{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:50:30Z","timestamp":1772913030614,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program","award":["2018YFB2100501"],"award-info":[{"award-number":["2018YFB2100501"]}]},{"name":"National Key R&amp;D Program","award":["41890823"],"award-info":[{"award-number":["41890823"]}]},{"name":"National Natural Science Foundation of China Program","award":["2018YFB2100501"],"award-info":[{"award-number":["2018YFB2100501"]}]},{"name":"National Natural Science Foundation of China Program","award":["41890823"],"award-info":[{"award-number":["41890823"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing is the primary way to extract the impervious surface areas (ISAs). However, the obstruction of vegetation is a long-standing challenge that prevents the accurate extraction of urban ISAs. Currently, there are no general and systematic methods to solve the problem. In this paper, we present a morphological feature-oriented algorithm, which can make use of the OSM road network information to remove the obscuring effects when the ISAs are extracted. Very high resolution (VHR) images of Wuhan, China, were used in experiments to verify the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can improve the accuracy and completeness of ISA extraction by our previous deep learning-based algorithm. In the proposed algorithm, the overall accuracy (OA) is 86.64%. The results show that the proposed algorithm is feasible and can extract the vegetation-obscured ISAs effectively and precisely.<\/jats:p>","DOI":"10.3390\/rs14102493","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T03:16:55Z","timestamp":1653362215000},"page":"2493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Morphological Feature-Oriented Algorithm for Extracting Impervious Surface Areas Obscured by Vegetation in Collaboration with OSM Road Networks in Urban Areas"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2189-8190","authenticated-orcid":false,"given":"Taomin","family":"Mao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Yewen","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Shuang","family":"Zhi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7266-8534","authenticated-orcid":false,"given":"Jinshan","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111338","DOI":"10.1016\/j.rse.2019.111338","article-title":"Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation","volume":"232","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Coseo, P., and Larsen, L. (2019). Accurate Characterization of Land Cover in Urban Environments: Determining the Importance of Including Obscured Impervious Surfaces in Urban Heat Island Models. Atmosphere, 10.","DOI":"10.3390\/atmos10060347"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1007\/s11069-014-1463-2","article-title":"Quantifying the impact of impervious surface location on flood peak discharge in urban areas","volume":"76","author":"Du","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shao, Z., Fu, H., Fu, P., and Yin, L. (2016). Mapping Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level. Remote Sens., 8.","DOI":"10.3390\/rs8110945"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jvcir.2018.12.051","article-title":"Automatic extraction of urban impervious surfaces based on deep learning and multi-source remote sensing data","volume":"60","author":"Huang","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Parekh, J.R., Poortinga, A., Bhandari, B., Mayer, T., Saah, D., and Chishtie, F. (2021). Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13163166"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-2046(02)00165-2","article-title":"Analyzing the land cover of an urban environment using high-resolution orthophotos","volume":"63","author":"Akbari","year":"2003","journal-title":"Landsc. Urban Plan."},{"key":"ref_9","unstructured":"Gray, K.A., and Finster, M.E. (1999). The Urban Heat Island, Photochemical Smog and Chicago: Local Features of the Problem and Solution, Atmospheric Pollution Prevention Division, U.S. Environmental Protection Agency."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1016\/j.rse.2009.06.004","article-title":"The influence of urban structures on impervious surface maps from airborne hyperspectral data","volume":"113","author":"Hostert","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"68","article-title":"A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States","volume":"10","author":"Weng","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1016\/j.rse.2009.05.014","article-title":"Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3722","DOI":"10.1080\/01431161.2014.915594","article-title":"Comparisons of regression tree models for sub-pixel imperviousness estimation in a Gulf Coast city of Mississippi, USA","volume":"35","author":"Gong","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"335","DOI":"10.14358\/PERS.82.5.335","article-title":"Extraction of Urban Impervious Surface Using Two-Season WorldView-2 Images: A Comparison","volume":"82","author":"Cai","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1080\/15481603.2018.1452588","article-title":"An Automated Algorithm for Mapping Building Impervious Areas from Airborne LiDAR Point-Cloud Data for Flood Hydrology","volume":"55","author":"Hung","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_16","unstructured":"Zhang, J., Li, P., Mazher, A., and Liu, L. (2012, January 16\u201318). Impervious Surface Extraction with Very High Resolution Imagery In Urban Areas: Reducing Tree Obscuring Effect. Proceedings of the Computer Vision in Remote Sensing (CVRS), Xiamen, China."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kaur, J., Singh, J., Sehra, S.S., and Rai, H.S. (2017, January 11\u201312). Systematic Literature Review of Data Quality within OpenStreetMap. Proceedings of the International Conference on Next Generation Computing and Information Systems (ICNGCIS), Model Inst Engn & Technol, Jammu, India.","DOI":"10.1109\/ICNGCIS.2017.35"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/JSTARS.2010.2074186","article-title":"A Multilevel Hierarchical Image Segmentation Method for Urban Impervious Surface Mapping Using Very High-Resolution Imagery","volume":"4","author":"Li","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","unstructured":"Jin, X. (2012). Segmentation-Based Image Processing System. (U.S. US8260048 B2)."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3233\/FI-2000-411207","article-title":"The Watershed Transform: Definitions, Algorithms, and Parallelization Strategies","volume":"41","author":"Roerdink","year":"2000","journal-title":"Fundam. Inform."},{"key":"ref_21","unstructured":"Robinson, D.J., Redding, N.J., and Crisp, D.J. (2002, January 1). Implementation of a Fast Algorithm for Segmenting Sar Imagery. Proceedings of the Scientific and Technical Report, Australia: Defense Science and Technology Organization."},{"key":"ref_22","first-page":"75","article-title":"Research on vegetation indices based on remote sensing images","volume":"24","author":"Luo","year":"2005","journal-title":"Ecol. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color Indices for Weed Identification under Various Soil, Residue and Lighting Conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. ASAE"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Ma, H., Cheng, X., Wang, X., and Yuan, J. (2013, January 16\u201318). Road Information Extraction from High-Resolution Remote Sensing Images Based on Threshold Segmentation and Mathematical Morphology. Proceedings of the 6th International Congress on Image and Signal Processing (CISP), Hangzhou, China.","DOI":"10.1109\/CISP.2013.6745242"},{"key":"ref_26","unstructured":"Zhou, H., Song, X., and Liu, G. (2017, January 28\u201329). Automatic Road Extraction from High Resolution Remote Sensing Image by Means of Topological Derivative and Mathematical Morphology. Proceedings of the Remote Sensing Image Processing and Geographic Information Systems. 10th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR)\u2014Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, Xiangyang, China."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/34.161346","article-title":"Thinning methodologies\u2014A comprehensive survey","volume":"14","author":"Lam","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","first-page":"1420","article-title":"Extraction of Urban Impervious Surface from High-Resolution Remote Sensing Imagery Based on Deep Learning","volume":"21","author":"Cai","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1080\/10095020.2021.1961567","article-title":"Spatiotemporal-Spectral-Angular Observation Model that Integrates Observations from UAV and Mobile Mapping Vehicle for Better Urban Mapping","volume":"24","author":"Shao","year":"2021","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1080\/10095020.2020.1864232","article-title":"Spatio-temporal-spectral observation model for urban remote sensing","volume":"24","author":"Shao","year":"2021","journal-title":"Geo-Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:16:50Z","timestamp":1760138210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":30,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102493"],"URL":"https:\/\/doi.org\/10.3390\/rs14102493","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,23]]}}}