visible band for lunar observations

NASA Astrophysics Data System (ADS)

Lim, Y.-M.; Choi, Y.-J.; Jo, Y.-S.; Lim, T.-H.; Ham, J.; Min, K. W.; Choi, Y.-W.

2013-06-01

A prototype hyper-spectral imager in the visible spectral band was developed for the planned Korean lunar missions in the 2020s. The instrument is based on simple refractive optics that adopted a linear variable filter and an interline charge-coupled device. This prototype imager is capable of mapping the lunar surface at wavelengths ranging from 450 to 900 nm with a spectral resolution of ˜8 nm and selectable channels ranging from 5 to 252. The anticipated spatial resolution is 17.2 m from an altitude of 100 km with a swath width of 21 km

High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose Contrast-Enhanced Digital Mammography.

PubMed

Pani, Silvia; Saifuddin, Sarene C; Ferreira, Filipa I M; Henthorn, Nicholas; Seller, Paul; Sellin, Paul J; Stratmann, Philipp; Veale, Matthew C; Wilson, Matthew D; Cernik, Robert J

2017-09-01

Contrast-enhanced digital mammography (CEDM) is an alternative to conventional X-ray mammography for imaging dense breasts. However, conventional approaches to CEDM require a double exposure of the patient, implying higher dose, and risk of incorrect image registration due to motion artifacts. A novel approach is presented, based on hyperspectral imaging, where a detector combining positional and high-resolution spectral information (in this case based on Cadmium Telluride) is used. This allows simultaneous acquisition of the two images required for CEDM. The approach was tested on a custom breast-equivalent phantom containing iodinated contrast agent (Niopam 150®). Two algorithms were used to obtain images of the contrast agent distribution: K-edge subtraction (KES), providing images of the distribution of the contrast agent with the background structures removed, and a dual-energy (DE) algorithm, providing an iodine-equivalent image and a water-equivalent image. The high energy resolution of the detector allowed the selection of two close-by energies, maximising the signal in KES images, and enhancing the visibility of details with the low surface concentration of contrast agent. DE performed consistently better than KES in terms of contrast-to-noise ratio of the details; moreover, it allowed a correct reconstruction of the surface concentration of the contrast agent in the iodine image. Comparison with CEDM with a conventional detector proved the superior performance of hyperspectral CEDM in terms of the image quality/dose tradeoff.

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

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

NRL Fact Book

DTIC Science & Technology

2008-01-01

Distributed network-based battle management High performance computing supporting uniform and nonuniform memory access with single and multithreaded...pallet Airborne EO/IR and radar sensors VNIR through SWIR hyperspectral systems VNIR, MWIR, and LWIR high-resolution sys- tems Wideband SAR systems...meteorological sensors Hyperspectral sensor systems (PHILLS) Mid-wave infrared (MWIR) Indium Antimonide (InSb) imaging system Long-wave infrared ( LWIR

Practical issues of hyperspectral imaging analysis of solid dosage forms.

PubMed

Amigo, José Manuel

2010-09-01

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

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.

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2010-01-01

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

Super-resolution reconstruction of hyperspectral images.

PubMed

Akgun, Toygar; Altunbasak, Yucel; Mersereau, Russell M

2005-11-01

Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying super-resolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super-resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions.

The role of the continuous wavelet transform in mineral identification using hyperspectral imaging in the long-wave infrared by using SVM classifier

NASA Astrophysics Data System (ADS)

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

2017-05-01

Hyperspectral imaging (HSI) in the long-wave infrared spectrum (LWIR) provides spectral and spatial information concerning the emissivity of the surface of materials, which can be used for mineral identification. For this, an endmember, which is the purest form of a mineral, is used as reference. All pure minerals have specific spectral profiles in the electromagnetic wavelength, which can be thought of as the mineral's fingerprint. The main goal of this paper is the identification of minerals by LWIR hyperspectral imaging using a machine learning scheme. The information of hyperspectral imaging has been recorded from the energy emitted from the mineral's surface. Solar energy is the source of energy in remote sensing, while a heating element is the energy source employed in laboratory experiments. Our work contains three main steps where the first step involves obtaining the spectral signatures of pure (single) minerals with a hyperspectral camera, in the long-wave infrared (7.7 to 11.8 μm), which measures the emitted radiance from the minerals' surface. The second step concerns feature extraction by applying the continuous wavelet transform (CWT) and finally we use support vector machine classifier with radial basis functions (SVM-RBF) for classification/identification of minerals. The overall accuracy of classification in our work is 90.23+/- 2.66%. In conclusion, based on CWT's ability to capture the information of signals can be used as a good marker for classification and identification the minerals substance.

Hyperspectral Raman imaging of bone growth and regrowth chemistry

NASA Astrophysics Data System (ADS)

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

1998-06-01

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

Some new classification methods for hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Du, Pei-jun; Chen, Yun-hao; Jones, Simon; Ferwerda, Jelle G.; Chen, Zhi-jun; Zhang, Hua-peng; Tan, Kun; Yin, Zuo-xia

2006-10-01

Hyperspectral Remote Sensing (HRS) is one of the most significant recent achievements of Earth Observation Technology. Classification is the most commonly employed processing methodology. In this paper three new hyperspectral RS image classification methods are analyzed. These methods are: Object-oriented FIRS image classification, HRS image classification based on information fusion and HSRS image classification by Back Propagation Neural Network (BPNN). OMIS FIRS image is used as the example data. Object-oriented techniques have gained popularity for RS image classification in recent years. In such method, image segmentation is used to extract the regions from the pixel information based on homogeneity criteria at first, and spectral parameters like mean vector, texture, NDVI and spatial/shape parameters like aspect ratio, convexity, solidity, roundness and orientation for each region are calculated, finally classification of the image using the region feature vectors and also using suitable classifiers such as artificial neural network (ANN). It proves that object-oriented methods can improve classification accuracy since they utilize information and features both from the point and the neighborhood, and the processing unit is a polygon (in which all pixels are homogeneous and belong to the class). HRS image classification based on information fusion, divides all bands of the image into different groups initially, and extracts features from every group according to the properties of each group. Three levels of information fusion: data level fusion, feature level fusion and decision level fusion are used to HRS image classification. Artificial Neural Network (ANN) can perform well in RS image classification. In order to promote the advances of ANN used for HIRS image classification, Back Propagation Neural Network (BPNN), the most commonly used neural network, is used to HRS image classification.

Calibration, characterization, and first results with the Ocean PHILLS hyperspectral imager

NASA Astrophysics Data System (ADS)

Davis, Curtiss O.; Kappus, Mary E.; Bowles, Jeffrey H.; Fisher, John; Antoniades, John A.; Carney, Megan

1999-10-01

The Ocean Portable Hyperspectral Imager for Low-Light spectroscopy (Ocean PHILLS), is a new hyperspectral imager specifically designed for imaging the coastal ocean. It uses a thinned, backside illuminated CCD for high sensitivity, and an all-reflective spectrograph with a convex grating in an Offner configuration to produce a distortion free image. Here we describe the instrument design and present the results of laboratory calibration and characterization and example results from a two week field experiment imaging the coastal waters off Lee Stocking, Island, Bahamas.

AOTF hyperspectral microscopic imaging for foodborne pathogenic bacteria detection

NASA Astrophysics Data System (ADS)

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

2011-06-01

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

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.

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.

Texture-adaptive hyperspectral video acquisition system with a spatial light modulator

NASA Astrophysics Data System (ADS)

Fang, Xiaojing; Feng, Jiao; Wang, Yongjin

2014-10-01

We present a new hybrid camera system based on spatial light modulator (SLM) to capture texture-adaptive high-resolution hyperspectral video. The hybrid camera system records a hyperspectral video with low spatial resolution using a gray camera and a high-spatial resolution video using a RGB camera. The hyperspectral video is subsampled by the SLM. The subsampled points can be adaptively selected according to the texture characteristic of the scene by combining with digital imaging analysis and computational processing. In this paper, we propose an adaptive sampling method utilizing texture segmentation and wavelet transform (WT). We also demonstrate the effectiveness of the sampled pattern on the SLM with the proposed method.

Hyperspectral imaging as a diagnostic tool for chronic skin ulcers

NASA Astrophysics Data System (ADS)

Denstedt, Martin; Pukstad, Brita S.; Paluchowski, Lukasz A.; Hernandez-Palacios, Julio E.; Randeberg, Lise L.

2013-03-01

The healing process of chronic wounds is complex, and the complete pathogenesis is not known. Diagnosis is currently based on visual inspection, biopsies and collection of samples from the wound surface. This is often time consuming, expensive and to some extent subjective procedures. Hyperspectral imaging has been shown to be a promising modality for optical diagnostics. The main objective of this study was to identify a suitable technique for reproducible classification of hyperspectral data from a wound and the surrounding tissue. Two statistical classification methods have been tested and compared to the performance of a dermatologist. Hyperspectral images (400-1000 nm) were collected from patients with venous leg ulcers using a pushbroom-scanning camera (VNIR 1600, Norsk Elektro Optikk AS).Wounds were examined regularly over 4 - 6 weeks. The patients were evaluated by a dermatologist at every appointment. One patient has been selected for presentation in this paper (female, age 53 years). The oxygen saturation of the wound area was determined by wavelength ratio metrics. Spectral angle mapping (SAM) and k-means clustering were used for classification. Automatic extraction of endmember spectra was employed to minimize human interaction. A comparison of the methods shows that k-means clustering is the most stable method over time, and shows the best overlap with the dermatologist's assessment of the wound border. The results are assumed to be affected by the data preprocessing and chosen endmember extraction algorithm. Results indicate that it is possible to develop an automated method for reliable classification of wounds based on hyperspectral data.

Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration

PubMed Central

Lohumi, Santosh; Lee, Hoonsoo; Kim, Moon S.; Qin, Jianwei; Kandpal, Lalit Mohan; Bae, Hyungjin; Rahman, Anisur

2018-01-01

The potential adulteration of foodstuffs has led to increasing concern regarding food safety and security, in particular for powdered food products where cheap ground materials or hazardous chemicals can be added to increase the quantity of powder or to obtain the desired aesthetic quality. Due to the resulting potential health threat to consumers, the development of a fast, label-free, and non-invasive technique for the detection of adulteration over a wide range of food products is necessary. We therefore report the development of a rapid Raman hyperspectral imaging technique for the detection of food adulteration and for authenticity analysis. The Raman hyperspectral imaging system comprises of a custom designed laser illumination system, sensing module, and a software interface. Laser illumination system generates a 785 nm laser line of high power, and the Gaussian like intensity distribution of laser beam is shaped by incorporating an engineered diffuser. The sensing module utilize Rayleigh filters, imaging spectrometer, and detector for collection of the Raman scattering signals along the laser line. A custom-built software to acquire Raman hyperspectral images which also facilitate the real time visualization of Raman chemical images of scanned samples. The developed system was employed for the simultaneous detection of Sudan dye and Congo red dye adulteration in paprika powder, and benzoyl peroxide and alloxan monohydrate adulteration in wheat flour at six different concentrations (w/w) from 0.05 to 1%. The collected Raman imaging data of the adulterated samples were analyzed to visualize and detect the adulterant concentrations by generating a binary image for each individual adulterant material. The results obtained based on the Raman chemical images of adulterants showed a strong correlation (R>0.98) between added and pixel based calculated concentration of adulterant materials. This developed Raman imaging system thus, can be considered as a powerful analytical technique for the quality and authenticity analysis of food products. PMID:29708973

Calibration and testing of a Raman hyperspectral imaging system to reveal powdered food adulteration.

PubMed

Lohumi, Santosh; Lee, Hoonsoo; Kim, Moon S; Qin, Jianwei; Kandpal, Lalit Mohan; Bae, Hyungjin; Rahman, Anisur; Cho, Byoung-Kwan

2018-01-01

The potential adulteration of foodstuffs has led to increasing concern regarding food safety and security, in particular for powdered food products where cheap ground materials or hazardous chemicals can be added to increase the quantity of powder or to obtain the desired aesthetic quality. Due to the resulting potential health threat to consumers, the development of a fast, label-free, and non-invasive technique for the detection of adulteration over a wide range of food products is necessary. We therefore report the development of a rapid Raman hyperspectral imaging technique for the detection of food adulteration and for authenticity analysis. The Raman hyperspectral imaging system comprises of a custom designed laser illumination system, sensing module, and a software interface. Laser illumination system generates a 785 nm laser line of high power, and the Gaussian like intensity distribution of laser beam is shaped by incorporating an engineered diffuser. The sensing module utilize Rayleigh filters, imaging spectrometer, and detector for collection of the Raman scattering signals along the laser line. A custom-built software to acquire Raman hyperspectral images which also facilitate the real time visualization of Raman chemical images of scanned samples. The developed system was employed for the simultaneous detection of Sudan dye and Congo red dye adulteration in paprika powder, and benzoyl peroxide and alloxan monohydrate adulteration in wheat flour at six different concentrations (w/w) from 0.05 to 1%. The collected Raman imaging data of the adulterated samples were analyzed to visualize and detect the adulterant concentrations by generating a binary image for each individual adulterant material. The results obtained based on the Raman chemical images of adulterants showed a strong correlation (R>0.98) between added and pixel based calculated concentration of adulterant materials. This developed Raman imaging system thus, can be considered as a powerful analytical technique for the quality and authenticity analysis of food products.

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

USDA-ARS?s Scientific Manuscript database

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...

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

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.

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.

Hyperspectral remote sensing for terrestrial applications

USGS Publications Warehouse

Thenkabail, Prasad S.; Teluguntla, Pardhasaradhi G.; Murali Krishna Gumma,; Venkateswarlu Dheeravath,

2015-01-01

Remote sensing data are considered hyperspectral when the data are gathered from numerous wavebands, contiguously over an entire range of the spectrum (e.g., 400–2500 nm). Goetz (1992) defines hyperspectral remote sensing as “The acquisition of images in hundreds of registered, contiguous spectral bands such that for each picture element of an image it is possible to derive a complete reflectance spectrum.” However, Jensen (2004) defines hyperspectral remote sensing as “The simultaneous acquisition of images in many relatively narrow, contiguous and/or non contiguous spectral bands throughout the ultraviolet, visible, and infrared portions of the electromagnetic spectrum.

Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery

NASA Astrophysics Data System (ADS)

Monteiro, Sildomar Takahashi; Minekawa, Yohei; Kosugi, Yukio; Akazawa, Tsuneya; Oda, Kunio

Hyperspectral image data provides a powerful tool for non-destructive crop analysis. This paper investigates a hyperspectral image data-processing method to predict the sweetness and amino acid content of soybean crops. Regression models based on artificial neural networks were developed in order to calculate the level of sucrose, glucose, fructose, and nitrogen concentrations, which can be related to the sweetness and amino acid content of vegetables. A performance analysis was conducted comparing regression models obtained using different preprocessing methods, namely, raw reflectance, second derivative, and principal components analysis. This method is demonstrated using high-resolution hyperspectral data of wavelengths ranging from the visible to the near infrared acquired from an experimental field of green vegetable soybeans. The best predictions were achieved using a nonlinear regression model of the second derivative transformed dataset. Glucose could be predicted with greater accuracy, followed by sucrose, fructose and nitrogen. The proposed method provides the possibility to provide relatively accurate maps predicting the chemical content of soybean crop fields.

Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning

NASA Astrophysics Data System (ADS)

Leavesley, Silas J.; Deal, Joshua; Hill, Shante; Martin, Will A.; Lall, Malvika; Lopez, Carmen; Rider, Paul F.; Rich, Thomas C.; Boudreaux, Carole W.

2018-02-01

Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied.

Integrated Ground-based Hyperspectral Imaging and Geochemical Study of the Eagle Ford Group in West Texas

NASA Astrophysics Data System (ADS)

Sun, L.; Khan, S.; Godet, A.

2017-12-01

This study used ground-based hyperspectral imaging to map an outcrop of the Eagle Ford Group in west Texas. The Eagle Ford Group consists of alternating layers of mudstone - wackestone, grainstone - packstone facies and volcanic ash deposits with high total organic carbon content deposited during the Late Cenomanian - Turonian time period. It is one of the few unconventional source rock and reservoirs that have surface representations. Ground-based hyperspectral imaging scanned an outcrop and hand samples at close ranges with very fine spatial resolution (centimeter to sub-millimeter). Spectral absorption modeling of clay minerals and calcite with the modified Gaussian model (MGM) allowed quantification of variations of mineral abundances. Petrographic analysis confirmed mineral identifications and shed light on sedimentary textures. Major element geochemistry confirmed the mineral quantification. Enrichment of molybdenum (Mo) and uranium (U) indicated "unrestricted marine" paleo-hydrogeology and anoxic to euxinic paleo-redox bottom water conditions. Mineral quantification resulted in mapping of mudstone - wackestone, grainstone - packstone facies and claystones (volcanic ash beds). The lack of spatial associations between the grainstones and claystones on the outcrop calls into question the hypothesis that the primary productivity is controlled by iron availability from volcanic ash beds. Hyperspectral remote sensing data also helped in creating a virtual outcrop model with detailed mineralogical compositions, and provided reservoir analog to extract compositional and geo-mechanical characteristics and variations. The utilization of these new techniques in geo-statistical analysis provides a workflow for employing remote sensing in resource exploration and exploitation.

Integrated ground-based hyperspectral imaging and geochemical study of the Eagle Ford Group in West Texas

NASA Astrophysics Data System (ADS)

Sun, Lei; Khan, Shuhab; Godet, Alexis

2018-01-01

This study used ground-based hyperspectral imaging to map an outcrop of the Eagle Ford Group in west Texas. The Eagle Ford Group consists of alternating layers of mudstone - wackestone, grainstone - packstone facies and volcanic ash deposits with high total organic content deposited during the Cenomanian - Turonian time period. It is one of the few unconventional source rock and reservoirs that have surface representations. Ground-based hyperspectral imaging scanned an outcrop and hand samples at close ranges with very fine spatial resolution (centimeter to sub-millimeter). Spectral absorption modeling of clay minerals and calcite with the modified Gaussian model (MGM) allowed quantification of variations of mineral abundances. Petrographic analysis confirmed mineral identifications and shed light on sedimentary textures, and major element geochemistry supported the mineral quantification. Mineral quantification resulted in mapping of mudstone - wackestone, grainstone - packstone facies and bentonites (volcanic ash beds). The lack of spatial associations between the grainstones and bentonites on the outcrop calls into question the hypothesis that the primary productivity is controlled by iron availability from volcanic ash beds. Enrichment of molybdenum (Mo) and uranium (U) indicated "unrestricted marine" paleo-hydrogeology and anoxic to euxinic paleo-redox bottom water conditions. Hyperspectral remote sensing data also helped in creating a virtual outcrop model with detailed mineralogical compositions, and provided reservoir analog to extract compositional and geo-mechanical characteristics and variations. The utilization of these new techniques in geo-statistical analysis provides a workflow for employing remote sensing in resource exploration and exploitation.

