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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11-20 订阅

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An automatic target recognition system for hyperspectral imagery using ORASIS

An automatic target recognition system for hyperspectral ima...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery Vii

作者： Gillis, D Palmadesso, P Bowles, J USN Res Lab Remote Sensing Div Washington DC 20375 USA

ISBN: (纸本)0819440760

We present an automatic target recognition system (ATR) for hyperspectral imagery. The system has been designed to use the output from ORASIS (the Optical Real-time Adaptive Spectral Identification System), a hyperspectral analysis package designed at the Naval Research Laboratory. The ATR system is capable of performing both target recognition (including subpixel identification) and anomaly detection, in near real-time and with no a priori scene knowledge. In this paper we discuss the algorithms we use in the ATR and include experimental results using the HYDICE Forest Radiance data set.

关键词： hyperspectral analysis ATR ORASIS

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Radiometric assessment of four pan-sharpening algorithms as applied to hyperspectral imagery 26

Radiometric assessment of four pan-sharpening algorithms as ...

引用

conference on algorithms, Technologies, and Applications for multispectral and hyperspectral imagery XXVI

作者： Ducay, Rey Messinger, David W. Rochester Inst Technol Chester F Carlson Ctr Imaging Sci 54 Lomb Mem Dr Rochester NY 14623 USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

Pan-sharpening {fusing the spatial and spectral information between panchromatic (PAN) and multispectral (MSI) or hyperspectral (HSI) imagery of a common scene is a hot topic in remote sensing due to a wide range of applications such as target detection, vegetation monitoring, and subsurface detection (e.g. landmines), among others. However, the focus of panchromatic sharpening is generally placed on visual quality of the resulting image and image-wide summary spectral accuracy metrics. Here we are interested in radiometrically accurate panchromatic sharpening of hyperspectral imagery with particular emphasis on spectral algorithm performance. Four pansharpening algorithms are applied to hyperspectral imagery and evaluated for spectral/radiometric fidelity. Two datasets from SHARE2012 were used: one which features rural scene elements and one which features an urban scene. Target detection was also performed to evaluate algorithm sharpening performance. We find that although visually the performance of the four algorithms were roughly similar, they differ in spectral/radiometric fidelity as well as performance in ACE target detection.

关键词： hyperspectral imagery panchromatic sharpening ACE target detection

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Determining the dimensionality of hyperspectral imagery for unsupervised band selection

Determining the dimensionality of hyperspectral imagery for ...

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

作者： Umaña-Díaz, A Vélez-Reyes, M Univ Puerto Rico Dept Elect & Comp Engn Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)0819449539

This paper addresses the problem of estimating the dimension of a hyperspectral image. Spanning and intrinsic dimension concepts are studied as ways to determine the number of degrees of freedom needed to represent a hyperspectral Image. algorithms for the estimation of spanning and intrinsic dimension are reviewed and applied to hyperspectral images. Estimators are evaluated and compared using simulated and AVIRIS data. The final objective of this work is to develop an algorithm to determine the number of bands to select in a band subset selection algorithm.

关键词： dimensionality reduction band selection data dimensionality

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Shadow-insensitive material detection/classification with atmospherically corrected hyperspectral imagery

Shadow-insensitive material detection/classification with at...

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conference on algorithms for multispectral, hyperspectral, and Ultraspectral imagery Vii

作者： Adler-Golden, SM Levine, RY Matthew, MW Richtsmeier, SC Bernstein, LS Gruninger, J Felde, G Hoke, M Anderson, GP Ratkowski, A Spectral Sciences Inc. (United States) Air Force Research Lab. (United States)

ISBN: (纸本)0819440760

Shadow-insensitive detection or classification of surface materials in atmospherically corrected hyperspectral imagery can be achieved by expressing the reflectance spectrum as a linear combination of spectra that correspond to illumination by the direct sun and by the sky. Some specific algorithm and applications are illustrated using hyperspectral Digital imagery Collection Experiment (HYDICE) data.

