IMAGE ANALYSIS

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

[+] Author Affiliations
Te-Ming Tu

Chung Cheng Institute of Technology, Department of Electrical Engineering, Taoyuan, Taiwan?33509

Opt. Eng. 39(4), 897-906 (Apr 01, 2000). doi:10.1117/1.602461
History: Received Apr. 2, 1999; Revised Sep. 10, 1999; Accepted Oct. 8, 1999
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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.

© 2000 Society of Photo-Optical Instrumentation Engineers

Citation

Te-Ming Tu
"Unsupervised signature extraction and separation in hyperspectral images: a noise-adjusted fast independent component analysis approach", Opt. Eng. 39(4), 897-906 (Apr 01, 2000). ; http://dx.doi.org/10.1117/1.602461


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