{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:43:37Z","timestamp":1769553817385,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Major Project of Inner Mongolia Autonomous Region","award":["2020ZD0021"],"award-info":[{"award-number":["2020ZD0021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Exposed mine gangue hills are prone to environmental problems such as soil erosion, surface water pollution, and dust. Revegetation of gangue hills can effectively combat the problem. Effective ground cover monitoring means can significantly improve the efficiency of vegetation restoration. We used UAV aerial photography to acquire data and used the Real-SR network to reconstruct the data in super-resolution; the Labv3+ network was used to segment the ground cover into green areas, open spaces, roads, and waters, and VDVI and Otsu were used to extract the vegetation from the green areas. The final ground-cover decomposition accuracy of this method can reach 82%. The application of a super-resolution reconstruction network improves the efficiency of UAV aerial photography; the ground interpretation method of deep learning combined with a vegetation index solves both the problem that vegetation index segmentation cannot cope with the complex ground and the problem of low accuracy due to little data for deep-learning image segmentation.<\/jats:p>","DOI":"10.3390\/rs14164043","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4043","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Luotuo Mountain Waste Dump Cover Interpretation Combining Deep Learning and VDVI Based on Data from an Unmanned Aerial Vehicle (UAV)"],"prefix":"10.3390","volume":"14","author":[{"given":"Yilin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Dongxu","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Liming","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Xinying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5412-5202","authenticated-orcid":false,"given":"Pengle","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Ying","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1093\/comjnl\/bxm028","article-title":"Super-resolution reconstruction algorithm to MODIS remote sensing images","volume":"52","author":"Shen","year":"2009","journal-title":"Comput. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.procs.2017.03.089","article-title":"Super-resolution reconstruction of satellite video images based on interpolation method","volume":"107","author":"Qifang","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_3","first-page":"171","article-title":"Remote Sensed Image Super-Resolution Reconstruction Based on a BP Neural Network","volume":"44","author":"Ding","year":"2008","journal-title":"Comput. Eng. Appl."},{"key":"ref_4","first-page":"555","article-title":"A Study on A New Method of Multi-spatial-resolution Remote Sensing Image Fusion Based on GA\u2014BP","volume":"22","author":"Wen","year":"2011","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1109\/TGRS.2018.2885506","article-title":"Achieving super-resolution remote sensing images via the wavelet transform combined with the recursive res-net","volume":"57","author":"Ma","year":"2019","journal-title":"ITGRS IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"420","DOI":"10.3390\/w7020420","article-title":"Determining characteristic vegetation areas by terrestrial laser scanning for floodplain flow modeling","volume":"7","author":"Jalonen","year":"2015","journal-title":"Water"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"130126","DOI":"10.1016\/j.chemosphere.2021.130126","article-title":"An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions","volume":"277","author":"Yaseen","year":"2021","journal-title":"Chemosphere"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Sharma, N., Lama, G.F.C., Hasan, M., Garg, R., Busico, G., and Alharbi, R.S. (2022). Three-Dimensional Hole Size (3DHS) Approach for Water Flow Turbulence Analysis over Emerging Sand Bars: Flume-Scale Experiments. Water, 14.","DOI":"10.3390\/w14121889"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/24705357.2021.1938255","article-title":"Velocity Uncertainty Quantification based on Riparian Vegetation Indices in open channels colonized by Phragmites australis","volume":"7","author":"Lama","year":"2022","journal-title":"J. Ecohydraulics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/jfm.2016.803","article-title":"Deep learning in fluid dynamics","volume":"814","author":"Kutz","year":"2017","journal-title":"JFM J. Fluid Mech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8558","DOI":"10.1029\/2018WR022643","article-title":"A transdisciplinary review of deep learning research and its relevance for water resources scientists","volume":"54","author":"Shen","year":"2018","journal-title":"WRR Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.compag.2014.02.009","article-title":"Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV","volume":"103","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","first-page":"527","article-title":"A topographyadjusted vegetation index (TAVI) and its application in vegetation fraction monitoring","volume":"38","author":"Hong","year":"2010","journal-title":"J. Fuzhou Univ. Nat. Sci. Ed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of color vegetation indices for automated crop imaging applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","first-page":"475","article-title":"Resnet-Based Tree Species Classification Using Uav Images","volume":"4213","author":"Natesan","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","first-page":"251","article-title":"Extraction of rural construction land from high-resolution remote sensing images based on SegNet semantic model","volume":"35","author":"Yang","year":"2019","journal-title":"Chin. J. Agric. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lobo Torres, D., Queiroz Feitosa, R., Nigri Happ, P., Elena Cu\u00e9 La Rosa, L., Marcato Junior, J., Martins, J., Ol\u00e3 Bressan, P., Gon\u00e7alves, W.N., and Liesenberg, V. (2020). Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery. Sensors, 20.","DOI":"10.3390\/s20020563"},{"key":"ref_19","first-page":"161","article-title":"Crop Classification by UAV Remote Sensing Based on Convolutional Neural Network","volume":"50","author":"Wang","year":"2019","journal-title":"J. Agric. Mach."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhou, C., Ye, H., Xu, Z., Hu, J., Shi, X., Hua, S., Yue, J., and Yang, G. (2019). Estimating maize-leaf coverage in field conditions by applying a machine learning algorithm to UAV remote sensing images. Appl. Sci., 9.","DOI":"10.3390\/app9112389"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 6\u20138). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Morales, G., Kemper, G., Sevillano, G., Arteaga, D., Ortega, I., and Telles, J. (2018). Automatic segmentation of Mauritia flexuosa in unmanned aerial vehicle (UAV) imagery using deep learning. Forests, 9.","DOI":"10.3390\/f9120736"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ji, X., Cao, Y., Tai, Y., Wang, C., Li, J., and Huang, F. (2020, January 14\u201319). Real-world super-resolution via kernel estimation and noise injection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00241"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Change Loy, C. (2018, January 10\u201314). Esrgan: Enhanced super-resolution generative adversarial networks. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., and Zhang, L. (2018, January 18\u201323). Learning a single convolutional super-resolution network for multiple degradations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00344"},{"key":"ref_27","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_31","first-page":"90","article-title":"Study on the extraction of exotic species Spartina alterniflora from UAV visible images","volume":"12","author":"Zhou","year":"2017","journal-title":"J. Subtrop. Resour. Environ."},{"key":"ref_32","first-page":"770","article-title":"Extraction of desert vegetation coverage based on visible light band information of unmanned aerial vehicle: A case study of Shapotou region","volume":"54","author":"Gao","year":"2018","journal-title":"J. Lanzhou Univ. Nat. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gonzalez, R.C. (2009). Digital Image Processing, Pearson Education India.","DOI":"10.1117\/1.3115362"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4043\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:12:06Z","timestamp":1760141526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4043"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,19]]},"references-count":36,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14164043"],"URL":"https:\/\/doi.org\/10.3390\/rs14164043","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,19]]}}}