An overview of ICA (independent component analysis) applications in remotely sensed data

C. H. Chen; Zhenhai Wang

doi:10.1117/12.618934

18 October 2005 An overview of ICA (independent component analysis) applications in remote sensed data

C. H. Chen, Zhenhai Wang

Proceedings Volume 5982, Image and Signal Processing for Remote Sensing XI; 59820J (2005) https://doi.org/10.1117/12.618934
Event: SPIE Remote Sensing, 2005, Bruges, Belgium

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.

Citation Download Citation

C. H. Chen and Zhenhai Wang "An overview of ICA (independent component analysis) applications in remote sensed data", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820J (18 October 2005); https://doi.org/10.1117/12.618934

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