{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:10:58Z","timestamp":1766049058578,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T00:00:00Z","timestamp":1538956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41501378"],"award-info":[{"award-number":["41501378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["BK20150835"],"award-info":[{"award-number":["BK20150835"]}]},{"name":"scientific research foundation of Nanjing University of Posts and Telecommunications","award":["NY214196"],"award-info":[{"award-number":["NY214196"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Very high spatial resolution (VHR) satellite images possess several advantages in terms of describing the details of ground targets. Extracting built-up areas from VHR images has received increasing attention in practical applications, such as land use planning, urbanization monitoring, geographic information database update. In this study, a novel method is proposed for built-up area detection and delineation on VHR satellite images, using multi-resolution space-frequency analysis, spatial dependence modelling and cross-scale feature fusion. First, the image is decomposed by multi-resolution wavelet transformation, and then the high-frequency information at different levels is employed to represent the multi-scale texture and structural characteristics of built-up areas. Subsequently, the local Getis-Ord statistic is introduced to model the spatial patterns of built-up area textures and structures by measuring the spatial dependence among frequency responses at different spatial positions. Finally, the saliency map of built-up areas is produced using a cross-scale feature fusion algorithm, followed by adaptive threshold segmentation to obtain the detection results. The experiments on ZY-3 and Quickbird datasets demonstrate the effectiveness and superiority of the proposed method through comparisons with existing algorithms.<\/jats:p>","DOI":"10.3390\/rs10101596","type":"journal-article","created":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T10:44:53Z","timestamp":1538995493000},"page":"1596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Delineation of Built-Up Areas from Very High-Resolution Satellite Imagery Using Multi-Scale Textures and Spatial Dependence"],"prefix":"10.3390","volume":"10","author":[{"given":"Yixiang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Zhiyong","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China"}]},{"given":"Yan","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/JSTARS.2011.2106332","article-title":"Foreword to the special issue on \u201chuman settlements: A global remote sensing challenge\u201d","volume":"4","author":"Gamba","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ehrlich, D., and Pesaresi, M. (2013, January 21\u201323). Do we need a global human settlement analysis system based on satellite imagery?. Proceedings of the Joint Urban Remote Sensing Event, Sao Paulo, Brazil.","DOI":"10.1109\/JURSE.2013.6550668"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2013.06.011","article-title":"A comprehensive review of earthquake-induced building damage detection with remote sensing techniques","volume":"84","author":"Dong","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2747\/1548-1603.42.1.80","article-title":"Population estimation methods in GIS and remote sensing: A review","volume":"42","author":"Wu","year":"2005","journal-title":"GISci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.apgeog.2011.05.008","article-title":"Preliminary mapping of high-resolution rural population distribution based on imagery from Google Earth: A case study in the Lake Tai basin, eastern China","volume":"32","author":"Yang","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.apgeog.2013.10.005","article-title":"Mapping recent built-up area changes in the city of Harare with high resolution satellite imagery","volume":"46","author":"Wania","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.compenvurbsys.2007.09.001","article-title":"Automatic identification of urban settlement boundaries for multiple representation databases","volume":"32","author":"Chaudhry","year":"2008","journal-title":"Comput. Environ. Urban. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/01431161.2010.481681","article-title":"Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach","volume":"1","author":"He","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7339","DOI":"10.3390\/rs6087339","article-title":"Urban built-up area extraction from landsat TM\/ETM+ images using spectral information and multivariate texture","volume":"6","author":"Zhang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1080\/17538947.2016.1168879","article-title":"Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data","volume":"9","author":"Sharma","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"7671","DOI":"10.3390\/rs70607671","article-title":"Regional urban extent extraction using multi-sensor data and one-class classification","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Corbane, C., Julea, A., Florczyk, A.J., Syrris, V., and Soille, P. (2016). Assessment of the added-value of sentinel-2 for detecting built-up areas. Remote Sens., 8.","DOI":"10.3390\/rs8040299"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hu, Z., Li, Q., Zhang, Q., and Wu, G. (2016). Representation of block-based image features in a multi-scale framework for built-up area detection. Remote Sens., 8.","DOI":"10.3390\/rs8020155"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1109\/LGRS.2014.2387850","article-title":"Built-up area detection from satellite images using multikernel learning, multifieldintegrating, and multihypothesis voting","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mboga, N., Persello, C., Bergado, J.R., and Stein, A. (2017). Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks. Remote Sens., 9.","DOI":"10.3390\/rs9111106"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.rse.2018.02.025","article-title":"Multi-sensor feature fusion for very high spatial resolution built-up area extraction in temporary settlements","volume":"209","author":"Pelizari","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/LGRS.2009.2028744","article-title":"Urban area detection using local feature points and spatial voting","volume":"7","author":"Sirmacek","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/LGRS.2013.2237751","article-title":"Unsupervised detection of built-up areas from multiple high-resolution remote sensing images","volume":"10","author":"Tao","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/LGRS.2012.2224315","article-title":"Improved Harris feature point set for orientation-sensitive urban-area detection in aerial images","volume":"10","author":"Kovacs","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, G., Xia, G., Huang, X., Yang, W., and Zhang, L. (2013, January 21\u201326). A perception-inspired building index for automatic built-up area detection in high-resolution satellite images. Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723490"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/LGRS.2015.2439696","article-title":"Accurate urban area detection in remote sensing images","volume":"12","author":"Shi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","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 IEEE International Symposium on Geoscience and Remote Sensing, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729108"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/LGRS.2012.2210188","article-title":"Local edge distributions for detection of salient structure textures and objects","volume":"10","author":"Hu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ning, X., and Lin, X. (2017). An index based on joint density of corners and line segments for built-up area detection from high resolution satellite imagery. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110338"},{"key":"ref_28","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 Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"You, Y., Wang, S., Ma, Y., Chen, G., Wang, B., Shen, M., and Liu, W. (2018). Building detection from VHR remote sensing imagery based on the morphological building index. Remote Sens., 10.","DOI":"10.3390\/rs10081287"},{"key":"ref_30","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 Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1109\/JSTARS.2011.2162579","article-title":"Toward global automatic built-up area recognition using optical VHR imagery","volume":"4","author":"Pesaresi","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1109\/JSTARS.2013.2271445","article-title":"A global human settlement layer from optical HR\/VHR RS data: Concept and first results","volume":"6","author":"Pesaresi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"721","DOI":"10.14358\/PERS.77.7.721","article-title":"A Multidirectional and Multiscale Morphological Index for Automatic Building Extraction from Multispectral GeoEye-1 Imagery","volume":"77","author":"Huang","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/JSTARS.2011.2168195","article-title":"Morphological Building\/Shadow Index for Building Extraction from High-Resolution Imagery over Urban Areas","volume":"5","author":"Huang","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1080\/17538940802420861","article-title":"An approach to extracting information of residential areas from Beijing-1 image based on Gabor texture segmentation","volume":"2","author":"Gong","year":"2009","journal-title":"Int. J. Digit. Earth"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1080\/2150704X.2014.889861","article-title":"BASI: A new index to extract built-up areas from high-resolution remote sensing images by visual attention model","volume":"5","author":"Shao","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.jvcir.2015.09.019","article-title":"Residential area extraction based on saliency analysis for high spatial resolution remote sensing images","volume":"33","author":"Zhang","year":"2015","journal-title":"J. Vis. Commun. Image R"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3750","DOI":"10.1109\/TGRS.2016.2527044","article-title":"Global and local saliency analysis for the extraction of residential areas in high-spatial-resolution remote sensing image","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/01431160903252327","article-title":"Contextual land-cover classification: Incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic","volume":"1","author":"Ghimire","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"467","DOI":"10.5194\/isprsarchives-XXXIX-B3-467-2012","article-title":"Feature modelling of high resolution remote sensing images considering spatial autocorrelation","volume":"XXXIX-B3","author":"Chen","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1109\/LGRS.2014.2306972","article-title":"Structural feature modeling of high-resolution remote sensing images using directional spatial correlation","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.1538-4632.1992.tb00261.x","article-title":"The analysis of spatial association by use of distance statistics","volume":"24","author":"Getis","year":"1992","journal-title":"Geogr. Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1111\/j.1538-4632.1995.tb00912.x","article-title":"Local spatial autocorrelation statistics: Distributional issues and an application","volume":"27","author":"Ord","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multi-resolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/TMM.2012.2225034","article-title":"A saliency detection model using low-level features based on wavelet transform","volume":"15","author":"Lin","year":"2013","journal-title":"IEEE Trans. Multimedia"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"803","DOI":"10.14358\/PERS.70.7.803","article-title":"Wavelets for urban spatial feature discrimination: Comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches","volume":"70","author":"Myint","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/01431160500295885","article-title":"On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation","volume":"27","author":"Ouma","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1080\/014311698214983","article-title":"Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistic","volume":"19","author":"Wulder","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compag.2014.12.011","article-title":"Getis\u2013Ord\u2019s hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data","volume":"111","author":"Peeters","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","first-page":"1957","article-title":"Practical approaches to principal component analysis in the presence of missing values","volume":"11","author":"Ilin","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"Threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1596\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:24:19Z","timestamp":1760196259000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1596"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,8]]},"references-count":51,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["rs10101596"],"URL":"https:\/\/doi.org\/10.3390\/rs10101596","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,10,8]]}}}