Classifying PolSAR Images Based on Multiview Unsupervised Feature Selection via View-Specific and Cross-View Representations

Multiview features can be accessed from multiple feature extractors or frequency bands to describe polarimetric synthetic aperture radar (PolSAR) images. These multiview features usually own high dimensionality and contain some redundant and irrelevant information leading to the classification performance deterioration. Additionally, acquiring label information of PolSAR data is challenging. To resolve these difficulties, we put forward a multiview unsupervised feature selection method via view-specific and cross-view representation (VSCVR) to classify PolSAR images, resulting in retaining datas global and local structure and exploring higher-order relationship between different views. Specifically, we learn view-specific self-representation constrained by tensor nuclear norm with the aim of the global structure preservation and exploration of high-order consensus and complementarity between different views. We adopt tensor nuclear norm by tensor singular value decomposition (t-TNN) as a low-rank constraint for multiview self-representation tensor. In addition, we incorporate crossview common representation with manifold regularization to sustain the local structure information and ensure consistency across multiple views. To accurately describe the local structure, we construct a similarity graph for manifold regularization via the Wishart distance reflecting the statistical property of PolSAR data. Furthermore, a group sparsity constraint for view-specific projection matrices is utilized for feature selection. We propose the corresponding algorithm to handle VSCVR model and conduct related analysis. Numerical and visual results on some real PolSAR datasets certify that VSCVR can acquire a few important features and improve classification performance effectively.