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Optical Engineering

Unsupervised signature extraction and separation in hyperspectral images: a noise-adjusted fast independent component analysis
Author(s): TeMing Tu
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Paper Abstract

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. © 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)02004-3]

Paper Details

Date Published: 1 April 2000
PDF: 10 pages
Opt. Eng. 39(4) doi: 10.1117/1.602461
Published in: Optical Engineering Volume 39, Issue 4
Show Author Affiliations
TeMing Tu, Chung Cheng Institute of Technology (Taiwan)


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