FSINet: A Robust Feature Separation and Integration Network for Multiscale SAR Object Detection

Deep learning has rapidly progressed in Synthetic Aperture Radar (SAR) image object detection. However, complex noise, intricate backgrounds, and multiscale objects impact the performance of SAR image object detection. To solve these problems, in this paper, we propose a Feature Separation and Integration Network (FSINet) to mitigate noise interference and improve the accuracy of multiscale objects for SAR images. FSINet is constructed in three steps. First, we construct the Feature Separation Module (FSM) as a preprocessing unit that decouples input images into distinct components, enabling collaboration with the downstream backbone to separate noise and enhance critical information. Subsequently, we propose an Adaptive Feature Integration Module (AFIM) designed to fuse cross-layer multiscale features, generating precise feature representations. Additionally, a new regression loss called Shape Normalized Wasserstein Distance (SNWD) is used to enhance the detection performance for multiscale objects. To validate the superiority of the proposed FSINet, a series of comparative experiments are conducted on three public SAR image object detection datasets, that is the High-Resolution SAR Image Dataset (HRSID), SAR-AIRCraft dataset, and MSAR dataset. The experimental results demonstrate the superiority of our method, revealing that our method achieves state-of-the-art performance in SAR image object detection.