The investigation into cloud detection algorithms holds significant potential for applications in disaster pre-diction,meteorological research,and beyond.The focus of this research endeavor lies in the development of a cloud de-tection algorithm tailored for MODIS imagery,leveraging the power of deep learning's semantic segmentation tech-niques to enhance the accuracy of cloud detection from MODIS data.This study introduces a novel deep learning mod-el,which integrates the strengths of U-Net,block self-attention mechanisms,and multi-scale network modules,to a-chieve a more precise differentiation between cloud and non-cloud regions in remote sensing images.Building upon the robust foundation of the U-Net architecture,our model incorporates attention modules and multi-scale network ele-ments.These enhancements are specifically designed to bolster the model's capability in identifying subtle features of cumulus humilis and fractocumulus clouds,addressing the limitations of traditional cloud detection algorithms in detec-ting thinner cloud layers.The attention mechanism employed in this work harmoniously combines block self-attention and multi-scale channel attention.The former enhances the model's sensitivity to global contextual information,thereby mitigating the challenge of poor detection in thin cloud layers.The latter,by extracting channel-wise relevant features,complements the detection of smaller cloud formations that might otherwise be overlooked.In the experimental phase,we meticulously detail the dataset utilized,including near-infrared spectral bands among other carefully selected data channels.The evaluation results showcase the model's remarkable performance,with precision and recall rates of 88.58％and 94.80％respectively for cloud detection.These findings conclusively demonstrate the effectiveness of our designed deep learning model in accurately detecting clouds from MODIS imagery,underscoring its promising ap-plications in advancing the field of remote sensing and related meteorological endeavors.
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