{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T07:11:25Z","timestamp":1772781085343,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62077038"],"award-info":[{"award-number":["62077038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672405"],"award-info":[{"award-number":["61672405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176196"],"award-info":[{"award-number":["62176196"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271374"],"award-info":[{"award-number":["62271374"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, convolutional neural networks (CNNs) have been widely used in the field of hyperspectral image (HSI) classification and achieved good classification results due to their excellent spectral\u2013spatial feature extraction ability. However, most methods use the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To solve the above problems, this article proposes a hyperspectral image classification method based on a Feature Embedding Network with Multiscale Attention (MAFEN). Firstly, a Multiscale Attention Module (MAM) is designed, which is able to not only learn multiscale information about features at different depths, but also extract effective information from them. Secondly, the deep semantic features can be embedded into the low-level features through the top-down channel, so that the features at all levels have rich semantic information. Finally, an Adaptive Spatial Feature Fusion (ASFF) strategy is introduced to adaptively fuse features from different levels. The experimental results show that the classification accuracies of MAFEN on four HSI datasets are better than those of the compared methods.<\/jats:p>","DOI":"10.3390\/rs15133338","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T01:02:41Z","timestamp":1688086961000},"page":"3338","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Feature Embedding Network with Multiscale Attention for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-0731","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jian","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jiajie","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4373-3661","authenticated-orcid":false,"given":"Caihong","family":"Mu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Collaborative Innovation Center of Quantum Information of Shaanxi Province, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9873816","DOI":"10.34133\/2021\/9873816","article-title":"Automatically Monitoring Impervio-us Surfaces Using Spectral Generalization and Time Series Landsat Imagery from1985 to 2020 in the Yangtze River Delta","volume":"2021","author":"Zhang","year":"2021","journal-title":"J. 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