Robust approach to independent component analysis for SAR image analysis

Abstract

This study proposes a method that improves the robustness of independent component analysis (ICA) by adding outlier rejection rule for solving synthetic aperture radar (SAR) image analysis problems. Since the noise in SAR images is multiplicative, the applicability of ICA is seriously reduced. The proposed robust approach includes three procedures. After a pre-processing stage of principal component analysis, the authors remove outliers by applying outlier rejection rule for multivariate data. Then the ICA method is applied on the clean data set. Its applications in SAR are discussed. The results show the potential usage of this robust approach in SAR image processing problems.