{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T23:21:42Z","timestamp":1779924102619,"version":"3.53.1"},"reference-count":78,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002830","name":"Centre National d\u2019Etudes Spatiales","doi-asserted-by":"publisher","award":["CNES\/INRIA no. 131024\/00"],"award-info":[{"award-number":["CNES\/INRIA no. 131024\/00"]}],"id":[{"id":"10.13039\/501100002830","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, a hierarchical probabilistic graphical model is proposed to tackle joint classification of multiresolution and multisensor remote sensing images of the same scene. This problem is crucial in the study of satellite imagery and jointly involves multiresolution and multisensor image fusion. The proposed framework consists of a hierarchical Markov model with a quadtree structure to model information contained in different spatial scales, a planar Markov model to account for contextual spatial information at each resolution, and decision tree ensembles for pixelwise modeling. This probabilistic graphical model and its topology are especially fit for application to very high resolution (VHR) image data. The theoretical properties of the proposed model are analyzed: the causality of the whole framework is mathematically proved, granting the use of time-efficient inference algorithms such as the marginal posterior mode criterion, which is non-iterative when applied to quadtree structures. This is mostly advantageous for classification methods linked to multiresolution tasks formulated on hierarchical Markov models. Within the proposed framework, two multimodal classification algorithms are developed, that incorporate Markov mesh and spatial Markov chain concepts. The results obtained in the experimental validation conducted with two datasets containing VHR multispectral, panchromatic, and radar satellite images, verify the effectiveness of the proposed framework. The proposed approach is also compared to previous methods that are based on alternate strategies for multimodal fusion.<\/jats:p>","DOI":"10.3390\/rs13050849","type":"journal-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T05:20:08Z","timestamp":1614230408000},"page":"849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3804-4768","authenticated-orcid":false,"given":"Martina","family":"Pastorino","sequence":"first","affiliation":[{"name":"DITEN Deptartment, University of Genoa, 16145 Genoa, Italy"},{"name":"Inria, Universit\u00e9 C\u00f4te d\u2019Azur, 06902 Sophia-Antipolis, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Montaldo","sequence":"additional","affiliation":[{"name":"DITEN Deptartment, University of Genoa, 16145 Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Fronda","sequence":"additional","affiliation":[{"name":"DITEN Deptartment, University of Genoa, 16145 Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ihsen","family":"Hedhli","sequence":"additional","affiliation":[{"name":"Institute on Intelligence and Data, Laval University, Quebec City, QC G1V0A6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3796-2938","authenticated-orcid":false,"given":"Gabriele","family":"Moser","sequence":"additional","affiliation":[{"name":"DITEN Deptartment, University of Genoa, 16145 Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastiano B.","family":"Serpico","sequence":"additional","affiliation":[{"name":"DITEN Deptartment, University of Genoa, 16145 Genoa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7444-0856","authenticated-orcid":false,"given":"Josiane","family":"Zerubia","sequence":"additional","affiliation":[{"name":"Inria, Universit\u00e9 C\u00f4te d\u2019Azur, 06902 Sophia-Antipolis, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/JPROC.2012.2211551","article-title":"Land-Cover Mapping by Markov Modeling of Spatial\u2013Contextual Information in Very-High-Resolution Remote Sensing Images","volume":"101","author":"Moser","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2013). Remote Sensing Digital Image Analysis: An Introduction, Springer. [5th ed.].","DOI":"10.1007\/978-3-642-30062-2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alparone, L., Aiazzi, B., Baronti, S., and Garzelli, G. (2015). Remote Sensing Image Fusion, CRC Press.","DOI":"10.1201\/b18189"},{"key":"ref_4","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."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1561\/2000000071","article-title":"Deep learning in object recognition, detection, and segmentation","volume":"8","author":"Wang","year":"2016","journal-title":"Found. Trends Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MSP.2017.2762355","article-title":"Conditional random fields meet deep neural networks for semantic segmentation: Combining probabilistic graphical models with deep learning for structured prediction","volume":"35","author":"Arnab","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_7","unstructured":"Li, S.Z. (2009). Markov Random Field Modeling in Image Analysis, Springer. [3rd ed.]."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1109\/89.917686","article-title":"A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces","volume":"9","author":"Baggenstoss","year":"2001","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1109\/83.826777","article-title":"Discrete Markov image modeling and inference on the quadtree","volume":"9","author":"Heitz","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1109\/TIT.1965.1053827","article-title":"Classification of binary random patterns","volume":"11","author":"Abend","year":"1965","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/TGRS.2003.809940","article-title":"Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields","volume":"41","author":"Fjortoft","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/02664769300000064","article-title":"Hidden Markov mesh random field models in image analysis","volume":"20","author":"Devijver","year":"1993","journal-title":"J. Appl. Stat."