{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:38Z","timestamp":1761176258370,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Cloud semantic segmentation, which assigns semantic labels to each pixel in multispectral images, plays a critical role in weather analysis and climate studies. Despite recent advancements in deep learning and the emergence of the Mamba architecture, existing methods for cloud segmentation continue to face significant challenges. In particular, current approaches often fall short in effectively modeling the complex relationships among spectral channels, which can lead to ambiguous representations and result in misclassification, especially of spectrally similar cloud types. Additionally, while Mamba excels at long-range modeling, it often overlooks local 2D structural dependencies, resulting in inaccuracies for clouds with complex spatial distributions. To address these challenges, we propose a novel cloud semantic classification model based on Spatial-Spectral Mamba. We design a spectral Mamba block (SpeMamba) to capture intricate intraspectral relationships to improve discrimination between confused cloud types, and also design a spatial Mamba block to model local-global dependencies through local-global scanning, preserving fine-grained spatial structures while maintaining global features. The proposed method is evaluated on the Himawari-8 image dataset, and the experimental results demonstrate the effectiveness of the proposed method, achieving the new state-of-the-art performance. Codes are available at https:\/\/github.com\/Zjut-MultimediaPlus\/LS-Mamba.<\/jats:p>","DOI":"10.3233\/faia251279","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:45Z","timestamp":1761127005000},"source":"Crossref","is-referenced-by-count":0,"title":["LS-Mamba: Spatial-Spectral Mamba for Multispectral Cloud Image Semantic Segmentation"],"prefix":"10.3233","author":[{"given":"Qiong","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science, Zhejiang University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiying","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251279","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:45Z","timestamp":1761127005000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251279","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}