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

共 408 条记录，以下是331-340 订阅

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Spectral/spatial annealing of hyperspectral imagery initialized by a supervised classification method

Spectral/spatial annealing of hyperspectral imagery initiali...

引用

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

作者： Rand, RS USA Engineer Res & Dev Ctr Alexandria VA 22315 USA

ISBN: (纸本)0819444758

A simulated annealing method of partitioning hyperspectral imagery, initialized by a supervised classification method, is investigated to provide spatially smooth class labeling for terrain mapping applications. The method is used to obtain an estimate of the mode a Gibbs distribution defined over a symmetric spatial neighborhood system that is based on an energy function characterizing spectral disparities in Euclidean distance and spectral angle. Experiments are conducted on a 210-band HYDICE scene that contains a diverse range of terrain features and that is supported with ground truth. Both visual and quantitative results demonstrate a clear benefit of this method as compared to spectral-only supervised classification or unsupervised annealing that has been initialized randomly.

关键词： Gibbs distribution Bayesian Markov Random Field supervised classification Euclidean distance Spectral Angle clustering hyperspectral imagery

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Restoration, target recognition, and countermeasure removal using the Computed Tomographic Imaging Spectrometer (CTIS)

Restoration, target recognition, and countermeasure removal ...

引用

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

作者： Schau, HC Soulon, RE Hall, GL Garrood, DJ Raytheon Electronic Systems (United States)

Computed Tomographic Imaging Spectrometers are described and shown to be capable of providing real-time flash multispectral imagery. Restoration equations and techniques for optimizing performance are described. Exper... 详细信息

ISBN: (纸本)0819444758

关键词： imaging spectrometer multispectral imagery diffractive optical elements

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Normal compositional models: Generalizations and applications

Normal compositional models: Generalizations and application...

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

作者： Stein, DWJ MIT Lincoln Lab Grp 97 Lexington MA 02420 USA

ISBN: (纸本)0819444758

The normal compositional model (NCM) is a descriptive model that explicitly accounts for sub-pixel mixing and random variation of the spectrum of a material. In this paper the normal compositional model, defined in an earlier work, is extended to include an additive term that may represent path radiance and additive sensor noise. If the covariance matrix of the additive term is non-singular, as may be assumed since it includes the covariance matrix of the additive noise, the covariance matrix of the other classes need not be non-singular. Thus the current model synthesizes the linear unmixing and Gaussian clustering algorithms. Anomaly and matched target detection algorithms based on these three models are compared using ocean hyperspectral imagery, and for these data the NCM approach reduces the false alarm probability by more than an order of magnitude. The linear mixture and normal compositional models separate surface reflections and upwelling light more effectively than the Gaussian clustering algorithm. Furthermore, greater inter-band correlation is estimated using the subpixel covariance estimation methodology than using the pure pixel modeling approach.

关键词： hyperspectral imagery sub-pixel analysis classification linear unmixing detection segmentation

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Further investigations into the use of linear and nonlinear mixing models for hyperspectral image analysis

Further investigations into the use of linear and nonlinear ...

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

作者： Guilfoyle, KJ Althouse, ML Chang, CI Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Remote Sensing & Image Proc Lab Baltimore MD 21250 USA

ISBN: (纸本)0819444758

This paper presents the latest results of a series of experiments designed to identify situations in which linear and nonlinear mixing models are expected to occur. It continues to investigate the possibility that there may be naturally occurring situations in which the typically used linear mixture model may not provide the most accurate spectrum. It has already been shown that for specific situations, in binary mixing cases, the nonlinear mixing model can produce more accurate endmember abundance estimates. These results are extended to include ternary and quaternary mixtures, as well as hyperspectral imagery collected over Cuprite, Nevada. In order to test these hypotheses, laboratory endmemeber and mixture data are collected in various scenarios for analysis. As shown in experiments, ternary and quaternary mixtures are more complicated than binary mixtures and the nonlinear mixing is more likely to occur in ternary and quaternary mixtures than in binary mixtures.

关键词： hyperspectral imagery linear mixing nonlinear mixing radial basis function neural network

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Two-stage band selection algorithm for hyperspectral imagery

Two-stage band selection algorithm for hyperspectral imagery

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

作者： Vélez-Reyes, M Linares, DM Jiménez, LO Univ Puerto Rico Dept Elect & Comp Engn Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819444758

This paper presents a two-stage band optimal band selection algorithm for hyperspectral imagery. The algorithm tries to compute the closest subset of bands to the principal components in the sense of having the smallest canonical correlation. The first stage of the algorithm computes and initial guess for the closest bands using matrix-factorization-based band subset selection. The second stage refines the subset of bands using a steepest ascent algorithm. Experimental results using AVIRIS imagery from the Cuprite Mining District are presented.

关键词： hyperspectral imagery dimensionality reduction band selection principal component analysis

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Automated coregistration of MTI spectral bands

Automated coregistration of MTI spectral bands

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

作者： Theiler, J Galbraith, A Pope, P Ramsey, K Szymanski, J Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)0819444758

In the focal plane of a pushbroom imager, a linear array of pixels is scanned across the scene, building up the image one row at a time. For the multispectral Thermal Imager (MTI), each of fifteen different spectral bands has its own linear array. These arrays are pushed across the scene together, but since each band's array is at a different position on the focal plane, a separate image is produced for each band. The standard MTI data products (LEVEL1B-R-COREG and LEVEL1B_R_GEO) resample these separate images to a common grid and produce coregistered multispectral image cubes. The coregistration software employs a direct "dead reckoning" approach. Every pixel in the calibrated image is mapped to an absolute position on the surface of the earth, and these are resampled to produce an undistorted. coregistered image of the scene. To do this requires extensive information regarding the satellite position and pointing as a function of time, the precise configuration of the focal plane, and the distortion due to the optics. These must be combined with knowledge about the position and altitude of the target on the rotating ellipsoidal earth. We will discuss the direct approach to MTI coregistration, as well as more recent attempts to "tweak" the precision of the band-to-band registration using correlations in the imagery itself.

