{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T09:37:02Z","timestamp":1771061822195,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ground-object classification using remote-sensing images of high resolution is widely used in land planning, ecological monitoring, and resource protection. Traditional image segmentation technology has poor effect on complex scenes in high-resolution remote-sensing images. In the field of deep learning, some deep neural networks are being applied to high-resolution remote-sensing image segmentation. The DeeplabV3+ network is a deep neural network based on encoder-decoder architecture, which is commonly used to segment images with high precision. However, the segmentation accuracy of high-resolution remote-sensing images is poor, the number of network parameters is large, and the cost of training network is high. Therefore, this paper improves the DeeplabV3+ network. Firstly, MobileNetV2 network was used as the backbone feature-extraction network, and an attention-mechanism module was added after the feature-extraction module and the ASPP module to introduce focal loss balance. Our design has the following advantages: it enhances the ability of network to extract image features; it reduces network training costs; and it achieves better semantic segmentation accuracy. Experiments on high-resolution remote-sensing image datasets show that the mIou of the proposed method on WHDLD datasets is 64.76%, 4.24% higher than traditional DeeplabV3+ network mIou, and the mIou on CCF BDCI datasets is 64.58%. This is 5.35% higher than traditional DeeplabV3+ network mIou and outperforms traditional DeeplabV3+, U-NET, PSP-NET and MACU-net networks.<\/jats:p>","DOI":"10.3390\/s22197477","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"7477","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Research on Ground Object Classification Method of High Resolution Remote-Sensing Images Based on Improved DeeplabV3+"],"prefix":"10.3390","volume":"22","author":[{"given":"Junjie","family":"Fu","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A and F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomei","family":"Yi","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A and F University, Hangzhou 311300, China"},{"name":"Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoying","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A and F University, Hangzhou 311300, China"},{"name":"Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lufeng","family":"Mo","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A and F University, Hangzhou 311300, China"},{"name":"Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8946-3447","authenticated-orcid":false,"given":"Peng","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A and F University, Hangzhou 311300, China"},{"name":"Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kasanda Ernest","family":"Kapula","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A and F University, Hangzhou 311300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shao, Z., Tang, P., Wang, Z., Saleem, N., Yam, S., and Sommai, C. 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