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检索条件"任意字段=Conference on Algorithms for Multispectral and Hyperspectral Imagery II"

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hyperspectral chemical plume quantification via background radiance estimation

Hyperspectral chemical plume quantification via background r...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Niu, Sidi Golowich, Steven E. Ingle, Vinay K. Manolakis, Dimitris G. Northeastern Univ 360 Huntington Ave Boston MA 02115 USA MIT Lincoln Lab Lexington MA 02420 USA

ISBN: (纸本)9780819495341

Existing chemical plume quantification algorithms assume that the off-plume radiance of a pixel containing the plume signal is unobservable. When the problem is limited to a single gas, the off-plume radiance may be estimated from the bands in which the gas absorption is nearly zero. It is then possible to compute the difference between the on- and off-plume radiances and solve for the plume strength from Beer's Law. The major advantage of this proposed method is that the gas strength can be resolved from the radiance difference so that the estimation error remains small for thick plumes.

关键词： hyperspectral imagery narrow-band gas background estimation chemical gaseous agent quantification

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Lossless to lossy compression for hyperspectral imagery based on wavelet and integerKLT transforms with 3D binary EZW

Lossless to lossy compression for hyperspectral imagery base...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Cheng, Kai-jen Dill, Jeffrey Ohio Univ Sch Elect Engn & Comp Sci Russ Coll Engn & Technol Stocker Ctr 329 Athens OH 45701 USA

ISBN: (纸本)9780819495341

In this paper, a lossless to lossy transform based image compression of hyperspectral images based on Integer Karhunen-Loeve Transform (IKLT) and Integer Discrete Wavelet Transform (IDWT) is proposed. Integer transforms are used to accomplish reversibility. The IKLT is used as a spectral decorrelator and the 2D-IDWT is used as a spatial decorrelator. The three-dimensional Binary Embedded Zerotree Wavelet (3D-BEZW) algorithm efficiently encodes hyperspectral volumetric image by implementing progressive bitplane coding. The signs and magnitudes of transform coefficients are encoded separately. Lossy and lossless compressions of signs are implemented by conventional EZW algorithm and arithmetic coding respectively. The efficient 3D-BEZW algorithm is applied to code magnitudes. Further compression can be achieved using arithmetic coding. The lossless and lossy compression performance is compared with other state of the art predictive and transform based image compression methods on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. Results show that the 3D-BEZW performance is comparable to predictive algorithms However, its computational cost is comparable to transform- based algorithms

关键词： predictive coding transform based coding hyperspectral images EZW SPIHT wavelet transform Karhunen-Loeve transform matrix factorization

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Concealed target detection using hyperspectral Imagers based on Intersection Kernel of SVM

Concealed target detection using Hyperspectral Imagers based...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Shim, Min-Sheob Kim, Sungho Yeungnam Univ Dept Elect Engn LED IT Fus Technol Res Ctr Kyongsan 712749 South Korea

ISBN: (纸本)9780819495341

This paper presents a concealed target detection based on the intersection kernel Support Vector Machine (SVM). hyperspectral imagers are widely used in the field of target detection and material analysis. In military applications, it can be used to border protection, concealed target detection, reconnaissance and surveillance. If disguised enemies not detected in advance, the damage of allies will be catastrophic by unexpected attack. Concealed object detection using radar and terahertz method is widely used. However, these active techniques are easily exposed to the enemy. Electronic Optical Counter Counter Measures (EOCCM) using hyperspectral imagers can be a feasible solution. We use the band selected feature directly and the intersection kernel based SVM. Different materials show different spectrums although they look similar in CCD camera. We propose novel concealed target detection method that consist of 4 step, Feature band selection, Feature Extraction, SVM learning and target detection.

关键词： hyperspectral imagery feature selection simulated annealing band ratio concealed target detection

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Multi-Modal Detection of Man-Made Objects in Simulated Aerial Images

Multi-Modal Detection of Man-Made Objects in Simulated Aeria...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Baran, Matthew S. Tutwiler, Richard L. Natale, Donald J. Bassett, Michael S. Harner, Matthew P. Penn State Univ State Coll PA 16802 USA SI Org Inc Valley Forge PA 19460 USA

ISBN: (纸本)9780819495341

This paper presents an approach to multi-modal detection of man-made objects from aerial imagery. Detections are made in polarization imagery, hyperspectral imagery, and LIDAR point clouds then fused into a single confidence map. The detections are based on reflective, spectral, and geometric features of man-made objects in airborne images. The polarization imagery detector uses the Stokes parameters and the degree of linear polarization to find highly polarizing objects. The hyperspectral detector matches scene spectra to a library of man-made materials using a combination of the spectral gradient angle and the generalized likelihood ratio test. The LIDAR detector clusters 3D points into objects using principle component analysis and prunes the detections by size and shape. Once the three channels are mapped into detection images, the information can be fused without some of the problems of multi-modal fusion, such as edge reversal. The imagery used in this system was simulated with a first-principles ray tracing image generator known as DIRSIG.

