Learning Scattering Similarity and Texture-Based Attention With Convolutional Neural Networks for PolSAR Image Classification

The varying polarimetric orientation angles (POAs) result in scattering diversity, leading to ambiguity in the interpretation of polarimetric synthetic aperture radar (PolSAR) images. Exploring the scattering characteristics in the polarimetric rotation domain (PRD) and the complementary features can help overcome the ambiguity. To address this, we propose a novel PolSAR image classification algorithm called learning scattering similarity and texture-based attention with convolutional neural networks (LSTCNNs). Three strategies are included in the proposed method. First, a pixel-level scattering similarity learning (SSL) module is proposed to analyze the scattering components of radar targets by learning the mapping from PolSAR data in the PRD to typical scattering models, with rotation angles as learnable parameters to utilize scattering diversity and avoid ambiguity. Second, a neighborhood-level texture-based attention (TA) module is proposed to learn the spatially enhanced features of PolSAR images, with the attention module design guided by the physical meaning of texture and consideration of channel and position importance. Finally, the proposed LSTCNN, which includes the SSL module, the TA module, and the classification module, combines pixel-level scattering features in the PRD and neighborhood-level texture features to increase the discriminability of features. The experimental results on three PolSAR images acquired by airborne SAR (AIRSAR) and experimental SAR (E-SAR) demonstrate the robustness and excellence of LSTCNN.