Exploiting SAR Tomography for Supervised Land-Cover Classification

dc.contributor.authorD’Hondt, Olivier
dc.contributor.authorHänsch, Ronny
dc.contributor.authorWagener, Nicolas
dc.contributor.authorHellwich, Olaf
dc.date.accessioned2019-09-11T15:25:00Z
dc.date.available2019-09-11T15:25:00Z
dc.date.issued2018-11-05
dc.date.updated2019-08-01T05:00:48Z
dc.description.abstractIn this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this objective. In particular, we show that classification based on TomoSAR significantly outperforms PolSAR data provided relevant features are extracted from the tomograms. We also provide a comparison of classification results obtained from covariance matrices versus tomogram features as well as obtained by different reference methods, i.e., the traditional Wishart classifier and the more sophisticated Random Forest. Extensive qualitative and quantitative results are shown on a fully polarimetric and multi-baseline dataset from the E-SAR sensor from the German Aerospace Center (DLR).en
dc.identifier.eissn2072-4292
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/9996
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-8987
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.otherSAR tomographyen
dc.subject.otherland-cover classificationen
dc.subject.otherfeature extractionen
dc.subject.otherrandom forestsen
dc.titleExploiting SAR Tomography for Supervised Land-Cover Classificationen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber1742en
dcterms.bibliographicCitation.doi10.3390/rs10111742en
dcterms.bibliographicCitation.issue11en
dcterms.bibliographicCitation.journaltitleRemote Sensingen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume10en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Technische Informatik und Mikroelektronik>FG Computer Vision & Remote Sensingde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Computer Vision & Remote Sensingde
tub.affiliation.instituteInst. Technische Informatik und Mikroelektronikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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