{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T20:50:17Z","timestamp":1762980617546,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T00:00:00Z","timestamp":1538697600000},"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":["61601465"],"award-info":[{"award-number":["61601465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20160244","BK20150189"],"award-info":[{"award-number":["BK20160244","BK20150189"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Applied Basic Research Project of Sichuan Province","award":["2018JY0318"],"award-info":[{"award-number":["2018JY0318"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The mean shift algorithm has been shown to perform well in optical image segmentation. However, the conventional mean shift algorithm performs poorly if it is directly used with Synthetic Aperture Radar (SAR) images due to the large dynamic range and strong speckle noise. Recently, the Generalized Mean Shift (GMS) algorithm with an adaptive variable asymmetric bandwidth has been proposed for Polarimetric SAR (PolSAR) image filtering. In this paper, the GMS algorithm is further developed for PolSAR image segmentation. A new merging predicate that is defined in the joint spatial-range domain is derived based on the GMS algorithm. A pre-sorting strategy and a post-processing step are also introduced into the GMS segmentation algorithm. The proposed algorithm can be directly used for PolSAR image superpixel segmentation without any pre-processing steps. Experiments using Airborne SAR (AirSAR) and Experimental SAR (ESAR) L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm. The parameter settings, stability, quality, and efficiency of the GMS algorithm are also discussed at the end of this paper.<\/jats:p>","DOI":"10.3390\/rs10101592","type":"journal-article","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T12:16:44Z","timestamp":1538741804000},"page":"1592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Superpixel Segmentation of Polarimetric Synthetic Aperture Radar (SAR) Images Based on Generalized Mean Shift"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3602-5731","authenticated-orcid":false,"given":"Fengkai","family":"Lang","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyong","family":"Yan","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fachao","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Land and Resources, China West Normal University, Nanchong 637002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,5]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer. Lecture Notes in Geoinformation and Cartography.","key":"ref_1","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/36.905240","article-title":"Segmentation and Classification of Vegetated Areas Using Polarimetric SAR Image Data","volume":"39","author":"Dong","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1109\/LGRS.2008.2002263","article-title":"Region-Based Classification of Polarimetric SAR Images Using Wishart MRF","volume":"5","author":"Wu","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/JSTARS.2010.2042280","article-title":"Unsupervised Full-Polarimetric SAR Data Segmentation as a Tool for Classification of Agricultural Areas","volume":"4","author":"Hoekman","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TGRS.2009.2024303","article-title":"Segmentation and Classification of Polarimetric SAR Data Using Spectral Graph Partitioning","volume":"48","author":"Ersahin","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/TGRS.2012.2203358","article-title":"Superpixel-Based Classification with an Adaptive Number of Classes for Polarimetric SAR Images","volume":"51","author":"Liu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2011.11.001","article-title":"A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data","volume":"118","author":"Qi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1109\/JSTARS.2013.2265331","article-title":"Polarimetric-Spatial Classification of SAR Images Based on the Fusion of Multiple Classifiers","volume":"7","author":"Ma","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2014.06.014","article-title":"Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data","volume":"96","author":"Jiao","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1080\/07038992.2015.1032901","article-title":"Land Cover Classification from Polarimetric SAR Data Based on Image Segmentation and Decision Trees","volume":"41","author":"Zhang","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_13","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_14","first-page":"12","article-title":"Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation","volume":"58","author":"Baatz","year":"2000","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2004.110","article-title":"Statistical Region Merging","volume":"26","author":"Nock","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1109\/TGRS.2003.814632","article-title":"Optimum model-based segmentation techniques for multifrequency polarimetric SAR images of urban areas","volume":"41","author":"Lombardo","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1109\/TPAMI.2006.191","article-title":"Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets","volume":"28","author":"Mitiche","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7222","DOI":"10.1109\/TGRS.2014.2309725","article-title":"A Modified Level Set Approach for Segmentation of Multiband Polarimetric SAR Images","volume":"52","author":"Yin","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1080\/2150704X.2015.1058984","article-title":"A level set method for segmentation of high-resolution polarimetric SAR images using a heterogeneous clutter model","volume":"6","author":"Zou","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1109\/TGRS.2011.2164085","article-title":"Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty","volume":"50","author":"Yu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/LGRS.2013.2271040","article-title":"Polarimetric SAR Image Segmentation Using Statistical Region