{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:09:57Z","timestamp":1762956597366,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,28]],"date-time":"2021-03-28T00:00:00Z","timestamp":1616889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1504805"],"award-info":[{"award-number":["2018YFC1504805"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806022","41941019","41874005"],"award-info":[{"award-number":["61806022","41941019","41874005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Geo-Information Engineering","award":["SKLGIE 2018-M-3-4"],"award-info":[{"award-number":["SKLGIE 2018-M-3-4"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["300102269103","300102269103","300102260301","300102260404"],"award-info":[{"award-number":["300102269103","300102269103","300102260301","300102260404"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201404910404"],"award-info":[{"award-number":["201404910404"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, deep learning methods, for example, convolutional neural networks (CNNs), have achieved high performance in hyperspectral image (HSI) classification. The limited training samples of HSI images make it hard to use deep learning methods with many layers and a large number of convolutional kernels as in large scale imagery tasks, and CNN-based methods usually need long training time. In this paper, we present a wide sliding window and subsampling network (WSWS Net) for HSI classification. It is based on layers of transform kernels with sliding windows and subsampling (WSWS). It can be extended in the wide direction to learn both spatial and spectral features more efficiently. The learned features are subsampled to reduce computational loads and to reduce memorization. Thus, layers of WSWS can learn higher level spatial and spectral features efficiently, and the proposed network can be trained easily by only computing linear weights with least squares. The experimental results show that the WSWS Net achieves excellent performance with different hyperspectral remotes sensing datasets compared with other shallow and deep learning methods. The effects of ratio of training samples, the sizes of image patches, and the visualization of features in WSWS layers are presented.<\/jats:p>","DOI":"10.3390\/rs13071290","type":"journal-article","created":{"date-parts":[[2021,3,28]],"date-time":"2021-03-28T23:27:25Z","timestamp":1616974045000},"page":"1290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2258-0993","authenticated-orcid":false,"given":"Jiangbo","family":"Xi","sequence":"first","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resources and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7626-0584","authenticated-orcid":false,"given":"Okan K.","family":"Ersoy","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Jianwu","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Transportation Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6101-2070","authenticated-orcid":false,"given":"Ming","family":"Cong","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resources and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0178-2342","authenticated-orcid":false,"given":"Tianjun","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Information Science, College of Science, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Chaoying","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resources and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-7449","authenticated-orcid":false,"given":"Zhenhong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resources and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral image classification using deep pixel-pair features","volume":"55","author":"Li","year":"2016","journal-title":"IEEE Trans. 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