Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping

dc.contributor.authorDuan, Puhong
dc.contributor.authorLai, Jibao
dc.contributor.authorGhamisi, Pedram
dc.contributor.authorKang, Xudong
dc.contributor.authorJackisch, Robert
dc.contributor.authorKang, Jian
dc.contributor.authorGloaguen, Richard
dc.date.accessioned2020-11-06T12:01:09Z
dc.date.available2020-11-06T12:01:09Z
dc.date.issued2020-09-07
dc.date.updated2020-10-07T17:36:20Z
dc.description.abstractCombining both spectral and spatial information with enhanced resolution provides not only elaborated qualitative information on surfacing mineralogy but also mineral interactions of abundance, mixture, and structure. This enhancement in the resolutions helps geomineralogic features such as small intrusions and mineralization become detectable. In this paper, we investigate the potential of the resolution enhancement of hyperspectral images (HSIs) with the guidance of RGB images for mineral mapping. In more detail, a novel resolution enhancement method is proposed based on component decomposition. Inspired by the principle of the intrinsic image decomposition (IID) model, the HSI is viewed as the combination of a reflectance component and an illumination component. Based on this idea, the proposed method is comprised of several steps. First, the RGB image is transformed into the luminance component, blue-difference and red-difference chroma components (YCbCr), and the luminance channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled luminance channel. Finally, the HSI with high resolution can be reconstructed by utilizing the obtained illumination and the reflectance components. Experimental results verify that the fused results can successfully achieve mineral mapping, producing better results qualitatively and quantitatively over single sensor data.en
dc.identifier.eissn2072-4292
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/11853
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-10743
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherhyperspectral imageen
dc.subject.othermineral mappingen
dc.subject.otherresolution enhancementen
dc.subject.otherintrinsic image decompositionen
dc.titleComponent Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mappingen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber2903en
dcterms.bibliographicCitation.doi10.3390/rs12182903en
dcterms.bibliographicCitation.issue18en
dcterms.bibliographicCitation.journaltitleRemote Sensingen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume12en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Technische Informatik und Mikroelektronik>FG Remote Sensing Image Analysis Groupde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Remote Sensing Image Analysis Groupde
tub.affiliation.instituteInst. Technische Informatik und Mikroelektronikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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