Merging","volume":"11","author":"Lang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/LGRS.2014.2322960","article-title":"Superpixel Segmentation for Polarimetric SAR Imagery Using Local Iterative Clustering","volume":"12","author":"Qin","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1109\/TGRS.2017.2662010","article-title":"Adaptive Superpixel Generation for Polarimetric SAR Images with Local Iterative Clustering and SIRV Model","volume":"55","author":"Xiang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5435","DOI":"10.1109\/TGRS.2015.2422737","article-title":"Adaptive-Window Polarimetric SAR Image Speckle Filtering Based on a Homogeneity Measurement","volume":"53","author":"Lang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1109\/JSTARS.2017.2708418","article-title":"Superpixel Segmentation of Polarimetric SAR Images Based on Integrated Distance Measure and Entropy Rate Method","volume":"10","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1109\/TGRS.2004.835302","article-title":"Segmentation of textured polarimetric SAR scenes by likelihood approximation","volume":"42","author":"Beaulieu","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1109\/TGRS.2010.2060730","article-title":"Hierarchical Segmentation of Polarimetric SAR Images Using Heterogeneous Clutter Models","volume":"49","author":"Bombrun","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TGRS.2011.2160647","article-title":"Filtering and Segmentation of Polarimetric SAR Data Based on Binary Partition Trees","volume":"50","author":"Salembier","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Chen, Q., Li, L., Xu, Q., Yang, S., Shi, X., and Liu, X. (2017). Multi-feature segmentation for high-resolution polarimetric SAR data based on fractal net evolution approach. Remote Sens., 9.","key":"ref_31","DOI":"10.3390\/rs9060570"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4440","DOI":"10.1109\/TGRS.2013.2282036","article-title":"Mean-Shift-Based Speckle Filtering of Polarimetric SAR Data","volume":"52","author":"Lang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The estimation of the gradient of a density function, with applications in pattern recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/34.400568","article-title":"Mean Shift, Mode Seeking, and Clustering","volume":"17","author":"Cheng","year":"1995","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Comaniciu, D., and Meer, P. (1999, January 20\u201327). Mean shift analysis and applications. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Kerkyra, Greece.","key":"ref_35","DOI":"10.1109\/ICCV.1999.790416"},{"unstructured":"Comaniciu, D., Ramesh, V., and Meer, P. (2000, January 13\u201315). Real-time tracking of non-rigid objects using mean shift. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head Island, SC, USA.","key":"ref_36"},{"key":"ref_37","first-page":"438","article-title":"The variable bandwidth mean shift and data-driven scale selection","volume":"Volume 1","author":"Comaniciu","year":"2001","journal-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV 2001)"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TPAMI.2003.1177159","article-title":"An algorithm for data-driven bandwidth selection","volume":"25","author":"Comaniciu","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"Cellier, F., Oriot, H., and Nicolas, J.M. (2004, January 1\u20133). Introduction of the mean shift algorithm in SAR imagery: Application to shadow extraction for building reconstruction. Proceedings of the IEEE International Workshop on Biomedical Circuits and Systems, Singapore.","key":"ref_39"},{"doi-asserted-by":"crossref","unstructured":"Jarabo-Amores, P., Rosa-Zurera, M., Mata-Moya, D., and Vicen-Bueno, R. (2009, January 5\u20137). \u201cMean-Shift\u201d filtering to reduce speckle noise in SAR images. Proceedings of the IEEE Intrumentation and Measurement Technology Conference, Singapore.","key":"ref_40","DOI":"10.1109\/IMTC.2009.5168635"},{"doi-asserted-by":"crossref","unstructured":"Beaulieu, J., and Touzi, R. (2010, January 25\u201330). Mean-Shift and Hierarchical Clustering for Textured Polarimetric SAR Image Segmentation\/Classification. Proceedings of the IEEE IGARSS 2010, Honolulu, HI, USA.","key":"ref_41","DOI":"10.1109\/IGARSS.2010.5653919"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/TIM.2010.2052478","article-title":"Spatial-Range Mean-Shift Filtering and Segmentation Applied to SAR Images","volume":"60","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"doi-asserted-by":"crossref","unstructured":"Lee, J.-S. (1980). Digital Image Enhancement and Noise Filtering by Use of Local Statistics. IEEE Trans. Pattern Anal. Mach. Intell., 165\u2013168.","key":"ref_43","DOI":"10.1109\/TPAMI.1980.4766994"},{"doi-asserted-by":"crossref","unstructured":"Kuan, D.T., Sawchuk, A.A., Member, S., Strand, T.C., and Chavel, P. (1985). Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise. IEEE Trans. Pattern Anal. Mach. Intell., 165\u2013177.","key":"ref_44","DOI":"10.1109\/TPAMI.1985.4767641"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TGRS.2008.2002881","article-title":"Improved Sigma Filter for Speckle Filtering of SAR Imagery","volume":"47","author":"Lee","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/36.789635","article-title":"Polarimetric SAR speckle filtering and its implication for classification","volume":"37","author":"Lee","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"unstructured":"Oliver, C., and Quegan, S. (2004). Understanding Synthetic Aperture Radar Images, SciTech Publishing, Inc.","key":"ref_47"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/TGRS.2014.2330857","article-title":"Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images","volume":"53","author":"Michel","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1592\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:24:07Z","timestamp":1760196247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,5]]},"references-count":48,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["rs10101592"],"URL":"https:\/\/doi.org\/10.3390\/rs10101592","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,10,5]]}}}