{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:35:37Z","timestamp":1776274537798,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T00:00:00Z","timestamp":1685923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Forestry Innovation Foundation of Guangdong Province","award":["2021KJCX001"],"award-info":[{"award-number":["2021KJCX001"]}]},{"name":"Forestry Innovation Foundation of Guangdong Province","award":["MEE(GYZX230202)"],"award-info":[{"award-number":["MEE(GYZX230202)"]}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","award":["2021KJCX001"],"award-info":[{"award-number":["2021KJCX001"]}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","award":["MEE(GYZX230202)"],"award-info":[{"award-number":["MEE(GYZX230202)"]}]},{"name":"Nanjing Institute of Environmental Sciences","award":["2021KJCX001"],"award-info":[{"award-number":["2021KJCX001"]}]},{"name":"Nanjing Institute of Environmental Sciences","award":["MEE(GYZX230202)"],"award-info":[{"award-number":["MEE(GYZX230202)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We studied the use of self-attention mechanism networks (SAN) and convolutional neural networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) remote sensing imagery in Dongtai Forest Farm, Jiangsu Province, China. We trained and validated representative CNN models, such as ResNet and ConvNeXt, as well as the SAN model, which incorporates Transformer models such as Swin Transformer and Vision Transformer (ViT). Our goal was to compare and evaluate the performance and accuracy of these networks when used in parallel. Due to various factors, such as noise, motion blur, and atmospheric scattering, the quality of low-altitude aerial images may be compromised, resulting in indistinct tree crown edges and deficient texture. To address these issues, we adopted Real-ESRGAN technology for image super-resolution reconstruction. Our results showed that the image dataset after reconstruction improved classification accuracy for both the CNN and Transformer models. The final classification accuracies, validated by ResNet, ConvNeXt, ViT, and Swin Transformer, were 96.71%, 98.70%, 97.88%, and 98.59%, respectively, with corresponding improvements of 1.39%, 1.53%, 0.47%, and 1.18%. Our study highlights the potential benefits of Transformer and CNN for forest tree species classification and the importance of addressing the image quality degradation issues in low-altitude aerial images.<\/jats:p>","DOI":"10.3390\/rs15112942","type":"journal-article","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T01:38:26Z","timestamp":1686015506000},"page":"2942","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Yingkang","family":"Huang","sequence":"first","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3568-0176","authenticated-orcid":false,"given":"Xiaorong","family":"Wen","sequence":"additional","affiliation":[{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Yuanyun","family":"Gao","sequence":"additional","affiliation":[{"name":"Ministry of Ecology and Environment, Nanjing Institute of Science, Nanjing 210037, China"}]},{"given":"Yanli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, Nacogdoches, TX 75962, USA"}]},{"given":"Guozhong","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, D., Pang, Y., Liu, L., and Li, Z. (2020). Individual tree classification using airborne LiDAR and hyperspectral data in a natural mixed forest of northeast China. Forests, 11.","DOI":"10.3390\/f11030303"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Marrs, J., and Ni-Meister, W. (2019). Machine learning techniques for tree species classification using co-registered LiDAR and hyperspectral data. Remote Sens., 11.","DOI":"10.3390\/rs11070819"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ballanti, L., Blesius, L., Hines, E., and Kruse, B. (2016). 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