{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T02:30:33Z","timestamp":1770517833301,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T00:00:00Z","timestamp":1572220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["Project Nos. 2018YFB0505400"],"award-info":[{"award-number":["Project Nos. 2018YFB0505400"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Project Nos. 41631178, 41871375, 41601354"],"award-info":[{"award-number":["Project Nos. 41631178, 41871375, 41601354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["(Project Nos. 22120180005"],"award-info":[{"award-number":["(Project Nos. 22120180005"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent advances in the fusion technology of remotely sensed data have led to an increased availability of extracted urban information from multiple spatial resolutions and multi-temporal acquisitions. Despite the existing extraction methods, there remains the challenging task of fully exploiting the characteristics of multisource remote sensing data, each of which has its own advantages. In this paper, a new fusion approach for accurately extracting urban built-up areas based on the use of multisource remotely sensed data, i.e., the DMSP-OLS nighttime light data, the MODIS land cover product (MCD12Q1) and Landsat 7 ETM+ images, was proposed. The proposed method mainly consists of two components: (1) the multi-level data fusion, including the initial sample selection, unified pixel resolution and feature weighted calculation at the feature level, as well as pixel attribution determination at decision level; and (2) the optimized sample selection with multi-factor constraints, which indicates that an iterative optimization with the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BSI), along with the sample training of the support vector machine (SVM) and the extraction of urban built-up areas, produces results with high credibility. Nine Chinese provincial capitals along the Silk Road Economic Belt, such as Chengdu, Chongqing, Kunming, Xining, and Nanning, were selected to test the proposed method with data from 2001 to 2010. Compared with the results obtained by the traditional threshold dichotomy and the improved neighborhood focal statistics (NFS) method, the following could be concluded. (1) The proposed approach achieved high accuracy and eliminated natural elements to a great extent while obtaining extraction results very consistent to those of the more precise improved NFS approach at a fine scale. The average overall accuracy (OA) and average Kappa values of the extracted urban built-up areas were 95% and 0.83, respectively. (2) The proposed method not only identified the characteristics of the urban built-up area from the nighttime light data and other daylight images at the feature level but also optimized the samples of the urban built-up area category at the decision level, making it possible to provide valuable information for urban planning, construction, and management with high accuracy.<\/jats:p>","DOI":"10.3390\/rs11212516","type":"journal-article","created":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T04:44:31Z","timestamp":1572237871000},"page":"2516","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3584-7378","authenticated-orcid":false,"given":"Xiaolong","family":"Ma","sequence":"first","affiliation":[{"name":"Institute of Cartography and Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, China"},{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Chengming","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Cartography and Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"given":"Xiaohua","family":"Tong","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1612-4844","authenticated-orcid":false,"given":"Sicong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,28]]},"reference":[{"key":"ref_1","first-page":"922","article-title":"Monitoring urban land cover and vegetation change by multi-temporal remote sensing information","volume":"20","author":"Du","year":"2010","journal-title":"Min. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, P., Sun, Q., Liu, M., Li, J., and Sun, D. (2017). A strategy of rapid extraction of built-up area using multi-seasonal Landsat-8 thermal infrared Band 10 images. Remote Sens., 9.","DOI":"10.3390\/rs9111126"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ge, W., Yang, H., Zhu, X., Ma, M., and Yang, Y. (2018). Ghost City Extraction and Rate Estimation in China Based on NPP-VIIRS Night-Time Light Data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7060219"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"083672","DOI":"10.1117\/1.JRS.8.083672","article-title":"Detection of built-up area in optical and synthetic aperture radar images using conditional random fields","volume":"8","author":"Tolpekin","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.apgeog.2010.01.009","article-title":"Per-pixel and object-oriented classification approachs for mapping urban features using Ikonos satellite data","volume":"30","author":"Bhaskaran","year":"2010","journal-title":"Appl. Geogr."