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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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Subspace selection for subpixel detection of 3D objects in hyperspectral imagery

Subspace selection for subpixel detection of 3D objects in h...

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

作者： Liu, Y Healey, G Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92717 USA

ISBN: (纸本)0819457914

Object detection in hyperspectral imagery benefits from the large amount of spectral information. The effective use of this information is crucial for a detection algorithm to achieve high accuracy under challenging conditions. In this paper, we establish subspace representations for 3D objects and backgrounds to improve discriminability for 3D detection invariant to unknown illumination and atmospheric conditions. Residual variance information is utilized to generate background and mixed residual statistics which improve the separation of target and background for detection. A new detection algorithm that uses these statistics in conjunction with a likelihood ratio test is proposed for the subpixel detection of complex 3D objects in cluttered backgrounds. Other existing algorithms, e.g. the generalized likelihood ratio test (GLRT), can be derived from this algorithm by introducing the appropriate assumptions. The new detection algorithm is evaluated for a number of images simulated using DIRSIG and also compared with other detection algorithms. The experimental results demonstrate accurate performance on these data sets.

关键词： 3D object detection subpixel hyperspectral image linear discriminant invariant DIRSIG

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Target Detection Using Artificial Neural Networks on LWIR hyperspectral imagery 24

Target Detection Using Artificial Neural Networks on LWIR Hy...

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24th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

作者： Martin, Jacob A. US Air Force Res Lab RYMT Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781510618008

ISBN: (纸本)9781510618008

Results using a neural network classifier for LWIR HSI target detection are presented. Detection performance using two different single layer networks with 10 and 20 nodes in the hidden layer are presented. Additionally, a two-layer network with 20 nodes in the first layer and 10 in the second is also tested. The larger networks generally performbetter, as expected, but this is not necessarily true for all targets. Neural networks significantly outperform ACE across several detection metric for eight different targets. Even when ACE is applied to atmospherically-corrected emissivity datacubes, neural networks applied without atmospheric compensation still performbetter. Finally, it is found that incorporating atmospheric metadata into the network inputs can improve detection performance but is again target-dependent.

关键词： Target detection Neural networks hyperspectral imaging Long wavelength infrared Atmospheric sensing Network architectures Artificial neural networks Machine learning Sensors hyperspectral target detection

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Resampling approach for anomaly detection in multispectral images

Resampling approach for anomaly detection in multispectral i...

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

作者： Theiler, J Cai, DM Los Alamos Natl Lab Space & Remote Sensing Sci Grp Los Alamos NM 87545 USA

ISBN: (纸本)0819449539

We propose a novel approach for identifying the "most unusual" samples in a data set, based on a resampling of data attributes. The resampling produces a "background class" and then binary classification is used to distinguish the original training set from the background. Those in the training set that are most like the background (i.e., most unlike the rest of the training set) are considered anomalous. Although by. their nature, anomalies do not permit a positive definition (if I knew what they were, I wouldn't call them anomalies), one can make "negative definitions" (I can say what does not qualify as an interesting anomaly). By choosing different resampling schemes, one can identify different kinds of anomalies. For multispectral images, anomalous pixels correspond to locations on the ground with unusual spectral signatures or, depending on how feature sets are constructed, unusual spatial textures.

关键词： anomaly detection machine learning multispectral imagery

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Symmetrized Regression for hyperspectral Background Estimation 21

Symmetrized Regression for Hyperspectral Background Estimati...

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

作者： Theiler, James Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9781628415889

We can improve the detection of targets and anomalies in a cluttered background by more effectively estimating that background. With a good estimate of what the target-free radiance or reflectance ought to be at a pixel, we have a point of comparison with what the measured value of that pixel actually happens to be. It is common to make this estimate using the mean of pixels in an annulus around the pixel of interest. But there is more information in the annulus than this mean value, and one can derive more general estimators than just the mean. The derivation pursued here is based on multivariate regression of the central pixel against the pixels in the surrounding annulus. This can be done on a band-by-band basis, or with multiple bands simultaneously. For overhead remote sensing imagery with square pixels, there is a natural eight-fold symmetry in the surrounding annulus, corresponding to reflection and right angle rotation. We can use this symmetry to impose constraints on the estimator function, and we can use these constraints to reduce the number or regressor variables in the problem. This paper investigates the utility of regression generally - and a variety of different symmetric regression schemes particularly - for hyperspectral background estimation in the context of generic target detection.

关键词： Background estimation Target Detection Anomaly Detection hyperspectral imagery

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hyperspectral Chemical Plume Quantification and Temperature Estimation

Hyperspectral Chemical Plume Quantification and Temperature ...

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20th SPIE conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery

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

ISBN: (纸本)9781628410259

Most hyperspectral chemical gaseous plume quantification algorithms assume a priori knowledge of the plume temperature either through direct measurement or an auxiliary temperature estimation approach. In this paper, we propose a new quantification algorithm that can simultaneously estimate the plume strength as well as its temperature. We impose only a mild spatial assumption, that at least one nearby pixel shares the same plume parameters as the target pixel, which we believe will be generally satisfied in practice. Simulations show that the performance loss incurred by estimating both the temperature and plume strength is small, as compared to the case when the plume temperature is known exactly.

