Spectral-Spatial Attentions and Deep Supervision for Change Detection in Remote Sensing Images

The task of remote sensing image change detection involves identifying differences between images captured in the same geographical area but at different times. When dealing with dual-time-series images, lighting and seasonal variations often make recognition challenging. To address the challenges, based on Unet++, we innovatively introduce the Spectral-Spatial Attention Module (SSAM) to better focus on fine-grained details. SSAM uses different frequency components to allocate differential weights to channels, allowing the network to pay more attention to the features relevant to the current task. Moreover, to better capture the change details, a multi-level deep supervision strategy is introduced to enhance the discriminative ability and robustness of early features. Our proposed method is named as SSUNet and has been validated on the CDD and LEVIRE-CD datasets, demonstrating significant advantages in detail recognition.