Unsupervised signature extraction and separation in hyperspectral images: a noise-adjusted fast independent component analysis

TeMing Tu

doi:10.1117/1.602461

1 April 2000 Unsupervised signature extraction and separation in hyperspectral images: a noise-adjusted fast independent component analysis

TeMing Tu

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Optical Engineering, Vol. 39, Issue 4, (April 2000). https://doi.org/10.1117/1.602461

Multispectral/hyperspectral imaging spectrometry in earth remote sensing applications mostly focuses on determining the identity and abundance of materials in a geographic area of interest. Without any prior knowledge, however, it is generally very difficult to identify and determine how many endmembers reside in a scene. We cope with this limitation by estimating the number of endmembers using a noise- adjusted version of the transformed Gerschgorin disk approach (NATGD). This estimated result is then applied to a noise-adjusted version of fast independent component analysis (NAFICA). Experimental results indicate that NAFICA offers a new approach for unsupervised signature extraction and separation in hyperspectral images. Â© 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)02004-3]

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TeMing Tu "Unsupervised signature extraction and separation in hyperspectral images: a noise-adjusted fast independent component analysis," Optical Engineering 39(4), (1 April 2000). https://doi.org/10.1117/1.602461

Published: 1 April 2000

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