{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:10:17Z","timestamp":1772043017363,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T00:00:00Z","timestamp":1545350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.<\/jats:p>","DOI":"10.3390\/sym11010003","type":"journal-article","created":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T09:24:11Z","timestamp":1545384251000},"page":"3","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Framework for Automatic Building Detection from Low-Contrast Satellite Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7679-0980","authenticated-orcid":false,"given":"Muhammad","family":"Aamir","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2975-4976","authenticated-orcid":false,"given":"Yi-Fei","family":"Pu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8233-567X","authenticated-orcid":false,"given":"Ziaur","family":"Rahman","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0289-1663","authenticated-orcid":false,"given":"Muhammad","family":"Tahir","sequence":"additional","affiliation":[{"name":"School of Software Technology, Dalian University of Technology (DUT), Dalian 116621, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1511-218X","authenticated-orcid":false,"given":"Hamad","family":"Naeem","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2839-4289","authenticated-orcid":false,"given":"Qiang","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"013106","DOI":"10.1117\/1.OE.56.1.013106","article-title":"Automatic segmentation of coronary arteries from computed tomography angiography data cloud using optimal thresholding","volume":"56","author":"Ansari","year":"2017","journal-title":"Opt. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, F., Ren, R., Van de Voorde, T., Xu, W., Zhou, G., and Zhou, Y. (2018). Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks. Remote Sens., 10.","DOI":"10.3390\/rs10030443"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"34","DOI":"10.5815\/ijigsp.2018.09.05","article-title":"A Video based Vehicle Detection, Counting and Classification System","volume":"9","author":"Memon","year":"2018","journal-title":"Image Graph. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yao, Y., Shi, Y., Weng, S., Guan, B., Yao, Y., Shi, Y., Weng, S., and Guan, B. (2017). Deep Learning for Detection of Object-Based Forgery in Advanced Video. Symmetry, 10.","DOI":"10.3390\/sym10010003"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Aamir, M., Pu, Y.-F., Abro, W.A., Naeem, H., and Rahman, Z. (2019). A Hybrid Approach for Object Proposal Generation, Springer.","DOI":"10.1007\/978-3-319-91659-0_18"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ayoub, N., Gao, Z., Chen, B., and Jian, M. (2018). A Synthetic Fusion Rule for Salient Region Detection under the Framework of DS-Evidence Theory. Symmetry, 10.","DOI":"10.3390\/sym10060183"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rahman, Z., Pu, Y.-F., Aamir, M., and Ullah, F. (2018). A framework for fast automatic image cropping based on deep saliency map detection and gaussian filter. Int. J. Comput. Appl.","DOI":"10.1080\/1206212X.2017.1422358"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1016\/j.ijleo.2017.05.018","article-title":"A new approach for image detection based on refined Bag of Words algorithm","volume":"140","author":"Naeem","year":"2017","journal-title":"Optik"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.worlddev.2017.07.015","article-title":"Breaking Ground: Unearthing the Potential of High-resolution, Remote-sensing Soil Data in Understanding Agricultural Profits and Technology Use in Sub-Saharan Africa","volume":"105","author":"Bhargava","year":"2018","journal-title":"World Dev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.ufug.2018.01.010","article-title":"Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft","volume":"30","author":"Honkavaara","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.epsl.2018.05.037","article-title":"Comparing dune migration measured from remote sensing with sand flux prediction based on weather data and model, a test case in Qatar","volume":"497","author":"Michel","year":"2018","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_12","first-page":"12","article-title":"Applying video summarization to aerial surveillance","volume":"Volume 10635","author":"Pham","year":"2018","journal-title":"Ground\/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, Z., and He, H. (2018). Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks. Symmetry, 10.","DOI":"10.3390\/sym10090375"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1111\/2041-210X.12941","article-title":"Measuring \u03b2-diversity by remote sensing: A challenge for biodiversity monitoring","volume":"9","author":"Rocchini","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Baughman, C.A., Jones, B.M., Bodony, K.L., Mann, D.H., Larsen, C.F., Himelstoss, E., and Smith, J. (2018). Remotely Sensing the Morphometrics and Dynamics of a Cold Region Dune Field Using Historical Aerial Photography and Airborne LiDAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10050792"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1080\/05704928.2018.1442346","article-title":"Proximal and remote sensing techniques for mapping of soil contamination with heavy metals","volume":"53","author":"Shi","year":"2018","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dahiya, S., Garg, P.K., Jat, M.K., and Garg, P.K. (2014, January 17\u201326). Building Extraction from High Resolution Satellite Images Using Matlab Software Building Extraction from High resolution Satellite Images. Proceedings of the International Multidisciplinary Scientific GeoConference & EXPO (SGEM), Albena, Bulgaria.","DOI":"10.5593\/SGEM2014\/B23\/S10.009"},{"key":"ref_18","unstructured":"Liu, W., and Prinet, V. (2005, January 29). Building Detection from High-resolution Satellite Image Using Probability Model. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium (IGARSS \u201905), Seoul, Korea."},{"key":"ref_19","unstructured":"Cui, S.Y., Yan, Q., Liu, Z.J., and Li, M. (2008, January 3\u201311). Building detection and recognition from high resolution remotely sensed imagery. Proceedings of the XXIst ISPRS Congress, Beijing, China."},{"key":"ref_20","first-page":"1063","article-title":"Building extraction from high resolution satellite images using hough transform","volume":"38","author":"Turker","year":"2010","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","unstructured":"Zhang, A., Liu, X., Gros, A., and Tiecke, T. (arXiv, 2017). Building Detection from Satellite Images on a Global Scale, arXiv."},{"key":"ref_22","first-page":"3","article-title":"Automatic Building Extraction from Satellite Imagery","volume":"13","author":"Theng","year":"2006","journal-title":"Eng. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., Chen, Z., Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1080\/01431160512331326675","article-title":"Model and context-driven building extraction in dense urban aerial images","volume":"26","author":"Peng","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/j.patrec.2004.09.033","article-title":"An improved snake model for building detection from urban aerial images","volume":"26","author":"Peng","year":"2005","journal-title":"Pattern Recognit. Lett."},{"key":"ref_26","first-page":"150","article-title":"Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours","volume":"12","author":"Ahmadi","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Akcay, H.G., and Aksoy, S. (2010, January 25\u201330). Building detection using directional spatial constraints. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652842"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1080\/01431161.2011.606852","article-title":"Unsupervised building detection in complex urban environments from multispectral satellite imagery","volume":"33","author":"Erener","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TPAMI.2011.94","article-title":"Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics","volume":"34","author":"Benedek","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.isprsjprs.2008.01.005","article-title":"A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data","volume":"63","author":"Durieux","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/TGRS.2008.2002027","article-title":"Recognition-Driven 2D Competing Priors Towards Automatic And Accurate Building Detection","volume":"47","author":"Karantzalos","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lefevre, S., Weber, J., and Sheeren, D. (2007, January 11\u201313). Automatic Building Extraction in VHR Images Using Advanced Morphological Operators. Proceedings of the 2007 Urban Remote Sensing Joint Event, Paris, France.","DOI":"10.1109\/URS.2007.371825"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1080\/01431160802509017","article-title":"A new approach to building identification from very-high-spatial-resolution images","volume":"30","author":"Lhomme","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/34.922708","article-title":"Detection and modeling of buildings from multiple aerial images","volume":"23","author":"Noronha","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1109\/TGRS.2011.2172995","article-title":"Three-Dimensional Polygonal Building Model Estimation From Single Satellite Images","volume":"50","author":"Izadi","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2013.09.004","article-title":"Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts","volume":"86","author":"Ok","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TGRS.2012.2200689","article-title":"Automatic Rooftop Extraction in Nadir Aerial Imagery of Suburban Regions Using Corners and Variational Level Set Evolution","volume":"51","author":"Cote","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1080\/01431160600868474","article-title":"A semi-automated approach for extracting buildings from QuickBird imagery applied to informal settlement mapping","volume":"28","author":"Mayunga","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/LGRS.2014.2347332","article-title":"An Efficient Approach for Automatic Rectangular Building Extraction From Very High Resolution Optical Satellite Imagery","volume":"12","author":"Wang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1109\/TGRS.2012.2207123","article-title":"Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery","volume":"51","author":"Ok","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1109\/JSTARS.2013.2249498","article-title":"Building Detection With Decision Fusion","volume":"6","author":"Senaras","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/34.761262","article-title":"Performance evaluation and analysis of monocular building extraction from aerial imagery","volume":"21","author":"Shufelt","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1109\/TGRS.2008.2008440","article-title":"Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory","volume":"47","author":"Sirmacek","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/TGRS.2010.2053713","article-title":"A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images","volume":"49","author":"Sirmacek","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4069","DOI":"10.1109\/JSTARS.2014.2308301","article-title":"Detection of Buildings in Multispectral Very High Spatial Resolution Images Using the Percentage Occupancy Hit-or-Miss Transform","volume":"7","author":"Stankov","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wegne, J.D., Soergel, U., and Rosenhahn, B. (2011, January 11\u201313). Segment-based building detection with conditional random fields. Proceedings of the 2011 Joint Urban Remote Sensing Event, Munich, Germany.","DOI":"10.1109\/JURSE.2011.5764756"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5391\/IJFIS.2015.15.1.35","article-title":"An Adaptive Histogram Equalization Based Local Technique for Contrast Preserving Image Enhancement","volume":"15","author":"Lee","year":"2015","journal-title":"Orig. Artic. Int. J. Fuzzy Log. Intell. