A Lightweight Cloud and Cloud Shadow Detection Transformer With Prior-Knowledge Guidance

In the field of remote sensing, cloud and cloud shadow detection (CCSD) presents a challenging task aimed at identifying inevitable clouds and cloud shadows (CCSs) within remote sensing images. Existing studies have tried to enhance detection performance through the design of intricate large-scale networks. These methods have obtained significant performance gains. However their high memory and computational overhead limit their applicability. Hence, in this letter, we propose a lightweight prior-knowledge guided transformer (LPGT). First, prior information from CCS is captured in prior-knowledge extraction (PKE), guiding the model to focus on the spatial relationships of CCS within the context, thereby further refining long-range dependencies. Next, a prior-guided efficient attention block (PEAB) is introduced as the fundamental feature extraction unit of LPGT, which contains depth-wise convolutions and expanded window multihead self-attention (SA) to enhance the computation capability of the model and reduces the intensive computational burden. Finally, a feature refinement block (FRB) is developed to improve the model’s local feature extraction capabilities for comprehensively understanding the contextual image maps, enabling accurate distinguishing between CCS. Extensive experimental results on the GF-1 wide field-of-view (WFV) dataset indicate that the proposed method achieves more competitive mean intersection over union (mIoU) performance while having fewer parameters and FLOPs.