Cloud detection is one of the primary challenges in preprocessing high-resolution remote sensing imagery, the accuracy of which is severely constrained by the multi-scale and complex morphological characteristics of clouds. Many approaches have been proposed to detect cloud. However, these methods still face significant challenges, particularly in handling the complexities of multi-scale cloud clusters and reliably distinguishing clouds from snow, ice and complex cloud shadows. To overcome these challenges, this paper proposes a novel cloud detection network based on the state space model (SSM), termed the Hierarchical Dilated Adaptive Mamba Network (HDAMNet). This network utilizes an encoder–decoder architecture, significantly expanding the receptive field and improving the capture of fine-grained cloud boundaries by introducing the Hierarchical Dilated Cross Scan (HDCS) mechanism in the encoder module. The multi-resolution adaptive feature extraction (MRAFE) integrates multi-scale semantic information to reduce channel confusion and emphasize essential features effectively. The Layer-wise Adaptive Attention (LAA) mechanism adaptively recalibrates features at skip connections, balancing fine-grained boundaries with global semantic information. On three public cloud detection datasets, HDAMNet achieves state-of-the-art performance across key evaluation metrics. Particularly noteworthy is its superior performance in identifying small-scale cloud clusters, delineating complex cloud–shadow boundaries, and mitigating interference from snow and ice.
Wang et al. (Thu,) studied this question.