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1109\/34.632989","article-title":"Computational Bayesian analysis of hidden Markov mesh models","volume":"19","author":"Dunmur","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1109\/TIP.2013.2246516","article-title":"Computationally tractable stochastic image modeling based on symmetric Markov mesh random fields","volume":"22","author":"Yousefi","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.1109\/TIP.2009.2029988","article-title":"Computation of image spatial entropy using quadrilateral Markov random field","volume":"18","author":"Razlighi","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Criminisi, A., and Shotton, J. (2013). Decision Forests for Computer Vision and Medical Image Analysis, Springer.","DOI":"10.1007\/978-1-4471-4929-3"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hedhli, I., Moser, G., Serpico, S.B., and Zerubia, J. (2017, January 6\u20138). Multi-resolution classification of urban areas using hierarchical symmetric Markov mesh models. Proceedings of the JURSE 2017, Dubai, United Arab Emirates.","DOI":"10.1109\/JURSE.2017.7924567"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Montaldo, A., Fronda, L., Hedhli, I., Moser, G., Serpico, S.B., and Zerubia, J. (2019, January 22\u201325). Causal Markov mesh hierarchical modeling for the contextual classification of multiresolution satellite images. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803351"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Montaldo, A., Fronda, L., Hedhli, I., Moser, G., Zerubia, J., and Serpico, S.B. (August, January 28). Joint classification of multiresolution and multisensor data using a multiscale Markov mesh model. Proceedings of the 2019 IEEE Geoscience and Remote Sensing Society (IGARSS), Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898060"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"269","DOI":"10.5194\/isprs-annals-V-3-2020-269-2020","article-title":"A causal hierarchical Markov framework for the classification of multiresolution and multisensor remote sensing images","volume":"V-3-2020","author":"Montaldo","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","article-title":"Rotation forest: A New classifier ensemble method","volume":"28","author":"Rodriguez","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/MGRS.2015.2432092","article-title":"Many hands make light work\u2014On ensemble learning techniques for data fusion in remote sensing","volume":"3","author":"Merentitis","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Pohl, C., and Van Genderen, J. (2016). Remote Sensing Image Fusion: A Practical Guide, CRC Press. [1st ed.].","DOI":"10.1201\/9781315370101"},{"key":"ref_29","unstructured":"Mallat, S. (2009). A Wavelet Tour of Signal Processing\u2014The Sparse Way, Academic Press. [3rd ed.]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/JSEN.2010.2041924","article-title":"Hybrid multiresolution method for multisensor multimodal image fusion","volume":"10","author":"Li","year":"2010","journal-title":"IEEE Sensors J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1016\/j.patcog.2004.03.010","article-title":"A wavelet-based image fusion tutorial","volume":"37","author":"Pajares","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1109\/TIP.2003.819237","article-title":"Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient","volume":"12","author":"Johnson","year":"2003","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/TGRS.2013.2281391","article-title":"A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information","volume":"52","author":"Gong","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/TIP.2005.847287","article-title":"Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery","volume":"14","author":"Zavorin","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3674","DOI":"10.1109\/TGRS.2006.881758","article-title":"Estimation of the mumber of decomposition levels for a wavelet-based multiresolution multisensor image fusion","volume":"44","author":"Pradhan","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2018.2890023","article-title":"Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art","volume":"7","author":"Ghamisi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_37","first-page":"459","article-title":"The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data","volume":"56","author":"Carper","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_38","first-page":"339","article-title":"Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis","volume":"55","author":"Chavez","year":"1989","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1109\/JSTSP.2011.2104938","article-title":"A theoretical analysis of the effects of aliasing and misregistration on pansharpened imagery","volume":"5","author":"Baronti","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1080\/014311600750037499","article-title":"Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details","volume":"21","author":"Liu","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"591","DOI":"10.14358\/PERS.72.5.591","article-title":"MTF-tailored multiscale fusion of high-resolution MS and pan imagery","volume":"72","author":"Aiazzi","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/36.763274","article-title":"Multiresolution-based image fusion with additive wavelet decomposition","volume":"37","author":"Nunez","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/TGRS.2005.856106","article-title":"Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods","volume":"43","author":"Otazu","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.inffus.2006.02.001","article-title":"Remote sensing image fusion using the curvelet transform","volume":"8","author":"Nencini","year":"2007","journal-title":"Inf. Fusion"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1109\/JSTARS.2011.2168317","article-title":"Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion","volume":"5","author":"Thoonen","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TGRS.2002.1000321","article-title":"Multisource data classification with dependence trees","volume":"40","author":"Datcu","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","unstructured":"Benediktsson, J.A., Swain, P.H., and