{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T06:11:17Z","timestamp":1770531077557,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Major Project of Yunnan Province (Science and Technology Special Project of Southwest United Graduate School-Major Projects of Basic Research and Applied Basic Research): Vegetation change monitoring and ecological restoration models in Jinsha River Basin mining area in Yunnan based on multi-modal remote sensing","award":["202302AO370003"],"award-info":[{"award-number":["202302AO370003"]}]},{"name":"Science and Technology Major Project of Yunnan Province (Science and Technology Special Project of Southwest United Graduate School-Major Projects of Basic Research and Applied Basic Research): Vegetation change monitoring and ecological restoration models in Jinsha River Basin mining area in Yunnan based on multi-modal remote sensing","award":["202301AT070173"],"award-info":[{"award-number":["202301AT070173"]}]},{"name":"Science and Technology Major Project of Yunnan Province (Science and Technology Special Project of Southwest United Graduate School-Major Projects of Basic Research and Applied Basic Research): Vegetation change monitoring and ecological restoration models in Jinsha River Basin mining area in Yunnan based on multi-modal remote sensing","award":["202401AT070103"],"award-info":[{"award-number":["202401AT070103"]}]},{"name":"Basic Research Project of Yunnan Province, Project name: Identification of high altitude Remote geological hazards in mountainous areas of Western Yunnan based on Multi-source Remote Sensing Technology\u2014A case study of the Ratuti River Basin","award":["202302AO370003"],"award-info":[{"award-number":["202302AO370003"]}]},{"name":"Basic Research Project of Yunnan Province, Project name: Identification of high altitude Remote geological hazards in mountainous areas of Western Yunnan based on Multi-source Remote Sensing Technology\u2014A case study of the Ratuti River Basin","award":["202301AT070173"],"award-info":[{"award-number":["202301AT070173"]}]},{"name":"Basic Research Project of Yunnan Province, Project name: Identification of high altitude Remote geological hazards in mountainous areas of Western Yunnan based on Multi-source Remote Sensing Technology\u2014A case study of the Ratuti River Basin","award":["202401AT070103"],"award-info":[{"award-number":["202401AT070103"]}]},{"name":"Remote sensing estimation of aboveground carbon sink of vegetation in the central Yunnan urban agglomeration and its response to climate change and human activities, supported by Yunnan Fundamental Research Projects","award":["202302AO370003"],"award-info":[{"award-number":["202302AO370003"]}]},{"name":"Remote sensing estimation of aboveground carbon sink of vegetation in the central Yunnan urban agglomeration and its response to climate change and human activities, supported by Yunnan Fundamental Research Projects","award":["202301AT070173"],"award-info":[{"award-number":["202301AT070173"]}]},{"name":"Remote sensing estimation of aboveground carbon sink of vegetation in the central Yunnan urban agglomeration and its response to climate change and human activities, supported by Yunnan Fundamental Research Projects","award":["202401AT070103"],"award-info":[{"award-number":["202401AT070103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>To address the challenges that convolutional neural networks (CNNs) face in extracting small objects and handling class imbalance in remote sensing imagery, this paper proposes a novel spatial contextual information and multiscale feature fusion encoding\u2013decoding network, SCIMF-Net. Firstly, SCIMF-Net employs an improved ResNeXt-101 deep backbone network, significantly enhancing the extraction capability of small object features. Next, a novel PMFF module is designed to effectively promote the fusion of features at different scales, deepening the model\u2019s understanding of global and local spatial contextual information. Finally, introducing a weighted joint loss function improves the SCIMF-Net model\u2019s performance in extracting LULC information under class imbalance conditions. Experimental results show that compared to other CNNs such as Res-FCN, U-Net, SE-U-Net, and U-Net++, SCIMF-Net improves PA by 0.68%, 0.54%, 1.61%, and 3.39%, respectively; MPA by 2.96%, 4.51%, 2.37%, and 3.45%, respectively; and MIOU by 3.27%, 4.89%, 4.2%, and 5.68%, respectively. Detailed comparisons of locally visualized LULC information extraction results indicate that SCIMF-Net can accurately extract information from imbalanced classes and small objects.<\/jats:p>","DOI":"10.3390\/ijgi13110386","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T09:57:36Z","timestamp":1730368656000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Research on Land Use and Land Cover Information Extraction Methods for Remote Sensing Images Based on Improved Convolutional Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Xue","family":"Ding","sequence":"first","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan Kunming, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoqian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangyun","family":"Peng","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Shao","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan Kunming, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruifang","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"ref_1","unstructured":"Mayer-Svh\u00f6nberger, V., and CuKier, K. 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