Multiview Subspace Tensor Self-Representation for SAR Image Semi-Supervised Classification

Synthetic aperture radar (SAR) image classification has proven its significant importance in automatic remote sensing. However, current methods demand a large volume of training data to ensure satisfactory generalization capability. Given the difficulty in obtaining sufficient labeled samples, semi-supervised learning for SAR image classification becomes extremely important. However, existing studies are hindered by sample relationship modeling and single sample descriptions, leading to limited performance. To address this issue, we propose an innovative approach: multiview subspace tensor self-representation learning with label propagation for SAR image classification. Initially, our method extracts global sample relationship matrices of SAR samples using subspace self-representation learning with affine and nonnegative constraints. To enhance the single sample feature description, we construct multiview tensor learning by stacking the subspace representation matrices of different SAR view features. Finally, the SAR image multiview subspace self-representation matrix is treated as the probability indication matrix for classification. The experiments on MSTAR using extreme 10% labeled samples have shown that the proposed method yields performance boosts of 22.3% and 12.2% to the state-of-the-art method under two rigorous evaluation settings, respectively. The remarkably superior experimental results effectively validate the effectiveness of our proposals.