关键词： Multi-modal Airborne imagery Object Detection DIRSIG

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Using multi-angle World View-2 imagery to determine bathymetry near Oahu, Hawaii

Using multi-angle World View-2 imagery to determine bathymet...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Lee, Krista R. Olsen, Richard C. Kruse, Fred A. Kim, Angela M. Naval Postgrad Sch Dept Phys 833 Dyer Rd Monterey CA 93943 USA

ISBN: (纸本)9780819495341

multispectral imaging (MSI) data collected at multiple angles over shallow water provide analysts with a unique perspective of bathymetry in coastal areas. Observations taken by DigitalGlobe's WorldView-2 (WV-2) sensor acquired at 39 different view angles on 30 July 2011 were used to determine the effect of acquisition angle on bathymetry derivation. The site used for this study was Kailua Bay (on the windward side of the island of Oahu). Satellite azimuth and elevation for these data ranged from 18.8 to 185.8 degrees and 24.9 (forward-looking) to 24.5 (backward-looking) degrees (respectively) with 90 degrees representing a nadir view. Bathymetry were derived directly from the WV-2 radiance data using a band ratio approach. Comparison of results to LiDAR-derived bathymetry showed that varying view angle impact the quality of the inferred bathymetry. Derived and reference bathymetly have a higher correlation as images are acquired closer to nadir. The band combination utilized for depth derivation also has an effect on derived bathymetry. Four band combinations were compared, and the Blue & Green combination provided the best results.

关键词： multispectral imaging WorldView-2 bathymetry derivation multi-angle imagery

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A method to generate sub-pixel classification maps for use in DIRSIG three-dimensional models

A method to generate sub-pixel classification maps for use i...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Givens, Ryan N. Walli, Karl C. Eismann, Michael T. US Air Force Inst Technol 2950 Hobson Way Wright Patterson AFB OH 45433 USA US Air Force Wright Patterson AFB Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819495341

Developing new remote sensing instruments is a costly and time consuming process. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model gives users the ability to create synthetic images for a proposed sensor before building it. However, to produce synthetic images, DIRSIG requires facetized, three-dimensional models attributed with spectral and texture information which can themselves be costly and time consuming to produce. Recent work has been successful in generating these scenes using an automated method when coincident Hyper Spectral imagery (HSI), LIght Detection and Ranging (LIDAR), and high-resolution imagery of a site are available. An important step in this process is attributing the three-dimensional information gained from the LIDAR with spectral information gained from the HSI. Previous work was able to do this attribution at the resolution of the HSI, but the HSI is generally at the lowest resolution of the three modalities. Due to the highly accurate method used to register the HSI, LIDAR, and high-resolution imagery, the potential for bringing additional information into the classification process exists. This paper will present a method to generate classification maps at or near the resolution of the high-resolution imagery component of the fused imagery. Initial results using this new method are provided and are promising in terms of their ability to ultimately help produce higher fidelity DIRSIG models.

关键词： Registration fusion synthetic imagery DIRSIG hyperspectral imagery classification

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Enhancement of hyperspectral imagery using Spectrally Weighted Tensor Anisotropic Nonlinear Diffusion for Classification.

Enhancement of Hyperspectral Imagery using Spectrally Weight...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Marin-McGee, Maider Velez-Reyes, Miguel Univ Puerto Rico Dept Math Sci Mayaguez PR 00680 USA Univ Texas El Paso Dept Elect & Comp Engn El Paso TX 79968 USA

ISBN: (纸本)9780819495341

Tensor Anisotropic Nonlinear Diffusion (TAND) is a divergence PDE-based diffusion technique that is "guided" by an edge descriptor, such as the structure tensor, to stir the diffusion. The structure tensor for vector valued images such as HSI is most often defined as the average of the scalar structure tensors for each band. The problem with this definition is the assumption that all bands provide the same amount of edge information giving them the same weights. As a result non-edge pixels can be reinforced and edges can be weakened resulting in poor performance by processes that depend on the structure tensor. Iterative processes such as TAND, in particular, are vulnerable to this phenomenon. Recently a weighted structure tensor based on the heat operator has been proposed [1]. The weights are based on the heat operator. This tensor takes advantage of the fact that, in HSI, neighboring spectral bands are highly correlated, as are the bands of its gradient. By taking advantage of local spectral information, the proposed scheme gives higher weighting to local spectral features that could be related to edge information allowing the diffusion process to better enhance edges while smoothing out uniform regions facilitating the process of classification. This article present how classification results are affected by using TAND based on the heat weighted structure tensor as an image enhancement step in a classification system.

