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Optimal band selection for supervised classification of multispectral data
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Dimesnionality reduction by optimal band selection for pixel classification of hyperspectral imagery
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A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification
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Hyperspectral, and Ultraspectral Imagery XI, Orlando, FL, Mar./Apr. 28-1
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B. Ramakrishna, J. Wang, A. Plaza, and C.-I Chang, "Spectral/spatial hyperspectral image compression in conjunction with virtual dimensionality," presented at the SPIE Symp. Defense and Security Conf. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, vol. 5806, Orlando, FL, Mar./Apr. 28-1, 2005.
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Dimensionality reduction by independent component analysis for hyperspectral image analysis
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Seoul, Korea, Jul. 25-29
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J. Wang and C.-I Chang, "Dimensionality reduction by independent component analysis for hyperspectral image analysis," presented at the IEEE Int. Geoscience Remote Sensing Symp., Seoul, Korea, Jul. 25-29, 2005.
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Target signature-constrained mixed pixel classification for hyperspectral imagery
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Detection of subpixel spectral signatures in hyperspectral image sequences
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Reno, NV
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Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery
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Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach
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Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis
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Jun
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Pasadena, CA
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Utility of virtual dimensionality in hyperspectral data exploitation
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Orlando, FL, Apr. 17-21
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C.-I Chang, "Utility of virtual dimensionality in hyperspectral data exploitation," presented at the SPIE Defense and Security Symp., Algorithms and Techniques for Multspectral, Hyperspectral, and Ultraspectral Imagery XII (OR52), Orlando, FL, Apr. 17-21, 2006.
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SPIE Defense and Security Symp., Algorithms and Techniques for Multspectral, Hyperspectral, and Ultraspectral Imagery XII (OR52)
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Chang, C.-I.1
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