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Independent Component Analysis by Using Joint Cumulants and its Application to Remote Sensing Images

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Abstract

In this paper, a joint cumulant independent component analysis (JC-ICA) algorithm is presented. It utilizes the higher order joint cumulants to extract independent components and can be implemented efficiently by a neural network. Its application in SAR (synthetic aperture radar) image analysis is presented and a comparison is also made with two other ICA methods. The results show the usage in image analysis and separation. Because the algorithm is based on statistics of order higher than the second, it is suitable also for applications to data with non-Gaussian distributions in blind signal processing.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of Massachusetts, Dartmouth, 285 Old Westport Road, North Dartmouth, MA, 02747, USA

    Xiaohui Zhang & C.H. Chen

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  1. Xiaohui Zhang
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  2. C.H. Chen
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Zhang, X., Chen, C. Independent Component Analysis by Using Joint Cumulants and its Application to Remote Sensing Images. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 37, 293–303 (2004). https://doi.org/10.1023/B:VLSI.0000027492.47536.d1

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  • DOI: https://doi.org/10.1023/B:VLSI.0000027492.47536.d1

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