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Generalized Scalable Neighborhood Component Analysis for Single and Multi-Label Remote Sensing Image Characterization
dc.contributor.author | kang, jian | |
dc.contributor.author | SCARANO, Antonio | |
dc.contributor.author | Plaza, Antonio | |
dc.date.accessioned | 2022-07-27T08:13:46Z | |
dc.date.available | 2022-07-27T08:13:46Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | KANG, Jian; FERNANDEZ-BELTRAN, Ruben; PLAZA, Antonio. Generalized Scalable Neighborhood Component Analysis for Single and Multi-Label Remote Sensing Image Characterization. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021. p. 2150-2153. | ca_CA |
dc.identifier.isbn | 9781665403696 | |
dc.identifier.uri | http://hdl.handle.net/10234/198762 | |
dc.description | Ponencia presentada en: IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) 2021, del 11 al 16 de julio de 2021 | |
dc.description.abstract | Deep metric learning has recently become a prominent technology for the semantic understanding ofremote sensing (RS) scenes due to its great potential for characterizing visual semantics. However, state-of-the-art deep metric learning models are often constrained in RS by the use of single-label annotations, which eventually reduce their capacity to characterize complex aerial scenes. Additionally, many of the existing works are specialized in particular RS applications which constrains the study of their associated metric spaces from a multi-task perspective. In this paper, we propose a new unified deep metric learning approach for both single- and multi-label RS scene characterization while also taking into account different downstream RS applications. Specifically, we extend the Scalable Neighborhood Component Analysis (SNCA) to the multi-label case and propose its generalized version, i.e., GSNCA. Extensive experiments on single- and multi-label RS benchmark datasets have been conducted to evaluate the effectiveness of the proposed method for RS image classification, clustering and retrieval. | ca_CA |
dc.format.extent | 4 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | IEEE | ca_CA |
dc.relation.isPartOf | IEEE, 2021. p. 2150-2153 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | deep metric learning | ca_CA |
dc.subject | neighbor embedding | ca_CA |
dc.subject | single- Multi-labels | ca_CA |
dc.subject | scene categorization | ca_CA |
dc.title | Generalized Scalable Neighborhood Component Analysis for Single and Multi-Label Remote Sensing Image Characterization | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.identifier.doi | https://doi.org/10.1109/IGARSS47720.2021.9553475 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |