{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T10:41:53Z","timestamp":1773744113550,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xihua University","award":["RZ2000002862"],"award-info":[{"award-number":["RZ2000002862"]}]},{"name":"Xihua University","award":["LGG22F020036"],"award-info":[{"award-number":["LGG22F020036"]}]},{"name":"Basic Public Welfare Research in Zhejiang Province of China","award":["RZ2000002862"],"award-info":[{"award-number":["RZ2000002862"]}]},{"name":"Basic Public Welfare Research in Zhejiang Province of China","award":["LGG22F020036"],"award-info":[{"award-number":["LGG22F020036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images (HSIs) are frequently contaminated by different noises (Gaussian noise, stripe noise, deadline noise, impulse noise) in the acquisition process as a result of the observation environment and imaging system limitations, which makes image information lost and difficult to recover. In this paper, we adopt a 3D-based SSCA block neural network of U-Net architecture for remote sensing HSI denoising, named SSCANet (Spatial and Spectral-Channel Attention Network), which is mainly constructed by a so-called SSCA block. By fully considering the characteristics of spatial-domain and spectral-domain of remote sensing HSIs, the SSCA block consists of a spatial attention (SA) block and a spectral-channel attention (SCA) block, in which the SA block is to extract spatial information and enhance spatial representation ability, as well as the SCA block to explore the band-wise relationship within HSIs for preserving spectral information. Compared to earlier 2D convolution, 3D convolution has a powerful spectrum preservation ability, allowing for improved extraction of HSIs characteristics. Experimental results demonstrate that our method holds better-restored results than other compared approaches, both visually and quantitatively.<\/jats:p>","DOI":"10.3390\/rs14143338","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T03:50:36Z","timestamp":1657597836000},"page":"3338","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Spatial and Spectral-Channel Attention Network for Denoising on Hyperspectral Remote Sensing Image"],"prefix":"10.3390","volume":"14","author":[{"given":"Hong-Xia","family":"Dou","sequence":"first","affiliation":[{"name":"School of Science, Xihua University, Chengdu 610039, China"}]},{"given":"Xiao-Miao","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, China"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, China"},{"name":"Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhoushan 316022, China"}]},{"given":"Hao-Zhen","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, China"}]},{"given":"Liang-Jian","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/LGRS.2010.2048696","article-title":"Improvement of Classification for Hyperspectral Images Based on Tensor Modeling","volume":"7","author":"Bourennane","year":"2010","journal-title":"IEEE Geosci. 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