Ersoy, O.K. (1989, January 10\u201314). Neural network approaches versus statistical methods in classification of multisource remote sensing data. Proceedings of the 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1109\/36.387573","article-title":"Classification of multi-sensor remote-sensing images by structured neural networks","volume":"33","author":"Serpico","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S1566-2535(01)00056-2","article-title":"Image fusion techniques for remote sensing applications","volume":"3","author":"Simone","year":"2002","journal-title":"Int. J. Inf. Fusion"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3858","DOI":"10.1109\/TGRS.2007.898446","article-title":"Fusion of support vector machines for classification of multisensor data","volume":"45","author":"Waske","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TGRS.2008.916201","article-title":"Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection","volume":"46","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"2832","DOI":"10.1109\/TGRS.2004.838344","article-title":"Landsat ETM+ and SAR image fusion based on generalized intensity Modulation","volume":"42","author":"Alparone","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1080\/01431160600606874","article-title":"Integration of panchromatic and SAR features into multispectral SPOT images using the \u2018\u00e0 trous\u2019 wavelet decomposition","volume":"28","author":"Chibani","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1109\/JSTARS.2013.2272773","article-title":"An area-based image fusion scheme for the integration of SAR and optical satellite imagery","volume":"6","author":"Byun","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1080\/01431160600649254","article-title":"Influence of image fusion approaches on classification accuracy: A case study","volume":"27","author":"Colditz","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"3346","DOI":"10.1109\/TGRS.2013.2272581","article-title":"Supervised classification of multisensor and multiresolution remote sensing images with a hierarchical copula-based approach","volume":"52","author":"Voisin","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"134677","DOI":"10.1109\/ACCESS.2019.2939152","article-title":"Temporal attention networks for multitemporal multisensor crop classification","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/JSTARS.2020.2971763","article-title":"A CNN-transformer hybrid approach for crop classification using multitemporal multisensor images","volume":"13","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1109\/JPROC.2002.800717","article-title":"Multiresolution Markov models for signal and image processing","volume":"90","author":"Willsky","year":"2002","journal-title":"Proc. IEEE"},{"key":"ref_63","unstructured":"Rudin, W. (1987). Real and Complex Analysis, McGraw-Hill, Inc.. [3rd ed.]."},{"key":"ref_64","first-page":"1085","article-title":"An empirical investigation of image resampling effects upon the spectral and textural supervised classification of a high spatial resolution multispectral image","volume":"62","author":"Dikshit","year":"1996","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1109\/TGRS.2006.882262","article-title":"Analysis of artifacts in subpixel remote sensing image registration","volume":"45","author":"Inglada","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","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_67","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/83.277898","article-title":"A multiscale random field model for Bayesian image segmentation","volume":"3","author":"Bouman","year":"1994","journal-title":"IEEE Trans. Image Process."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"6333","DOI":"10.1109\/TGRS.2016.2580321","article-title":"A new cascade model for the hierarchical joint classification of multitemporal and multiresolution remote sensing data","volume":"54","author":"Hedhli","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1049\/el.2011.1364","article-title":"Generating symmetric causal Markov random fields","volume":"47","author":"Yousefi","year":"2011","journal-title":"Electron. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF00116037","article-title":"The strength of weak learnability","volume":"5","author":"Schapire","year":"1990","journal-title":"Mach. Learn."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1109\/TGRS.2002.802476","article-title":"Multiple classifiers applied to multisource remote sensing data","volume":"40","author":"Briem","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1080\/01431161.2011.602651","article-title":"Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data","volume":"33","author":"Miao","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/S0034-4257(00)00142-5","article-title":"Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data","volume":"74","author":"DeFries","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1109\/TPAMI.2006.200","article-title":"Convergent tree-reweighted message passing for energy minimization","volume":"28","author":"Kolmogorov","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_75","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_76","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning, MIT Press."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"3641","DOI":"10.1109\/JSTARS.2017.2693993","article-title":"Adaptive scale selection for multiscale segmentation of satellite images","volume":"10","author":"Zhou","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"103026","DOI":"10.1016\/j.cviu.2020.103026","article-title":"Low-level multiscale image segmentation and a benchmark for its evaluation","volume":"199","author":"Akbas","year":"2020","journal-title":"Comput. Vis. Image Underst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:28:09Z","timestamp":1760160489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,25]]},"references-count":78,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050849"],"URL":"https:\/\/doi.org\/10.3390\/rs13050849","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,25]]}}}