Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11036
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dc.contributor.authorKang, Jian-
dc.contributor.authorFernández-Beltrán, Rubén-
dc.contributor.authorYe, Zhen-
dc.contributor.authorTong, Xiaohua-
dc.contributor.authorGhamisi, Pedram-
dc.contributor.authorPlaza, Antonio-
dc.date.accessioned2020-12-09T14:38:37Z-
dc.date.available2020-12-09T14:38:37Z-
dc.date.issued2020-08-12-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12162-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-11036-
dc.description.abstractDeep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task in large-scale RS archives, which may eventually constrain the availability of sufficient training data for this kind of models. To address this issue, we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. The conducted experiments (including different state-of-the-art methods and two benchmark RS archives) validate the effectiveness of the proposed approach for RS image classification, clustering and retrieval tasks. The codes of this paper are publicly available.en
dc.description.sponsorshipEC/H2020/734541/EU/TOOLS FOR MAPPING HUMAN EXPOSURE TO RISKY ENVIRONMENTAL CONDITIONS BY MEANS OF GROUND AND EARTH OBSERVATION DATA/EOXPOSUREen
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherdeep metric learningen
dc.subject.otherremote sensingen
dc.subject.otherimage characterizationen
dc.subject.othersemi-supervised learningen
dc.titleHigh-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imageryen
dc.typeArticleen
dc.date.updated2020-09-02T18:57:31Z-
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn2072-4292-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.3390/rs12162603en
dcterms.bibliographicCitation.journaltitleRemote Sensingen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume12en
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.issue16en
dcterms.bibliographicCitation.articlenumber2603en
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