{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T07:00:47Z","timestamp":1773385247630,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"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":[[2023,9,28]]},"abstract":"<jats:p>Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud cover significantly impedes its application. Although existing deep cloud removal models have achieved notable outcomes, they scarcely consider contextual information. This study introduces a high-performance cloud removal architecture, termed Progressive Multi-scale Attention Autoencoder (PMAA), which concurrently harnesses global and local information to construct robust contextual dependencies using a novel Multi-scale Attention Module (MAM) and a novel Local Interaction Module (LIM). PMAA establishes long-range dependencies of multi-scale features using MAM and modulates the reconstruction of fine-grained details utilizing LIM, enabling simultaneous representation of fine- and coarse-grained features at the same level. With the help of diverse and multi-scale features, PMAA consistently outperforms the previous state-of-the-art model CTGAN on two benchmark datasets. Moreover, PMAA boasts considerable efficiency advantages, with only 0.5% and 14.6% of the parameters and computational complexity of CTGAN, respectively. These comprehensive results underscore PMAA\u2019s potential as a lightweight cloud removal network suitable for deployment on edge devices to accomplish large-scale cloud removal tasks. Our source code and pre-trained models are available at https:\/\/github.com\/XavierJiezou\/PMAA.<\/jats:p>","DOI":"10.3233\/faia230636","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:22:45Z","timestamp":1695979365000},"source":"Crossref","is-referenced-by-count":27,"title":["PMAA: A Progressive Multi-Scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-Temporal Satellite Imagery"],"prefix":"10.3233","author":[{"given":"Xuechao","family":"Zou","sequence":"first","affiliation":[{"name":"Department of Computer Technology and Applications, Qinghai University, Xining, China"}]},{"given":"Kai","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"given":"Junliang","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"given":"Pin","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Computer Technology and Applications, Qinghai University, Xining, China"},{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"given":"Yachao","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Computer Technology and Applications, Qinghai University, Xining, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230636","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:22:46Z","timestamp":1695979366000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230636"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230636","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}