关键词： MTI multispectral imagery coregistration

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Efficient anomaly detection and discrimination for hyperspectral imagery

Efficient anomaly detection and discrimination for hyperspec...

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

作者： Ren, H Du, Q Jensen, J USA Edgewood Chem & Biol Ctr Aberdeen Proving Ground MD 21010 USA

ISBN: (纸本)0819444758

With the improvement of remote sensing sensor techniques, hyperspectral imagery is widely used today. Hundreds of frequency channels are used to collect radiance from the ground, which results in hundreds of co-registered images. How to process this huge amount of data is a great challenge, especially when no information of the image scene is available. Under this circumstance, anomaly detection becomes more difficult. Several methods are devoted to this problem, such as the well-known RX algorithm which takes advantage of the second-order statistics. In this paper we propose an effective algorithm for anomaly detection and discrimination based on high-order statistics. They include the normalized third central moment referred to as skewness and the normalized fourth central moment referred to as kurtosis, which measure the asymmetry and the flatness of a distribution respectively. The Gaussian distribution is completely determined by the first two statistics and has zero skewness and kurtosis, so these two indices tell us the deviation of a distribution from the Gaussian and are suitable to anomaly detection. The proposed algorithm can be generalized to use any high-order moment. The experimental results with AVIRIS data demonstrate that it can provide comparable detection results with low computational complexity.

关键词： anomaly detection high-order statistics hyperspectral imagery skewness kurtosis

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Performance analysis for CEM and OSP

Performance analysis for CEM and OSP

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

作者： Du, Q Ren, H Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX 78363 USA

ISBN: (纸本)0819444758

We investigate two well-known techniques Constrained Energy Minimization (CEM) and Orthogonal Subspace Projection (OSP) and find out that they can be theoretically treated as equivalent methods when the noise is independent and identically distributed (i.i.d) and its variance is small enough compared to signals, i.e., SNR is large enough. In order to make the condition true, two approaches are applied to modify the OSP technique. One is to estimate the noise covariance matrix and whiten the noise to be i.i.d and unit variance. The other is to estimate sufficient undesired signatures such that the noise approaches i.i.d when the data is projected onto the orthogonal subspace of undesired signatures. The results using simulated and real data demonstrate that our conclusion on the relationship between the OSP and the CEM is correct. It will be instructive to algorithm selection in practical application.

关键词： Orthogonal Subspace Projection (OSP) Constrained Energy Minimization (CEM) detection classification hyperspectral imagery

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Utilizing spatial features in classifying high-resolution imagery data

Utilizing spatial features in classifying high-resolution im...

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

作者： Kiang, RK NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA

ISBN: (纸本)0819444758

The spatial resolution of spaceborne instrument has increased substantially in the three decades since Landsat-1 was launched. Higher spatial resolution has made some applications possible. But it has also brought about new challenges in ground cover classification. At a resolution around 1 meter, vegetation often displays distinct textures. Hence texture may make differentiation among some cover types possible. Ikonos panchromatic and multispectral data are used to examine how spatial features improve classification accuracy. In this study, textural features are extracted from co-occurrence matrices, contextual features are derived from neighborhood properties, and maximum likelihood method is used for classifications. It is shown that for the test data both types of spatial features, and especially the contextual measures, can significantly improve the classification accuracies. Discrete wavelet transform is used to extract textural features for two types of vegetation. Transformed divergence, a measure of separability, is shown much enhanced when textural features are included.

关键词： spatial textural contextual Ikonos wavelet transform

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Automated material map generation using hyperspectral data: a case study using AVIRIS imagery

Automated material map generation using hyperspectral data: ...

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

作者： Lucey, P Winter, M Winter, E Steutel, D Univ Hawaii Manoa HIGP Honolulu HI 96822 USA

ISBN: (纸本)0819444758

The University of Hawaii's Efficient Materials Mapping program aims to automatically and rapidly produce material maps from hyperspectral scenes. The program combines an end-member determination algorithm and a material identification algorithm to produce context maps in real time without user intervention. The material identification algorithm is a combination of a spectral database and analytic code;each spectrum in the library augmented with computer readable diagnostic instructions. At present, the material library consists of over three hundred different spectra, generally geological materials from the USGS digital spectral library, however selected spectra from other libraries have been incorporated. Our method has been applied to an AVIRIS scene taken over Kaneohe bay, Hawaii. This scene contains large expanses of ocean, developed and undeveloped land, thus providing a good test bed for the program. The results of applying this methodology were verified by ground truth where possible by team equipped with a hand held spectrometer. Algorithm derived archetypical end-member locations were well matched well by the material identification database, however the end-member determination itself operated sub-optimally on this scene. These results will guide progress with respect to the continued development of this program.

关键词： AVIRIS N-FINDR hyperspectral mapping

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