Convolutional Autoencoder-Based Multispectral Image Fusion

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dc.contributor.ORCID0000-0001-8520-8457 (Azarang, A)
dc.contributor.ORCID0000-0001-5183-6359 (Kehtarnavaz, N)
dc.contributor.VIAF166234961 (Kehtarnavaz, N)
dc.contributor.authorAzarang, Arian
dc.contributor.authorManoochehri, Hafez E.
dc.contributor.authorKehtarnavaz, Nasser
dc.contributor.utdAuthorAzarang, Arian
dc.contributor.utdAuthorManoochehri, Hafez E.
dc.contributor.utdAuthorKehtarnavaz, Nasser
dc.date.accessioned2019-10-31T15:09:51Z
dc.date.available2019-10-31T15:09:51Z
dc.date.created2019-03-18
dc.description.abstractThis paper presents a deep learning-based pansharpening method for fusion of panchromatic and multispectral images in remote sensing applications. This method can be categorized as a component substitution method in which a convolutional autoencoder network is trained to generate original panchromatic images from their spatially degraded versions. Low resolution multispectral images are then fed into the trained convolutional autoencoder network to generate estimated high resolution multispectral images. The fusion is achieved by injecting the detail map of each spectral band into the corresponding estimated high resolution multispectral bands. Full reference and no-reference metrics are computed for the images of three satellite datasets. These measures are compared with the existing fusion methods whose codes are publicly available. The results obtained indicate the effectiveness of the developed deep learning-based method for multispectral image fusion. © 2019 IEEE.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.bibliographicCitationAzarang, A., H. E. Manoochehri, and N. Kehtarnavaz. 2019. "Convolutional Autoencoder-Based Multispectral Image Fusion." IEEE Access 7: 35673-35683, doi: 10.1109/ACCESS.2019.2905511
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10735.1/7050
dc.identifier.volume7
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.urihttps://dx.doi.org/10.1109/ACCESS.2019.2905511
dc.rightsIEEE Open Access. Commercial reuse is prohibited.
dc.rights©2019 IEEE
dc.rightshttps://open.ieee.org/index.php/publishing-options/ieee-access-journal/
dc.source.journalIEEE Access
dc.subjectConvolutions (Mathematics)
dc.subjectDeep learning
dc.subjectRemote sensing
dc.subjectMultispectral imaging
dc.subjectRemote sensing
dc.titleConvolutional Autoencoder-Based Multispectral Image Fusion
dc.type.genrearticle

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