Cloud Detection Method Based on Spatial–Spectral Features and Encoder–Decoder Feature Fusion

Cloud obscuration in remote sensing images affects Earth observation tasks by causing blurred and incomplete surface observation information. Regarding this, cloud detection is crucial in the processing of remote sensing images. However, existing cloud detection methods present some challenges, such as missed detection of thin cloud areas and false detection caused by confusing clouds with highlighted areas such as snow and ice. To address these problems, in this article, we proposed a cloud detection network that incorporates spectral feature enhancement and spatial–spectral feature fusion. Based on the difference in reflectivity of clouds and ground objects in the atmosphere, we proposed a short-wave infrared cloud index (SWIR-Index) and designed a feature-guided module to incorporate the spectral feature into the network and guide the training of the network to enhance the network’s ability to learn differential features of snow, ice, and clouds. To fully utilize the spectral band information and spatial features of remote sensing images, we developed a spatial–spectral feature fusion module (SSFFM) that extracts spatial features at different scales and performs inter-spectral information fusion of spectral bands. Furthermore, we proposed a encoder–decoder feature fusion module that automatically calculates pixel weights by using a weight extraction block. The ablation study proves that our method can improve the feature extraction ability, reduce the leakage and misdetection, and improve the detection accuracy. Experimental results on Sentinel-2A images demonstrate the superior performance of the method, reaching 98.65(%) overall accuracy (OA) on the WHUS2-CD dataset, 97.50(%) on the S2-CMC dataset, and 92.36(%) on the CloudSEN12 dataset, which outperforms other algorithms.