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging.

PubMed

Ravi, Daniele; Fabelo, Himar; Callic, Gustavo Marrero; Yang, Guang-Zhong

2017-09-01

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Physically motivated correlation formalism in hyperspectral imaging

NASA Astrophysics Data System (ADS)

Roy, Ankita; Rafert, J. Bruce

2004-05-01

Most remote sensing data-sets contain a limiting number of independent spatial and spectral measurements, beyond which no effective increase in information is achieved. This paper presents a Physically Motivated Correlation Formalism (PMCF) ,which places both Spatial and Spectral data on an equivalent mathematical footing in the context of a specific Kernel, such that, optimal combinations of independent data can be selected from the entire Hypercube via the method of "Correlation Moments". We present an experimental and computational analysis of Hyperspectral data sets using the Michigan Tech VFTHSI [Visible Fourier Transform Hyperspectral Imager] based on a Sagnac Interferometer, adjusted to obtain high SNR levels. The captured Signal Interferograms of different targets - aerial snaps of Houghton and lab-based data (white light , He-Ne laser , discharge tube sources) with the provision of customized scan of targets with the same exposures are processed using inverse imaging transformations and filtering techniques to obtain the Spectral profiles and generate Hypercubes to compute Spectral/Spatial/Cross Moments. PMCF answers the question of how optimally the entire hypercube should be sampled and finds how many spatial-spectral pixels are required for a particular target recognition.

Performance limitations of temperature-emissivity separation techniques in long-wave infrared hyperspectral imaging applications

NASA Astrophysics Data System (ADS)

Pieper, Michael; Manolakis, Dimitris; Truslow, Eric; Cooley, Thomas; Brueggeman, Michael; Jacobson, John; Weisner, Andrew

2017-08-01

Accurate estimation or retrieval of surface emissivity from long-wave infrared or thermal infrared (TIR) hyperspectral imaging data acquired by airborne or spaceborne sensors is necessary for many scientific and defense applications. This process consists of two interwoven steps: atmospheric compensation and temperature-emissivity separation (TES). The most widely used TES algorithms for hyperspectral imaging data assume that the emissivity spectra for solids are smooth compared to the atmospheric transmission function. We develop a model to explain and evaluate the performance of TES algorithms using a smoothing approach. Based on this model, we identify three sources of error: the smoothing error of the emissivity spectrum, the emissivity error from using the incorrect temperature, and the errors caused by sensor noise. For each TES smoothing technique, we analyze the bias and variability of the temperature errors, which translate to emissivity errors. The performance model explains how the errors interact to generate temperature errors. Since we assume exact knowledge of the atmosphere, the presented results provide an upper bound on the performance of TES algorithms based on the smoothness assumption.

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;

Onboard Classification of Hyperspectral Data on the Earth Observing One Mission

NASA Technical Reports Server (NTRS)

Chien, Steve; Tran, Daniel; Schaffer, Steve; Rabideau, Gregg; Davies, Ashley Gerard; Doggett, Thomas; Greeley, Ronald; Ip, Felipe; Baker, Victor; Doubleday, Joshua;

Mapping rock forming minerals at Boundary Canyon, Death Valey National Park, California, using aerial SEBASS thermal infrared hyperspectral image data

NASA Astrophysics Data System (ADS)

Aslett, Zan; Taranik, James V.; Riley, Dean N.

2018-02-01

Aerial spatially enhanced broadband array spectrograph system (SEBASS) long-wave infrared (LWIR) hyperspectral image data were used to map the distribution of rock-forming minerals indicative of sedimentary and meta-sedimentary lithologies around Boundary Canyon, Death Valley, California, USA. Collection of data over the Boundary Canyon detachment fault (BCDF) facilitated measurement of numerous lithologies representing a contact between the relatively unmetamorphosed Grapevine Mountains allochthon and the metamorphosed core complex of the Funeral Mountains autochthon. These included quartz-rich sandstone, quartzite, conglomerate, and alluvium; muscovite-rich schist, siltstone, and slate; and carbonate-rich dolomite, limestone, and marble, ranging in age from late Precambrian to Quaternary. Hyperspectral data were reduced in dimensionality and processed to statistically identify and map unique emissivity spectra endmembers. Some minerals (e.g., quartz and muscovite) dominate multiple lithologies, resulting in a limited ability to differentiate them. Abrupt variations in image data emissivity amongst pelitic schists corresponded to amphibolite; these rocks represent gradation from greenschist- to amphibolite-metamorphic facies lithologies. Although the full potential of LWIR hyperspectral image data may not be fully utilized within this study area due to lack of measurable spectral distinction between rocks of similar bulk mineralogy, the high spectral resolution of the image data was useful in characterizing silicate- and carbonate-based sedimentary and meta-sedimentary rocks in proximity to fault contacts, as well as for interpreting some mineral mixtures.

Classification of Hyperspectral Data Based on Guided Filtering and Random Forest

NASA Astrophysics Data System (ADS)

Ma, H.; Feng, W.; Cao, X.; Wang, L.

2017-09-01

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to "the curse of dimensionality". In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging

PubMed Central

Pandey, Piyush; Ge, Yufeng; Stoerger, Vincent; Schnable, James C.

2017-01-01

Image-based high-throughput plant phenotyping in greenhouse has the potential to relieve the bottleneck currently presented by phenotypic scoring which limits the throughput of gene discovery and crop improvement efforts. Numerous studies have employed automated RGB imaging to characterize biomass and growth of agronomically important crops. The objective of this study was to investigate the utility of hyperspectral imaging for quantifying chemical properties of maize and soybean plants in vivo. These properties included leaf water content, as well as concentrations of macronutrients nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), and sulfur (S), and micronutrients sodium (Na), iron (Fe), manganese (Mn), boron (B), copper (Cu), and zinc (Zn). Hyperspectral images were collected from 60 maize and 60 soybean plants, each subjected to varying levels of either water deficit or nutrient limitation stress with the goal of creating a wide range of variation in the chemical properties of plant leaves. Plants were imaged on an automated conveyor belt system using a hyperspectral imager with a spectral range from 550 to 1,700 nm. Images were processed to extract reflectance spectrum from each plant and partial least squares regression models were developed to correlate spectral data with chemical data. Among all the chemical properties investigated, water content was predicted with the highest accuracy [R2 = 0.93 and RPD (Ratio of Performance to Deviation) = 3.8]. All macronutrients were also quantified satisfactorily (R2 from 0.69 to 0.92, RPD from 1.62 to 3.62), with N predicted best followed by P, K, and S. The micronutrients group showed lower prediction accuracy (R2 from 0.19 to 0.86, RPD from 1.09 to 2.69) than the macronutrient groups. Cu and Zn were best predicted, followed by Fe and Mn. Na and B were the only two properties that hyperspectral imaging was not able to quantify satisfactorily (R2 < 0.3 and RPD < 1.2). This study suggested the potential usefulness of hyperspectral imaging as a high-throughput phenotyping technology for plant chemical traits. Future research is needed to test the method more thoroughly by designing experiments to vary plant nutrients individually and cover more plant species, genotypes, and growth stages. PMID:28824683

High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging.

PubMed

Pandey, Piyush; Ge, Yufeng; Stoerger, Vincent; Schnable, James C

2017-01-01

Image-based high-throughput plant phenotyping in greenhouse has the potential to relieve the bottleneck currently presented by phenotypic scoring which limits the throughput of gene discovery and crop improvement efforts. Numerous studies have employed automated RGB imaging to characterize biomass and growth of agronomically important crops. The objective of this study was to investigate the utility of hyperspectral imaging for quantifying chemical properties of maize and soybean plants in vivo . These properties included leaf water content, as well as concentrations of macronutrients nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), and sulfur (S), and micronutrients sodium (Na), iron (Fe), manganese (Mn), boron (B), copper (Cu), and zinc (Zn). Hyperspectral images were collected from 60 maize and 60 soybean plants, each subjected to varying levels of either water deficit or nutrient limitation stress with the goal of creating a wide range of variation in the chemical properties of plant leaves. Plants were imaged on an automated conveyor belt system using a hyperspectral imager with a spectral range from 550 to 1,700 nm. Images were processed to extract reflectance spectrum from each plant and partial least squares regression models were developed to correlate spectral data with chemical data. Among all the chemical properties investigated, water content was predicted with the highest accuracy [ R 2 = 0.93 and RPD (Ratio of Performance to Deviation) = 3.8]. All macronutrients were also quantified satisfactorily ( R 2 from 0.69 to 0.92, RPD from 1.62 to 3.62), with N predicted best followed by P, K, and S. The micronutrients group showed lower prediction accuracy ( R 2 from 0.19 to 0.86, RPD from 1.09 to 2.69) than the macronutrient groups. Cu and Zn were best predicted, followed by Fe and Mn. Na and B were the only two properties that hyperspectral imaging was not able to quantify satisfactorily ( R 2 < 0.3 and RPD < 1.2). This study suggested the potential usefulness of hyperspectral imaging as a high-throughput phenotyping technology for plant chemical traits. Future research is needed to test the method more thoroughly by designing experiments to vary plant nutrients individually and cover more plant species, genotypes, and growth stages.

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

NASA Astrophysics Data System (ADS)

Bostater, Charles R.; Oney, Taylor

2016-10-01

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

Simulation of Hyperspectral Images

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Discrimination methods for biological contaminants in fresh-cut lettuce based on VNIR and NIR hyperspectral imaging

NASA Astrophysics Data System (ADS)

Mo, Changyeun; Kim, Giyoung; Kim, Moon S.; Lim, Jongguk; Lee, Seung Hyun; Lee, Hong-Seok; Cho, Byoung-Kwan

2017-09-01

The rapid detection of biological contaminants such as worms in fresh-cut vegetables is necessary to improve the efficiency of visual inspections carried out by workers. Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms in fresh-cut lettuce. The optimal wavebands that can detect worms in fresh-cut lettuce were investigated for each type of HSI using one-way ANOVA. Worm-detection imaging algorithms for VNIR and NIR imaging exhibited prediction accuracies of 97.00% (RI547/945) and 100.0% (RI1064/1176, SI1064-1176, RSI-I(1064-1173)/1064, and RSI-II(1064-1176)/(1064+1176)), respectively. The two HSI techniques revealed that spectral images with a pixel size of 1 × 1 mm or 2 × 2 mm had the best classification accuracy for worms. The results demonstrate that hyperspectral reflectance imaging techniques have the potential to detect worms in fresh-cut lettuce. Future research relating to this work will focus on a real-time sorting system for lettuce that can simultaneously detect various defects such as browning, worms, and slugs.

Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging.

PubMed

Lee, Hoonsoo; Kim, Moon S; Song, Yu-Rim; Oh, Chang-Sik; Lim, Hyoun-Sub; Lee, Wang-Hee; Kang, Jum-Soon; Cho, Byoung-Kwan

2017-03-01

There is a need to minimize economic damage by sorting infected seeds from healthy seeds before seeding. However, current methods of detecting infected seeds, such as seedling grow-out, enzyme-linked immunosorbent assays, the polymerase chain reaction (PCR) and the real-time PCR have a critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to evaluate the potential of visible/near-infrared (Vis/NIR) hyperspectral imaging system for detecting bacteria-infected watermelon seeds. A hyperspectral Vis/NIR reflectance imaging system (spectral region of 400-1000 nm) was constructed to obtain hyperspectral reflectance images for 336 bacteria-infected watermelon seeds, which were then subjected to partial least square discriminant analysis (PLS-DA) and a least-squares support vector machine (LS-SVM) to classify bacteria-infected watermelon seeds from healthy watermelon seeds. The developed system detected bacteria-infected watermelon seeds with an accuracy > 90% (PLS-DA: 91.7%, LS-SVM: 90.5%), suggesting that the Vis/NIR hyperspectral imaging system is effective for quarantining bacteria-infected watermelon seeds. The results of the present study show that it is possible to use the Vis/NIR hyperspectral imaging system for detecting bacteria-infected watermelon seeds. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Detection of triterpene acids distribution in loquat (Eriobotrya japonica) leaf using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Shi, Jiyong; Chen, Wu; Zou, Xiaobo; Xu, Yiwei; Huang, Xiaowei; Zhu, Yaodi; Shen, Tingting

2018-01-01

Hyperspectral images (431-962 nm) and partial least squares (PLS) were used to detect the distribution of triterpene acids within loquat (Eriobotrya japonica) leaves. 72 fresh loquat leaves in the young group, mature group and old group were collected for hyperspectral imaging; and triterpene acids content of the loquat leaves was analyzed using high performance liquid chromatography (HPLC). Then the spectral data of loquat leaf hyperspectral images and the triterpene acids content were employed to build calibration models. After spectra pre-processing and wavelength selection, an optimum calibration model (Rp = 0.8473, RMSEP = 2.61 mg/g) for predicting triterpene acids was obtained by synergy interval partial least squares (siPLS). Finally, spectral data of each pixel in the loquat leaf hyperspectral image were extracted and substituted into the optimum calibration model to predict triterpene acids content of each pixel. Therefore, the distribution map of triterpene acids content was obtained. As shown in the distribution map, triterpene acids are accumulated mainly in the leaf mesophyll regions near the main veins, and triterpene acids concentration of young group is less than that of mature and old groups. This study showed that hyperspectral imaging is suitable to determine the distribution of active constituent content in medical herbs in a rapid and non-invasive manner.

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

Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.

PubMed

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

2015-11-20

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

PET and PVC separation with hyperspectral imagery.

PubMed

Moroni, Monica; Mei, Alessandro; Leonardi, Alessandra; Lupo, Emanuela; Marca, Floriana La

2015-01-20

Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers--polyethylene terephthalate (PET) and polyvinyl chloride (PVC)--in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900-1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

PET and PVC Separation with Hyperspectral Imagery

PubMed Central

Moroni, Monica; Mei, Alessandro; Leonardi, Alessandra; Lupo, Emanuela; La Marca, Floriana

2015-01-01

Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET) and polyvinyl chloride (PVC)—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry. PMID:25609050

Scene-based method for spatial misregistration detection in hyperspectral imagery.

PubMed

Dell'Endice, Francesco; Nieke, Jens; Schläpfer, Daniel; Itten, Klaus I

2007-05-20

Hyperspectral imaging (HSI) sensors suffer from spatial misregistration, an artifact that prevents the accurate acquisition of the spectra. Physical considerations let us assume that the influence of the spatial misregistration on the acquired data depends both on the wavelength and on the across-track position. A scene-based method, based on edge detection, is therefore proposed. Such a procedure measures the variation on the spatial location of an edge between its various monochromatic projections, giving an estimation for spatial misregistration, and also allowing identification of misalignments. The method has been applied to several hyperspectral sensors, either prism, or grating-based designs. The results confirm the dependence assumptions on lambda and theta, spectral wavelength and across-track pixel, respectively. Suggestions are also given to correct for spatial misregistration.

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

USDA-ARS?s Scientific Manuscript database

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...

Design and laboratory calibration of the compact pushbroom hyperspectral imaging system

NASA Astrophysics Data System (ADS)

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

2009-11-01

The designed hyperspectral imaging system is composed of three main parts, that is, optical subsystem, electronic subsystem and capturing subsystem. And a three-dimensional "image cube" can be obtained through push-broom. The fore-optics is commercial-off-the-shelf with high speed and three continuous zoom ratios. Since the dispersive imaging part is based on Offner relay configuration with an aberration-corrected convex grating, high power of light collection and variable view field are obtained. The holographic recording parameters of the convex grating are optimized, and the aberration of the Offner configuration dispersive system is balanced. The electronic system adopts module design, which can minimize size, mass, and power consumption. Frame transfer area-array CCD is chosen as the image sensor and the spectral line can be binned to achieve better SNR and sensitivity without any deterioration in spatial resolution. The capturing system based on the computer can set the capturing parameters, calibrate the spectrometer, process and display spectral imaging data. Laboratory calibrations are prerequisite for using precise spectral data. The spatial and spectral calibration minimize smile and keystone distortion caused by optical system, assembly and so on and fix positions of spatial and spectral line on the frame area-array CCD. Gases excitation lamp is used in smile calibration and the keystone calculation is carried out by different viewing field point source created by a series of narrow slit. The laboratory and field imaging results show that this pushbroom hyperspectral imaging system can acquire high quality spectral images.

Wavelet compression techniques for hyperspectral data

NASA Technical Reports Server (NTRS)

Evans, Bruce; Ringer, Brian; Yeates, Mathew

1994-01-01

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

M-estimation for robust sparse unmixing of hyperspectral images

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Imaging spectroscopy using embedded diffractive optical arrays

NASA Astrophysics Data System (ADS)

Hinnrichs, Michele; Hinnrichs, Bradford

2017-09-01

Pacific Advanced Technology (PAT) has developed an infrared hyperspectral camera based on diffractive optic arrays. This approach to hyperspectral imaging has been demonstrated in all three infrared bands SWIR, MWIR and LWIR. The hyperspectral optical system has been integrated into the cold-shield of the sensor enabling the small size and weight of this infrared hyperspectral sensor. This new and innovative approach to an infrared hyperspectral imaging spectrometer uses micro-optics that are made up of an area array of diffractive optical elements where each element is tuned to image a different spectral region on a common focal plane array. The lenslet array is embedded in the cold-shield of the sensor and actuated with a miniature piezo-electric motor. This approach enables rapid infrared spectral imaging with multiple spectral images collected and processed simultaneously each frame of the camera. This paper will present our optical mechanical design approach which results in an infrared hyper-spectral imaging system that is small enough for a payload on a small satellite, mini-UAV, commercial quadcopter or man portable. Also, an application of how this spectral imaging technology can easily be used to quantify the mass and volume flow rates of hydrocarbon gases. The diffractive optical elements used in the lenslet array are blazed gratings where each lenslet is tuned for a different spectral bandpass. The lenslets are configured in an area array placed a few millimeters above the focal plane and embedded in the cold-shield to reduce the background signal normally associated with the optics. The detector array is divided into sub-images covered by each lenslet. We have developed various systems using a different number of lenslets in the area array. Depending on the size of the focal plane and the diameter of the lenslet array will determine the number of simultaneous different spectral images collected each frame of the camera. A 2 x 2 lenslet array will image four different spectral images of the scene each frame and when coupled with a 512 x 512 focal plane array will give spatial resolution of 256 x 256 pixel each spectral image. Another system that we developed uses a 4 x 4 lenslet array on a 1024 x 1024 pixel element focal plane array which gives 16 spectral images of 256 x 256 pixel resolution each frame. This system spans the SWIR and MWIR bands with a single optical array and focal plane array.

