{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T19:49:41Z","timestamp":1768420181473,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,22]],"date-time":"2018-10-22T00:00:00Z","timestamp":1540166400000},"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":["No. 61701166"],"award-info":[{"award-number":["No. 61701166"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["No. 2018M632215"],"award-info":[{"award-number":["No. 2018M632215"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. 2018B16314"],"award-info":[{"award-number":["No. 2018B16314"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Projects in the National Science &amp; Technology Pillar Program during the Twelfth Five-year Plan Period","award":["No. 2015BAB07B01"],"award-info":[{"award-number":["No. 2015BAB07B01"]}]},{"name":"Regional Program of National Natural Science Foundation of China","award":["No. 51669014"],"award-info":[{"award-number":["No. 51669014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible\/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy.<\/jats:p>","DOI":"10.3390\/s18103587","type":"journal-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T08:43:36Z","timestamp":1540284216000},"page":"3587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6643-2039","authenticated-orcid":false,"given":"Chenming","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6888-7993","authenticated-orcid":false,"given":"Simon X.","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-2464","authenticated-orcid":false,"given":"Hongmin","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Qu","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongchang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianbing","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer and Information, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,22]]},"reference":[{"key":"ref_1","first-page":"64","article-title":"Application of remote sensing technology in hydrology and water resources","volume":"2","author":"Qiu","year":"2018","journal-title":"Jiangsu Water Resour."},{"key":"ref_2","first-page":"236","article-title":"Review of hyperspectral remote sensing image classification","volume":"20","author":"Du","year":"2016","journal-title":"J. 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