Cloud detection network based on context feature enhancement for remote sensing images

Cloud detection aims to identify cloud regions in satellite remote sensing images. This task remains particularly challenging due to the diverse morphological characteristics of clouds and their spectral similarity to bright ground surfaces, such as snow or sand. Most existing methods still suffer from unsatisfactory performance, primarily due to their inability to effectively model global information, which restricts the model’s understanding of the differences between cloud regions and the background. To address these issues, we propose a novel framework called the Contextual Feature Enhancement Network (CFEnet), which enhances contextual representations by incorporating global and multi-scale information. Specifically, we first introduce the global information modeling module (GIMM), which captures long-range dependencies and global context at multiple scales, enabling the model to comprehensively perceive image features. Subsequently, we design a multi-scale feature pyramid (MSFP) to gradually integrate high-resolution low-level features with low-resolution high-level features. Finally, a feature fusion module (FFM) is employed to fuse feature maps from different modules. Our method’s effectiveness was evaluated on three public datasets: GF-1 WFV, LandSat8, and SPARCS. Experimental results demonstrated that CFEnet significantly outperforms several state-of-the-art methods, achieving an average improvement of 1.77% in detection precision.