Basics of image analysis

USDA-ARS?s Scientific Manuscript database

Hyperspectral imaging technology has emerged as a powerful tool for quality and safety inspection of food and agricultural products and in precision agriculture over the past decade. Image analysis is a critical step in implementing hyperspectral imaging technology; it is aimed to improve the qualit...

Excitation-scanning hyperspectral imaging as a means to discriminate various tissues types

NASA Astrophysics Data System (ADS)

Deal, Joshua; Favreau, Peter F.; Lopez, Carmen; Lall, Malvika; Weber, David S.; Rich, Thomas C.; Leavesley, Silas J.

2017-02-01

Little is currently known about the fluorescence excitation spectra of disparate tissues and how these spectra change with pathological state. Current imaging diagnostic techniques have limited capacity to investigate fluorescence excitation spectral characteristics. This study utilized excitation-scanning hyperspectral imaging to perform a comprehensive assessment of fluorescence spectral signatures of various tissues. Immediately following tissue harvest, a custom inverted microscope (TE-2000, Nikon Instruments) with Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) were used to acquire hyperspectral image data from each sample. Scans utilized excitation wavelengths from 340 nm to 550 nm in 5 nm increments. Hyperspectral images were analyzed with custom Matlab scripts including linear spectral unmixing (LSU), principal component analysis (PCA), and Gaussian mixture modeling (GMM). Spectra were examined for potential characteristic features such as consistent intensity peaks at specific wavelengths or intensity ratios among significant wavelengths. The resultant spectral features were conserved among tissues of similar molecular composition. Additionally, excitation spectra appear to be a mixture of pure endmembers with commonalities across tissues of varied molecular composition, potentially identifiable through GMM. These results suggest the presence of common autofluorescent molecules in most tissues and that excitationscanning hyperspectral imaging may serve as an approach for characterizing tissue composition as well as pathologic state. Future work will test the feasibility of excitation-scanning hyperspectral imaging as a contrast mode for discriminating normal and pathological tissues.

A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

PubMed Central

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

2014-01-01

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

Infrared hyperspectral imaging for chemical vapour detection

NASA Astrophysics Data System (ADS)

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

2012-10-01

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

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 for shorter wavelengths studied in this work as well as in spectral areas that experience absorption by the atmosphere.

Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks.

PubMed

Lin, Jianyu; Clancy, Neil T; Qi, Ji; Hu, Yang; Tatla, Taran; Stoyanov, Danail; Maier-Hein, Lena; Elson, Daniel S

2018-06-15

Surgical guidance and decision making could be improved with accurate and real-time measurement of intra-operative data including shape and spectral information of the tissue surface. In this work, a dual-modality endoscopic system has been proposed to enable tissue surface shape reconstruction and hyperspectral imaging (HSI). This system centers around a probe comprised of an incoherent fiber bundle, whose fiber arrangement is different at the two ends, and miniature imaging optics. For 3D reconstruction with structured light (SL), a light pattern formed of randomly distributed spots with different colors is projected onto the tissue surface, creating artificial texture. Pattern decoding with a Convolutional Neural Network (CNN) model and a customized feature descriptor enables real-time 3D surface reconstruction at approximately 12 frames per second (FPS). In HSI mode, spatially sparse hyperspectral signals from the tissue surface can be captured with a slit hyperspectral imager in a single snapshot. A CNN based super-resolution model, namely "super-spectral-resolution" network (SSRNet), has also been developed to estimate pixel-level dense hypercubes from the endoscope cameras standard RGB images and the sparse hyperspectral signals, at approximately 2 FPS. The probe, with a 2.1 mm diameter, enables the system to be used with endoscope working channels. Furthermore, since data acquisition in both modes can be accomplished in one snapshot, operation of this system in clinical applications is minimally affected by tissue surface movement and deformation. The whole apparatus has been validated on phantoms and tissue (ex vivo and in vivo), while initial measurements on patients during laryngeal surgery show its potential in real-world clinical applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Snapshot hyperspectral fovea vision system (HyperVideo)

NASA Astrophysics Data System (ADS)

Kriesel, Jason; Scriven, Gordon; Gat, Nahum; Nagaraj, Sheela; Willson, Paul; Swaminathan, V.

2012-06-01

The development and demonstration of a new snapshot hyperspectral sensor is described. The system is a significant extension of the four dimensional imaging spectrometer (4DIS) concept, which resolves all four dimensions of hyperspectral imaging data (2D spatial, spectral, and temporal) in real-time. The new sensor, dubbed "4×4DIS" uses a single fiber optic reformatter that feeds into four separate, miniature visible to near-infrared (VNIR) imaging spectrometers, providing significantly better spatial resolution than previous systems. Full data cubes are captured in each frame period without scanning, i.e., "HyperVideo". The current system operates up to 30 Hz (i.e., 30 cubes/s), has 300 spectral bands from 400 to 1100 nm (~2.4 nm resolution), and a spatial resolution of 44×40 pixels. An additional 1.4 Megapixel video camera provides scene context and effectively sharpens the spatial resolution of the hyperspectral data. Essentially, the 4×4DIS provides a 2D spatially resolved grid of 44×40 = 1760 separate spectral measurements every 33 ms, which is overlaid on the detailed spatial information provided by the context camera. The system can use a wide range of off-the-shelf lenses and can either be operated so that the fields of view match, or in a "spectral fovea" mode, in which the 4×4DIS system uses narrow field of view optics, and is cued by a wider field of view context camera. Unlike other hyperspectral snapshot schemes, which require intensive computations to deconvolve the data (e.g., Computed Tomographic Imaging Spectrometer), the 4×4DIS requires only a linear remapping, enabling real-time display and analysis. The system concept has a range of applications including biomedical imaging, missile defense, infrared counter measure (IRCM) threat characterization, and ground based remote sensing.

Optimisation and evaluation of hyperspectral imaging system using machine learning algorithm

NASA Astrophysics Data System (ADS)

Suthar, Gajendra; Huang, Jung Y.; Chidangil, Santhosh

2017-10-01

Hyperspectral imaging (HSI), also called imaging spectrometer, originated from remote sensing. Hyperspectral imaging 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 objects physiology, morphology, and composition. The present work involves testing and evaluating the performance of the hyperspectral imaging system. The methodology involved manually taking reflectance of the object in many images or scan of the object. The object used for the evaluation of the system was cabbage and tomato. The data is further converted to the required format and the analysis is done using machine learning algorithm. The machine learning algorithms applied were able to distinguish between the object present in the hypercube obtain by the scan. It was concluded from the results that system was working as expected. This was observed by the different spectra obtained by using the machine-learning algorithm.

Depth resolved hyperspectral imaging spectrometer based on structured light illumination and Fourier transform interferometry

PubMed Central

Choi, Heejin; Wadduwage, Dushan; Matsudaira, Paul T.; So, Peter T.C.

2014-01-01

A depth resolved hyperspectral imaging spectrometer can provide depth resolved imaging both in the spatial and the spectral domain. Images acquired through a standard imaging Fourier transform spectrometer do not have the depth-resolution. By post processing the spectral cubes (x, y, λ) obtained through a Sagnac interferometer under uniform illumination and structured illumination, spectrally resolved images with depth resolution can be recovered using structured light illumination algorithms such as the HiLo method. The proposed scheme is validated with in vitro specimens including fluorescent solution and fluorescent beads with known spectra. The system is further demonstrated in quantifying spectra from 3D resolved features in biological specimens. The system has demonstrated depth resolution of 1.8 μm and spectral resolution of 7 nm respectively. PMID:25360367

Derivative spectra matching for wetland vegetation identification and classification by hyperspectral image

NASA Astrophysics Data System (ADS)

Wang, Jinnian; Zheng, Lanfen; Tong, Qingxi

1998-08-01

In this paper, we reported some research result in applying hyperspectral remote sensing data in identification and classification of wetland plant species and associations. Hyperspectral data were acquired by Modular Airborne Imaging Spectrometer (MAIS) over Poyang Lake wetland, China. A derivative spectral matching algorithm was used in hyperspectral vegetation analysis. The field measurement spectra were as reference for derivative spectral matching. In the study area, seven wetland plant associations were identified and classified with overall average accuracy is 84.03%.

Hyperspectral Imager-Tracker

NASA Technical Reports Server (NTRS)

Agurok, Llya

2013-01-01

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

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.

Spectroscopic imaging using acousto-optic tunable filters

NASA Astrophysics Data System (ADS)

Bouhifd, Mounir; Whelan, Maurice

2007-07-01

We report on novel hyper-spectral imaging filter-modules based on acousto-optic tuneable filters (AOTF). The AOTF functions as a full-field tuneable bandpass filter which offers fast continuous or random access tuning with high filtering efficiency. Due to the diffractive nature of the device, the unfiltered zero-order and the filtered first-order images are geometrically separated. The modules developed exploit this feature to simultaneously route both the transmitted white-light image and the filtered fluorescence image to two separate cameras. Incorporation of prisms in the optical paths and careful design of the relay optics in the filter module have overcome a number of aberrations inherent to imaging through AOTFs, leading to excellent spatial resolution. A number of practical uses of this technique, both for in vivo auto-fluorescence endoscopy and in vitro fluorescence microscopy were demonstrated. We describe the operational principle and design of recently improved prototype instruments for fluorescence-based diagnostics and demonstrate their performance by presenting challenging hyper-spectral fluorescence imaging applications.

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.

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.

Validation of Spectral Unmixing Results from Informed Non-Negative Matrix Factorization (INMF) of Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Wright, L.; Coddington, O.; Pilewskie, P.

2017-12-01

Hyperspectral instruments are a growing class of Earth observing sensors designed to improve remote sensing capabilities beyond discrete multi-band sensors by providing tens to hundreds of continuous spectral channels. Improved spectral resolution, range and radiometric accuracy allow the collection of large amounts of spectral data, facilitating thorough characterization of both atmospheric and surface properties. We describe the development of an Informed Non-Negative Matrix Factorization (INMF) spectral unmixing method to exploit this spectral information and separate atmospheric and surface signals based on their physical sources. INMF offers marked benefits over other commonly employed techniques including non-negativity, which avoids physically impossible results; and adaptability, which tailors the method to hyperspectral source separation. The INMF algorithm is adapted to separate contributions from physically distinct sources using constraints on spectral and spatial variability, and library spectra to improve the initial guess. Using this INMF algorithm we decompose hyperspectral imagery from the NASA Hyperspectral Imager for the Coastal Ocean (HICO), with a focus on separating surface and atmospheric signal contributions. HICO's coastal ocean focus provides a dataset with a wide range of atmospheric and surface conditions. These include atmospheres with varying aerosol optical thicknesses and cloud cover. HICO images also provide a range of surface conditions including deep ocean regions, with only minor contributions from the ocean surfaces; and more complex shallow coastal regions with contributions from the seafloor or suspended sediments. We provide extensive comparison of INMF decomposition results against independent measurements of physical properties. These include comparison against traditional model-based retrievals of water-leaving, aerosol, and molecular scattering radiances and other satellite products, such as aerosol optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS).

[An improved low spectral distortion PCA fusion method].

PubMed

Peng, Shi; Zhang, Ai-Wu; Li, Han-Lun; Hu, Shao-Xing; Meng, Xian-Gang; Sun, Wei-Dong

2013-10-01

Aiming at the spectral distortion produced in PCA fusion process, the present paper proposes an improved low spectral distortion PCA fusion method. This method uses NCUT (normalized cut) image segmentation algorithm to make a complex hyperspectral remote sensing image into multiple sub-images for increasing the separability of samples, which can weaken the spectral distortions of traditional PCA fusion; Pixels similarity weighting matrix and masks were produced by using graph theory and clustering theory. These masks are used to cut the hyperspectral image and high-resolution image into some sub-region objects. All corresponding sub-region objects between the hyperspectral image and high-resolution image are fused by using PCA method, and all sub-regional integration results are spliced together to produce a new image. In the experiment, Hyperion hyperspectral data and Rapid Eye data were used. And the experiment result shows that the proposed method has the same ability to enhance spatial resolution and greater ability to improve spectral fidelity performance.

Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization.

PubMed

Hakala, Teemu; Markelin, Lauri; Honkavaara, Eija; Scott, Barry; Theocharous, Theo; Nevalainen, Olli; Näsi, Roope; Suomalainen, Juha; Viljanen, Niko; Greenwell, Claire; Fox, Nigel

2018-05-03

Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK).

Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization

PubMed Central

Hakala, Teemu; Scott, Barry; Theocharous, Theo; Näsi, Roope; Suomalainen, Juha; Greenwell, Claire; Fox, Nigel

2018-01-01

Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK). PMID:29751560

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.

Data Processing for the Space-Based Desis Hyperspectral Sensor

NASA Astrophysics Data System (ADS)

Carmona, E.; Avbelj, J.; Alonso, K.; Bachmann, M.; Cerra, D.; Eckardt, A.; Gerasch, B.; Graham, L.; Günther, B.; Heiden, U.; Kerr, G.; Knodt, U.; Krutz, D.; Krawcyk, H.; Makarau, A.; Miller, R.; Müller, R.; Perkins, R.; Walter, I.

2017-05-01

The German Aerospace Center (DLR) and Teledyne Brown Engineering (TBE) have established a collaboration to develop and operate a new space-based hyperspectral sensor, the DLR Earth Sensing Imaging Spectrometer (DESIS). DESIS will provide spacebased hyperspectral data in the VNIR with high spectral resolution and near-global coverage. While TBE provides the platform and infrastructure for operation of the DESIS instrument on the International Space Station, DLR is responsible for providing the instrument and the processing software. The DESIS instrument is equipped with novel characteristics for an imaging spectrometer such high spectral resolution (2.55 nm), a mirror pointing unit or a CMOS sensor operated in rolling shutter mode. We present here an overview of the DESIS instrument and its processing chain, emphasizing the effect of the novel characteristics of DESIS in the data processing and final data products. Furthermore, we analyse in more detail the effect of the rolling shutter on the DESIS data and possible mitigation/correction strategies.

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

Spatial/Spectral Identification of Endmembers from AVIRIS Data using Mathematical Morphology

NASA Technical Reports Server (NTRS)

Plaza, Antonio; Martinez, Pablo; Gualtieri, J. Anthony; Perez, Rosa M.

2001-01-01

During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or improved for remote sensing applications. Imaging spectrometry allows the detection of materials, objects and regions in a particular scene with a high degree of accuracy. Hyperspectral data typically consist of hundreds of thousands of spectra, so the analysis of this information is a key issue. Mathematical morphology theory is a widely used nonlinear technique for image analysis and pattern recognition. Although it is especially well suited to segment binary or grayscale images with irregular and complex shapes, its application in the classification/segmentation of multispectral or hyperspectral images has been quite rare. In this paper, we discuss a new completely automated methodology to find endmembers in the hyperspectral data cube using mathematical morphology. The extension of classic morphology to the hyperspectral domain allows us to integrate spectral and spatial information in the analysis process. In Section 3, some basic concepts about mathematical morphology and the technical details of our algorithm are provided. In Section 4, the accuracy of the proposed method is tested by its application to real hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imaging spectrometer. Some details about these data and reference results, obtained by well-known endmember extraction techniques, are provided in Section 2. Finally, in Section 5 we expose the main conclusions at which we have arrived.

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

Target Detection Using an AOTF Hyperspectral Imager

NASA Technical Reports Server (NTRS)

Cheng, L-J.; Mahoney, J.; Reyes, F.; Suiter, H.

1994-01-01

This paper reports results of a recent field experiment using a prototype system to evaluate the acousto-optic tunable filter polarimetric hyperspectral imaging technology for target detection applications.

Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal Carcinoma In Situ.

PubMed

Khouj, Yasser; Dawson, Jeremy; Coad, James; Vona-Davis, Linda

2018-01-01

Hyperspectral imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients. Samples from each patient included both normal and ductal carcinoma tissue, both stained with hematoxylin and eosin stain and unstained. Slides were imaged using a snapshot HSI system, and the spectral reflectance differences were evaluated. Analysis of the spectral reflectance values indicated that wavelengths near 550 nm showed the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. The K-means method was applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with true negative rate of 95.8%, and false positive rate of 4.2%. These results were verified by ground-truth marking of the tissue samples by a pathologist. In the hyperspectral image analysis, the image processing algorithm, K-means, shows the greatest potential for building a semi-automated system that could identify and sort between normal and ductal carcinoma in situ tissues.

Anomaly detection in hyperspectral imagery: statistics vs. graph-based algorithms

NASA Astrophysics Data System (ADS)

Berkson, Emily E.; Messinger, David W.

2016-05-01

Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution; however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

Hyperspectral imaging using near infrared spectroscopy to monitor coat thickness uniformity in the manufacture of a transdermal drug delivery system.

PubMed

Pavurala, Naresh; Xu, Xiaoming; Krishnaiah, Yellela S R

2017-05-15

Hyperspectral imaging using near infrared spectroscopy (NIRS) integrates spectroscopy and conventional imaging to obtain both spectral and spatial information of materials. The non-invasive and rapid nature of hyperspectral imaging using NIRS makes it a valuable process analytical technology (PAT) tool for in-process monitoring and control of the manufacturing process for transdermal drug delivery systems (TDS). The focus of this investigation was to develop and validate the use of Near Infra-red (NIR) hyperspectral imaging to monitor coat thickness uniformity, a critical quality attribute (CQA) for TDS. Chemometric analysis was used to process the hyperspectral image and a partial least square (PLS) model was developed to predict the coat thickness of the TDS. The goodness of model fit and prediction were 0.9933 and 0.9933, respectively, indicating an excellent fit to the training data and also good predictability. The % Prediction Error (%PE) for internal and external validation samples was less than 5% confirming the accuracy of the PLS model developed in the present study. The feasibility of the hyperspectral imaging as a real-time process analytical tool for continuous processing was also investigated. When the PLS model was applied to detect deliberate variation in coating thickness, it was able to predict both the small and large variations as well as identify coating defects such as non-uniform regions and presence of air bubbles. Published by Elsevier B.V.