关键词： hyperspectral Images Structure Tensor Tensor nonlinear anisotropic diffusion

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A Semi-Supervised Classification Algorithm Using the TAD-Derived Background as Training Data

A Semi-Supervised Classification Algorithm Using the TAD-Der...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Fan, Lei Ambeau, Brittany Messinger, David W. Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA

ISBN: (纸本)9780819495341

In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's), or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi-supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

关键词： TAD graph theory classification spectral imagery mutual nearest neighbor GML MDM

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An analysis of the probability distribution of spectral angle and Euclidean distance in hyperspectral remote sensing using microspectroscopy

An analysis of the probability distribution of spectral angl...

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conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XIX

作者： Resmini, Ronald G. Deloye, Christopher J. Allen, David W. Mitre Corp 7594 Colshire DrMS T630 Mclean VA 22102 USA Natl Inst Stand & Technol Gaithersburg MD 20899 USA

ISBN: (纸本)9780819495341

Determining the probability distribution of hyperspectral imagery (HSI) data and of the results of algorithms applied to those data, is critical to understanding algorithm performance and for establishing performance metrics such as probability of detection, false alarm rate, and minimum detectable and identifiable quantities. The results of analyses of visible/near-infrared (VNIR;400 nm to 900 nm) HSI microscopy data of small fragments (similar to 1.25 cm in size) of minerals are presented. HST microscopy, also known as microspectroscopy, is the acquisition of HST data cubes of fields of view ranging from centimeters to millimeters in size. It is imaging spectrometry but at a small spatial scale. With HST microspectroscopy, several thousand spectral signatures may be easily acquired of individual target materials samples of which may be quite small With such data, probability distributions may be very precisely determined. For faceted/irregularly shaped samples and mixtures (checkerboard, intimate, or microscopic), HSI microscopy data readily facilitate a detailed assessment of the contribution of the materials, their morphology, spectral mixing interactions, radiative transfer processes, view/illumination geometry contributions, etc., to the observed probability distribution(s) of the HST data and of algorithm output. Here, spectral angle, the individual components of spectral angle (e.g., the inner product or numerator of the spectral angle equation), Euclidean distance, and L1 norm values are calculated. Regions of interest (ROT) on the fragments are easily defined that contain thousands of spectra far from the fragments' edges though translucency sometimes remains a factor impacting spectral signatures. The aforementioned metrics are derived for the spectra in an ROT of individual mineral fragments;across ROIs of different minerals;and with an ROT of an inert background. The resulting probability distributions of the various populations of the metrics are decid

关键词： hyperspectral remote sensing hyperspectral imagery HSI microscopy HSI microscopy spectral angle microspectroscopy Euclidean distance probability distribution L1 norm

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Fuzzy Decision Tree Model Adaptation to Multi- and hyperspectral imagery Supervised Classification

Fuzzy Decision Tree Model Adaptation to Multi- and Hyperspec...

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9th International conference on Digital Technologies (DT)

作者： Stankevich, Sergey Levashenko, Vitaly Zaitseva, Elena Natl Acad Sci Ukraine Sci Ctr Aerosp Res Earth Kiev Ukraine Univ Zilina Dept Informat Zilina Slovakia

ISBN: (纸本)9781479909247;9781479909230

Now the land cover classification system is very important for various remote sensing applications and many sectors of economy. Therefore, development of algorithms for multi- and hyperspectral imagery classification is an urgent task. In this paper we present a new efficient algorithm for multi- and hyperspectral imagery classification based on fuzzy decision tree approach. We use the multispectral imagery spectral bands as fuzzy data source attributes and cumulative mutual information between them and the resulting fuzzy classification as decision tree inducing criterion. Proposed algorithm provides classification accuracy than traditional ones and significant data dimensionality reduction by means of informative spectral bands selection.

关键词： remote sensing imagery classification fuzzy decision trees spectral band selection

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