{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:05:23Z","timestamp":1760241923225,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T00:00:00Z","timestamp":1540252800000},"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>In the framework of synthetic aperture radar (SAR) systems, current satellite missions make it possible to acquire images at very high and multiple spatial resolutions with short revisit times. This scenario conveys a remarkable potential in applications to, for instance, environmental monitoring and natural disaster recovery. In this context, data fusion and change detection methodologies play major roles. This paper proposes an unsupervised change detection algorithm for the challenging case of multimodal SAR data collected by sensors operating at multiple spatial resolutions. The method is based on Markovian probabilistic graphical models, graph cuts, linear mixtures, generalized Gaussian distributions, Gram\u2013Charlier approximations, maximum likelihood and minimum mean squared error estimation. It benefits from the SAR images acquired at multiple spatial resolutions and with possibly different modalities on the considered acquisition times to generate an output change map at the finest observed resolution. This is accomplished by modeling the statistics of the data at the various spatial scales through appropriate generalized Gaussian distributions and by iteratively estimating a set of virtual images that are defined on the pixel grid at the finest resolution and would be collected if all the sensors could work at that resolution. A Markov random field framework is adopted to address the detection problem by defining an appropriate multimodal energy function that is minimized using graph cuts.<\/jats:p>","DOI":"10.3390\/rs10111671","type":"journal-article","created":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T02:59:40Z","timestamp":1540349980000},"page":"1671","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Markovian Approach to Unsupervised Change Detection with Multiresolution and Multimodality SAR Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3861-7447","authenticated-orcid":false,"given":"David","family":"Solarna","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture, University of Genoa, Via All\u2019Opera Pia 11A, 16145 Genoa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3796-2938","authenticated-orcid":false,"given":"Gabriele","family":"Moser","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture, University of Genoa, Via All\u2019Opera Pia 11A, 16145 Genoa, Italy"}]},{"given":"Sebastiano B.","family":"Serpico","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture, University of Genoa, Via All\u2019Opera Pia 11A, 16145 Genoa, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ban, Y. (2016). Multitemporal Remote Sensing, Springer-Verlag.","DOI":"10.1007\/978-3-319-47037-5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2015.2443494","article-title":"The Time Variable in Data Fusion: A Change Detection Perspective","volume":"3","author":"Bovolo","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2946","DOI":"10.1109\/JPROC.2012.2198030","article-title":"Information extraction from remote sensing images for flood monitoring and damage evaluation","volume":"100","author":"Serpico","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/014311699213659","article-title":"Monitoring land-cover changes: A comparison of change detection techniques","volume":"20","author":"Mas","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3288","DOI":"10.1117\/1.1518995","article-title":"Unsupervised change-detection methods for remote-sensing images","volume":"41","author":"Melgani","year":"2002","journal-title":"Opt. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6497","DOI":"10.1109\/TGRS.2016.2585495","article-title":"A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images","volume":"54","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schaum, A.P., and Stocker, A. (2004). Hyperspectral change detection and supervised matched filtering based on covariance equalization. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, SPIE Press.","DOI":"10.1117\/12.544026"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/TGRS.2007.907973","article-title":"Hyperspectral Change Detection in the Presenceof Diurnal and Seasonal Variations","volume":"46","author":"Eismann","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","unstructured":"Oliver, C., and Quegan, S. (2004). Understanding Synthetic Aperture Radar Images, SciTech Publications."