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.

Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content.

PubMed

Sun, Ye; Wang, Yihang; Xiao, Hui; Gu, Xinzhe; Pan, Leiqing; Tu, Kang

2017-11-15

Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617nm, 675nm, and 818nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

NASA Tech Briefs, February 2010

NASA Technical Reports Server (NTRS)

2010-01-01

Topics covered include: Insulation-Testing Cryostat With Lifting Mechanism; Optical Testing of Retroreflectors for Cryogenic Applications; Measuring Cyclic Error in Laser Heterodyne Interferometers; Self-Referencing Hartmann Test for Large-Aperture Telescopes; Measuring a Fiber-Optic Delay Line Using a Mode-Locked Laser; Reconfigurable Hardware for Compressing Hyperspectral Image Data; Spatio-Temporal Equalizer for a Receiving-Antenna Feed Array; High-Speed Ring Bus; Nanoionics-Based Switches for Radio-Frequency Applications; Lunar Dust-Tolerant Electrical Connector; Compact, Reliable EEPROM Controller; Quad-Chip Double-Balanced Frequency Tripler; Ka-Band Waveguide Two-Way Hybrid Combiner for MMIC Amplifiers; Radiation-Hardened Solid-State Drive; Use of Nanofibers to Strengthen Hydrogels of Silica, Other Oxides, and Aerogels; Two Concepts for Deployable Trusses; Concentric Nested Toroidal Inflatable Structures; Investigating Dynamics of Eccentricity in Turbomachines; Improved Low-Temperature Performance of Li-Ion Cells Using New Electrolytes; Integrity Monitoring of Mercury Discharge Lamps; White-Light Phase-Conjugate Mirrors as Distortion Correctors; Biasable, Balanced, Fundamental Submillimeter Monolithic Membrane Mixer; ICER-3D Hyperspectral Image Compression Software; and Context Modeler for Wavelet Compression of Spectral Hyperspectral Images.

Investigation of carbonates in the Sutter's Mill meteorite grains with hyperspectral infrared imaging micro-spectroscopy

NASA Astrophysics Data System (ADS)

Yesiltas, Mehmet

2018-04-01

Synchrotron-based high spatial resolution hyperspectral infrared imaging technique provides thousands of infrared spectra with high resolution, thus allowing us to acquire detailed spatial maps of chemical molecular structures for many grains in short times. Utilizing this technique, thousands of infrared spectra were analyzed at once instead of inspecting each spectrum separately. Sutter's Mill meteorite is a unique carbonaceous type meteorite with highly heterogeneous chemical composition. Multiple grains from the Sutter's Mill meteorite have been studied using this technique and the presence of both hydrous and anhydrous silicate minerals have been observed. It is observed that the carbonate mineralogy varies from simple to more complex carbonates even within a few microns in the meteorite grains. These variations, the type and distribution of calcite-like vs. dolomite-like carbonates are presented by means of hyperspectral FTIR imaging spectroscopy with high resolution. Various scenarios for the formation of different carbonate compositions in the Sutter's Mill parent body are discussed.

LWIR hyperspectral micro-imager for detection of trace explosive particles

NASA Astrophysics Data System (ADS)

Bingham, Adam L.; Lucey, Paul G.; Akagi, Jason T.; Hinrichs, John L.; Knobbe, Edward T.

2014-05-01

Chemical micro-imaging is a powerful tool for the detection and identification of analytes of interest against a cluttered background (i.e. trace explosive particles left behind in a fingerprint). While a variety of groups have demonstrated the efficacy of Raman instruments for these applications, point by point or line by line acquisition of a targeted field of view (FOV) is a time consuming process if it is to be accomplished with useful spatial resolutions. Spectrum Photonics has developed and demonstrated a prototype system utilizing long wave infrared hyperspectral microscopy, which enables the simultaneous collection of LWIR reflectance spectra from 8-14 μm in a 30 x 7 mm FOV with 30 μm spatial resolution in 30 s. An overview of the uncooled Sagnac-based LWIR HSM system will be given, emphasizing the benefits of this approach. Laboratory Hyperspectral data collected from custom mixtures and fingerprint residues is shown, focusing on the ability of the LWIR chemical micro-imager to detect chemicals of interest out of a cluttered background.

Semi-Supervised Data Summarization: Using Spectral Libraries to Improve Hyperspectral Clustering

NASA Technical Reports Server (NTRS)

Wagstaff, K. L.; Shu, H. P.; Mazzoni, D.; Castano, R.

2005-01-01

Hyperspectral imagers produce very large images, with each pixel recorded at hundreds or thousands of different wavelengths. The ability to automatically generate summaries of these data sets enables several important applications, such as quickly browsing through a large image repository or determining the best use of a limited bandwidth link (e.g., determining which images are most critical for full transmission). Clustering algorithms can be used to generate these summaries, but traditional clustering methods make decisions based only on the information contained in the data set. In contrast, we present a new method that additionally leverages existing spectral libraries to identify materials that are likely to be present in the image target area. We find that this approach simultaneously reduces runtime and produces summaries that are more relevant to science goals.

ℓ0 -based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation

NASA Astrophysics Data System (ADS)

Xu, Xia; Shi, Zhenwei; Pan, Bin

2018-07-01

Sparse unmixing aims at recovering pure materials from hyperpspectral images and estimating their abundance fractions. Sparse unmixing is actually ℓ0 problem which is NP-h ard, and a relaxation is often used. In this paper, we attempt to deal with ℓ0 problem directly via a multi-objective based method, which is a non-convex manner. The characteristics of hyperspectral images are integrated into the proposed method, which leads to a new spectra and multi-objective based sparse unmixing method (SMoSU). In order to solve the ℓ0 norm optimization problem, the spectral library is encoded in a binary vector, and a bit-wise flipping strategy is used to generate new individuals in the evolution process. However, a multi-objective method usually produces a number of non-dominated solutions, while sparse unmixing requires a single solution. How to make the final decision for sparse unmixing is challenging. To handle this problem, we integrate the spectral characteristic of hyperspectral images into SMoSU. By considering the spectral correlation in hyperspectral data, we improve the Tchebycheff decomposition function in SMoSU via a new regularization item. This regularization item is able to enforce the individual divergence in the evolution process of SMoSU. In this way, the diversity and convergence of population is further balanced, which is beneficial to the concentration of individuals. In the experiments part, three synthetic datasets and one real-world data are used to analyse the effectiveness of SMoSU, and several state-of-art sparse unmixing algorithms are compared.

hyperspectral characterization of tissue simulating phantoms using a supercontinuum laser in a spatial frequency domain imaging instrument

NASA Astrophysics Data System (ADS)

Torabzadeh, Mohammad; Stockton, Patrick; Kennedy, Gordon T.; Saager, Rolf B.; Durkin, Anthony J.; Bartels, Randy A.; Tromberg, Bruce J.

2018-02-01

Hyperspectral Imaging (HSI) is a growing field in tissue optics due to its ability to collect continuous spectral features of a sample without a contact probe. Spatial Frequency Domain Imaging (SFDI) is a non-contact wide-field spectral imaging technique that is used to quantitatively characterize tissue structure and chromophore concentration. In this study, we designed a Hyperspectral SFDI (H-SFDI) instrument which integrated a supercontinuum laser source to a wavelength tuning optical configuration and a sCMOS camera to extract spatial (Field of View: 2cm×2cm) and broadband spectral features (580nm-950nm). A preliminary experiment was also performed to integrate the hyperspectral projection unit to a compressed single pixel camera and Light Labeling (LiLa) technique.

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.

Hyperspectral Image Analysis for Skin Tumor Detection

NASA Astrophysics Data System (ADS)

Kong, Seong G.; Park, Lae-Jeong

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

Analysis of hyperspectral fluorescence images for poultry skin tumor inspection

NASA Astrophysics Data System (ADS)

Kong, Seong G.; Chen, Yud-Ren; Kim, Intaek; Kim, Moon S.

2004-02-01

We present a hyperspectral fluorescence imaging system with a fuzzy inference scheme for detecting skin tumors on poultry carcasses. Hyperspectral images reveal spatial and spectral information useful for finding pathological lesions or contaminants on agricultural products. Skin tumors are not obvious because the visual signature appears as a shape distortion rather than a discoloration. Fluorescence imaging allows the visualization of poultry skin tumors more easily than reflectance. The hyperspectral image samples obtained for this poultry tumor inspection contain 65 spectral bands of fluorescence in the visible region of the spectrum at wavelengths ranging from 425 to 711 nm. The large amount of hyperspectral image data is compressed by use of a discrete wavelet transform in the spatial domain. Principal-component analysis provides an effective compressed representation of the spectral signal of each pixel in the spectral domain. A small number of significant features are extracted from two major spectral peaks of relative fluorescence intensity that have been identified as meaningful spectral bands for detecting tumors. A fuzzy inference scheme that uses a small number of fuzzy rules and Gaussian membership functions successfully detects skin tumors on poultry carcasses. Spatial-filtering techniques are used to significantly reduce false positives.

Security inspection in ports by anomaly detection using hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Rivera, Javier; Valverde, Fernando; Saldaña, Manuel; Manian, Vidya

2013-05-01

Applying hyperspectral imaging technology in port security is crucial for the detection of possible threats or illegal activities. One of the most common problems that cargo suffers is tampering. This represents a danger to society because it creates a channel to smuggle illegal and hazardous products. If a cargo is altered, security inspections on that cargo should contain anomalies that reveal the nature of the tampering. Hyperspectral images can detect anomalies by gathering information through multiple electromagnetic bands. The spectrums extracted from these bands can be used to detect surface anomalies from different materials. Based on this technology, a scenario was built in which a hyperspectral camera was used to inspect the cargo for any surface anomalies and a user interface shows the results. The spectrum of items, altered by different materials that can be used to conceal illegal products, is analyzed and classified in order to provide information about the tampered cargo. The image is analyzed with a variety of techniques such as multiple features extracting algorithms, autonomous anomaly detection, and target spectrum detection. The results will be exported to a workstation or mobile device in order to show them in an easy -to-use interface. This process could enhance the current capabilities of security systems that are already implemented, providing a more complete approach to detect threats and illegal cargo.

An explorative chemometric approach applied to hyperspectral images for the study of illuminated manuscripts

NASA Astrophysics Data System (ADS)

Catelli, Emilio; Randeberg, Lise Lyngsnes; Alsberg, Bjørn Kåre; Gebremariam, Kidane Fanta; Bracci, Silvano

2017-04-01

Hyperspectral imaging (HSI) is a fast non-invasive imaging technology recently applied in the field of art conservation. With the help of chemometrics, important information about the spectral properties and spatial distribution of pigments can be extracted from HSI data. With the intent of expanding the applications of chemometrics to the interpretation of hyperspectral images of historical documents, and, at the same time, to study the colorants and their spatial distribution on ancient illuminated manuscripts, an explorative chemometric approach is here presented. The method makes use of chemometric tools for spectral de-noising (minimum noise fraction (MNF)) and image analysis (multivariate image analysis (MIA) and iterative key set factor analysis (IKSFA)/spectral angle mapper (SAM)) which have given an efficient separation, classification and mapping of colorants from visible-near-infrared (VNIR) hyperspectral images of an ancient illuminated fragment. The identification of colorants was achieved by extracting and interpreting the VNIR spectra as well as by using a portable X-ray fluorescence (XRF) spectrometer.

Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging

PubMed Central

Chu, Bingquan; Yu, Keqiang; Zhao, Yanru

2018-01-01

This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874–1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans. PMID:29671781

Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging.

PubMed

Chu, Bingquan; Yu, Keqiang; Zhao, Yanru; He, Yong

2018-04-19

This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874⁻1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans.

The French proposal for a high spatial resolution Hyperspectral mission

NASA Astrophysics Data System (ADS)

Carrère, Véronique; Briottet, Xavier; Jacquemoud, Stéphane; Marion, Rodolphe; Bourguignon, Anne; Chami, Malik; Chanussot, Jocelyn; Chevrel, Stéphane; Deliot, Philippe; Dumont, Marie; Foucher, Pierre-Yves; Gomez, Cécile; Roman-Minghelli, Audrey; Sheeren, David; Weber, Christiane; Lefèvre, Marie-José; Mandea, Mioara

2014-05-01

More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion or HICO have clearly demonstrated the ability of such a remote sensing technique to produce value added information regarding surface composition and physical properties for a large variety of applications. Scheduled missions such as EnMAP and PRISMA prove the increased interest of the scientific community for such a type of remote sensing data. In France, a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyperspectral, GSH) established an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution. From these conclusions, CNES (French Space Agency) decided a phase 0 study for an hyperspectral mission concept, named at this time HYPXIM (HYPerspectral-X IMagery), the main fields of applications are vegetation biodiversity, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. Results pointed out applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. The phase A started at the beginning of 2013 based on the following HYPXIM characteristics: a hyperspectral camera covering the [0.4 - 2.5 µm] spectral range with a 8 m ground sampling distance (GSD) and a PAN camera with a 1.85 m GSD, onboard a mini-satellite platform. This phase A is currently stopped due to budget constraints. Nevertheless, the Science team is currently focusing on the preparation for the next CNES prospective meeting (March, 2014), an important step for the future of the mission. This paper will provide an update of the status of this mission and of new results obtained by the Science team.

Methods for gas detection using stationary hyperspectral imaging sensors

DOEpatents

Conger, James L [San Ramon, CA; Henderson, John R [Castro Valley, CA

2012-04-24

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

Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging.

PubMed

Li, Jiangbo; Rao, Xiuqin; Ying, Yibin

2012-01-15

Automated discrimination of fruits with canker from other fruit with normal surface and different type of peel defects has become a helpful task to enhance the competitiveness and profitability of the citrus industry. Over the last several years, hyperspectral imaging technology has received increasing attention in the agricultural products inspection field. This paper studied the feasibility of classification of citrus canker from other peel conditions including normal surface and nine peel defects by hyperspectal imaging. A combination algorithm based on principal component analysis and the two-band ratio (Q(687/630)) method was proposed. Since fewer wavelengths were desired in order to develop a rapid multispectral imaging system, the canker classification performance of the two-band ratio (Q(687/630)) method alone was also evaluated. The proposed combination approach and two-band ratio method alone resulted in overall classification accuracy for training set samples and test set samples of 99.5%, 84.5% and 98.2%, 82.9%, respectively. The proposed combination approach was more efficient for classifying canker against various conditions under reflectance hyperspectral imagery. However, the two-band ratio (Q(687/630)) method alone also demonstrated effectiveness in discriminating citrus canker from normal fruit and other peel diseases except for copper burn and anthracnose. Copyright © 2011 Society of Chemical Industry.

Detection of disturbed earth using hyperspectral LWIR imaging data

NASA Astrophysics Data System (ADS)

Hubbard, Wendy; Bishop, Gary; Gagnon, Jean-Philippe; Lagueux, Philippe; Hannuna, Sion; Campbell, Neill

2010-10-01

The Reststrahlen effect has been investigated for detecting regions of recently disturbed earth, by taking images where metallic objects had been buried in a sandy soil and comparing with images of undisturbed soil. The images were taken with a Long wave Infrared (LWIR) Hyperspectral Sensor, the Hyper-Cam.

[Estimation and Visualization of Nitrogen Content in Citrus Canopy Based on Two Band Vegetation Index (TBVI)].

PubMed

Wang, Qiao-nan; Ye, Xu-jun; Li, Jin-meng; Xiao, Yu-zhao; He, Yong

2015-03-01

Nitrogen is a necessary and important element for the growth and development of fruit orchards. Timely, accurate and nondestructive monitoring of nitrogen status in fruit orchards would help maintain the fruit quality and efficient production of the orchard, and mitigate the pollution of water resources caused by excessive nitrogen fertilization. This study investigated the capability of hyperspectral imagery for estimating and visualizing the nitrogen content in citrus canopy. Hyperspectral images were obtained for leaf samples in laboratory as well as for the whole canopy in the field with ImSpector V10E (Spectral Imaging Ltd., Oulu, Finland). The spectral datas for each leaf sample were represented by the average spectral data extracted from the selected region of interest (ROI) in the hyperspectral images with the aid of ENVI software. The nitrogen content in each leaf sample was measured by the Dumas combustion method with the rapid N cube (Elementar Analytical, Germany). Simple correlation analysis and the two band vegetation index (TBVI) were then used to develop the spectra data-based nitrogen content prediction models. Results obtained through the formula calculation indicated that the model with the two band vegetation index (TBVI) based on the wavelengths 811 and 856 nm achieved the optimal estimation of nitrogen content in citrus leaves (R2 = 0.607 1). Furthermore, the canopy image for the identified TBVI was calculated, and the nitrogen content of the canopy was visualized by incorporating the model into the TBVI image. The tender leaves, middle-aged leaves and elder leaves showed distinct nitrogen status from highto low-levels in the canopy image. The results suggested the potential of hyperspectral imagery for the nondestructive detection and diagnosis of nitrogen status in citrus canopy in real time. Different from previous studies focused on nitrogen content prediction at leaf level, this study succeeded in predicting and visualizing the nutrient content of fruit trees at canopy level. This would provide valuable information for the implementation of individual tree-based fertilization schemes in precision orchard management practices.

Airborne measurements in the infrared using FTIR-based imaging hyperspectral sensors

NASA Astrophysics Data System (ADS)

Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Lagueux, P.; Farley, V.; Marcotte, F.; Chamberland, M.

