{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:20:29Z","timestamp":1777486829539,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["18A014"],"award-info":[{"award-number":["18A014"]}]},{"name":"Construction Program for the First-Class Disciplines (Geography) of Hunan Province, China","award":["18A014"],"award-info":[{"award-number":["18A014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fine classification of urban nighttime lighting is a key prerequisite step for small-scale nighttime urban research. In order to fill the gap of high-resolution urban nighttime light image classification and recognition research, this paper is based on a small rotary-wing UAV platform, taking the nighttime static monocular tilted light images of communities near Meixi Lake in Changsha City as research data. Using an object-oriented classification method to fully extract the spectral, textural and geometric features of urban nighttime lights, we build four types of classification models based on random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN) and decision tree (DT), respectively, to finely extract five types of nighttime lights: window light, neon light, road reflective light, building reflective light and background. The main conclusions are as follows: (i) The equal division of the image into three regions according to the visual direction can alleviate the variable scale problem of monocular tilted images, and the multiresolution segmentation results combined with Canny edge detection are more suitable for urban nighttime lighting images; (ii) RF has the highest classification accuracy among the four classification algorithms, with an overall classification accuracy of 95.36% and a kappa coefficient of 0.9381 in the far view region, followed by SVM, KNN and DT as the worst; (iii) Among the fine classification results of urban light types, window light and background have the highest classification accuracy, with both UA and PA above 93% in the RF classification model, while road reflective light has the lowest accuracy; (iv) Among the selected classification features, the spectral features have the highest contribution rates, which are above 59% in all three regions, followed by the textural features and the geometric features with the smallest contribution rates. This paper demonstrates the feasibility of nighttime UAV static monocular tilt image data for fine classification of urban light types based on an object-oriented classification approach, provides data and technical support for small-scale urban nighttime research such as community building identification and nighttime human activity perception.<\/jats:p>","DOI":"10.3390\/s23042180","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T04:47:24Z","timestamp":1676436444000},"page":"2180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8443-8143","authenticated-orcid":false,"given":"Daoquan","family":"Zhang","sequence":"first","affiliation":[{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China"},{"name":"School of Geographic Sciences, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deping","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China"},{"name":"School of Geographic Sciences, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China"},{"name":"School of Geographic Sciences, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiejie","family":"Wu","sequence":"additional","affiliation":[{"name":"Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China"},{"name":"School of Geographic Sciences, Hunan Normal University, Changsha 410081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111942","DOI":"10.1016\/j.rse.2020.111942","article-title":"Monitoring hourly night-time light by an unmanned aerial vehicle and its implications to satellite remote sensing","volume":"247","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"342","DOI":"10.11834\/jrs.20211018","article-title":"Nighttime light remote sensing and urban studies: Data, methods, applications, and prospects","volume":"25","author":"Yu","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications\u2014A review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_4","first-page":"505","article-title":"Research advance and application prospect of unmanned aerial vehicle remote sensing system","volume":"39","author":"Deren","year":"2014","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_5","first-page":"1046","article-title":"UAV remote sensing: Popularization and expand application development trend","volume":"23","author":"Liao","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1111\/j.1468-2427.2012.01109.x","article-title":"Spatial analyses of the urban village development process in Shenzhen, China","volume":"37","author":"Hao","year":"2013","journal-title":"Int. J. Urban Reg. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111443","DOI":"10.1016\/j.rse.2019.111443","article-title":"Remote sensing of night lights: A review and an outlook for the future","volume":"237","author":"Levin","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Timilsina, S., Aryal, J., and Kirkpatrick, J.B. (2020). Mapping urban tree cover changes using object-based convolution neural network (OB-CNN). Remote Sens., 12.","DOI":"10.3390\/rs12183017"},{"key":"ref_10","first-page":"121","article-title":"Unmanned image classification in karst area combining topographic factors and stratification strategy","volume":"2","author":"Yu","year":"2022","journal-title":"Bull. Surv. Mapp."