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/TGRS.2009.2012407","article-title":"Unsupervised Change Detection from Multichannel SAR Data by Markovian Data Fusion","volume":"47","author":"Moser","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1109\/TGRS.2007.893568","article-title":"A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis","volume":"45","author":"Inglada","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1109\/TGRS.2008.916476","article-title":"Conditional copulas for change detection in heterogeneous remote sensing images","volume":"46","author":"Mercier","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: the wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1002\/cpa.3160410705","article-title":"Orthonormal bases of compactly supported wavelets","volume":"41","author":"Daubechies","year":"1988","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1109\/TGRS.2005.857987","article-title":"A detail-preserving scale-driven approach to change detection in multitemporal SAR images","volume":"43","author":"Bovolo","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moser, G., and Serpico, S.B. (2010, January 25\u201330). Unsupervised change detection with very high resolution SAR images by multiscale analysis and Markov random fields. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652435"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1109\/TGRS.2010.2066979","article-title":"Multitemporal Image Change Detection Using Undecimated Discrete Wavelet Transform and Active Contours","volume":"49","author":"Celik","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chabira, B., Skanderi, T., and Aissa, A.B. (2013, January 25\u201327). Unsupervised change detection from multitemporal multichannel SAR images based on stationary wavelet transform. Proceedings of the MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Banff, AB, Canada.","DOI":"10.1109\/Multi-Temp.2013.6866025"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Moser, G., Serpico, S.B., and Vernazza, G. (2013, January 21\u201326). Multiresolution SAR data fusion for unsupervised change detection. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723003"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dellinger, F., Delon, J., Gousseau, Y., Michel, J., and Tupin, F. (2014, January 13\u201318). Change detection for high resolution satellite images, based on SIFT descriptors and an a contrario approach. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6946667"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TGRS.2014.2323552","article-title":"SAR-SIFT: A SIFT-Like Algorithm for SAR Images","volume":"53","author":"Dellinger","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1109\/TGRS.2011.2174155","article-title":"A New Coherent Similarity Measure for Temporal Multichannel Scene Characterization","volume":"50","author":"Erten","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1109\/TGRS.2012.2211883","article-title":"A New Polarimetric Change Detector in Radar Imagery","volume":"51","author":"Marino","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7483","DOI":"10.1109\/TGRS.2014.2310451","article-title":"Change Detection of Multilook Polarimetric SAR Images Using Heterogeneous Clutter Models","volume":"52","author":"Liu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.patcog.2014.09.027","article-title":"A New Patch Based Change Detector for Polarimetric SAR Data","volume":"48","author":"Liu","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Akbari, V., Anfinsen, S.N., Doulgeris, A.P., and Eltoft, T. (2015, January 26\u201331). A change detector for polarimetric SAR data based on the relaxed Wishart distribution. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326653"},{"key":"ref_27","first-page":"4041","article-title":"Change Detection in Full and Dual Polarization, Single and Multifrequency SAR Data","volume":"8","author":"Nielsen","year":"2015","journal-title":"IEEE J-STARS"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3953","DOI":"10.1109\/TGRS.2016.2532320","article-title":"Polarimetric SAR Change Detection with the Complex Hotelling-Lawley Trace Statistic","volume":"54","author":"Akbari","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2000000035","article-title":"Markov Random Fields in Image Segmentation","volume":"5","author":"Kato","year":"2012","journal-title":"Found. Trends Signal Process."},{"key":"ref_30","unstructured":"Li, S.Z. (2009). Markov Random Field Modeling in Image Analysis, Springer. Advances in Computer Vision and Pattern Recognition."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TPAMI.2004.1262177","article-title":"What energy functions can be minimized via graph cuts?","volume":"26","author":"Kolmogorov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TGRS.2004.841395","article-title":"A bayesian approach to classification of multiresolution remote sensing data","volume":"43","author":"Storvik","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5054","DOI":"10.1109\/TGRS.2016.2547027","article-title":"Multiresolution Supervised Classification of Panchromatic and Multispectral Images by Markov Random Fields and Graph Cuts","volume":"54","author":"Moser","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0165-1684(97)00223-5","article-title":"HOS-based generalized noise pdf models for signal detection optimization","volume":"65","author":"Tesei","year":"1998","journal-title":"Signal Process."