Polsar Image Feature Extraction Based on Co-Regularization

Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (Pol-SAR) image classification. This paper proposes a feature extraction method by combing the two distances for Pol-SAR image classification. Firstly, two weight graphs are constructed based on the two distances to represent the local information of the data. Specifically, the neighbouring samples are sought in a local region to reduce the computation burden and utilize the spatial information. Then the dimensionality reduction model is constructed based on the two weight graphs and co-regularization. The co-regularization aims to minimize the dissimilarity of low-dimensional features corresponding to two graphs. Finally, the obtained low-dimensional features are used for PolSAR image classification. Experimental results on real PolSAR datasets demonstrate the effectiveness and superiority of the proposed method.