2009-09-01

Hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. Push-broom dispersive sensors have been typically used for airborne hyperspectral mapping. However, extending the spectral range towards the mid-wave and long-wave infrared brings performance limitations due to the self emission of the sensor itself. The Fourier-transform spectrometer technology has been extensively used in the infrared spectral range due to its high transmittance as well as throughput and multiplex advantages, thereby reducing the sensor self-emission problem. Telops has developed the Hyper-Cam, a rugged and compact infrared hyperspectral imager. The Hyper-Cam is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides passive signature measurement capability, with up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The Hyper-Cam has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. A special pointing module was designed to compensate for airplane attitude and forward motion. To our knowledge, the Hyper-Cam is the first commercial airborne hyperspectral imaging sensor based on Fourier-transform infrared technology. The first airborne measurements and some preliminary performance criteria for the Hyper-Cam are presented in this paper.

Airborne measurements in the infrared using FTIR-based imaging hyperspectral sensors

NASA Astrophysics Data System (ADS)

Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Farley, V.; Lagueux, P.; Marcotte, F.; Chamberland, M.

2009-05-01

Hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. Push-broom dispersive sensors have been typically used for airborne hyperspectral mapping. However, extending the spectral range towards the mid-wave and long-wave infrared brings performance limitations due to the self emission of the sensor itself. The Fourier-transform spectrometer technology has been extensively used in the infrared spectral range due to its high transmittance as well as throughput and multiplex advantages, thereby reducing the sensor self-emission problem. Telops has developed the Hyper-Cam, a rugged and compact infrared hyperspectral imager. The Hyper-Cam is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides passive signature measurement capability, with up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The Hyper-Cam has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. A special pointing module was designed to compensate for airplane attitude and forward motion. To our knowledge, the Hyper-Cam is the first commercial airborne hyperspectral imaging sensor based on Fourier-transform infrared technology. The first airborne measurements and some preliminary performance criteria for the Hyper-Cam are presented in this paper.

UAV-Based Hyperspectral Remote Sensing for Precision Agriculture: Challenges and Opportunities

NASA Astrophysics Data System (ADS)

Angel, Y.; Parkes, S. D.; Turner, D.; Houborg, R.; Lucieer, A.; McCabe, M.

2017-12-01

Modern agricultural production relies on monitoring crop status by observing and measuring variables such as soil condition, plant health, fertilizer and pesticide effect, irrigation and crop yield. Managing all of these factors is a considerable challenge for crop producers. As such, providing integrated technological solutions that enable improved diagnostics of field condition to maximize profits, while minimizing environmental impacts, would be of much interest. Such challenges can be addressed by implementing remote sensing systems such as hyperspectral imaging to produce precise biophysical indicator maps across the various cycles of crop development. Recent progress in unmanned aerial vehicles (UAVs) have advanced traditional satellite-based capabilities, providing a capacity for high-spatial, spectral and temporal response. However, while some hyperspectral sensors have been developed for use onboard UAVs, significant investment is required to develop a system and data processing workflow that retrieves accurately georeferenced mosaics. Here we explore the use of a pushbroom hyperspectral camera that is integrated on-board a multi-rotor UAV system to measure the surface reflectance in 272 distinct spectral bands across a wavelengths range spanning 400-1000 nm, and outline the requirement for sensor calibration, integration onto a stable UAV platform enabling accurate positional data, flight planning, and development of data post-processing workflows for georeferenced mosaics. The provision of high-quality and geo-corrected imagery facilitates the development of metrics of vegetation health that can be used to identify potential problems such as production inefficiencies, diseases and nutrient deficiencies and other data-streams to enable improved crop management. Immense opportunities remain to be exploited in the implementation of UAV-based hyperspectral sensing (and its combination with other imaging systems) to provide a transferable and scalable integrated framework for crop growth monitoring and yield prediction. Here we explore some of the challenges and issues in translating the available technological capacity into a useful and useable image collection and processing flow-path that enables these potential applications to be better realized.

Hyperspectral Cubesat Constellation for Rapid Natural Hazard Response

NASA Technical Reports Server (NTRS)

Mandl, Daniel; Huemmrich, Karl; Crum, Gary; Ly, Vuong; Handy, Matthew; Ong, Lawrence

2015-01-01

Earth Observing 1 (E0-1) satellite has an imaging spectrometer (hyperspectral) instrument called Hyperion. The satellite is able to image any spot on Earth in the nadir looking direction every 16 days. With slewing of the satellite and allowing for up to a 23 degree view angle, any spot on the Earth can be imaged approximately every 2 to 3 days. EO-1 has been used to track many natural hazards such as wildfires, volcanoes and floods. An enhanced capability that is sought is the ability to image natural hazards in a daily time series for space based imaging spectrometers. The Hyperion can not provide this capability on EO-1 with the present polar orbit. However, a constellation of cubesats, each with the same imaging spectrometer, positioned strategically in the same orbit, can be used to provide daily coverage, cost-effectively.

Bridging research with innovative products: a compact hyperspectral camera for investigating artworks: a feasibility study

NASA Astrophysics Data System (ADS)

Cucci, Costanza; Casini, Andrea; Stefani, Lorenzo; Picollo, Marcello; Jussila, Jouni

2017-07-01

For more than a decade, a number of studies and research projects have been devoted to customize hyperspectral imaging techniques to the specific needs of conservation and applications in museum context. A growing scientific literature definitely demonstrated the effectiveness of reflectance hyperspectral imaging for non-invasive diagnostics and highquality documentation of 2D artworks. Additional published studies tackle the problems of data-processing, with a focus on the development of algorithms and software platforms optimised for visualisation and exploitation of hyperspectral bigdata sets acquired on paintings. This scenario proves that, also in the field of Cultural Heritage (CH), reflectance hyperspectral imaging has nowadays reached the stage of mature technology, and is ready for the transition from the R&D phase to the large-scale applications. In view of that, a novel concept of hyperspectral camera - featuring compactness, lightness and good usability - has been developed by SPECIM, Spectral Imaging Ltd. (Oulu, Finland), a company in manufacturing products for hyperspectral imaging. The camera is proposed as new tool for novel applications in the field of Cultural Heritage. The novelty of this device relies in its reduced dimensions and weight and in its user-friendly interface, which make this camera much more manageable and affordable than conventional hyperspectral instrumentation. The camera operates in the 400-1000nm spectral range and can be mounted on a tripod. It can operate from short-distance (tens of cm) to long distances (tens of meters) with different spatial resolutions. The first release of the prototype underwent a preliminary in-depth experimentation at the IFAC-CNR laboratories. This paper illustrates the feasibility study carried out on the new SPECIM hyperspectral camera, tested under different conditions on laboratory targets and artworks with the specific aim of defining its potentialities and weaknesses in its use in the Cultural Heritage field.

Combined spectral-domain optical coherence tomography and hyperspectral imaging applied for tissue analysis: Preliminary results

NASA Astrophysics Data System (ADS)

Dontu, S.; Miclos, S.; Savastru, D.; Tautan, M.

2017-09-01

In recent years many optoelectronic techniques have been developed for improvement and the development of devices for tissue analysis. Spectral-Domain Optical Coherence Tomography (SD-OCT) is a new medical interferometric imaging modality that provides depth resolved tissue structure information with resolution in the μm range. However, SD-OCT has its own limitations and cannot offer the biochemical information of the tissue. These data can be obtained with hyperspectral imaging, a non-invasive, sensitive and real time technique. In the present study we have combined Spectral-Domain Optical Coherence Tomography (SD-OCT) with Hyperspectral imaging (HSI) for tissue analysis. The Spectral-Domain Optical Coherence Tomography (SD-OCT) and Hyperspectral imaging (HSI) are two methods that have demonstrated significant potential in this context. Preliminary results using different tissue have highlighted the capabilities of this technique of combinations.

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

[Identification of green tea brand based on hyperspectra imaging technology].

PubMed

Zhang, Hai-Liang; Liu, Xiao-Li; Zhu, Feng-Le; He, Yong

2014-05-01

Hyperspectral imaging technology was developed to identify different brand famous green tea based on PCA information and image information fusion. First 512 spectral images of six brands of famous green tea in the 380 approximately 1 023 nm wavelength range were collected and principal component analysis (PCA) was performed with the goal of selecting two characteristic bands (545 and 611 nm) that could potentially be used for classification system. Then, 12 gray level co-occurrence matrix (GLCM) features (i. e., mean, covariance, homogeneity, energy, contrast, correlation, entropy, inverse gap, contrast, difference from the second-order and autocorrelation) based on the statistical moment were extracted from each characteristic band image. Finally, integration of the 12 texture features and three PCA spectral characteristics for each green tea sample were extracted as the input of LS-SVM. Experimental results showed that discriminating rate was 100% in the prediction set. The receiver operating characteristic curve (ROC) assessment methods were used to evaluate the LS-SVM classification algorithm. Overall results sufficiently demonstrate that hyperspectral imaging technology can be used to perform classification of green tea.

Optimizing Radiometric Processing and Feature Extraction of Drone Based Hyperspectral Frame Format Imagery for Estimation of Yield Quantity and Quality of a Grass Sward

NASA Astrophysics Data System (ADS)

Näsi, R.; Viljanen, N.; Oliveira, R.; Kaivosoja, J.; Niemeläinen, O.; Hakala, T.; Markelin, L.; Nezami, S.; Suomalainen, J.; Honkavaara, E.

2018-04-01

Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10 % better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40 % compared to approaches where only spectral features were applied. The best estimation RMSE of 239 kg/ha (6.0 %) was obtained with multiview anisotropy corrected data set and the 3D features.

Mapping vegetation cover and biomass on the Qinghai-Tibet-Plateau using hyperspectral measurements and multispectral satellite images

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Lehnert, Lukas W.; Wang, Yun; Reudenbach, Christoph; Nauss, Thomas; Bendix, Jörg

2016-04-01

Pastoralism is the dominant land-use on the Qinghai-Tibet-Plateau (QTP) providing the major economic resource for the local population. However, the pastures are highly supposed to be affected by ongoing degradation whose extent is still disputed. This study uses hyperspectral in situ measurements and multispectral satellite images to assess vegetation cover and above ground biomass (AGB) as proxies of pasture degradation on a regional scale. Using Random Forests in conjunction with recursive feature selection as modeling tool, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate vegetation cover and AGB. To regionalize pasture degradation proxies, the transferability of the locally derived models to high resolution multispectral satellite data is assessed. For this purpose, 1183 hyperspectral measurements and vegetation records were sampled at 18 locations on the QTP. AGB was determined on 25 0.5x0.5m plots. Proxies for pasture degradation were derived from the spectra by calculating narrow-band indices (NBI). Using the NBI as predictor variables vegetation cover and AGB were modeled. Models were calculated using the hyperspectral data as well as the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used. In contrast, errors in AGB estimations were considerably higher. Only small differences in accuracy were observed between the models based on hyper- compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite data, the results still show a high potential to map pasture degradation proxies on the QTP even for larger scales.

Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering

NASA Astrophysics Data System (ADS)

Rodríguez, Aida; Nieves, Juan Luis; Valero, Eva; Garrote, Estíbaliz; Hernández-Andrés, Javier; Romero, Javier

2012-01-01

We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images. Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing the number of correctly classified pixels.

Method for hyperspectral imagery exploitation and pixel spectral unmixing

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang (Inventor)

2003-01-01

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

Bandwidth efficient channel estimation method for airborne hyperspectral data transmission in sparse doubly selective communication channels

NASA Astrophysics Data System (ADS)

Vahidi, Vahid; Saberinia, Ebrahim; Regentova, Emma E.

2017-10-01

A channel estimation (CE) method based on compressed sensing (CS) is proposed to estimate the sparse and doubly selective (DS) channel for hyperspectral image transmission from unmanned aircraft vehicles to ground stations. The proposed method contains three steps: (1) the priori estimate of the channel by orthogonal matching pursuit (OMP), (2) calculation of the linear minimum mean square error (LMMSE) estimate of the received pilots given the estimated channel, and (3) estimate of the complex amplitudes and Doppler shifts of the channel using the enhanced received pilot data applying a second round of a CS algorithm. The proposed method is named DS-LMMSE-OMP, and its performance is evaluated by simulating transmission of AVIRIS hyperspectral data via the communication channel and assessing their fidelity for the automated analysis after demodulation. The performance of the DS-LMMSE-OMP approach is compared with that of two other state-of-the-art CE methods. The simulation results exhibit up to 8-dB figure of merit in the bit error rate and 50% improvement in the hyperspectral image classification accuracy.

Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

NASA Astrophysics Data System (ADS)

Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

2017-04-01

Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

Radiometric characterization of hyperspectral imagers using multispectral sensors

NASA Astrophysics Data System (ADS)

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

2009-08-01

The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these tests sites are not always successful due to weather and funding availability. Therefore, RSG has also employed automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor. This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of MODIS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most bands as well as similar agreement between results that employ the different MODIS sensors as a reference.

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.

Potential use of MCR-ALS for the identification of coeliac-related biochemical changes in hyperspectral Raman maps from pediatric intestinal biopsies.

PubMed

Fornasaro, Stefano; Vicario, Annalisa; De Leo, Luigina; Bonifacio, Alois; Not, Tarcisio; Sergo, Valter

2018-05-14

Raman hyperspectral imaging is an emerging practice in biological and biomedical research for label free analysis of tissues and cells. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. The current study reports the first Raman microspectroscopic characterisation of colon tissues from patients with Coeliac Disease (CD). The aim was to assess if Raman imaging coupled with hyperspectral multivariate image analysis is capable of detecting the alterations in the biochemical composition of intestinal tissues associated with CD. The analytical approach was based on a multi-step methodology: duodenal biopsies from healthy and coeliac patients were measured and processed with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS). Based on the distribution maps and the pure spectra of the image constituents obtained from MCR-ALS, interesting biochemical differences between healthy and coeliac patients has been derived. Noticeably, a reduced distribution of complex lipids in the pericryptic space, and a different distribution and abundance of proteins rich in beta-sheet structures was found in CD patients. The output of the MCR-ALS analysis was then used as a starting point for two clustering algorithms (k-means clustering and hierarchical clustering methods). Both methods converged with similar results providing precise segmentation over multiple Raman images of studied tissues.

A hierarchical classification approach for recognition of low-density (LDPE) and high-density polyethylene (HDPE) in mixed plastic waste based on short-wave infrared (SWIR) hyperspectral imaging

NASA Astrophysics Data System (ADS)

Bonifazi, Giuseppe; Capobianco, Giuseppe; Serranti, Silvia

2018-06-01

The aim of this work was to recognize different polymer flakes from mixed plastic waste through an innovative hierarchical classification strategy based on hyperspectral imaging, with particular reference to low density polyethylene (LDPE) and high-density polyethylene (HDPE). A plastic waste composition assessment, including also LDPE and HDPE identification, may help to define optimal recycling strategies for product quality control. Correct handling of plastic waste is essential for its further "sustainable" recovery, maximizing the sorting performance in particular for plastics with similar characteristics as LDPE and HDPE. Five different plastic waste samples were chosen for the investigation: polypropylene (PP), LDPE, HDPE, polystyrene (PS) and polyvinyl chloride (PVC). A calibration dataset was realized utilizing the corresponding virgin polymers. Hyperspectral imaging in the short-wave infrared range (1000-2500 nm) was thus applied to evaluate the different plastic spectral attributes finalized to perform their recognition/classification. After exploring polymer spectral differences by principal component analysis (PCA), a hierarchical partial least squares discriminant analysis (PLS-DA) model was built allowing the five different polymers to be recognized. The proposed methodology, based on hierarchical classification, is very powerful and fast, allowing to recognize the five different polymers in a single step.

Effectiveness of Spectral Similarity Measures to Develop Precise Crop Spectra for Hyperspectral Data Analysis

NASA Astrophysics Data System (ADS)

Chauhan, H.; Krishna Mohan, B.

2014-11-01

The present study was undertaken with the objective to check effectiveness of spectral similarity measures to develop precise crop spectra from the collected hyperspectral field spectra. In Multispectral and Hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to select precise crop spectra from the set of field spectra. Conventionally crop spectra are developed after rejecting outliers based only on broad-spectrum analysis. Here a successful attempt has been made to develop precise crop spectra based on spectral similarity. As unevaluated data usage leads to uncertainty in the image classification, it is very crucial to evaluate the data. Hence, notwithstanding the conventional method, the data precision has been performed effectively to serve the purpose of the present research work. The effectiveness of developed precise field spectra was evaluated by spectral discrimination measures and found higher discrimination values compared to spectra developed conventionally. Overall classification accuracy for the image classified by field spectra selected conventionally is 51.89% and 75.47% for the image classified by field spectra selected precisely based on spectral similarity. KHAT values are 0.37, 0.62 and Z values are 2.77, 9.59 for image classified using conventional and precise field spectra respectively. Reasonable higher classification accuracy, KHAT and Z values shows the possibility of a new approach for field spectra selection based on spectral similarity measure.

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

2015-01-01

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

Spectral-spatial classification of hyperspectral imagery with cooperative game

NASA Astrophysics Data System (ADS)

Zhao, Ji; Zhong, Yanfei; Jia, Tianyi; Wang, Xinyu; Xu, Yao; Shu, Hong; Zhang, Liangpei

2018-01-01

Spectral-spatial classification is known to be an effective way to improve classification performance by integrating spectral information and spatial cues for hyperspectral imagery. In this paper, a game-theoretic spectral-spatial classification algorithm (GTA) using a conditional random field (CRF) model is presented, in which CRF is used to model the image considering the spatial contextual information, and a cooperative game is designed to obtain the labels. The algorithm establishes a one-to-one correspondence between image classification and game theory. The pixels of the image are considered as the players, and the labels are considered as the strategies in a game. Similar to the idea of soft classification, the uncertainty is considered to build the expected energy model in the first step. The local expected energy can be quickly calculated, based on a mixed strategy for the pixels, to establish the foundation for a cooperative game. Coalitions can then be formed by the designed merge rule based on the local expected energy, so that a majority game can be performed to make a coalition decision to obtain the label of each pixel. The experimental results on three hyperspectral data sets demonstrate the effectiveness of the proposed classification algorithm.