关键词： detection classification hyperspectral image shadow

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Advanced band sharpening study

Advanced band sharpening study

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conference on algorithms for multispectral and hyperspectral imagery iiI

作者： Vrabel, J Booz*Allen & Hamilton Inc. (United States)

ISBN: (纸本)0819424862

Band sharpening involving multi-sensor and multi-resolution imagery is an excellent means of utilizing the complementary nature of various data types. The synergistic use of these imagery types can provide additional information that is not independently available in each source. In the case of band sharpening, a higher spatial resolution panchromatic (pan) image is fused with a lower spatial resolution multispectral image. This fusion creates a product with the spectral characteristics of the multispectral image and a spatial resolution approaching that of the panchromatic image. The goal of this project was to evaluate MSI band sharpening in four research areas. The first area explored the ''Effective Ground Sample Distance (GSD)'' and relative utility of multispectral imagery sharpened with panchromatic imagery. The second area examined interactions between data compression and the band sharpening process. The third area determined the effectiveness of band sharpening using a pair of high resolution sharpening bands covering different regions of the electromagnetic spectrum. The fourth area determined the effect of band sharpening on the accuracy of automated exploitation algorithms such as terrain categorization (TERCAT) and normalized difference vegetation index (NDVI).

关键词： multispectral fusion multi-sensor sharpening hyperspectral imagery band sharpening exploitation compression imagery analysis

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Nonparametric classification of subpixel materials in multispectral imagery 2

Nonparametric classification of subpixel materials in multis...

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conference on algorithms for multispectral and hyperspectral imagery ii

作者： Boudreau, E Huguenin, R Karaska, M APPL ANAL INC BILLERICAMA 01821

ISBN: (纸本)0819421391

An effective process for the automatic classification of subpixel materials in multispectral imagery has been developed. The applied analysis spectral analytical process (AASAP) isolates the contribution of specific materials of interest (MOI) within mixed pixels. AASAP consists of a suite of algorithms that perform environmental correction, signature derivation, and subpixel classification. Atmospheric and sun angle correction factors are extracted directly from imagery, allowing signatures produced from a given image to be applied to other images. AASAP signature derivation extracts a component of the pixel spectra that is most common to the training set to produce a signature spectrum and nonparametric feature space. The subpixel classifier applies a background estimation technique to a given pixel under test to produce a residual. A detection occurs when the residual falls within the signature feature space. AASAP was employed to detect stands of Loblolly Pine in a landsat TM scene that contained a variety of species of southern yellow pine. An independent field evaluation indicated that 85% of the detections contained over 20% Loblolly, and that 91% of the known Loblolly stands were detected. For another application, a crop signature derived from a scene in Texas detected occurrences of the same crop in scenes from Kansas and Mexico. AASAP has also been used to locate subpixel occurrences of soil contamination, wetlands species, and lines of communications.

关键词： multispectral subpixel mixed pixel classification signature atmospheric correction spectral analysis MSI remote sensing

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Spatial and temporal variability of hyperspectral signatures of terrain - art. no. 69660L

Spatial and temporal variability of hyperspectral signatures...

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

作者： Jones, K. F. Perovich, D. K. Koenig, G. G. USA Cold Reg Res & Engn Lab Hanover NH 03755 USA

ISBN: (纸本)9780819471574

Electromagnetic signatures of terrain exhibit significant spatial heterogeneity on a range of scales as well as considerable temporal variability. A statistical characterization of the spatial heterogeneity and spatial scaling algorithms of terrain electromagnetic signatures are required to extrapolate measurements to larger scales. Basic terrain elements including bare soil, grass, deciduous, and coniferous trees were studied in a quasi-laboratory setting using instrumented test sites in Hanover, NH and Yuma, AZ. Observations were made using a visible and near infrared spectroradiometer (350 - 2500 nm) and hyperspectral camera (400 - 1100 nm). Results are reported illustrating: i) several difference scenes;ii) a terrain scene time series sampled over an annual cycle;and iii) the detection -of artifacts in scenes. A principal component analysis indicated that the first three principal components typically explained between 90 and 99% of the. variance of the 30 to 40-channel hyperspectral images. Higher order principal com ponents of hyperspectral images are useful for detecting artifacts in scenes.

