Cross-dimensional feature attention aggregation network for cloud and snow recognition of high satellite images

Cloud and snow in remote sensing images typically block the underlying surface information and interfere with the extraction of available information, so detecting cloud and snow becomes a critical problem in remotely sensed image processing. The current methods for detecting clouds and snow are susceptible to interference from complex background, making it difficult to recover cloud edge details and causing missing and false detection phenomena. To address these issues, a cross-dimensional feature attention aggregation network is suggested to realize the segmentation of clouds and snow. To address the problem of interference induced by the similar spectral characteristics of clouds and snow, the context attention aggregation module is added to conflate feature maps of various dimensions and screen the information. Multi-scale strip convolution module (MSSCM) and its improved version MSSCMs are used to extract edge characteristics at different scales and improve the harsh segmentation border. Also, adding deep feature semantic information extraction module to deep features to guide the classification of the model to avoid the interference of complex background. Finally, a ’los beatles’ module is used to replace the traditional linear combination in the decoding stage, and the feature information of different granularity is fused and extracted to enhance the model’s detection efficiency. In this paper, experiments are carried out on the public datasets: CSWV, HRC_\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\_$$\end{document}WHU and L8_\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\_$$\end{document}SPARCS. The MIOU scores on the three datasets are 89.507%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 91.674%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} and 80.722%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, respectively. Comparative experiment findings demonstrate that the network presented in this article can attain the highest detection accuracy and good detection efficiency with low parameters.