{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:29:53Z","timestamp":1772724593284,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T00:00:00Z","timestamp":1542412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001655","name":"Deutscher Akademischer Austauschdienst","doi-asserted-by":"publisher","award":["57144088"],"award-info":[{"award-number":["57144088"]}],"id":[{"id":"10.13039\/501100001655","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High-Resolution (VHR) satellite images especially in complex urban scenes. In this paper, we develop a superpixel algorithm as a modified edge-based version of Simple Linear Iterative Clustering (SLIC), which is here called ESLIC, compatible with VHR satellite images. Then, based on the modified properties of generated superpixels, a heuristic multi-scale approach for building extraction is proposed, based on the stereo satellite imagery along with the corresponding Digital Surface Model (DSM). First, to generate the modified superpixels, an edge-preserving term is applied to retain the main building boundaries and edges. The resulting superpixels are then used to initially refine the stereo-extracted DSM. After shadow and vegetation removal, a rough building mask is obtained from the normalized DSM, which highlights the appropriate regions in the image, to be used as the input of a multi-scale superpixel segmentation of the proper areas to determine the superpixels inside the building. Finally, these building superpixels with different scales are integrated and the output is a unified building mask. We have tested our methods on building samples from a WorldView-2 dataset. The results are promising, and the experiments show that superpixels generated with the proposed ESLIC algorithm are more adherent to the building boundaries, and the resulting building mask retains urban object shape better than those generated with the original SLIC algorithm.<\/jats:p>","DOI":"10.3390\/rs10111824","type":"journal-article","created":{"date-parts":[[2018,11,21]],"date-time":"2018-11-21T11:23:27Z","timestamp":1542799407000},"page":"1824","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Modified Superpixel Segmentation for Digital Surface Model Refinement and Building Extraction from Satellite Stereo Imagery"],"prefix":"10.3390","volume":"10","author":[{"given":"Zeinab","family":"Gharibbafghi","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaojiao","family":"Tian","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-1475","authenticated-orcid":false,"given":"Peter","family":"Reinartz","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,17]]},"reference":[{"key":"ref_1","unstructured":"Dey, V., Zhang, Y., and Zhong, M. (2010, January 5\u20137). A review on image segmentation techniques with remote sensing perspective. Proceedings of the Technical Commission VII Symposium, Vienna, Austria."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"040901","DOI":"10.1117\/1.JEI.21.4.040901","article-title":"Survey of contemporary trends in color image segmentation","volume":"21","author":"Vantaram","year":"2012","journal-title":"J. Electron. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1016\/0031-3203(93)90135-J","article-title":"A review on image segmentation techniques","volume":"26","author":"Pal","year":"1993","journal-title":"Pattern Recognit."},{"key":"ref_4","first-page":"555","article-title":"Object-oriented image processing in an integrated GIS\/remote sensing environment and perspectives for environmental applications","volume":"2","author":"Blaschke","year":"2000","journal-title":"Environ. Inf. Plan. Polit. Public"},{"key":"ref_5","first-page":"12","article-title":"What\u2019s wrong with pixels? Some recent developments interfacing remote sensing and GIS","volume":"6","author":"Blaschke","year":"2001","journal-title":"GeoBIT\/GIS"},{"key":"ref_6","unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and S\u00fcsstrunk, S. (2010). Slic Superpixels, EPFL. Technical Report."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TIP.2008.2002304","article-title":"Minimization of region-scalable fitting energy for image segmentation","volume":"17","author":"Li","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1109\/TIP.2011.2146190","article-title":"A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI","volume":"20","author":"Li","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1109\/TPAMI.2006.233","article-title":"Random walks for image segmentation","volume":"28","author":"Grady","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11263-006-8711-1","article-title":"A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape","volume":"72","author":"Cremers","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1109\/LGRS.2013.2259214","article-title":"Superpixel generating algorithm based on pixel intensity and location similarity for SAR image classification","volume":"10","author":"Xiang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/LGRS.2016.2618840","article-title":"Polarimetric SAR image classification using deep convolutional neural networks","volume":"13","author":"Zhou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, S., Fu, W., and Fang, L. (2017). Multiscale superpixel-based sparse representation for hyperspectral image classification. Remote Sens., 9.","DOI":"10.3390\/rs9020139"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/JSTARS.2018.2849363","article-title":"Building Footprint Extraction From VHR Remote Sensing Images Combined with Normalized DSMs Using Fused Fully Convolutional Networks","volume":"11","author":"Bittner","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","article-title":"Efficient graph-based image segmentation","volume":"59","author":"Felzenszwalb","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11263-006-7934-5","article-title":"Graph cuts and efficient ND image segmentation","volume":"70","author":"Boykov","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yi, F., and Moon, I. (2012, January 19\u201320). Image segmentation: A survey of graph-cut methods. Proceedings of the 2012 IEEE International Conference on Systems and Informatics (ICSAI), Yantai, China.","DOI":"10.1109\/ICSAI.2012.6223428"},{"key":"ref_20","unstructured":"Cour, T., Benezit, F., and