A Novel ResUNet Architecture for Thin Cloud and Boundary Detection in Landsat 8 Remote Sensing Imagery

To address the challenges of thin cloud detection and imprecise cloud boundary segmentation in Landsat 8 remote sensing imagery, this paper proposes a systematic approach that comprehensively enhances cloud detection accuracy from data preprocessing to network architecture optimisation. First, through empirical analysis, an optimised band input combination was determined (removing the panchromatic Band 8 and thermal infrared Band 11), effectively suppressing urban background noise. Subsequently, an enhanced ResUNet model was designed, innovatively integrating an Atrous Spatial Pyramid Pooling (ASPP) module with an attention gate (AG) mechanism. The ASPP module enhances detection capabilities for thin clouds and diffuse cloud masses by aggregating multi-scale global contextual information. The attention-gated mechanism finely tunes feature fusion during the decoding phase, suppressing interference from highly reflective surface features to achieve precise cloud boundary segmentation. Experiments conducted on the Landsat 8 dataset featuring typical urban scenes demonstrate that the proposed method significantly outperforms mainstream models across both conventional and boundary-specific metrics, achieving an overall accuracy (OA) of 0.9717, a mean intersection over union (mIoU) of 0.8102, and, notably, a mean bounding box intersection over union (mB-IoU) of 0.4154 and a mean bounding box F1 score of 0.5356, representing improvements of 16.3% and 12.5%, respectively, over existing methods. This research provides an efficient and robust technical framework for cloud detection tasks in complex urban environments, laying the foundation for high-precision processing of remote sensing imagery and subsequent quantitative analysis.