Bi-dimensional Attention-Based SAR Change Detection with SEBlock and MHSA

Synthetic aperture radar (SAR) image change detection (CD) involves identifying changes between images captured at different times over the same geographical region. SAR provides unique advantages for remote sensing applications, such as disaster monitoring, due to its capability to penetrate clouds and operate under all-weather conditions. However, the presence of speckle noise remains a significant challenge, hindering accurate change detection. To address this issue, this paper introduces a novel SEBlock and Multi-Head Self-Attention-Enhanced Bi-dimensional Aggregation Module (SEBAM) for robust feature extraction and noise suppression. SEBAM combines the Squeeze-and-Excitation (SE) block and Multi-Head Self-Attention (MHSA) mechanism to adaptively emphasise critical channel-wise and spatial dependencies, thereby enhancing the network’s ability to differentiate subtle changes in complex SAR images. Extensive experiments on three SAR datasets demonstrate that the proposed method significantly outperforms state-of-the-art techniques, achieving higher accuracy and robustness in change detection tasks.