Shi, J. (2005, January 20\u201325). Spectral segmentation with multiscale graph decomposition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005 (CVPR 2005), San Diego, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6061","DOI":"10.1109\/TGRS.2016.2580643","article-title":"Multiscale and multifeature normalized cut segmentation for high spatial resolution remote sensing imagery","volume":"54","author":"Zhong","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1109\/TIP.2017.2651389","article-title":"Linear spectral clustering superpixel","volume":"26","author":"Chen","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jvcir.2015.10.012","article-title":"Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation","volume":"34","author":"Zhu","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/TIP.2014.2302892","article-title":"Lazy random walks for superpixel segmentation","volume":"23","author":"Shen","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","article-title":"Turbopixels: Fast superpixels using geometric flows","volume":"31","author":"Levinshtein","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Veksler, O., Boykov, Y., and Mehrani, P. (2010, January 5\u201311). Superpixels and supervoxels in an energy optimization framework. Proceedings of the European Conference on Computer Vision, Heraklion, Greece.","DOI":"10.1007\/978-3-642-15555-0_16"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Csillik, O. (2017). Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels. Remote Sens., 9.","DOI":"10.3390\/rs9030243"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Moore, A.P., Prince, S.J., Warrell, J., Mohammed, U., and Jones, G. (2008, January 23\u201328). Superpixel lattices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587471"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Van den Bergh, M., Boix, X., Roig, G., de Capitani, B., and Van Gool, L. (2012, January 7\u201313). Seeds: Superpixels extracted via energy-driven sampling. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33786-4_2"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Soatto, S. (2008, January 12\u201318). Quick shift and kernel methods for mode seeking. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88693-8_52"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3243","DOI":"10.1109\/TIP.2010.2069690","article-title":"Distance regularized level set evolution and its application to image segmentation","volume":"19","author":"Li","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cui, J., Wang, W., and Lin, C. (2017). A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors, 17.","DOI":"10.3390\/s17071474"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_36","unstructured":"Rubner, Y., and Tomasi, C. (2013). Perceptual Metrics for Image Database Navigation, Springer Science & Business Media."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"745309","DOI":"10.1155\/ASP.2005.2196","article-title":"Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information","volume":"2005","author":"Jin","year":"2005","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5234","DOI":"10.1080\/01431161.2016.1230287","article-title":"Building extraction from high-resolution satellite images in urban areas: Recent methods and strategies against significant challenges","volume":"37","author":"Ghanea","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1109\/JSTARS.2016.2616446","article-title":"A Probabilistic Framework for Building Extraction From Airborne Color Image and DSM","volume":"10","author":"Chai","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","first-page":"57","article-title":"Building reconstruction: The dilemma of generic versus specific models","volume":"15","author":"Heuel","year":"2001","journal-title":"K\u00fcnstliche Intelligenz"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo processing by semiglobal matching and mutual information","volume":"30","author":"Hirschmuller","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","first-page":"B1","article-title":"Improving semi-global matching: Cost aggregation and confidence measure","volume":"41","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"55","DOI":"10.5194\/isprs-annals-III-1-55-2016","article-title":"A New Algorithm for Void Filling in a DSM from Stereo Satellite Images in Urban Areas","volume":"3","author":"Bafghi","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_44","first-page":"1685","article-title":"A novel spectral index for automatic shadow detection in urban mapping based on WorldView-2 satellite imagery","volume":"8","author":"Shahi","year":"2014","journal-title":"Int. J. Comput. Electr. Autom. Control Inf. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s11263-009-0251-z","article-title":"Benchmarking image segmentation algorithms","volume":"85","author":"Estrada","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, Z., Cui, Y., and Wu, G. (2017). Local competition-based superpixel segmentation algorithm in remote sensing. Sensors, 17.","DOI":"10.3390\/s17061364"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"393","DOI":"10.5194\/isprs-archives-XLII-1-W1-393-2017","article-title":"Automatic Rooftop Extraction in Stereo Imagery Using Distance and Building Shape Regularized Level Set Evolution","volume":"42","author":"Tian","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TGRS.2013.2240692","article-title":"Building change detection based on satellite stereo imagery and digital surface models","volume":"52","author":"Tian","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Tian, J. (2013). 3D Change Detection From High and Very High Resolution Satellite Stereo Imagery. [Ph.D. Thesis, Universit\u00e4t Osnabr\u00fcck]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1824\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:25Z","timestamp":1760196625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1824"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,17]]},"references-count":49,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111824"],"URL":"https:\/\/doi.org\/10.3390\/rs10111824","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,17]]}}}