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Proceedings Paper

An overview of ICA (independent component analysis) applications in remotely sensed data
Author(s): C. H. Chen; Zhenhai Wang
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Paper Abstract

ICA has been a well studied subject in recent years. Its implementation may employ neural networks or adaptive learning techniques. In contrast to PCA, the objective of ICA is to extract components with high-order statistical independence. The concept and process of deriving the independent components have had motivated the development of many mathematical algorithms. In fact it is not necessary to achieve perfect statistical independence in this process. ICA is particularly, perhaps uniquely also, useful in blind source separation problem which is to determine from the received signals the original signals from different physical sources which are considered as independent. ICA has significant impact on many applications such as in remote sensing, medical testing, face recognition, direction of arrival estimatin and other areas The purpose of this paper is to examine some of these applications including SAR images, sonar signals, and exploration seismic data.

Paper Details

Date Published: 18 October 2005
PDF: 15 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820J (18 October 2005); doi: 10.1117/12.618934
Show Author Affiliations
C. H. Chen, Univ. of Massachusetts (United States)
Zhenhai Wang, Univ. of Massachusetts (United States)

Published in SPIE Proceedings Vol. 5982:
Image and Signal Processing for Remote Sensing XI
Lorenzo Bruzzone, Editor(s)

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