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

NASA Astrophysics Data System (ADS)

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

2013-08-01

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

In vivo use of hyperspectral imaging to develop a noncontact endoscopic diagnosis support system for malignant colorectal tumors

NASA Astrophysics Data System (ADS)

Han, Zhimin; Zhang, Aoyu; Wang, Xiguang; Sun, Zongxiao; Wang, May D.; Xie, Tianyu

2016-01-01

The early detection and diagnosis of malignant colorectal tumors enables the initiation of early-stage therapy and can significantly increase the survival rate and post-treatment quality of life among cancer patients. Hyperspectral imaging (HSI) is recognized as a powerful tool for noninvasive cancer detection. In the gastrointestinal field, most of the studies on HSI have involved ex vivo biopsies or resected tissues. In the present study, we aimed to assess the difference in the in vivo spectral reflectance of malignant colorectal tumors and normal mucosa. A total of 21 colorectal tumors or adenomatous polyps from 12 patients at Shanghai Zhongshan Hospital were examined using a flexible hyperspectral (HS) colonoscopy system that can obtain in vivo HS images of the colorectal mucosa. We determined the optimal wavelengths for differentiating tumors from normal tissue based on these recorded images. The application of the determined wavelengths in spectral imaging in clinical trials indicated that such a clinical support system comprising a flexible HS colonoscopy unit and band selection unit is useful for outlining the tumor region and enhancing the display of the mucosa microvascular pattern in vivo.

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 (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing 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 (MC) 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.

A portable confocal hyperspectral microscope without any scan or tube lens and its application in fluorescence and Raman spectral imaging

NASA Astrophysics Data System (ADS)

Li, Jingwei; Cai, Fuhong; Dong, Yongjiang; Zhu, Zhenfeng; Sun, Xianhe; Zhang, Hequn; He, Sailing

2017-06-01

In this study, a portable confocal hyperspectral microscope is developed. In traditional confocal laser scanning microscopes, scan lens and tube lens are utilized to achieve a conjugate relationship between the galvanometer and the back focal plane of the objective, in order to achieve a better resolution. However, these lenses make it difficult to scale down the volume of the system. In our portable confocal hyperspectral microscope (PCHM), the objective is placed directly next to the galvomirror. Thus, scan lens and tube lens are not included in our system and the size of this system is greatly reduced. Furthermore, the resolution is also acceptable in many biomedical and food-safety applications. Through reducing the optical length of the system, the signal detection efficiency is enhanced. This is conducive to realizing both the fluorescence and Raman hyperspectral imaging. With a multimode fiber as a pinhole, an improved image contrast is also achieved. Fluorescent spectral images for HeLa cells/fingers and Raman spectral images of kumquat pericarp are present. The spectral resolution and spatial resolutions are about 0.4 nm and 2.19 μm, respectively. These results demonstrate that this portable hyperspectral microscope can be used in in-vivo fluorescence imaging and in situ Raman spectral imaging.

Identification of seedling cabbages and weeds using hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

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...

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.

Standoff midwave infrared hyperspectral imaging of ship plumes

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

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.

Camouflaged target detection based on polarized spectral features

NASA Astrophysics Data System (ADS)

Tan, Jian; Zhang, Junping; Zou, Bin

2016-05-01

The polarized hyperspectral images (PHSI) include polarization, spectral, spatial and radiant features, which provide more information about objects and scenes than traditional intensity or spectrum ones. And polarization can suppress the background and highlight the object, leading to the high potential to improve camouflaged target detection. So polarized hyperspectral imaging technique has aroused extensive concern in the last few years. Nowadays, the detection methods are still not very mature, most of which are rooted in the detection of hyperspectral image. And before using these algorithms, Stokes vector is used to process the original four-dimensional polarized hyperspectral data firstly. However, when the data is large and complex, the amount of calculation and error will increase. In this paper, tensor is applied to reconstruct the original four-dimensional data into new three-dimensional data, then, the constraint energy minimization (CEM) is used to process the new data, which adds the polarization information to construct the polarized spectral filter operator and takes full advantages of spectral and polarized information. This way deals with the original data without extracting the Stokes vector, so as to reduce the computation and error greatly. The experimental results also show that the proposed method in this paper is more suitable for the target detection of the PHSI.

Wide area Hyperspectral Motion Imaging

DTIC Science & Technology

2017-02-03

LEXINGTON, MASSACHUSETTS 02420-9108 (781) 981-1343 3 February 2017 TO: FROM: Dr. Joseph Lin (joseph.lin@ll.mit.edu), Advanced Imager ...Technology SUBJECT: Wide-area Hyperspectral Motion Imaging Introduction Wide-area motion imaging (WAMI) has received increased attention in...fielded imaging spectrometers use either dispersive or interferometric techniques. A dispersive spectrometer uses a grating or prism to disperse the

Concept and integration of an on-line quasi-operational airborne hyperspectral remote sensing system

NASA Astrophysics Data System (ADS)

Schilling, Hendrik; Lenz, Andreas; Gross, Wolfgang; Perpeet, Dominik; Wuttke, Sebastian; Middelmann, Wolfgang

2013-10-01

Modern mission characteristics require the use of advanced imaging sensors in reconnaissance. In particular, high spatial and high spectral resolution imaging provides promising data for many tasks such as classification and detecting objects of military relevance, such as camouflaged units or improvised explosive devices (IEDs). Especially in asymmetric warfare with highly mobile forces, intelligence, surveillance and reconnaissance (ISR) needs to be available close to real-time. This demands the use of unmanned aerial vehicles (UAVs) in combination with downlink capability. The system described in this contribution is integrated in a wing pod for ease of installation and calibration. It is designed for the real-time acquisition and analysis of hyperspectral data. The main component is a Specim AISA Eagle II hyperspectral sensor, covering the visible and near-infrared (VNIR) spectral range with a spectral resolution up to 1.2 nm and 1024 pixel across track, leading to a ground sampling distance below 1 m at typical altitudes. The push broom characteristic of the hyperspectral sensor demands an inertial navigation system (INS) for rectification and georeferencing of the image data. Additional sensors are a high resolution RGB (HR-RGB) frame camera and a thermal imaging camera. For on-line application, the data is preselected, compressed and transmitted to the ground control station (GCS) by an existing system in a second wing pod. The final result after data processing in the GCS is a hyperspectral orthorectified GeoTIFF, which is filed in the ERDAS APOLLO geographical information system. APOLLO allows remote access to the data and offers web-based analysis tools. The system is quasi-operational and was successfully tested in May 2013 in Bremerhaven, Germany.

Efficient hyperspectral image segmentation using geometric active contour formulation

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Superpixel-Augmented Endmember Detection for Hyperspectral Images

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

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.

Informed Source Separation of Atmospheric and Surface Signal Contributions in Shortwave Hyperspectral Imagery using Non-negative Matrix Factorization

NASA Astrophysics Data System (ADS)

Wright, L.; Coddington, O.; Pilewskie, P.

2015-12-01

Current challenges in Earth remote sensing require improved instrument spectral resolution, spectral coverage, and radiometric accuracy. Hyperspectral instruments, deployed on both aircraft and spacecraft, are a growing class of Earth observing sensors designed to meet these challenges. They collect large amounts of spectral data, allowing thorough characterization of both atmospheric and surface properties. The higher accuracy and increased spectral and spatial resolutions of new imagers require new numerical approaches for processing imagery and separating surface and atmospheric signals. One potential approach is source separation, which allows us to determine the underlying physical causes of observed changes. Improved signal separation will allow hyperspectral instruments to better address key science questions relevant to climate change, including land-use changes, trends in clouds and atmospheric water vapor, and aerosol characteristics. In this work, we investigate a Non-negative Matrix Factorization (NMF) method for the separation of atmospheric and land surface signal sources. NMF offers marked benefits over other commonly employed techniques, including non-negativity, which avoids physically impossible results, and adaptability, which allows the method to be tailored to hyperspectral source separation. We adapt our NMF algorithm to distinguish between contributions from different physically distinct sources by introducing constraints on spectral and spatial variability and by using library spectra to inform separation. We evaluate our NMF algorithm with simulated hyperspectral images as well as hyperspectral imagery from several instruments including, the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), NASA Hyperspectral Imager for the Coastal Ocean (HICO) and National Ecological Observatory Network (NEON) Imaging Spectrometer.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Hyperresolution: an hyperspectral and high resolution imager for Earth observation

NASA Astrophysics Data System (ADS)

De Vidi, R.; Chiarantini, L.; Bini, A.

2017-11-01

Hyperspectral space imagery is an emerging technology that supports many scientific, civil, security and defence operational applications. The main advantage of this remote sensing technique is that it allows the so-called Feature Extraction: in fact the spectral signature allows the recognition of the materials composing the scene. Hyperspectral Products and their applications have been investigated in the past years by Galileo Avionica to direct the instrument characteristics design. Sample products have been identified in the civil / environment monitoring fields (such as coastal monitoring, vegetation, hot spot and urban classification) and in defense / security applications: their performances have been verified by means of airborne flight campaigns. The Hyperspectral and High Resolution Imager is a space-borne instrument that implement a pushbroom technique to get strip spectral images over the Hyperspectral VNIR and SWIR bands, with a ground sample distance at nadir of 20m in a 20 km wide ground swath, with 200 spectral channels, realizing an average spectral resolution of 10nm. The High Resolution Panchromatic Channel insists in the same swath to allow for multiresolution data fusion of hyperspectral imagery.

The Hyperspectral Imager for the Coastal Ocean (HICO (trademark)) Provides a New View of the Coastal Ocean

DTIC Science & Technology

2012-02-09

The calibrated data are then sent to NRL Stennis Space Center (NRL-SSC) for further processing using the NRL SSC Automated Processing System (APS...hyperspectral sensor in space we have not previously developed automated processing for hyperspectral ocean color data. The hyperspectral processing branch

Improved classification and visualization of healthy and pathological hard dental tissues by modeling specular reflections in NIR hyperspectral images

NASA Astrophysics Data System (ADS)

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

2012-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots, which are difficult to diagnose. Near-infrared (NIR) hyperspectral imaging is a new promising technique for early detection of demineralization which can classify healthy and pathological dental tissues. However, due to non-ideal illumination of the tooth surface the hyperspectral images can exhibit specular reflections, in particular around the edges and the ridges of the teeth. These reflections significantly affect the performance of automated classification and visualization methods. Cross polarized imaging setup can effectively remove the specular reflections, however is due to the complexity and other imaging setup limitations not always possible. In this paper, we propose an alternative approach based on modeling the specular reflections of hard dental tissues, which significantly improves the classification accuracy in the presence of specular reflections. The method was evaluated on five extracted human teeth with corresponding gold standard for 6 different healthy and pathological hard dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized regions. Principal component analysis (PCA) was used for multivariate local modeling of healthy and pathological dental tissues. The classification was performed by employing multiple discriminant analysis. Based on the obtained results we believe the proposed method can be considered as an effective alternative to the complex cross polarized imaging setups.

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

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Hyperspectral discrimination of camouflaged target

NASA Astrophysics Data System (ADS)

Bárta, Vojtěch; Racek, František

2017-10-01

The article deals with detection of camouflaged objects during winter season. Winter camouflage is a marginal affair in most countries due to short time period of the snow cover. In the geographical condition of Central Europe the winter period with snow occurs less than 1/12 of year. The LWIR or SWIR spectral areas are used for detection of camouflaged objects. For those spectral regions the difference in chemical composition and temperature express in spectral features. Exploitation of the LWIR and SWIR devices is demanding due to their large dimension and expensiveness. Therefore, the article deals with estimation of utilization of VIS region for detecting of camouflaged object on snow background. The multispectral image output for the various spectral filters is simulated. Hyperspectral indices are determined to detect the camouflaged objects in the winter. The multispectral image simulation is based on the hyperspectral datacube obtained in real conditions.

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.

Classification by diagnosing all absorption features (CDAF) for the most abundant minerals in airborne hyperspectral images

NASA Astrophysics Data System (ADS)

Mobasheri, Mohammad Reza; Ghamary-Asl, Mohsen

2011-12-01

Imaging through hyperspectral technology is a powerful tool that can be used to spectrally identify and spatially map materials based on their specific absorption characteristics in electromagnetic spectrum. A robust method called Tetracorder has shown its effectiveness at material identification and mapping, using a set of algorithms within an expert system decision-making framework. In this study, using some stages of Tetracorder, a technique called classification by diagnosing all absorption features (CDAF) is introduced. This technique enables one to assign a class to the most abundant mineral in each pixel with high accuracy. The technique is based on the derivation of information from reflectance spectra of the image. This can be done through extraction of spectral absorption features of any minerals from their respected laboratory-measured reflectance spectra, and comparing it with those extracted from the pixels in the image. The CDAF technique has been executed on the AVIRIS image where the results show an overall accuracy of better than 96%.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-01-01

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

Whole-surface round object imaging method using line-scan hyperspectral imaging system

USDA-ARS?s Scientific Manuscript database

To achieve comprehensive online quality and safety inspection of fruits, whole-surface sample presentation and imaging regimes must be considered. Specifically, a round object sample presentation method is under development to achieve effective whole-surface sample evaluation based on the use of a s...

Fluorescence hyperspectral imaging technique for foreign substance detection on fresh-cut lettuce.

PubMed

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

2017-09-01

Non-destructive methods based on fluorescence hyperspectral imaging (HSI) techniques were developed to detect worms on fresh-cut lettuce. The optimal wavebands for detecting the worms were investigated using the one-way ANOVA and correlation analyses. The worm detection imaging algorithms, RSI-I (492-626)/492 , provided a prediction accuracy of 99.0%. The fluorescence HSI techniques indicated that the spectral images with a pixel size of 1 × 1 mm had the best classification accuracy for worms. The overall results demonstrate that fluorescence HSI techniques have the potential to detect worms on fresh-cut lettuce. In the future, we will focus on developing a multi-spectral imaging system to detect foreign substances such as worms, slugs and earthworms on fresh-cut lettuce. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

LED lighting for use in multispectral and hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

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...

Multimodal hyperspectral optical microscopy

DOE PAGES

Novikova, Irina V.; Smallwood, Chuck R.; Gong, Yu; ...

2017-09-02

We describe a unique and convenient approach to multimodal hyperspectral optical microscopy, herein achieved by coupling a portable and transferable hyperspectral imager to various optical microscopes. The experimental and data analysis schemes involved in recording spectrally and spatially resolved fluorescence, dark field, and optical absorption micrographs are illustrated through prototypical measurements targeting selected model systems. Namely, hyperspectral fluorescence micrographs of isolated fluorescent beads are employed to ensure spectral calibration of our detector and to gauge the attainable spatial resolution of our measurements; the recorded images are diffraction-limited. Moreover, spatially over-sampled absorption spectroscopy of a single lipid (18:1 Liss Rhod PE)more » layer reveals that optical densities on the order of 10-3 may be resolved by spatially averaging the recorded optical signatures. We also briefly illustrate two applications of our setup in the general areas of plasmonics and cell biology. Most notably, we deploy hyperspectral optical absorption microscopy to identify and image algal pigments within a single live Tisochrysis lutea cell. Overall, this work paves the way for multimodal multidimensional spectral imaging measurements spanning the realms of several scientific disciples.« less

Multimodal hyperspectral optical microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Novikova, Irina V.; Smallwood, Chuck R.; Gong, Yu

We describe a unique and convenient approach to multimodal hyperspectral optical microscopy, herein achieved by coupling a portable and transferable hyperspectral imager to various optical microscopes. The experimental and data analysis schemes involved in recording spectrally and spatially resolved fluorescence, dark field, and optical absorption micrographs are illustrated through prototypical measurements targeting selected model systems. Namely, hyperspectral fluorescence micrographs of isolated fluorescent beads are employed to ensure spectral calibration of our detector and to gauge the attainable spatial resolution of our measurements; the recorded images are diffraction-limited. Moreover, spatially over-sampled absorption spectroscopy of a single lipid (18:1 Liss Rhod PE)more » layer reveals that optical densities on the order of 10-3 may be resolved by spatially averaging the recorded optical signatures. We also briefly illustrate two applications of our setup in the general areas of plasmonics and cell biology. Most notably, we deploy hyperspectral optical absorption microscopy to identify and image algal pigments within a single live Tisochrysis lutea cell. Overall, this work paves the way for multimodal multidimensional spectral imaging measurements spanning the realms of several scientific disciples.« less

Blind image fusion for hyperspectral imaging with the directional total variation

NASA Astrophysics Data System (ADS)

Bungert, Leon; Coomes, David A.; Ehrhardt, Matthias J.; Rasch, Jennifer; Reisenhofer, Rafael; Schönlieb, Carola-Bibiane

2018-04-01

Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.

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

PubMed Central

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

2014-01-01

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

Radiation calibration for LWIR Hyperspectral Imager Spectrometer

NASA Astrophysics Data System (ADS)

Yang, Zhixiong; Yu, Chunchao; Zheng, Wei-jian; Lei, Zhenggang; Yan, Min; Yuan, Xiaochun; Zhang, Peizhong

2014-11-01

The radiometric calibration of LWIR Hyperspectral imager Spectrometer is presented. The lab has been developed to LWIR Interferometric Hyperspectral imager Spectrometer Prototype(CHIPED-I) to study Lab Radiation Calibration, Two-point linear calibration is carried out for the spectrometer by using blackbody respectively. Firstly, calibration measured relative intensity is converted to the absolute radiation lightness of the object. Then, radiation lightness of the object is is converted the brightness temperature spectrum by the method of brightness temperature. The result indicated †that this method of Radiation Calibration calibration was very good.

Atmospheric correction for hyperspectral ocean color sensors

NASA Astrophysics Data System (ADS)

Ibrahim, A.; Ahmad, Z.; Franz, B. A.; Knobelspiesse, K. D.

2017-12-01

NASA's heritage Atmospheric Correction (AC) algorithm for multi-spectral ocean color sensors is inadequate for the new generation of spaceborne hyperspectral sensors, such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and by aerosols from the measured top-of-atmosphere (TOA) radiance. Further, it must also compensate for the absorption by atmospheric gases and correct for reflection and refraction of the air-sea interface. We present and evaluate an improved AC for hyperspectral sensors beyond the heritage approach by utilizing the additional spectral information of the hyperspectral sensor. The study encompasses a theoretical radiative transfer sensitivity analysis as well as a practical application of the Hyperspectral Imager for the Coastal Ocean (HICO) and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensors.

