A new PCA-based method for data compression and enhancement of multi-frequency polarimetric SAR imagery

Article type: Research Article

Authors: Chitroub, Salim | Houacine, Amrane | Sansal, Boualem

Affiliations: Signal Processing Laboratory, Electrical Engineering Faculty, University of Sciences and Technology of Houari Boumediene, P.O. Box 32, El-Alia, Bab-Ezzouar, 16111, Algiers, Algeria. Fax: +213 21 24 71 87; E-mail: S_chitroub@hotmail.com

Abstract: A new PCA-based method for an optimal representation of multi-frequency polarimetric SAR images is proposed. The method performs the simultaneous diagonalization of the signal and multiplicative noise covariance matrices via one orthogonal matrix. The covariance matrix of the multiplicative noise becomes an identity matrix, which implies that the variance of the noise in each new image is unity, and is uncorrelated between transformed images. The covariance matrix of the SAR images is transformed to a diagonal matrix whose diagonal elements are ordered in decreasing value, which means that the new images are uncorrelated and will be ordered by their variances (qualities). The theoretical analysis and the implementation procedure of the method are given. The method has been applied on real SAR images. The compression ability of the method is proved via a reconstitution process of the original SAR images from a small number of new images with a minimal loss of information.

Keywords: principal component analysis, data compression, Synthetic Aperture Radar (SAR) image; remote sensing

DOI: 10.3233/IDA-2002-6206

Journal: Intelligent Data Analysis, vol. 6, no. 2, pp. 187-207, 2002