SHARE 2012: subpixel detection and unmixing experiments

Kerekes, John P.; Ludgate, Kyle; Giannandrea, AnneMarie; Raqueño, Nina G.; Goldberg, Daniel S.

doi:10.1117/12.2016274

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…1 shows ROC curves for detection of painted panels (green and yellow) in imagery collected as part of the SHARE 2012 campaign. 30,31 There are only a few targets, and they are spatially clumped, so they do not give a robust statistical characterization of algorithm performance. They do show, however, that these algorithms can detect real targets in real backgrounds.…”

Section: Methodsmentioning

confidence: 99%

Anisotropic background models for spectral target detection

Theiler

2023

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX

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Algorithms are derived for detecting targets in cluttered backgrounds, where the background is modeled as a product of univariate distributions independently fit to each of the principal component projections. Thus, fatter-than-Gaussian tails are fit to the data, with a different fatness parameter for each principal component. Comparisons are made to elliptically-contoured distributions (which, unlike these product distributions, are isotropic in the whitened space), including the multivariate t and the Gaussian. Numerical experiments are performed on hyperspectral data from the SHARE 2012 exercise, with target detection performance evaluated on both actual and simulated targets. Both direct and residual data are considered, with the residual data obtained from local background subtraction -these residual data are found to exhibit not only lower variance, but qualitatively different tail statistics. More direct target-agnostic measures are also employed to asses how well these models fit the different kinds of background clutter.

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Section: Methodsmentioning

confidence: 99%

Anisotropic background models for spectral target detection

Theiler

2023

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX

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“…Other novel target designs are featured in spectral unmixing datasets, using ground [18] and remote sensing [8], [19], [20] imagers. The design of the SHARE 2010 and 2012 targets developed by RIT consist of a checkerboard pattern of 2 materials.…”

Section: Review Of Other Target Designsmentioning

confidence: 99%

Design and Demonstration of a Lattice-Based Target for Hyperspectral Subpixel Target Detection Experiments

Cañas,

Kerekes

2024

IEEE Trans. Geosci. Remote Sensing

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A lattice-based target design is presented for expanding research capabilities in subpixel target detection. The targets generate large numbers of subpixel samples with a priori knowledge of the exact subpixel fractions. This contrasts with traditional targets, where subpixel fractions are either unknown or estimated with significant uncertainty, with limited samples available in historical datasets. The subpixel targets diminish these drawbacks, and generate constant subpixel samples invariant to effects of the system (e.g. image distortions, scan pattern) which would typically induce uncertainty. Simulations were performed to assess the accuracy of the proposed method of achieving samples with constant fractions. To validate and demonstrate the functionality of the design, four targets were fabricated with constant subpixel fractions (0.2, 0.4, 0.6, 0.8) and were deployed into a hyperspectral data collection. Spectral unmixing validated the retrieval of samples with constant fractions, and a general target detection scenario was demonstrated using 300 − 400 samples of each constant fraction. The impacts of a limited number of target samples (e.g. n = 5, 10) on ROC curves were empirically assessed, with significant reduction of variability observed when n > 100, illustrating the advantages when large sample sizes are available. Design limitations are discussed, along with applications (e.g. algorithm comparison) for the community.

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“…Types of these fabrics are blue felt, blue cotton, yellow felt, yellow cotton, pink felt and gold felt as explained in Ref. 7 and shown in the aerial view in Figure 1. Six of these targets are pure fabrics and their sizes are (12'×12').…”

Section: Introductionmentioning

confidence: 99%

“…The image is extracted from RIT WASP imagery collected at 1551Z with approximately 6' resolution Ref. 7 For testing purposes, first, the original ground truth spectra (OG) and the a manually chosen ground truth spectra (MCG) were compared. The OG had been obtained from a spectrometer measurement, and was provided with the dataset.…”

Section: Introductionmentioning

confidence: 99%

Effect of endmember clustering on proportion estimation: results on the SHARE 2012 dataset

Güneş

Yüksel

2015

SPIE Proceedings

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Estimating the number of endmembers and their spectrum is a challenging task. For one, endmember detection algorithms may over or underestimate the number of endmembers in a given scene. Further, even if the number of endmembers are known beforehand, result of the endmember detection algorithms may not be accurate. They may find multiple endmembers representing the same class, while completely missing some of the endmembers representing the other classes. This hinders the performance of unmixing, resulting in incorrect endmember proportion estimates. In this study, SHARE-2012 AVON data pertaining to the unmixing experiment was considered. It was cropped to include only the eight pieces of cloth and a portion of the surrounding asphalt and grass. This data was used to evaluate the performance of five endmember detection algorithms, namely the PPI, VCA, N-FINDR, ICE and SPICE; none of which found the endmember spectra correctly. All of these algorithms generated multiple endmembers corresponding to the same class or they completely missed some of the endmembers. Hence, the peak-aware N-FINDR algorithm was devised to group the endmembers of the same class so as not to over or under-estimate the true endmembers. The comparisons with or without this refinement for the N-FINDR algorithm are demonstrated.

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SHARE 2012: subpixel detection and unmixing experiments

Cited by 18 publications

References 9 publications

Anisotropic background models for spectral target detection

Anisotropic background models for spectral target detection

Design and Demonstration of a Lattice-Based Target for Hyperspectral Subpixel Target Detection Experiments

Effect of endmember clustering on proportion estimation: results on the SHARE 2012 dataset

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