Active Volcano Monitoring using a Space-based Hyperspectral Imager

NASA Astrophysics Data System (ADS)

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

2010-12-01

Active volcanoes occur on every continent, often in close proximity to heavily populated areas. While ground-based studies are essential for scientific research and disaster mitigation, remote sensing from space can provide rapid and continuous monitoring of active and potentially active volcanoes [Ramsey and Flynn, 2004]. In this paper, we report on hyperspectral measurements of Kilauea volcano, Hawaii. Hyperspectral images obtained by the US Air Force TacSat-3/ARTEMIS sensor [Lockwood et al, 2006] are used to obtain estimates of the surface temperatures for the volcano. ARTEMIS measures surface-reflected light in the visible, near-infrared, and short-wave infrared bands (VNIR-SWIR). The SWIR bands are known to be sensitive to thermal radiation [Green, 1996]. For example, images from the NASA Hyperion hyperspectral sensor have shown the extent of wildfires and active volcanoes [Young, 2009]. We employ the methodology described by Dennison et al, (2006) to obtain an estimate of the temperature of the active region of Kilauea. Both day and night-time images were used in the analysis. To improve the estimate, we aggregated neighboring pixels. The active rim of the lava lake is clearly discernable in the temperature image, with a measured temperature exceeding 1100o C. The temperature decreases markedly on the exterior of the summit crater. While a long-wave infrared (LWIR) sensor would be ideal for volcano monitoring, we have shown that the thermal state of an active volcano can be monitored using the SWIR channels of a reflective hyperspectral imager. References: Dennison, Philip E., Kraivut Charoensiri, Dar A. Roberts, Seth H. Peterson, and Robert O. Green (2006). Wildfire temperature and land cover modeling using hyperspectral data, Remote Sens. Environ., vol. 100, pp. 212-222. Green, R. O. (1996). Estimation of biomass fire temperature and areal extent from calibrated AVIRIS spectra, in Summaries of the 6th Annual JPL Airborne Earth Science Workshop, Pasadena, CA JPL Publ. 96-4, vol. 1, pp. 105-113. Lockwood, Ronald B., Thomas W. Cooley, Richard M. Nadile, James A. Gardner, Peter S. Armstrong, Abraham M. Payton, Thom M. Davis, Stanley D. Straight, Thomas G. Chrien, Edward L. Gussin, and David Makowski (2006). Advanced Responsive Tactically-Effective Military Imaging Spectrometer (ARTEMIS) Design, in Proceedings of the 2006 IEEE International Geoscience and Remote Sensing Symposium, 31 July-4 August 2006, Denver, Colorado. Ramsey, Michael S., and Luke P. Flynn (2004). Strategies, insights, and the recent advances in volcanic monitoring and mapping with data from NASA’s Earth Observing System, Jour. of Volcanology and Geothermal Research, vol. 135, pp. 1-11. Young, Joseph (2009). EO-1 Weekly status report for September 24-30, 2009, Earth Science Mission Operations (ESMO) Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771.

Hyperspectral Based Skin Detection for Person of Interest Identification

DTIC Science & Technology

2015-03-01

burns, tans minimally. Type III 14-20 Cream white; fair with any hair or eye color ; quite common. Sometimes mild burn, tans uniformly. Type IV 21-27...90 viii List of Figures Figure Page 2.1 Hyperspectral Cube: The colors underneath the image are the different layers. Each...and 1348 nm). Group one has the melanin concentration distribution of 8% or less. Each color line is a sample of a class

Derivative Analysis of AVIRIS Hyperspectral Data for the Detection of Plant Stress

NASA Technical Reports Server (NTRS)

Estep, Lee; Berglund, Judith

2001-01-01

A remote sensing campaign was conducted over a U.S. Department of Agriculture test site at Shelton, Nebraska. The test field was set off in blocks that were differentially treated with nitrogen. Four replicates of 0-kg/ha to 200-kg/ha, in 50-kg/ha increments, were present. Low-altitude AVIRIS hyperspectral data were collected over the site in 224 spectral bands. Simultaneously, ground data were collected to support the airborne imagery. In an effort to evaluate published, derivative-based algorithms for the detection of plant stress, different derivative-based approaches were applied to the collected AVIRIS image cube. The results indicate that, given good quality hyperspectral imagery, derivative techniques compare favorably with simple, well known band ratio algorithms for detection of plant stress.

Hyperspectral imaging for simultaneous measurements of two FRET biosensors in pancreatic β-cells.

PubMed

Elliott, Amicia D; Bedard, Noah; Ustione, Alessandro; Baird, Michelle A; Davidson, Michael W; Tkaczyk, Tomasz; Piston, David W

2017-01-01

Fluorescent protein (FP) biosensors based on Förster resonance energy transfer (FRET) are commonly used to study molecular processes in living cells. There are FP-FRET biosensors for many cellular molecules, but it remains difficult to perform simultaneous measurements of multiple biosensors. The overlapping emission spectra of the commonly used FPs, including CFP/YFP and GFP/RFP make dual FRET measurements challenging. In addition, a snapshot imaging modality is required for simultaneous imaging. The Image Mapping Spectrometer (IMS) is a snapshot hyperspectral imaging system that collects high resolution spectral data and can be used to overcome these challenges. We have previously demonstrated the IMS's capabilities for simultaneously imaging GFP and CFP/YFP-based biosensors in pancreatic β-cells. Here, we demonstrate a further capability of the IMS to image simultaneously two FRET biosensors with a single excitation band, one for cAMP and the other for Caspase-3. We use these measurements to measure simultaneously cAMP signaling and Caspase-3 activation in pancreatic β-cells during oxidative stress and hyperglycemia, which are essential components in the pathology of diabetes.

Hyper-spectral image segmentation using spectral clustering with covariance descriptors

NASA Astrophysics Data System (ADS)

Kursun, Olcay; Karabiber, Fethullah; Koc, Cemalettin; Bal, Abdullah

2009-02-01

Image segmentation is an important and difficult computer vision problem. Hyper-spectral images pose even more difficulty due to their high-dimensionality. Spectral clustering (SC) is a recently popular clustering/segmentation algorithm. In general, SC lifts the data to a high dimensional space, also known as the kernel trick, then derive eigenvectors in this new space, and finally using these new dimensions partition the data into clusters. We demonstrate that SC works efficiently when combined with covariance descriptors that can be used to assess pixelwise similarities rather than in the high-dimensional Euclidean space. We present the formulations and some preliminary results of the proposed hybrid image segmentation method for hyper-spectral images.

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.

Red to far-red multispectral fluorescence image fusion for detection of fecal contamination on apples

USDA-ARS?s Scientific Manuscript database

This research developed a multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet/blue LED excitation for detection of fecal contamination on Golden Delicious apples. Using a hyperspectral line-scan imaging system consisting of an EMCCD camera, spectrograph, an...

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

USDA-ARS?s Scientific Manuscript database

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...

Design of light guide sleeve on hyperspectral imaging system for skin diagnosis

NASA Astrophysics Data System (ADS)

Yan, Yung-Jhe; Chang, Chao-Hsin; Huang, Ting-Wei; Chiang, Hou-Chi; Wu, Jeng-Fu; Ou-Yang, Mang

2017-08-01

A hyperspectral imaging system is proposed for early study of skin diagnosis. A stable and high hyperspectral image quality is important for analysis. Therefore, a light guide sleeve (LGS) was designed for the embedded on a hyperspectral imaging system. It provides a uniform light source on the object plane with the determined distance. Furthermore, it can shield the ambient light from entering the system and increasing noise. For the purpose of producing a uniform light source, the LGS device was designed in the symmetrical double-layered structure. It has light cut structures to adjust distribution of rays between two layers and has the Lambertian surface in the front-end to promote output uniformity. In the simulation of the design, the uniformity of illuminance was about 91.7%. In the measurement of the actual light guide sleeve, the uniformity of illuminance was about 92.5%.

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.

Visual Analysis for Detection and Quantification of Pseudomonas cichorii Disease Severity in Tomato Plants.

PubMed

Rajendran, Dhinesh Kumar; Park, Eunsoo; Nagendran, Rajalingam; Hung, Nguyen Bao; Cho, Byoung-Kwan; Kim, Kyung-Hwan; Lee, Yong Hoon

2016-08-01

Pathogen infection in plants induces complex responses ranging from gene expression to metabolic processes in infected plants. In spite of many studies on biotic stress-related changes in host plants, little is known about the metabolic and phenotypic responses of the host plants to Pseudomonas cichorii infection based on image-based analysis. To investigate alterations in tomato plants according to disease severity, we inoculated plants with different cell densities of P. cichorii using dipping and syringe infiltration methods. High-dose inocula (≥ 10(6) cfu/ml) induced evident necrotic lesions within one day that corresponded to bacterial growth in the infected tissues. Among the chlorophyll fluorescence parameters analyzed, changes in quantum yield of PSII (ΦPSII) and non-photochemical quenching (NPQ) preceded the appearance of visible symptoms, but maximum quantum efficiency of PSII (Fv/Fm) was altered well after symptom development. Visible/near infrared and chlorophyll fluorescence hyperspectral images detected changes before symptom appearance at low-density inoculation. The results of this study indicate that the P. cichorii infection severity can be detected by chlorophyll fluorescence assay and hyperspectral images prior to the onset of visible symptoms, indicating the feasibility of early detection of diseases. However, to detect disease development by hyperspectral imaging, more detailed protocols and analyses are necessary. Taken together, change in chlorophyll fluorescence is a good parameter for early detection of P. cichorii infection in tomato plants. In addition, image-based visualization of infection severity before visual damage appearance will contribute to effective management of plant diseases.

Novel snapshot hyperspectral imager for fluorescence imaging

NASA Astrophysics Data System (ADS)

Chandler, Lynn; Chandler, Andrea; Periasamy, Ammasi

2018-02-01

Hyperspectral imaging has emerged as a new technique for the identification and classification of biological tissue1. Benefitting recent developments in sensor technology, the new class of hyperspectral imagers can capture entire hypercubes with single shot operation and it shows great potential for real-time imaging in biomedical sciences. This paper explores the use of a SnapShot imager in fluorescence imaging via microscope for the very first time. Utilizing the latest imaging sensor, the Snapshot imager is both compact and attachable via C-mount to any commercially available light microscope. Using this setup, fluorescence hypercubes of several cells were generated, containing both spatial and spectral information. The fluorescence images were acquired with one shot operation for all the emission range from visible to near infrared (VIS-IR). The paper will present the hypercubes obtained images from example tissues (475-630nm). This study demonstrates the potential of application in cell biology or biomedical applications for real time monitoring.

Hyperspectral Imager for the Coastal Ocean: instrument description and first images.

PubMed

Lucke, Robert L; Corson, Michael; McGlothlin, Norman R; Butcher, Steve D; Wood, Daniel L; Korwan, Daniel R; Li, Rong R; Snyder, Willliam A; Davis, Curt O; Chen, Davidson T

2011-04-10

The Hyperspectral Imager for the Coastal Ocean (HICO) is the first spaceborne hyperspectral sensor designed specifically for the coastal ocean and estuarial, riverine, or other shallow-water areas. The HICO generates hyperspectral images, primarily over the 400-900 nm spectral range, with a ground sample distance of ≈90 m (at nadir) and a high signal-to-noise ratio. The HICO is now operating on the International Space Station (ISS). Its cross-track and along-track fields of view are 42 km (at nadir) and 192 km, respectively, for a total scene area of 8000 km(2). The HICO is an innovative prototype sensor that builds on extensive experience with airborne sensors and makes extensive use of commercial off-the-shelf components to build a space sensor at a small fraction of the usual cost and time. Here we describe the instrument's design and characterization and present early images from the ISS. © 2011 Optical Society of America

Algorithm for Lossless Compression of Calibrated Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Kiely, Aaron B.; Klimesh, Matthew A.

2010-01-01

A two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.

Physical Modeling for Processing Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) Hyperspectral Data

DTIC Science & Technology

2003-09-30

Physical Modeling for Processing Geosynchronous Imaging Fourier Transform Spectrometer ( GIFTS ) Hyperspectral Data Dr. Allen H.-L. Huang...ssec.wisc.edu Award Number: N000140110850 Grant Number: 144KE70 http://www.ssec.wisc.edu/ gifts /navy/ LONG-TERM GOALS This Office of Naval...objective of this DoD research effort is to develop and demonstrate a fully functional GIFTS hyperspectral data processing system with the potential for a

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

NASA Astrophysics Data System (ADS)

Hu, Yan-Yan; Li, Dong-Sheng

2016-01-01

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

Combining hyperspectral imaging and Raman spectroscopy for remote chemical sensing

NASA Astrophysics Data System (ADS)

Ingram, John M.; Lo, Edsanter

2008-04-01

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

Broadband hyperspectral coherent anti-Stokes Raman scattering microscopy for stain-free histological imaging with principal component analysis

NASA Astrophysics Data System (ADS)

Xu, Jingjiang; Guo, Baoshan; Wong, Kenneth K. Y.; Tsia, Kevin K.

2014-02-01

Routine procedures in standard histopathology involve laborious steps of tissue processing and staining for final examination. New techniques which can bypass these procedures and thus minimize the tissue handling error would be of great clinical value. Coherent anti-Stokes Raman scattering (CARS) microscopy is an attractive tool for label-free biochemical-specific characterization of biological specimen. However, a vast majority of prior works on CARS (or stimulated Raman scattering (SRS)) bioimaging restricted analyses on a narrowband or well-distinctive Raman spectral signatures. Although hyperspectral SRS/CARS imaging has recently emerged as a better solution to access wider-band spectral information in the image, studies mostly focused on a limited spectral range, e.g. CH-stretching vibration of lipids, or non-biological samples. Hyperspectral image information in the congested fingerprint spectrum generally remains untapped for biological samples. In this regard, we further explore ultrabroadband hyperspectral multiplex (HM-CARS) to perform chemoselective histological imaging with the goal of exploring its utility in stain-free clinical histopathology. Using the supercontinuum Stokes, our system can access the CARS spectral window as wide as >2000cm-1. In order to unravel the congested CARS spectra particularly in the fingerprint region, we first employ a spectral phase-retrieval algorithm based on Kramers-Kronig (KK) transform to minimize the non-resonant background in the CARS spectrum. We then apply principal component analysis (PCA) to identify and map the spatial distribution of different biochemical components in the tissues. We demonstrate chemoselective HM-CARS imaging of a colon tissue section which displays the key cellular structures that correspond well with standard stained-tissue observation.

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.

Near-infrared hyperspectral imaging of water evaporation dynamics for early detection of incipient caries.

PubMed

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

2014-10-01

Incipient caries is characterized as demineralization of the tooth enamel reflecting in increased porosity of enamel structure. As a result, the demineralized enamel may contain increased amount of water, and exhibit different water evaporation dynamics than the sound enamel. The objective of this paper is to assess the applicability of water evaporation dynamics of sound and demineralized enamel for detection and quantification of incipient caries using near-infrared hyperspectral imaging. The time lapse of water evaporation from enamel samples with artificial and natural caries lesions of different stages was imaged by a near-infrared hyperspectral imaging system. Partial least squares regression was used to predict the water content from the acquired spectra. The water evaporation dynamics was characterized by a first order logarithmic drying model. The calculated time constants of the logarithmic drying model were used as the discriminative feature. The conducted measurements showed that demineralized enamel contains more water and exhibits significantly faster water evaporation than the sound enamel. By appropriate modelling of the water evaporation process from the enamel surface, the contrast between the sound and demineralized enamel observed in the individual near infrared spectral images can be substantially enhanced. The presented results indicate that near-infrared based prediction of water content combined with an appropriate drying model presents a strong foundation for development of novel diagnostic tools for incipient caries detection. The results of the study enhance the understanding of the water evaporation process from the sound and demineralized enamel and have significant implications for the detection of incipient caries by near-infrared hyperspectral imaging. Copyright © 2014 Elsevier Ltd. All rights reserved.

Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

NASA Astrophysics Data System (ADS)

Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

2017-10-01

Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

Quantitative methods and detection techniques in hyperspectral imaging involving medical and other applications

NASA Astrophysics Data System (ADS)

Roy, Ankita

2007-12-01

This research using Hyperspectral imaging involves recognizing targets through spatial and spectral matching and spectral un-mixing of data ranging from remote sensing to medical imaging kernels for clinical studies based on Hyperspectral data-sets generated using the VFTHSI [Visible Fourier Transform Hyperspectral Imager], whose high resolution Si detector makes the analysis achievable. The research may be broadly classified into (I) A Physically Motivated Correlation Formalism (PMCF), which places both spatial and spectral data on an equivalent mathematical footing in the context of a specific Kernel and (II) An application in RF plasma specie detection during carbon nanotube growing process. (III) Hyperspectral analysis for assessing density and distribution of retinopathies like age related macular degeneration (ARMD) and error estimation enabling the early recognition of ARMD, which is treated as an ill-conditioned inverse imaging problem. The broad statistical scopes of this research are two fold-target recognition problems and spectral unmixing problems. All processes involve experimental and computational analysis of Hyperspectral data sets is presented, which is based on the principle of a Sagnac Interferometer, calibrated to obtain high SNR levels. PMCF computes spectral/spatial/cross moments and answers the question of how optimally the entire hypercube should be sampled and finds how many spatial-spectral pixels are required precisely for a particular target recognition. Spectral analysis of RF plasma radicals, typically Methane plasma and Argon plasma using VFTHSI has enabled better process monitoring during growth of vertically aligned multi-walled carbon nanotubes by instant registration of the chemical composition or density changes temporally, which is key since a significant correlation can be found between plasma state and structural properties. A vital focus of this dissertation is towards medical Hyperspectral imaging applied to retinopathies like age related macular degeneration targets taken with a Fundus imager, which is akin to the VFTHSI. Detection of the constituent components in the diseased hyper-pigmentation area is also computed. The target or reflectance matrix is treated as a highly ill-conditioned spectral un-mixing problem, to which methodologies like inverse techniques, principal component analysis (PCA) and receiver operating curves (ROC) for precise spectral recognition of infected area. The region containing ARMD was easily distinguishable from the spectral mesh plots over the entire band-pass area. Once the location was detected the PMCF coefficients were calculated by cross correlating a target of normal oxygenated retina with the deoxygenated one. The ROCs generated using PMCF shows 30% higher detection probability with improved accuracy than ROCs based on Spectral Angle Mapper (SAM). By spectral unmixing methods, the important endmembers/carotenoids of the MD pigment were found to be Xanthophyl and lutein, while beta-carotene which showed a negative correlation in the unconstrained inverse problem is a supplement given to ARMD patients to prevent the disease and does not occur in the eye. Literature also shows degeneration of meso-zeaxanthin. Ophthalmologists may assert the presence of ARMD and commence the diagnosis process if the Xanthophyl pigment have degenerated 89.9%, while the lutein has decayed almost 80%, as found deduced computationally. This piece of current research takes it to the next level of precise investigation in the continuing process of improved clinical findings by correlating the microanatomy of the diseased fovea and shows promise of an early detection of this disease.

