MFCSD: A Cloud Detection Network Based on New Gaofen Satellite Optical Remote Sensing Dataset

With advances in aerospace technology, high-resolution optical imagery from satellites, such as China’s Gaofen series, is vital for climate monitoring and agriculture. However, cloud cover significantly impedes the utility of optical remote sensing imagery, making effective cloud detection essential. Currently, Gaofen optical data cloud detection faces two difficulties: one is that the variable shapes and scales of clouds and their shadows pose difficulties for segmentation tasks, and the other is the scarcity of sufficient and well-annotated datasets. This work addresses these challenges through two main contributions. First, we propose the MaskFormer-based cloud and shadow detection (MFCSD) model, an extended MaskFormer architecture that innovatively integrates an atrous spatial pyramid pooling (ASPP) module for multiscale feature extraction and a channel attention mechanism to enhance feature representation. More distinctively, based on the geometric properties of clouds and its shadow, we introduce a novel Shadow Shift Loss that explicitly quantifies and corrects the positional shift between clouds and their shadows, thereby significantly improving the classification accuracy of cloud shadows in remote sensing imagery. Second, we construct a manually annotated cloud detection dataset from Gaofen imagery, covering diverse geographic and climatic conditions across China to serve as a comprehensive benchmark. Extensive experiments demonstrate that our model achieves excellent performance, outperforming previous leading methods, including SwinTransformer by 0.06 in mIoU and 0.05 in fwIoU, while maintaining lower computational complexity. By integrating these innovations, this work provides an effective solution and valuable data resource for advancing cloud detection in remote sensing.