{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:07:56Z","timestamp":1774498076799,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T00:00:00Z","timestamp":1625788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vietnam National Foundation for Science and Technology Development (NAFOSTED)","award":["105.99-2020.09"],"award-info":[{"award-number":["105.99-2020.09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient boosting algorithms. In this regard, this study investigates three robust classifiers, namely XGBoost, LightGBM, and Catboost, in combination with a CNN for a land cover study in Hanoi, Vietnam. The experiments were implemented using SPOT7 imagery through (1) image segmentation and extraction of features, including spectral information and spatial metrics, (2) normalization of attribute values and generation of graphs, and (3) using graphs as the input dataset to the investigated models for classifying six land cover classes, namely House, Bare land, Vegetation, Water, Impervious Surface, and Shadow. The results show that CNN-based XGBoost (Overall accuracy = 0.8905), LightGBM (0.8956), and CatBoost (0.8956) outperform the other methods used for comparison. It can be seen that the combination of object-based image analysis and CNN-based gradient boosting algorithms significantly improves classification accuracies and can be considered as alternative methods for land cover analysis.<\/jats:p>","DOI":"10.3390\/rs13142709","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T10:50:38Z","timestamp":1625827838000},"page":"2709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Gradient Boosting Machine and Object-Based CNN for Land Cover Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5059-9731","authenticated-orcid":false,"given":"Quang-Thanh","family":"Bui","sequence":"first","affiliation":[{"name":"Center for Applied Research in Remote Sensing and GIS (CARGIS), Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Hanoi 11416, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8676-7832","authenticated-orcid":false,"given":"Tien-Yin","family":"Chou","sequence":"additional","affiliation":[{"name":"Geographic Information Systems Research Center, Feng Chia University, Taichung 40724, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1869-7726","authenticated-orcid":false,"given":"Thanh-Van","family":"Hoang","sequence":"additional","affiliation":[{"name":"Geographic Information Systems Research Center, Feng Chia University, Taichung 40724, Taiwan"}]},{"given":"Yao-Min","family":"Fang","sequence":"additional","affiliation":[{"name":"Geographic Information Systems Research Center, Feng Chia University, Taichung 40724, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3391-9657","authenticated-orcid":false,"given":"Ching-Yun","family":"Mu","sequence":"additional","affiliation":[{"name":"Geographic Information Systems Research Center, Feng Chia University, Taichung 40724, Taiwan"}]},{"given":"Pi-Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Geographic Information Systems Research Center, Feng Chia University, Taichung 40724, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4196-5717","authenticated-orcid":false,"given":"Vu-Dong","family":"Pham","sequence":"additional","affiliation":[{"name":"Center for Applied Research in Remote Sensing and GIS (CARGIS), Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Hanoi 11416, Vietnam"}]},{"given":"Quoc-Huy","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Center for Applied Research in Remote Sensing and GIS (CARGIS), Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Hanoi 11416, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2926-526X","authenticated-orcid":false,"given":"Do Thi Ngoc","family":"Anh","sequence":"additional","affiliation":[{"name":"Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Hanoi 11416, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0006-0727","authenticated-orcid":false,"given":"Van-Manh","family":"Pham","sequence":"additional","affiliation":[{"name":"Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Hanoi 11416, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8322-3055","authenticated-orcid":false,"given":"Michael E.","family":"Meadows","sequence":"additional","affiliation":[{"name":"Department of Environmental & Geographical Science, University of Capetown, Rondebosh 7701, South Africa"},{"name":"School of Geographic Sciences, East China Normal University, Shanghai 200241, China"},{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. 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