关键词： hyperspectral imagery principal component analysis reflectance detection

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Bobcat 2013: a hyperspectral data collection supporting the development and evaluation of spatial-spectral algorithms

Bobcat 2013: a hyperspectral data collection supporting the ...

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

作者： Kaufman, Jason Celenk, Mehmet White, A. K. Stocker, Alan D. Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA Space Comp Corp Los Angeles CA 90025 USA

ISBN: (纸本)9781628410259

The amount of hyperspectral imagery (HSI) data currently available is relatively small compared to other imaging modalities, and what is suitable for developing, testing, and evaluating spatial-spectral algorithms is virtually nonexistent. In this work, a significant amount of coincident airborne hyperspectral and high spatial resolution panchromatic imagery that supports the advancement of spatial-spectral feature extraction algorithms was collected to address this need. The imagery was collected in April 2013 for Ohio University by the Civil Air Patrol, with their Airborne Real-time Cueing hyperspectral Enhanced Reconnaissance (ARCHER) sensor. The target materials, shapes, and movements throughout the collection area were chosen such that evaluation of change detection algorithms, atmospheric compensation techniques, image fusion methods, and material detection and identification algorithms is possible. This paper describes the collection plan, data acquisition, and initial analysis of the collected imagery.

关键词： hyperspectral imagery high spatial resolution imagery spatial-spectral feature extraction image resolution enhancement pansharpening image fusion material detection and identification

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Unsupervised unmixing analysis based on multiscale representation

Unsupervised unmixing analysis based on multiscale represent...

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Annual conference on algorithms and Technologies for multispectral, hyperspectral, and Ultraspectral imagery XViiI

作者： Torres-Madronero, Maria C. Velez-Reyes, Miguel Univ Puerto Rico Lab Appl Remote Sensing & Image Proc Mayaguez PR 00681 USA

ISBN: (纸本)9780819490681

Automated unmixing consists of finding the number of endmembers, their spectral signatures and their abundances from a hyperspectral image. Most unmixing techniques are pixel-to-pixel procedures that do not take advantage of spatial information provided by hyperspectral sensor. This paper explores a new approach for unmixing analysis of hyperspectral imagery based on a multiscale representation for the joint estimation of the number of endmember and their spectral signatures. Experimental results using an AVIRIS image is presented.

关键词： hyperspectral imagery unsupervised unmixing multiscale representation

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Ellipsoids for Anomaly Detection in Remote Sensing imagery 21

Ellipsoids for Anomaly Detection in Remote Sensing Imagery

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

作者： Grosklos, Guen Theiler, James Los Alamos Natl Lab POB 1663 Los Alamos NM 87545 USA

ISBN: (纸本)9781628415889

For many target and anomaly detection algorithms, a key step is the estimation of a centroid (relatively easy) and a covariance matrix (somewhat harder) that characterize the background clutter. For a background that can be modeled as a multivariate Gaussian, the centroid and covariance lead to an explicit probability density function that can be used in likelihood ratio tests for optimal detection statistics. But ellipsoidal contours can characterize a much larger class of multivariate density function, and the ellipsoids that characterize the outer periphery of the distribution are most appropriate for detection in the low false alarm rate regime. Traditionally the sample mean and sample covariance are used to estimate ellipsoid location and shape, but these quantities are confounded both by large lever-arm outliers and non-Gaussian distributions within the ellipsoid of interest. This paper compares a variety of centroid and covariance estimation schemes with the aim of characterizing the periphery of the background distribution. In particular, we will consider a robust variant of the Khachiyan algorithm for minimum-volume enclosing ellipsoid. The performance of these different approaches is evaluated on multispectral and hyperspectral remote sensing imagery using coverage plots of ellipsoid volume versus false alarm rate.

关键词： Anomaly Detection multispectral imagery hyperspectral imagery Background Estimation Low False Alarm Rate

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