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Here, we propose a deep learning fusion network that effectively utilizes NDVI, called the Dense-Spectral-Location-NDVI network (DSLN). In DSLN, we first extract spatial location information from NDVI data at the same time as remote sensing image data to enhance the boundary information. Then, the spectral features are put into the encoding-decoding structure to abstract the depth features and restore the spatial information. The NDVI fusion module is used to fuse the NDVI information and depth features to improve the separability of land cover information. Experiments on the GF-1 dataset show that the mean OA (mOA) and the mean value of the Kappa coefficient (mKappa) of the DSLN network model reach 0.8069 and 0.7161, respectively, which have good applicability to temporal and spatial distribution. The comparison of the forest area released by Xuancheng Forestry Bureau and the forest area in Xuancheng produced by the DSLN model shows that the former is consistent with the latter. In conclusion, the DSLN network model is effectively applied in practice and can provide more accurate land cover data for regional ESV analysis.<\/jats:p>","DOI":"10.3390\/rs14215455","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T10:47:57Z","timestamp":1667126877000},"page":"5455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Land Cover Classification Method for High-Resolution Remote Sensing Images Based on NDVI Deep Learning Fusion Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Jingzheng","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Liyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Engineering Technology Research Institute, CCCC Second Highway Consultants Co., Ltd., Wuhan 430056, China"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1983-5978","authenticated-orcid":false,"given":"Penghai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-7953","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"}]},{"given":"Chengrong","family":"Pan","sequence":"additional","affiliation":[{"name":"Anhui Eco-Environment Monitoring Center, Hefei 230601, China"}]},{"given":"Yanlan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"School of Artificial Intelligence, Anhui University, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/S1066-7938(00)80033-0","article-title":"Nature\u2019s Services: Societal Dependence on Natural Ecosystems Edited by Gretchen C. 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