MultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networks

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dc.authorscopusid55292760600
dc.authorscopusid57221815490
dc.authorscopusid55666247200
dc.contributor.authorKaraca, Ali Can
dc.contributor.authorKara, Ozan
dc.contributor.authorGüllü, Mehmet Kemal
dc.date.accessioned2022-02-15T16:57:56Z
dc.date.available2022-02-15T16:57:56Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractMultispectral satellites that measure the reflected energy from the different regions on the Earth generate the multispectral (MS) images continuously. The following MS image for the same region can be acquired with respect to the satellite revisit period. The images captured at different times over the same region are called multitemporal images. Traditional compression methods generally benefit from spectral and spatial correlation within the MS image. However, there is also a temporal correlation between multitemporal images. To this end, we propose a novel generative adversarial network (GAN) based prediction method called MultiTempGAN for compression of multitemporal MS images. The proposed method defines a lightweight GAN-based model that learns to transform the reference image to the target image. Here, the generator parameters of MultiTempGAN are saved for the reconstruction purpose in the receiver system. Due to MultiTempGAN has a low number of parameters, it provides efficiency in multitemporal MS image compression. Experiments were carried out on three Sentinel-2 MS image pairs belonging to different geographical regions. We compared the proposed method with JPEG2000-based conventional compression methods and three deep learning methods in terms of signal-tonoise ratio, mean spectral angle, mean spectral correlation, and laplacian mean square error metrics. Additionally, we have also evaluated the change detection performances and visual maps of the methods. Experimental results demonstrate that MultiTempGAN not only achieves the best metric values among the other methods at high compression ratios but also presents convincing performances in change detection applications.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [119E405]en_US
dc.description.sponsorshipThis study has been supported by The Scientific and Technological Research Council of Turkey (TUBITAK) [119E405] .en_US
dc.identifier.doi10.1016/j.jvcir.2021.103385
dc.identifier.issn1047-3203
dc.identifier.issn1095-9076
dc.identifier.scopus2-s2.0-85119176940en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jvcir.2021.103385
dc.identifier.urihttps://hdl.handle.net/20.500.14034/312
dc.identifier.volume81en_US
dc.identifier.wosWOS:000727378900003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAcademic Press Inc Elsevier Scienceen_US
dc.relation.journalJournal Of Visual Communication And Image Representationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMultispectral image compressionen_US
dc.subjectGenerative adversarial networksen_US
dc.subjectBig dataen_US
dc.subjectRemote sensingen_US
dc.subjectMultitemporal imagesen_US
dc.subjectHyperspectral Imageen_US
dc.titleMultiTempGAN: Multitemporal multispectral image compression framework using generative adversarial networksen_US
dc.typeArticleen_US

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