{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:27:42Z","timestamp":1770294462953,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T00:00:00Z","timestamp":1582243200000},"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":["61972056"],"award-info":[{"award-number":["61972056"]}],"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":["61772454"],"award-info":[{"award-number":["61772454"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hunan Province of China","award":["2019JJ50666"],"award-info":[{"award-number":["2019JJ50666"]}]},{"name":"the &quot;Double First-class&quot; International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology","award":["2019IC34"],"award-info":[{"award-number":["2019IC34"]}]},{"name":"the Postgraduate Training Innovation Base Construction Project of Hunan Province","award":["2019-248-51"],"award-info":[{"award-number":["2019-248-51"]}]},{"name":"the Postgraduate Scientific Research Innovation Fund of Hunan Province","award":["CX20190696"],"award-info":[{"award-number":["CX20190696"]}]},{"name":"the Postgraduate Scientific Research Innovation Fund of Hunan Province","award":["CX20190697"],"award-info":[{"award-number":["CX20190697"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Many remote sensing scene classification algorithms improve their classification accuracy by additional modules, which increases the parameters and computing overhead of the model at the inference stage. In this paper, we explore how to improve the classification accuracy of the model without adding modules at the inference stage. First, we propose a network training strategy of training with multi-size images. Then, we introduce more supervision information by triplet loss and design a branch for the triplet loss. In addition, dropout is introduced between the feature extractor and the classifier to avoid over-fitting. These modules only work at the training stage and will not bring about the increase in model parameters at the inference stage. We use Resnet18 as the baseline and add the three modules to the baseline. We perform experiments on three datasets: AID, NWPU-RESISC45, and OPTIMAL. Experimental results show that our model combined with the three modules is more competitive than many existing classification algorithms. In addition, ablation experiments on OPTIMAL show that dropout, triplet loss, and training with multi-size images improve the overall accuracy of the model on the test set by 0.53%, 0.38%, and 0.7%, respectively. The combination of the three modules improves the overall accuracy of the model by 1.61%. It can be seen that the three modules can improve the classification accuracy of the model without increasing model parameters at the inference stage, and training with multi-size images brings a greater gain in accuracy than the other two modules, but the combination of the three modules will be better.<\/jats:p>","DOI":"10.3390\/s20041188","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T10:49:16Z","timestamp":1582282156000},"page":"1188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4278-0805","authenticated-orcid":false,"given":"Jianming","family":"Zhang","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China"}]},{"given":"Chaoquan","family":"Lu","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5473-8738","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China"},{"name":"School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China"}]},{"given":"Xiao-Guang","family":"Yue","sequence":"additional","affiliation":[{"name":"Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand"},{"name":"Department of Computer Science and Engineering, School of Sciences, European University Cyprus, Nicosia 1516, Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3249-495X","authenticated-orcid":false,"given":"Se-Jung","family":"Lim","sequence":"additional","affiliation":[{"name":"Liberal Arts &amp; Convergence Studies, Honam University, Gwangju 62399, Korea"}]},{"given":"Zafer","family":"Al-Makhadmeh","sequence":"additional","affiliation":[{"name":"Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3439-6413","authenticated-orcid":false,"given":"Amr","family":"Tolba","sequence":"additional","affiliation":[{"name":"Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia"},{"name":"Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin-El-kom 32511, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. 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