关键词： hyperspectral imagery chemical agent quantification background estimation selected-band algorithm

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A generalized linear mixing model for hyperspectral imagery

A generalized linear mixing model for hyperspectral imagery

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

作者： Gillis, David Bowles, Jeffrey Ientilucci, Emmett J. Messinger, David W. USN Res Lab Washington DC 20375 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780819471574

We continue previous work that generalizes the traditional linear mixing model from a combination of endmember vectors to a combination of multi-dimensional affine endmember subspaces. This generalization allows the model to handle the natural variation that is present is real-world hyperspectral imagery. Once the endmember subspaces have been defined, the scene may be demixed as usual, allowing for existing post-processing algorithms (classification, etc.) to proceed as-is. In addition, the endmember subspace model naturally incorporates the use of physics-based modeling approaches ('target spaces') in order to identify sub-pixel targets. In this paper, we present a modification to our previous model that uses affine subspaces (as opposed to true linear subspaces) and a new demixing algorithm. We also include experimental results on both synthetic and real-world data, and include a discussion on bow well the model fits the real-world data sets.

关键词： hyperspectral linear mixing endmembers physics-based modeling

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A 2DPCA-based method for automatic selection of hyperspectral image bands for color visualization

A 2DPCA-based method for automatic selection of hyperspectra...

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

作者： Kaufman, Jason Celenk, Mehmet Vongsy, Karmon Adv Syst Grp 4200 Colonel Glenn Hwy Beavercreek OH 45431 USA Ohio Univ Sch Elec Eng & Comp Sci Athens OH 45701 USA

ISBN: (纸本)9780819471574

hyperspectral imagery (HSI) is a relatively new technology capable of relaying intensity information gathered from both visible and non-visible ranges of the electromagnetic spectrum. HSI images can contain hundreds of bands, which present a problem when an image analyst must select the most relevant bands from such an image for visualization, particularly when the bands that are within the range of human vision are either not present or heavily distorted. it is proposed here that two-dimensional principal component analysis (2DPCA) can aid in the automatic selection of the bands from an HSI image that would best reflect visual information. The method requires neither prior knowledge of the image contents nor the association between spectral bands and their center wavelengths.

关键词： hyperspectral imagery visualization 2DPCA

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A high-speed implementation of manifold coordinate representations of hyperspectral imagery: A GPU-based approach to rapid nonlinear modeling

A high-speed implementation of manifold coordinate represent...

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

作者： Topping, T. Russell French, James Hancock, Monte F., Jr. Celestech Inc Phoenix AZ 85048 USA

ISBN: (纸本)9780819481597

Working with the Naval Research Laboratory, Celestech has implemented advanced non-linear hyperspectral image (HSI) processing algorithms optimized for Graphics Processing Units (GPU). These algorithms have demonstrated performance improvements of nearly 2 orders of magnitude over optimal CPU-based implementations. The paper briefly covers the architecture of the NIVIDIA GPU to provide a basis for discussing GPU optimization challenges and strategies. The paper then covers optimization approaches employed to extract performance from the GPU implementation of Dr. Bachmann's algorithms including memory utilization and process thread optimization considerations. The paper goes on to discuss strategies for deploying GPU-enabled servers into enterprise service oriented architectures. Also discussed are Celestech's on-going work in the area of middleware frameworks to provide an optimized multi-GPU utilization and scheduling approach that supports both multiple GPUs in a single computer as well as across multiple computers. This paper is a complementary work to the paper submitted by Dr. Charles Bachmann entitled "A Scalable Approach to Modeling Nonlinear Structure in hyperspectral imagery and Other High-Dimensional Data Using Manifold Coordinate Representations". Dr. Bachmann's paper covers the algorithmic and theoretical basis for the HSI processing approach.

关键词： hyperspectral Image Processing Graphics Processing Units GPU Non-linear

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Linearly constrained band selection for hyperspectral imagery

Linearly constrained band selection for hyperspectral imager...

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

作者： Wang, Su Chang, Chein-, I Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)0819462896

Linearly constrained adaptive beamforming has been used to design hyperspectral target detection algorithms such as constrained energy minimization (CEM) and linearly constrained minimum variance (LCMV). It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper investigates this idea and further uses it to develop a new approach to band selection, referred to as linear constrained band selection (LCBS) for hyperspectral imagery. It interprets a band image as a desired target signature while considering other band images as unknown signatures. With this interpretation, the proposed LCBS linearly constrains a band image while also minimizing band correlation or dependence caused by other band images. As a result, two different methods referred to as Band Correlation Minimization (BCM) and Band Correlation Constraint (BCC) can be developed for band selection. Such LCBS allows one to select desired bands for data analysis. In order to determine the number of bands required to select, p, a recently developed concept, called virtual dimensionality (VD) is used to estimate the p. Once the p is determined, a set of p desired bands can be selected by LCBS. Finally, experiments are conducted to substantiate the proposed LCBS.

关键词： Target detection hyperspectral imaging Algorithm development Image processing Detection and tracking algorithms Linear filtering Image filtering Optical filters hyperspectral target detection Principal component analysis

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Geo-positional accuracy evaluation of QuickBird Ortho-Ready Standard 2A multispectral imagery

Geo-positional accuracy evaluation of QuickBird Ortho-Ready ...

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conference onalgorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery X

作者： Ager, TP Natl Geospatial Intelligence Agcy Reston VA USA

ISBN: (纸本)081945348X

The NGA has initiated a program to evaluate high resolution commercial sensors developed by Space Imaging, DigitalGlobe, Orbimage and others. Recent evaluations have involved QuickBird panchromatic products including the Basic 1B, Ortho-Ready Standard 2A, orthorectified, and Basic Stereo Pair products. This paper presents the results of an additional geo-positional accuracy evaluation of multispectral QuickBird Ortho-Ready Standard 2A images. These products were compared to a globally distributed set of Ground Control Points (GCPs), and the calculated geo-positional accuracy was compared to the published QuickBird specifications and to the panchromatic Ortho-Ready Standard 2A product. The results of the panchromatic and multispectral bands are compared, and the hand-to-hand registration of the multispectral arrays is evaluated.

关键词： band-to-band registration Ortho-Ready accuracy multispectral QuickBird

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