{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:35:23Z","timestamp":1762324523889,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T00:00:00Z","timestamp":1564531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laboratory of Green Platemaking and Standardization for Flexography Printing","award":["ZBKT201710"],"award-info":[{"award-number":["ZBKT201710"]}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai","doi-asserted-by":"publisher","award":["19ZR1435900"],"award-info":[{"award-number":["19ZR1435900"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Bidding Project of the Shanghai Academy of Publishing and Media in 2018","award":["SAYB1803"],"award-info":[{"award-number":["SAYB1803"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The connection structure in the convolutional layers of most deep learning-based algorithms used for the classification of hyperspectral images (HSIs) has typically been in the forward direction. In this study, an end-to-end alternately updated spectral\u2013spatial convolutional network (AUSSC) with a recurrent feedback structure is used to learn refined spectral and spatial features for HSI classification. The proposed AUSSC includes alternating updated blocks in which each layer serves as both an input and an output for the other layers. The AUSSC can refine spectral and spatial features many times under fixed parameters. A center loss function is introduced as an auxiliary objective function to improve the discrimination of features acquired by the model. Additionally, the AUSSC utilizes smaller convolutional kernels than other convolutional neural network (CNN)-based methods to reduce the number of parameters and alleviate overfitting. The proposed method was implemented on four HSI data sets, as follows: Indian Pines, Kennedy Space Center, Salinas Scene, and Houston. Experimental results demonstrated that the proposed AUSSC outperformed the HSI classification accuracy obtained by state-of-the-art deep learning-based methods with a small number of training samples.<\/jats:p>","DOI":"10.3390\/rs11151794","type":"journal-article","created":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T11:37:07Z","timestamp":1564573027000},"page":"1794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Alternately Updated Spectral\u2013Spatial Convolution Network for the Classification of Hyperspectral Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8549-4710","authenticated-orcid":false,"given":"Wenju","family":"Wang","sequence":"first","affiliation":[{"name":"College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3231-8817","authenticated-orcid":false,"given":"Shuguang","family":"Dou","sequence":"additional","affiliation":[{"name":"College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, Shanghai Baosight Software Co., Ltd., Shanghai 200940, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/MSP.2013.2279507","article-title":"Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection","volume":"31","author":"Willett","year":"2014","journal-title":"IEEE Signal Process. 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