Unsupervised hyperspectral image analysis using independent component analysis (ICA)

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In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the designed ... continued below

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Chiang, S. S. & Ginsberg, I. W. June 30, 2000.

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In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the designed learning algorithm is able to converge to non-orthogonal independent components. This is particularly useful in hyperspectral image analysis since many materials extracted from a hyperspectral image may have similar spectral signatures and may not be orthogonal. The AVIRIS experiments have demonstrated that the proposed ICA provides an effective unsupervised technique for hyperspectral image classification.

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OSTI as DE00758100

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  • Conference title not supplied, University of Maryland, College Park, MD (US), No date supplied

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  • Report No.: DOE/NV/11718--433
  • Grant Number: AC08-96NV11718
  • Office of Scientific & Technical Information Report Number: 758100
  • Archival Resource Key: ark:/67531/metadc702773

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  • June 30, 2000

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  • Sept. 12, 2015, 6:31 a.m.

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  • Feb. 11, 2016, 9:09 p.m.

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Chiang, S. S. & Ginsberg, I. W. Unsupervised hyperspectral image analysis using independent component analysis (ICA), article, June 30, 2000; Nevada. (digital.library.unt.edu/ark:/67531/metadc702773/: accessed April 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.