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.

Hyperspectral imaging from space: Warfighter-1

NASA Astrophysics Data System (ADS)

Cooley, Thomas; Seigel, Gary; Thorsos, Ivan

1999-01-01

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

Oil Spill AISA+ Hyperspectral Data Detection Based on Different Sea Surface Glint Suppression Methods

NASA Astrophysics Data System (ADS)

Yang, J.; Ren, G.; Ma, Y.; Dong, L.; Wan, J.

2018-04-01

The marine oil spill is a sudden event, and the airborne hyperspectral means to detect the oil spill is an important part of the rapid response. Sun glint, the specular reflection of sun light from water surface to sensor, is inevitable due to the limitation of observation geometry, which makes so much bright glint in image that it is difficult to extract oil spill feature information from the remote sensing data. This paper takes AISA+ airborne hyperspectral oil spill image as data source, using multi-scale wavelet transform, enhanced Lee filter, enhanced Frost filter and mean filter method for sea surface glint suppression of images. And then the classical SVM method is used for the oil spill information detection, and oil spill information distribution map obtained by human-computer interactive interpretation is used to verify the accuracy of oil spill detection. The results show that the above methods can effectively suppress the sea surface glints and improve the accuracy of oil spill detection. The enhanced Lee filter method has the highest detection accuracy of 88.28 %, which is 12.2 % higher than that of the original image.

BCB Bonding Technology of Back-Side Illuminated COMS Device

NASA Astrophysics Data System (ADS)

Wu, Y.; Jiang, G. Q.; Jia, S. X.; Shi, Y. M.

2018-03-01

Back-side illuminated CMOS(BSI) sensor is a key device in spaceborne hyperspectral imaging technology. Compared with traditional devices, the path of incident light is simplified and the spectral response is planarized by BSI sensors, which meets the requirements of quantitative hyperspectral imaging applications. Wafer bonding is the basic technology and key process of the fabrication of BSI sensors. 6 inch bonding of CMOS wafer and glass wafer was fabricated based on the low bonding temperature and high stability of BCB. The influence of different thickness of BCB on bonding strength was studied. Wafer bonding with high strength, high stability and no bubbles was fabricated by changing bonding conditions.

Label-free hyperspectral dark-field microscopy for quantitative scatter imaging

NASA Astrophysics Data System (ADS)

Cheney, Philip; McClatchy, David; Kanick, Stephen; Lemaillet, Paul; Allen, David; Samarov, Daniel; Pogue, Brian; Hwang, Jeeseong

2017-03-01

A hyperspectral dark-field microscope has been developed for imaging spatially distributed diffuse reflectance spectra from light-scattering samples. In this report, quantitative scatter spectroscopy is demonstrated with a uniform scattering phantom, namely a solution of polystyrene microspheres. A Monte Carlo-based inverse model was used to calculate the reduced scattering coefficients of samples of different microsphere concentrations from wavelength-dependent backscattered signal measured by the dark-field microscope. The results are compared to the measurement results from a NIST double-integrating sphere system for validation. Ongoing efforts involve quantitative mapping of scattering and absorption coefficients in samples with spatially heterogeneous optical properties.

[Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

PubMed

Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

2013-03-01

Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

Hyperspectral stimulated emission depletion microscopy and methods of use thereof

DOEpatents

Timlin, Jerilyn A; Aaron, Jesse S

2014-04-01

A hyperspectral stimulated emission depletion ("STED") microscope system for high-resolution imaging of samples labeled with multiple fluorophores (e.g., two to ten fluorophores). The hyperspectral STED microscope includes a light source, optical systems configured for generating an excitation light beam and a depletion light beam, optical systems configured for focusing the excitation and depletion light beams on a sample, and systems for collecting and processing data generated by interaction of the excitation and depletion light beams with the sample. Hyperspectral STED data may be analyzed using multivariate curve resolution analysis techniques to deconvolute emission from the multiple fluorophores. The hyperspectral STED microscope described herein can be used for multi-color, subdiffraction imaging of samples (e.g., materials and biological materials) and for analyzing a tissue by Forster Resonance Energy Transfer ("FRET").

Novel Hyperspectral Anomaly Detection Methods Based on Unsupervised Nearest Regularized Subspace

NASA Astrophysics Data System (ADS)

Hou, Z.; Chen, Y.; Tan, K.; Du, P.

2018-04-01

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

PubMed Central

Li, Na; Xu, Zhaopeng; Zhao, Huijie; Huang, Xinchen; Drummond, Jane; Wang, Daming

2018-01-01

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively. PMID:29510547

Determination of total iron-reactive phenolics, anthocyanins and tannins in wine grapes of skins and seeds based on near-infrared hyperspectral imaging.

PubMed

Zhang, Ni; Liu, Xu; Jin, Xiaoduo; Li, Chen; Wu, Xuan; Yang, Shuqin; Ning, Jifeng; Yanne, Paul

2017-12-15

Phenolics contents in wine grapes are key indicators for assessing ripeness. Near-infrared hyperspectral images during ripening have been explored to achieve an effective method for predicting phenolics contents. Principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) models were built, respectively. The results show that SVR behaves globally better than PLSR and PCR, except in predicting tannins content of seeds. For the best prediction results, the squared correlation coefficient and root mean square error reached 0.8960 and 0.1069g/L (+)-catechin equivalents (CE), respectively, for tannins in skins, 0.9065 and 0.1776 (g/L CE) for total iron-reactive phenolics (TIRP) in skins, 0.8789 and 0.1442 (g/L M3G) for anthocyanins in skins, 0.9243 and 0.2401 (g/L CE) for tannins in seeds, and 0.8790 and 0.5190 (g/L CE) for TIRP in seeds. Our results indicated that NIR hyperspectral imaging has good prospects for evaluation of phenolics in wine grapes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hyperspectral image denoising and anomaly detection based on low-rank and sparse representations

NASA Astrophysics Data System (ADS)

Zhuang, Lina; Gao, Lianru; Zhang, Bing; Bioucas-Dias, José M.

2017-10-01

The very high spectral resolution of Hyperspectral Images (HSIs) enables the identification of materials with subtle differences and the extraction subpixel information. However, the increasing of spectral resolution often implies an increasing in the noise linked with the image formation process. This degradation mechanism limits the quality of extracted information and its potential applications. Since HSIs represent natural scenes and their spectral channels are highly correlated, they are characterized by a high level of self-similarity and are well approximated by low-rank representations. These characteristic underlies the state-of-the-art in HSI denoising. However, in presence of rare pixels, the denoising performance of those methods is not optimal and, in addition, it may compromise the future detection of those pixels. To address these hurdles, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI denoiser, which implements explicit low-rank representation, promotes self-similarity, and, by using a form of collaborative sparsity, preserves rare pixels. The denoising and detection effectiveness of the proposed robust HSI denoiser is illustrated using semi-real data.

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.

Assessing diabetic foot ulcer development risk with hyperspectral tissue oximetry

NASA Astrophysics Data System (ADS)

Yudovsky, Dmitry; Nouvong, Aksone; Schomacker, Kevin; Pilon, Laurent

2011-02-01

Foot ulceration remains a serious health concern for diabetic patients and has a major impact on the cost of diabetes treatment. Early detection and preventive care, such as offloading or improved hygiene, can greatly reduce the risk of further complications. We aim to assess the use of hyperspectral tissue oximetry in predicting the risk of diabetic foot ulcer formation. Tissue oximetry measurements are performed during several visits with hyperspectral imaging of the feet in type 1 and 2 diabetes mellitus subjects that are at risk for foot ulceration. The data are retrospectively analyzed at 21 sites that ulcerated during the course of our study and an ulceration prediction index is developed. Then, an image processing algorithm based on this index is implemented. This algorithm is able to predict tissue at risk of ulceration with a sensitivity and specificity of 95 and 80%, respectively, for images taken, on average, 58 days before tissue damage is apparent to the naked eye. Receiver operating characteristic analysis is also performed to give a range of sensitivity/specificity values resulting in a Q-value of 89%.

Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control.

PubMed

Kim, Heekang; Kwon, Soon; Kim, Sungho

2016-07-08

This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen).

Characterization of E coli biofim formations on baby spinach leaf surfaces using hyperspectral fluorescence imaging

NASA Astrophysics Data System (ADS)

Cho, Hyunjeong; Baek, Insuck; Oh, Mirae; Kim, Sungyoun; Lee, Hoonsoo; Kim, Moon S.

2017-05-01

Bacterial biofilm formed by pathogens on fresh produce surfaces is a food safety concern because the complex extracellular matrix in the biofilm structure reduces the reduction and removal efficacies of washing and sanitizing processes such as chemical or irradiation treatments. Therefore, a rapid and nondestructive method to identify pathogenic biofilm on produce surfaces is needed to ensure safe consumption of fresh, raw produce. This research aimed to evaluate the feasibility of hyperspectral fluorescence imaging for detecting Escherichia.coli (ATCC 25922) biofilms on baby spinach leaf surfaces. Samples of baby spinach leaves were immersed and inoculated with five different levels (from 2.6x104 to 2.6x108 CFU/mL) of E.coli and stored at 4°C for 24 h and 48 h to induce biofilm formation. Following the two treatment days, individual leaves were gently washed to remove excess liquid inoculums from the leaf surfaces and imaged with a hyperspectral fluorescence imaging system equipped with UV-A (365 nm) and violet (405 nm) excitation sources to evaluate a spectral-image-based method for biofilm detection. The imaging results with the UV-A excitation showed that leaves even at early stages of biofilm formations could be differentiated from the control leaf surfaces. This preliminary investigation demonstrated the potential of fluorescence imaging techniques for detection of biofilms on leafy green surfaces.

Hyperspectral fluorescence imaging system for biomedical diagnostics

NASA Astrophysics Data System (ADS)

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

2006-02-01

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

Flag-based detection of weak gas signatures in long-wave infrared hyperspectral image sequences

NASA Astrophysics Data System (ADS)

Marrinan, Timothy; Beveridge, J. Ross; Draper, Bruce; Kirby, Michael; Peterson, Chris

2016-05-01

We present a flag manifold based method for detecting chemical plumes in long-wave infrared hyperspectral movies. The method encodes temporal and spatial information related to a hyperspectral pixel into a flag, or nested sequence of linear subspaces. The technique used to create the flags pushes information about the background clutter, ambient conditions, and potential chemical agents into the leading elements of the flags. Exploiting this temporal information allows for a detection algorithm that is sensitive to the presence of weak signals. This method is compared to existing techniques qualitatively on real data and quantitatively on synthetic data to show that the flag-based algorithm consistently performs better on data when the SINRdB is low, and beats the ACE and MF algorithms in probability of detection for low probabilities of false alarm even when the SINRdB is high.

5-ALA induced fluorescent image analysis of actinic keratosis

NASA Astrophysics Data System (ADS)

Cho, Yong-Jin; Bae, Youngwoo; Choi, Eung-Ho; Jung, Byungjo

2010-02-01

In this study, we quantitatively analyzed 5-ALA induced fluorescent images of actinic keratosis using digital fluorescent color and hyperspectral imaging modalities. UV-A was utilized to induce fluorescent images and actinic keratosis (AK) lesions were demarcated from surrounding the normal region with different methods. Eight subjects with AK lesion were participated in this study. In the hyperspectral imaging modality, spectral analysis method was utilized for hyperspectral cube image and AK lesions were demarcated from the normal region. Before image acquisition, we designated biopsy position for histopathology of AK lesion and surrounding normal region. Erythema index (E.I.) values on both regions were calculated from the spectral cube data. Image analysis of subjects resulted in two different groups: the first group with the higher fluorescence signal and E.I. on AK lesion than the normal region; the second group with lower fluorescence signal and without big difference in E.I. between two regions. In fluorescent color image analysis of facial AK, E.I. images were calculated on both normal and AK lesions and compared with the results of hyperspectral imaging modality. The results might indicate that the different intensity of fluorescence and E.I. among the subjects with AK might be interpreted as different phases of morphological and metabolic changes of AK lesions.

Airborne hyperspectral remote sensing in Italy

NASA Astrophysics Data System (ADS)

Bianchi, Remo; Marino, Carlo M.; Pignatti, Stefano

1994-12-01

The Italian National Research Council (CNR) in the framework of its `Strategic Project for Climate and Environment in Southern Italy' established a new laboratory for airborne hyperspectral imaging devoted to environmental problems. Since the end of June 1994, the LARA (Laboratorio Aereo per Ricerche Ambientali -- Airborne Laboratory for Environmental Studies) Project is fully operative to provide hyperspectral data to the national and international scientific community by means of deployments of its CASA-212 aircraft carrying the Daedalus AA5000 MIVIS (multispectral infrared and visible imaging spectrometer) system. MIVIS is a modular instrument consisting of 102 spectral channels that use independent optical sensors simultaneously sampled and recorded onto a compact computer compatible magnetic tape medium with a data capacity of 10.2 Gbytes. To support the preprocessing and production pipeline of the large hyperspectral data sets CNR housed in Pomezia, a town close to Rome, a ground based computer system with a software designed to handle MIVIS data. The software (MIDAS-Multispectral Interactive Data Analysis System), besides the data production management, gives to users a powerful and highly extensible hyperspectral analysis system. The Pomezia's ground station is designed to maintain and check the MIVIS instrument performance through the evaluation of data quality (like spectral accuracy, signal to noise performance, signal variations, etc.), and to produce, archive, and diffuse MIVIS data in the form of geometrically and radiometrically corrected data sets on low cost and easy access CC media.

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.

The application of hypserspectral imaging analysis to fresh food safety inspection

USDA-ARS?s Scientific Manuscript database

Line-scan hyperspectral images of fresh matured tomatoes were collected for image analysis. Algorithms were developed, based on spectral analysis, to detect defect of cracks on fresh produce. Four wavebands of 569 nm, 645 nm, 702 nm and 887 nm were selected from spectra analysis to use the relative...

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

USDA-ARS?s Scientific Manuscript database

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...

Hyperspectral fluorescence imaging using violet LEDs as excitation sources for fecal matter contaminate identification on spinach leaves

USDA-ARS?s Scientific Manuscript database

Food safety in the production of fresh produce for human consumption is a worldwide issue and needs to be addressed to decrease foodborne illnesses and resulting costs. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for detection of fecal contaminates on spina...

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

USDA-ARS?s Scientific Manuscript database

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

A hierarchical classification approach for recognition of low-density (LDPE) and high-density polyethylene (HDPE) in mixed plastic waste based on short-wave infrared (SWIR) hyperspectral imaging.

PubMed

Bonifazi, Giuseppe; Capobianco, Giuseppe; Serranti, Silvia

2018-06-05

The aim of this work was to recognize different polymer flakes from mixed plastic waste through an innovative hierarchical classification strategy based on hyperspectral imaging, with particular reference to low density polyethylene (LDPE) and high-density polyethylene (HDPE). A plastic waste composition assessment, including also LDPE and HDPE identification, may help to define optimal recycling strategies for product quality control. Correct handling of plastic waste is essential for its further "sustainable" recovery, maximizing the sorting performance in particular for plastics with similar characteristics as LDPE and HDPE. Five different plastic waste samples were chosen for the investigation: polypropylene (PP), LDPE, HDPE, polystyrene (PS) and polyvinyl chloride (PVC). A calibration dataset was realized utilizing the corresponding virgin polymers. Hyperspectral imaging in the short-wave infrared range (1000-2500nm) was thus applied to evaluate the different plastic spectral attributes finalized to perform their recognition/classification. After exploring polymer spectral differences by principal component analysis (PCA), a hierarchical partial least squares discriminant analysis (PLS-DA) model was built allowing the five different polymers to be recognized. The proposed methodology, based on hierarchical classification, is very powerful and fast, allowing to recognize the five different polymers in a single step. Copyright © 2018 Elsevier B.V. All rights reserved.

The development of a line-scan imaging algorithm for the detection of fecal contamination on leafy geens

NASA Astrophysics Data System (ADS)

Yang, Chun-Chieh; Kim, Moon S.; Chuang, Yung-Kun; Lee, Hoyoung

2013-05-01

This paper reports the development of a multispectral algorithm, using the line-scan hyperspectral imaging system, to detect fecal contamination on leafy greens. Fresh bovine feces were applied to the surfaces of washed loose baby spinach leaves. A hyperspectral line-scan imaging system was used to acquire hyperspectral fluorescence images of the contaminated leaves. Hyperspectral image analysis resulted in the selection of the 666 nm and 688 nm wavebands for a multispectral algorithm to rapidly detect feces on leafy greens, by use of the ratio of fluorescence intensities measured at those two wavebands (666 nm over 688 nm). The algorithm successfully distinguished most of the lowly diluted fecal spots (0.05 g feces/ml water and 0.025 g feces/ml water) and some of the highly diluted spots (0.0125 g feces/ml water and 0.00625 g feces/ml water) from the clean spinach leaves. The results showed the potential of the multispectral algorithm with line-scan imaging system for application to automated food processing lines for food safety inspection of leafy green vegetables.

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Development of TMA-based imaging system for hyperspectral application

NASA Astrophysics Data System (ADS)

Choi, Young-Wan; Yang, Seung-Uk; Kang, Myung-Seok; Kim, Ee-Eul

2017-11-01

Funded by the Ministry of Commerce, Industry, and Energy of Korea, SI initiated the development of the prototype model of TMA-based electro-optical system as part of the national space research and development program. Its optical aperture diameter is 120 mm, the effective focal length is 462 mm, and its full field-of-view is 5.08 degrees. The dimension is of about 600 mm × 400 mm × 400 mm and the weight is less than 15 kg. To demonstrate its performance, hyper-spectral imaging based on linear spectral filter is selected for the application of the prototype. The spectral resolution will be less than 10 nm and the number of channels will be more than 40 in visible and nearinfrared region. In this paper, the progress made so far on the prototype development will be presented