{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:46:00Z","timestamp":1765356360553,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T00:00:00Z","timestamp":1523404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing (RS) image segmentation is an essential step in geographic object-based image analysis (GEOBIA) to ultimately derive \u201cmeaningful objects\u201d. While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA). Specifically, a minimum spanning tree (MST) algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR) algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the \u201creverse searching-forward processing\u201d chain based on message passing interface (MPI) parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2) and different selected landscapes (residential\/industrial, residential\/agriculture) covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral), while the accuracy is comparable with that of the FNEA method.<\/jats:p>","DOI":"10.3390\/rs10040590","type":"journal-article","created":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T12:16:50Z","timestamp":1523449010000},"page":"590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery"],"prefix":"10.3390","volume":"10","author":[{"given":"Haiyan","family":"Gu","sequence":"first","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China"}]},{"given":"Yanshun","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China"}]},{"given":"Haitao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0303-6290","authenticated-orcid":false,"given":"Zhengjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China"}]},{"given":"Uwe","family":"Soergel","sequence":"additional","affiliation":[{"name":"Institute for Photogrammetry, University of Stuttgart, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-8458","authenticated-orcid":false,"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria"}]},{"given":"Shiyong","family":"Cui","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute (IMF), Earth Observation Center (EOC), German Aerospace Center (DLR), 82234 We\u00dfling, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,11]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-based Image Analysis: A new paradigm in Remote Sensing and Geographic Information Science","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TGRS.2012.2234755","article-title":"Remote Sensing Image Segmentation by Combining Spectral and Texture Features","volume":"52","author":"Yuan","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11769-009-0083-3","article-title":"Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm","volume":"19","author":"Wang","year":"2009","journal-title":"Chin. Geogr. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.3390\/rs6053583","article-title":"An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery","volume":"6","author":"Judah","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"667","DOI":"10.14358\/PERS.75.6.667","article-title":"A Region-based Level Set Segmentation for Automatic Detection of Man-made Objects from Aerial and Satellite Images","volume":"75","author":"Karantzalos","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2987","DOI":"10.1109\/TGRS.2014.2367129","article-title":"Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images","volume":"53","author":"Gaetano","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","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."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/14498596.2010.487850","article-title":"Enhanced evaluation of image segmentation results","volume":"55","author":"Marpu","year":"2010","journal-title":"J. Spat. Sci."},{"key":"ref_10","unstructured":"Strobl, J., Blaschke, T., and Griesebner, G. (2000). Multiresolution segmentaion: An optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII, Wichmann."},{"key":"ref_11","first-page":"95","article-title":"An Improved Method of FNEA for High Resolution Remote Sensing Image Segmentation","volume":"16","author":"Deng","year":"2014","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1016\/j.patcog.2012.09.015","article-title":"A Survey of Graph Theoretical Approaches to Image Segmentation","volume":"46","author":"Peng","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/34.16711","article-title":"Hierarchy in Picture Segmentation: A Stepwise Optimization Approach","volume":"11","author":"Beaulieu","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","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_15","unstructured":"Wang, S., and Siskind, J.M. (2001, January 7\u201314). Image Segmentation with Minimum Mean Cut. Proceedings of the 8th IEEE International Conference on Computer Vision, Vancouver, BC, Canada."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/TPAMI.2003.1201819","article-title":"Image Segmentation with Ratio Cut","volume":"25","author":"Wang","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalised cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","first-page":"12","article-title":"Experimental study on graph-based image segmentation methods in the classification of satellite images","volume":"11","author":"Giachetta","year":"2012","journal-title":"EARSeL eProc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bue, B.D., Thompson, D.R., Gilmore, M.S., and Castano, R. (2011, January 6\u20139). Metric learning for hyperspectral image segmentation. Proceedings of the 3rd IEEE WHISPERS, Lisbon, Portugal.","DOI":"10.1109\/WHISPERS.2011.6080873"},{"key":"ref_20","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","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2015.01.018","article-title":"A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data","volume":"104","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","first-page":"202","article-title":"A Graph-Based Image Segmentation Approach for Image Classification and Its Application on SAR Images","volume":"89","author":"Sharifi","year":"2013","journal-title":"Prz. Elektrotech."