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Demirel, H., Anbarjafari, G., and Jahromi, M.N.S. (2008, January 27\u201329). Image equalization based on singular value decomposition. Proceedings of the 2008 23rd International Symposium on Computer and Information Sciences, Istanbul, Turkey.","DOI":"10.1109\/ISCIS.2008.4717878"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1109\/JSTSP.2011.2112332","article-title":"Image Features Extraction and Fusion Based on Joint Sparse Representation","volume":"5","author":"Yu","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_50","first-page":"1165","article-title":"Local Discriminant Wavelet Packet Coordinates for Face Recognition","volume":"8","author":"Liu","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/TIP.2002.1014998","article-title":"The curvelet transform for image denoising","volume":"11","author":"Starck","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/LGRS.2009.2034873","article-title":"Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition","volume":"7","author":"Demirel","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lamard, M., Daccache, W., Cazuguel, G., Roux, C., and Cochener, B. (2005, January 17\u201318). Use of a JPEG-2000 Wavelet Compression Scheme for Content-Based Ophtalmologic Retinal Images Retrieval. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1615341"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1016\/j.isatra.2014.04.007","article-title":"Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT\u2013SVD","volume":"53","author":"Bhandari","year":"2014","journal-title":"ISA Trans."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1109\/TGRS.2011.2136381","article-title":"Use of Salient Features for the Design of a Multistage Framework to Extract Roads From High-Resolution Multispectral Satellite Images","volume":"49","author":"Das","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","unstructured":"Abujarad, F., Nadim, G., and Omar, A. (2005, January 2\u20133). Clutter reduction and detection of landmine objects in ground penetrating radar data using singular value decomposition (SVD). Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar (IWAGPR 2005), Delft, The Netherlands."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1109\/LGRS.2009.2028440","article-title":"Satellite Image Resolution Enhancement Using Complex Wavelet Transform","volume":"7","author":"Demirel","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1016\/j.patrec.2011.06.001","article-title":"EDLines: A real-time line segment detector with a false detection control","volume":"32","author":"Akinlar","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TPAMI.1986.4767808","article-title":"Extracting Straight Lines","volume":"PAMI-8","author":"Burns","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Topal, C., Akinlar, C., and Genc, Y. (2010, January 23\u201326). Edge Drawing: A Heuristic Approach to Robust Real-Time Edge Detection. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.593"},{"key":"ref_62","first-page":"14","article-title":"QuickBird-A Milestone for High Resolution Mapping","volume":"11","author":"Cheng","year":"2002","journal-title":"Earth Obs. Mag."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"22034","DOI":"10.1109\/ACCESS.2018.2819705","article-title":"Building Extraction From RGB VHR Images Using Shifted Shadow Algorithm","volume":"6","author":"Gao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/03772063.2017.1351320","article-title":"A Cognitive Viewpoint on Building Detection from Remotely Sensed Multispectral Images","volume":"64","author":"Chandra","year":"2018","journal-title":"IETE J. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1080\/22797254.2017.1416676","article-title":"Automatic building footprint extraction from high-resolution satellite image using mathematical morphology","volume":"51","author":"Gavankar","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s12524-017-0694-6","article-title":"Object-Based Rule Sets and Its Transferability for Building Extraction from High Resolution Satellite Imagery","volume":"46","author":"Attarzadeh","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1109\/JSTARS.2016.2587324","article-title":"A New Building Extraction Postprocessing Framework for High-Spatial- Resolution Remote-Sensing Imagery","volume":"10","author":"Huang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Li, W., He, C., Fang, J., and Fu, H. (2018, January 18\u201322). Semantic Segmentation based Building Extraction Method using Multi-Source GIS Map Datasets and Satellite Imagery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00043"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Li, S., Tang, H., Huang, X., Mao, T., Niu, X., Li, S., Tang, H., Huang, X., Mao, T., and Niu, X. (2017). Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters. Remote Sens., 9.","DOI":"10.3390\/rs9111177"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Chen, R., Li, X., Li, J., Chen, R., Li, X., and Li, J. (2018). Object-Based Features for House Detection from RGB High-Resolution Images. Remote Sens., 10.","DOI":"10.3390\/rs10030451"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/1\/3\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:35:22Z","timestamp":1760196922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/1\/3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,21]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["sym11010003"],"URL":"https:\/\/doi.org\/10.3390\/sym11010003","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,21]]}}}