},{"key":"ref_6","first-page":"42","article-title":"Urban growth and environmental impacts in Jing-Jin-Ji, the Yangtze, River Delta and the Pearl River Delta","volume":"30","author":"Haas","year":"2014","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.landurbplan.2012.02.013","article-title":"Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008","volume":"106","author":"Liu","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.habitatint.2017.01.001","article-title":"Spatial-temporal evolution and classification of marginalization of cultivated land in the process of urbanization","volume":"61","author":"Li","year":"2017","journal-title":"Habitat Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1109\/JSTARS.2018.2824302","article-title":"A Multisource Remotely Sensed Data Oriented approach for Ghost City\u201d Phenomenon Identification","volume":"99","author":"Ma","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_10","first-page":"62","article-title":"Spatial Difference Pattern of House Vacancy in China from Nighttime Light View","volume":"37","author":"Dong","year":"2017","journal-title":"Econ. Geogr."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, S., Tong, X., Bruzzone, L., and Du, P. (2017, January 23\u201328). A novel semisupervised framework for multiple change detection in hyperspectral images. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8126922"},{"key":"ref_12","first-page":"258","article-title":"Integrated approach to extract information from high and very high resolution RS images for urban planning","volume":"2","author":"Amarsaikhan","year":"2009","journal-title":"J. Geogr. Reg. Plan."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, K., and Chen, Y. (2018). A Genetic Algorithm-Based Urban Cluster Automatic threshold approach by Combining VIIRS DNB, NDVI, and NDBI to Monitor Urbanization. Remote Sens., 10.","DOI":"10.3390\/rs10020277"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.1080\/01431160802039957","article-title":"A new index for delineating built-up land features in satellite imagery","volume":"29","author":"Xu","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xiang, D., Tang, T., Hu, C., Fan, Q., and Su, Y. (2016). Built-up area extraction from polSAR imagery with model-based decomposition and polarimetric coherence. Remote Sens., 8.","DOI":"10.3390\/rs8080685"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/JSTARS.2008.2002869","article-title":"A robust built-up area presence index by anisotropic rotation-invariant textural measure","volume":"1","author":"Pesaresi","year":"2008","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_17","first-page":"1","article-title":"Development of new indices for extraction of built-up area and bare soil from Landsat Data","volume":"1","author":"Waqar","year":"2012","journal-title":"Open Access Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1109\/JSTARS.2015.2394504","article-title":"Cauchy graph embedding optimization for built-up areas detection from high-resolution remote sensing images","volume":"8","author":"Li","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Y., Qin, K., Jiang, H., Wu, T., and Zhang, Y. (2016, January 10\u201315). Built-up area extraction using data field from high-resolution satellite images. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729108"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, S., Du, P., Liang, H., Xia, J., and Li, Y. (2018). Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sens., 10.","DOI":"10.3390\/rs10020276"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1080\/15481603.2015.1007778","article-title":"A new approach for extracting built-up urban areas using DMSP-OLS nighttime stable lights: A case study in the Pearl River Delta, Southern China","volume":"52","author":"Su","year":"2015","journal-title":"GISci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6110","DOI":"10.1080\/01431161.2017.1312623","article-title":"Monitoring urban expansion using time series of night-time light data: A case study in Wuhan. China","volume":"38","author":"Xin","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2018.08.017","article-title":"A novel co-training approach for urban land cover mapping with unclear Landsat time series imagery","volume":"217","author":"Hu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2018.2837222","article-title":"Monitoring of Urban Impervious Surfaces Using Time Series of High-Resolution Remote Sensing Images in Rapidly Urbanized Areas: A Case Study of Shenzhen","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1080\/15481603.2014.939539","article-title":"Built-up area extraction using Landsat 8 OLI imagery","volume":"51","author":"Bhatti","year":"2014","journal-title":"GISci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6708","DOI":"10.1080\/01431161.2014.960623","article-title":"A maximum entropy approach to extract urban land by combining MODIS reflectance, MODIS NDVI, and DMSP-OLS data","volume":"35","author":"Lin","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4258","DOI":"10.1109\/TGRS.2018.2805829","article-title":"Mapping Urban Areas in China Using Multisource Data with a Novel Ensemble SVM Method","volume":"56","author":"Huang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shi, K., Chen, Y., Yu, B., Xu, T., Li, L., Huang, C., Liu, R., Chen, Z., and Wu, J. (2016). Urban expansion and agricultural land loss in China: A multiscale perspective. Sustainability, 8.","DOI":"10.3390\/su8080790"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/j.energy.2018.03.020","article-title":"Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road","volume":"150","author":"Shi","year":"2018","journal-title":"Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.apgeog.2015.06.016","article-title":"Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data","volume":"63","author":"Zhou","year":"2015","journal-title":"Appl. Geogr."