Ship Detection Based on Feature Erasure and Channel-Wise Global Pooling Attention in SAR Images

Ship detection in synthetic aperture radar (SAR) images is an essential research direction in the field of remote sensing image interpretation. However, the performance of SAR image ship detection is inevitably susceptible to the influence of complex background and surrounding environment. To alleviate these issues, we propose a ship detection method based on feature erasure and attention mechanism. The Attention-based Feature Erasure Layer (AFELayer) is utilized to capture background information around the ship as a sub-significant feature to assist network localization. In addition, a Channel-wise Global Pooling Attention module (CGPA) is employed to correlate the significance of channels with each other on a global scale. Compared with existing algorithms, the experimental results show that the detection accuracy of proposed method reaches 75.5% on SSDD dataset, which shows superior performance in SAR image ship detection.