One of the primary challenges in Synthetic Aperture Radar (SAR) object detection lies in the pervasive influence of coherent noise. As a common practice, most existing methods, whether handcrafted approaches or deep learning-based methods, employ the analysis or enhancement of object spatial-domain characteristics to achieve implicit denoising. In this paper, we propose DenoDet V2, which explores a completely novel and different perspective to deconstruct and modulate the features in the transform domain via a carefully designed attention architecture. Compared to DenoDet V1, DenoDet V2 is a major advancement that exploits the complementary nature of amplitude and phase information through a band-wise mutual modulation mechanism, which enables a reciprocal enhancement between phase and amplitude spectra. Extensive experiments on various SAR datasets demonstrate the state-of-the-art performance of DenoDet V2. Notably, DenoDet V2 achieves a significant 0.8% improvement on SARDet-100K dataset compared to DenoDet V1, while reducing the model complexity by half.

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 cannot 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 (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 by 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.