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/JSTARS.2009.2022047","article-title":"An efficient multi-scale SRMMHR (statistical region merging and minimum heterogeneity rule) segmentation method for high-resolution remote sensing imagery","volume":"2","author":"Li","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","first-page":"1213","article-title":"A Multiresolution Remotely Sensed Image Segmentation Method Combining Rainfalling Watershed Algorithm and Fast Region Merging","volume":"37","author":"Wang","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"197","DOI":"10.5194\/isprsarchives-XL-7-W4-197-2015","article-title":"High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach","volume":"40","author":"Yang","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","first-page":"133","article-title":"High-efficiency Remotely Sensed Image Parallel Processing Method Study Based on MPI","volume":"12","author":"Shen","year":"2007","journal-title":"J. Image Graph."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, G.Q., and Liu, D.S. (2005, January 22\u201325). Key Technologies Research on Building a Cluster-based Parallel Computing System for Remote Sensing. Proceedings of the 5th International Conference on Computational Science, Atlanta, GA, USA.","DOI":"10.1007\/11428862_66"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Schott, J.R. (2007). Remote Sensing: The Image Chain Approach, Oxford University Press.","DOI":"10.1093\/oso\/9780195178173.001.0001"},{"key":"ref_29","unstructured":"ISPRS Benchmarks (2016, September 15). 2D Semantic Labeling Contest\u2014Potsdam. Available online: http:\/\/www2.isprs.org\/commissions\/comm3\/wg4\/2d-sem-label-potsdam.html."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1080\/01431161003777189","article-title":"Optimal region growing segmentation and its effect on classification accuracy","volume":"32","author":"Gao","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Achanccaray, P., Ayma, V.A., Jimenez, L.I., Bernabe, S., Happ, P.N., Costa, G.A.O.P., Feitosa, R.Q., and Plaza, A. (2015, January 26\u201331). SPT 3.1: A free software tool for automatic tuning of segmentation parameters in optical, hyperspectral and SAR images. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326785"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4928","DOI":"10.1109\/TGRS.2011.2151866","article-title":"Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis","volume":"49","author":"Martha","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land-cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3285","DOI":"10.1080\/01431161003745657","article-title":"Object-based urban detailed land-cover classification with high spatial resolution IKONOS imagery","volume":"32","author":"Pu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","unstructured":"Johnson, B.A. (2012). Mapping Urban Land Cover Using Multi-Scale and Spatial Autocorrelation Information in High Resolution Imagery. [Ph.D. Thesis, Florida Atlantic University]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1016\/0031-3203(95)00169-7","article-title":"A survey on evaluation methods for image segmentation","volume":"29","author":"Zhang","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.isprsjprs.2014.07.002","article-title":"Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery","volume":"96","author":"Belgiu","year":"2014","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1109\/34.506791","article-title":"An Experimental Comparison of Range Image Segmentation Algorithms","volume":"18","author":"Hoover","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"Lucieer, A. (2004). Uncertainties in Segmentation and Their Visualisation. [Ph.D. Thesis, Utrecht University and International Institute for Geo-Information Science and Earth Observation (ITC)]."},{"key":"ref_42","unstructured":"Neubert, M., and Meinel, G. (2003, January 6\u20138). Evaluation of Segmentation programs for high resolution remote sensing applications. Proceedings of the Joint ISPRS\/EARSeL Workshop \u201cHigh Resolution Mapping from Space 2003\u201d, Hannover, Germany."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","article-title":"Objective criteria for the evaluation of clustering methods","volume":"336","author":"Rand","year":"1971","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pont-Tuset, J., and Marques, F. (2013, January 23\u201328). Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.277"},{"key":"ref_45","unstructured":"Feitosa, R.Q., Costa, G.A.O.P., Cazes, T.B., and Feijo, B. (2006, January 4\u20135). A genetic approach for the automatic adaptation of segmentation parameters. Proceedings of the 1st International Conference on Object Based Image Analysis, Salzburg, Austria."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ma, L., Fu, T.Y., Blaschke, T., Li, M.C., Tiede, D., Zhou, Z.J., Ma, X.X., and Chen, D.L. (2017). Evaluation of feature selection methods for object-based land cover mapping of UAV imagery by RF and SVM classifiers. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020051"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/590\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:00:20Z","timestamp":1760194820000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/590"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,11]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040590"],"URL":"https:\/\/doi.org\/10.3390\/rs10040590","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,4,11]]}}}