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dc.contributor.authorLuppino, Luigi Tommaso
dc.contributor.authorBianchi, Filippo Maria
dc.contributor.authorMoser, Gabriele
dc.contributor.authorAnfinsen, Stian Normann
dc.date.accessioned2022-11-03T07:35:44Z
dc.date.available2022-11-03T07:35:44Z
dc.date.issued2018-11-01
dc.description.abstractChange detection in heterogeneous multitemporal satellite images is an emerging topic in remote sensing. In this paper we propose a framework, based on image regression, to perform change detection in heterogeneous multitemporal satellite images, which has become a main topic in remote sensing. Our method learns a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian processes, support vector machines, random forests, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate not only potentials and limitations of our framework, but also the pros and cons of each regression method, we perform experiments on two data sets. The results indicates that random forests achieve good performance, are fast and robust to hyperparameters, whereas the homogeneous pixel transformation method can achieve better accuracy at the cost of a higher complexity.en_US
dc.description© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.identifier.citationLuppino LT, Bianchi FM, Moser G, Anfinsen SN: Remote sensing image regression for heterogeneous change detection. In: IEEE SPS. 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), 2018. IEEE Signal Processing Societyen_US
dc.identifier.cristinIDFRIDAID 1658459
dc.identifier.doi10.1109/MLSP.2018.8517033
dc.identifier.isbn978-1-5386-5477-4
dc.identifier.urihttps://hdl.handle.net/10037/27238
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.projectIDNorges forskningsråd: 251327en_US
dc.relation.urihttps://ieeexplore.ieee.org/abstract/document/8517033
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright © 2018 IEEEen_US
dc.subjectVDP::Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.subjectVDP::Mathematics and natural scienses: 400::Information and communication science: 420en_US
dc.subjectBildebehandling / Image processingen_US
dc.subjectKunstig intelligens / Artificial intelligenceen_US
dc.subjectMaskinlæring / Machine learningen_US
dc.subjectMønstergjenkjenning / Pattern Recognitionen_US
dc.titleRemote sensing image regression for heterogeneous change detectionen_US
dc.type.versionacceptedVersionen_US
dc.typeConference objecten_US
dc.typeKonferansebidragen_US


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