},{"key":"ref_36","unstructured":"Kendall, M.G., and Stuart, A. (1958). The Advanced Theory of Statistics\/Sir Maurice Kendall and Alan Stuart, C. Griffin."},{"key":"ref_37","unstructured":"Papoulis, A., and Pillai, S.U. (2002). Probability, Random Variables, and Stochastic Processes, McGraw Hill. [4th ed.]."},{"key":"ref_38","unstructured":"Van Trees, H., Bell, K., and Tian, Z. (2013). Detection Estimation and Modulation Theory, Part I: Detection, Estimation, and Filtering Theory, Wiley."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TPAMI.1984.4767596","article-title":"Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images","volume":"6","author":"Geman","year":"1984","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/JPROC.2012.2211551","article-title":"Land-cover mapping by Markov modeling of spatial-contextual information in very-high resolution remote sensing images","volume":"101","author":"Moser","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/36.481897","article-title":"A Markov random field model for classification of multisource satellite imagery","volume":"34","author":"Solberg","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.1109\/TGRS.2006.876288","article-title":"Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery","volume":"44","author":"Moser","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TGRS.2004.842441","article-title":"An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images","volume":"43","author":"Bazi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","unstructured":"Cram\u00e9r, H. (1999). Mathematical Methods of Statistics (PMS-9), Princeton University Press."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0898-1221(00)00101-2","article-title":"A unifying information-theoretic framework for independent component analysis","volume":"39","author":"Lee","year":"2000","journal-title":"Comput. Math. Appl."},{"key":"ref_46","unstructured":"Powell, M.J.D. (2009). The BOBYQA Algorithm for Bound Constrained Optimization without Derivatives, University of Cambridge. Technical Report."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1111\/j.2517-6161.1974.tb00999.x","article-title":"Spatial Interaction and the Statistical Analysis of Lattice Systems","volume":"36","author":"Besag","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1111\/j.2517-6161.1989.tb01764.x","article-title":"Exact Maximum A Posteriori Estimation for Binary Images","volume":"51","author":"Greig","year":"1989","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TPAMI.2004.60","article-title":"An experimental comparison of min-cut\/max- flow algorithms for energy minimization in vision","volume":"26","author":"Boykov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"179","DOI":"10.2307\/2987782","article-title":"Statistical Analysis of Non-Lattice Data","volume":"24","author":"Besag","year":"1975","journal-title":"Statistician"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1093\/biomet\/64.3.616","article-title":"Efficiency of Pseudolikelihood Estimation for Simple Gaussian Fields","volume":"64","author":"Besag","year":"1977","journal-title":"Biometrika"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1137\/1026034","article-title":"Mixture Densities, Maximum Likelihood and the Em Algorithm","volume":"26","author":"Redner","year":"1984","journal-title":"SIAM Rev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1561\/2200000013","article-title":"An introduction to conditional random fields","volume":"4","author":"Sutton","year":"2011","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.1109\/TGRS.2009.2014364","article-title":"Unsupervised synthetic aperture radar image segmentation using Fisher distributions","volume":"47","author":"Galland","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","first-page":"2763","article-title":"Exploration of multitemporal COSMO-SkyMed data via interactive tree-structured MRF segmentation","volume":"7","author":"Gaetano","year":"2014","journal-title":"IEEE J-STARS"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3277","DOI":"10.1109\/TGRS.2018.2797316","article-title":"Decision Fusion with Multiple Spatial Supports by Conditional Random Fields","volume":"56","author":"Tuia","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1109\/JPROC.2015.2449668","article-title":"Multimodal Classification of Remote Sensing Images: A Review and Future Directions","volume":"103","author":"Tuia","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_58","first-page":"2448","article-title":"Classification of Multisensor and Multiresolution Remote Sensing Images Through Hierarchical Markov Random Fields","volume":"14","author":"Hedhli","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1671\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:25:35Z","timestamp":1760196335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,23]]},"references-count":58,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111671"],"URL":"https:\/\/doi.org\/10.3390\/rs10111671","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,10,23]]}}}