A Statistical-Texture Feature Learning Network for PolSAR Image Classification

Both traditional and deep-learning-based methods have limitations in extracting statistical features from polarimetric synthetic aperture radar (PolSAR) images that contain regions with different levels of heterogeneity. To address this issue, we present a statistical-texture feature learning network (STLNet) for PolSAR image classification. Our approach includes several strategies. First, we propose a novel Nth-order statistical feature learning (N-SL) module as the statistical modeling interface to be combined with the network. In addition, we propose a multilevel high-order statistical feature learning (MSL) module based on the N-SL module to represent the statistical characteristics of PolSAR images. Second, we propose a texture feature learning (TL) module to explore the spatial relationships among pixels and supplement the learned statistical features. Experimental results on the experimental synthetic aperture radar (E-SAR) and airborne synthetic aperture radar (AIRSAR) datasets demonstrate that the proposed MSL and TL modules can effectively improve classification performance. Furthermore, STLNet outperforms other networks of comparable size.