Supervised Polsar Image Classification By Combiningmultiple Features

For polarimetric synthetic aperture radar (PolSAR) image classification, each pixel can be represented by multiple features from different perspectives, such as polarimetric feature (PF), texture feature (TF) and color feature (CF). Both multi-view canonical correlation analysis (MCCA) and multi-view spectral embedding (MSE) are two unsupervised multi-view subspace learning methods which search for different projection matrices for different features to combine multiple features in a common low-dimensional feature space. However, MCCA emphasizes the correlation of multiple features and MSE learns the complementarity of multiple features. To deeply learn the relation of multiple features, we incorporate MCCA with MSE based on the label information and a symmetric version of revised Wishart (SRW) distance for supervised PolSAR image feature extraction. Experimental results confirm that the proposed method can improve the classification performance.