Compound PCA-ICA neural network model for enhancement and feature extraction of multi-frequency polarimetric SAR imagery
Salim Chitroub, Amrane Houacine, Boualem Sansal · 2002
Through its demixing operation, the potential use of independent components analysis (ICA) for multi-frequency polarimetric SAR imagery enhancement and feature extraction is demonstrated. A compound PCA-ICA neural network model is proposed, which consists of two levels of processing. The first one is the simultaneous diagonalization of the signal and signal-dependent noise covariance matrices using PCA transforms. The goal is to provide the PC images that are decorrelated and in which the SNR is improved. The second one consists of separating the noise from these images by providing new IC images in which the speckle is reduced. These images approach the PC ones and may be different only in their order and contrast. As a quantitative criterion, the contrast ratio is used, which value is smaller when the speckle is reduced. The model has been applied to the SIR-C data. The extracted features are quite effective for scene interpretation.