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhang, H., Wang, F., and Lai, R. (2022). Extraction of Broad-Leaved tree crown based on UAV visible images and OBIA-RF model: A case study for Chinese Olive Trees. Remote Sens., 14.","DOI":"10.3390\/rs14102469"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Guo, Q., Zhang, J., Guo, S., Ye, Z., Deng, H., Hou, X., and Zhang, H. (2022). Urban tree classification based on object-oriented approach and random forest algorithm using unmanned aerial vehicle (uav) multispectral imagery. Remote Sens., 14.","DOI":"10.3390\/rs14163885"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. (2018). Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_14","first-page":"225","article-title":"Extraction of urban impervious surface based on the visible images of UAV and OBIA-RF algorithm","volume":"38","author":"Ye","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Eng. (Trans. CSAE)"},{"key":"ref_15","first-page":"180","article-title":"Object-oriented classification of unmanned aerial vehicle image for thermal erosion gully boundary extraction","volume":"31","author":"LIANG","year":"2019","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106905","DOI":"10.1016\/j.compag.2022.106905","article-title":"Vineyard classification using OBIA on UAV-based RGB and multispectral data: A case study in different wine regions","volume":"196","author":"Matese","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","first-page":"505","article-title":"Detection of Tilted Aerial Photography Right-Angled Image Control Points Target based on LSD Algorithm","volume":"23","year":"2021","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_18","first-page":"192","article-title":"Radiometric consistency correction of UAV multispectral images in strong reflective water environment","volume":"38","author":"Shunzhong","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_19","first-page":"692","article-title":"Data processing and landslide information extraction based on UAV remote sensing","volume":"19","author":"Chen","year":"2017","journal-title":"J. Geo.-Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"361","DOI":"10.3233\/JIFS-179092","article-title":"Intelligent image segmentation model for remote sensing applications","volume":"37","author":"Shen","year":"2019","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, X., Qiu, J., Zhang, W., Zhou, Z., and Kang, Y. (2022). Soybean Seedling Root Segmentation Using Improved U-Net Network. Sensors, 22.","DOI":"10.3390\/s22228904"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, Y., and Li, Q. (2022). The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient. Sensors, 22.","DOI":"10.3390\/s22218202"},{"key":"ref_23","unstructured":"Chuyue, P., Xiao, C., and Linyuan, X. (2021). Study on Recognizing the Penguin Population in UAV Image Based on Object Otiented Classification. Geomat. Inf. Sci. Wuhan Univ., 1\u201315."},{"key":"ref_24","first-page":"324","article-title":"A Advanced Multi-Scale Fractal Net Evolution Approach","volume":"29","author":"Junjie","year":"2014","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land cover classification using Google Earth Engine and random forest classifier\u2014The role of image composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, L., Liu, Z., Ren, T., Liu, D., Ma, Z., Tong, L., Zhang, C., Zhou, T., Zhang, X., and Li, S. (2020). Identification of seed maize fields with high spatial resolution and multiple spectral remote sensing using random forest classifier. Remote Sens., 12.","DOI":"10.3390\/rs12030362"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MGRS.2016.2641240","article-title":"Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques","volume":"5","author":"Maulik","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sahour, H., Kemink, K.M., and O\u2019Connell, J. (2022). Integrating SAR and optical remote sensing for conservation-targeted wetlands mapping. Remote Sens., 14.","DOI":"10.3390\/rs14010159"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Taunk, K., De, S., Verma, S., and Swetapadma, A. (2019, January 15\u201317). A brief review of nearest neighbor algorithm for learning and classification. Proceedings of the 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India.","DOI":"10.1109\/ICCS45141.2019.9065747"},{"key":"ref_33","first-page":"1127","article-title":"Method of remote sensing extraction of cultivated land area under complex conditions in southern region","volume":"35","author":"Mu","year":"2020","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_34","unstructured":"Sen, P.C., Hajra, M., and Ghosh, M. (2020). Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhou, R., Yang, C., Li, E., Cai, X., Yang, J., and Xia, Y. (2021). Object-based wetland vegetation classification using multi-feature selection of unoccupied aerial vehicle RGB imagery. Remote Sens., 13.","DOI":"10.3390\/rs13234910"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Islam, N., Rashid, M.M., Wibowo, S., Xu, C.-Y., Morshed, A., Wasimi, S.A., Moore, S., and Rahman, S.M. (2021). Early weed detection using image processing and machine learning techniques in an Australian chilli farm. Agriculture, 11.","DOI":"10.3390\/agriculture11050387"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vieira, C.C., Sarkar, S., Tian, F., Zhou, J., Jarquin, D., Nguyen, H.T., Zhou, J., and Chen, P. (2022). Differentiate soybean response to off-target dicamba damage based on UAV imagery and machine learning. Remote Sens., 14.","DOI":"10.3390\/rs14071618"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2180\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:36:17Z","timestamp":1760121377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/2180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,15]]},"references-count":37,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23042180"],"URL":"https:\/\/doi.org\/10.3390\/s23042180","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,15]]}}}