},{"key":"ref_31","first-page":"172","article-title":"Research on \u201cGhost Town\u201d Index Based on Landsat Data Products and DMSP\/OLS Nighttime Light Data: A Case Research of Anhui Province","volume":"6","author":"Li","year":"2017","journal-title":"Sci. Mosaic"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"637","DOI":"10.3390\/rs9060637","article-title":"A stepwise calibration of global DMSP\/OLS stable nighttime light data (1992\u20132013)","volume":"9","author":"Li","year":"2017","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.3390\/ijgi4042519","article-title":"Evaluation of the consistency of MODIS land cover product (MCD12Q1) based on Chinese 30 m GlobeLand30 datasets: A case study in Anhui Province, China","volume":"4","author":"Liang","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.ecolind.2015.03.037","article-title":"Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI\/TIRS imageries: A comparative assessment of various spectral indices","volume":"56","author":"Estoque","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_35","unstructured":"National Bureau of Statistics of the People\u2019s Republic of China (2002). China City Statistical Yearbook."},{"key":"ref_36","unstructured":"National Bureau of Statistics of the People\u2019s Republic of China (2006). China City Statistical Yearbook."},{"key":"ref_37","unstructured":"National Bureau of Statistics of the People\u2019s Republic of China (2011). China City Statistical Yearbook."},{"key":"ref_38","unstructured":"(2019, May 07). National Fundamental Geographic Information System, National Geomatics Center of China, Available online: http:\/\/ngcc.sbsm.gov.cn\/."},{"key":"ref_39","first-page":"1092","article-title":"Correction of DMSP\/OLS night-time light images and its application in China","volume":"17","author":"Cao","year":"2015","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01431161.2017.1339927","article-title":"A novel approach for urban area extraction from VIIRS DNB and MODIS NDVI data: A case study of Chinese cities","volume":"38","author":"Zhang","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","first-page":"771","article-title":"Study on extraction approachs for water information in Nantong city, China using Landsat7 ETM+ data","volume":"51","author":"Chao","year":"2011","journal-title":"Int. Conf. Remote Sens."},{"key":"ref_42","first-page":"103","article-title":"Study on urban heat island effect in Nanchang based on landsat 8 satellite images","volume":"29","author":"Chen","year":"2017","journal-title":"Acta Agric. Jiangxi"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ma, X., Tong, X., Liu, S., and Ma, Z. (2017, January 23\u201328). Extraction of built-up areas in Chinese silk road economic belt based on DMSP-OLS data. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128346"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jiang, W., He, G.J., Long, T.F., Wang, C., Ni, Y., and Ma, R.Q. (2017). Assessing light pollution in China based on nighttime light imagery. Remote Sens., 9.","DOI":"10.3390\/rs9020135"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2005.02.002","article-title":"Spatial Analysis of Global Urban Extent from DMSPOLS Night Lights","volume":"96","author":"Small","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1080\/2150704X.2014.905728","article-title":"Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas","volume":"5","author":"Shi","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_47","first-page":"51","article-title":"Unmixing of Hyperspectral Imagery Based on Probabilistic Outputs of Support Vector Machines","volume":"31","author":"Wu","year":"2006","journal-title":"Geomat. Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ma, X., Tong, X., Liu, S., Luo, X., Xie, H., and Li, C. (2017). Optimized sample selection in SVM classification by combining with DMSP-OLS, Landsat NDVI and globeland30 products for extracting urban built-up areas. Remote Sens., 9.","DOI":"10.3390\/rs9030236"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1007\/s11434-006-2006-3","article-title":"Study on the Reconstruction of China\u2019s Urbanization Process in 1990s Based on DMSP\/OLS Night Light Data and Statistical Data","volume":"51","author":"He","year":"2006","journal-title":"Chin. Sci. Bull."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2516\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:29:49Z","timestamp":1760189389000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/21\/2516"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,28]]},"references-count":49,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11212516"],"URL":"https:\/\/doi.org\/10.3390\/rs11212516","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,28]]}}}