DJSPNet: Deep Joint Statistical–Spatial Pooling Network for High-Resolution SAR Image Classification

Abstract

The previous approaches based on statistical features or spatial features have achieved promising performance on pixel-wise high-resolution (HR) synthetic aperture radar (SAR) image classification, but these methods always can not capture local spatial features and global statistical properties efficiently because of the complex spatial structural patterns and statistical nature in SAR patches. Inspired by this, we propose a deep joint statistical-spatial pooling network, named DJSPNet, for HR SAR image classification, which combines a group second-order statistical feature learning (GSFL) block and an efficient feature-fusion style (EFS) into an end-to-end feature learning block. GSFL block is designed with a group second-order feature learning method in two steps, where the first step divides convolutional channels into several semantic groups; The second step collects second-order feature statistics through calculating pairwise feature interactions within each group. EFS models second-order attentional statistics between statistical characteristics and spatial features by polynomial kernel approximation and guides the discriminative feature activations in SAR patches. More specifically, both GSFL and EFS are stacked and plugged into the encoder stage of conventional U-Net for distinguishable feature learning. Experimental results suggest that the proposed DJSPNet gives better classification performance compared with related deep feature learning networks on a real TerraSAR-X dataset.