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Deep Robust Hashing Using Self-Distillation for Remote Sensing Image Retrieval

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dc.contributor.authorhan,lirong
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorHaut, Juan Mario
dc.contributor.authorPlaza, Antonio
dc.contributor.orcidhttps://orcid.org/0000-0002-8613-7037
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.contributor.orcidhttps://orcid.org/0000-0002-9613-1659
dc.coverage.spatialAthens, Greece
dc.coverage.temporal2024-07-12
dc.date.accessioned2024-11-21T08:24:55Z
dc.date.available2024-11-21T08:24:55Z
dc.date.issued2024
dc.descriptionThe registered version of this article, first published in “IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium", is available online at the publisher's website: IEEE, https://doi.org/10.1109/IGARSS53475.2024.10641118 La versión registrada de este artículo, publicado por primera vez en “IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/IGARSS53475.2024.10641118
dc.description.abstractThis paper presents a novel self-distillation based deep robust hash for fast remote sensing (RS) image retrieval. Specifically, there are two primary processes in our proposed model: teacher learning (TL) and student learning (SL). Two transformed samples are produced from one sample image through nuanced and signalized transformations, respectively. Transformed samples are fed into both the TL and the SL flows. To reduce discrepancies in the processed samples and guarantee a consistent hash code, the parameters are shared by the two modules during the training stage. Then, a resilient module is employed to enhance the image features in order to ensure more dependable hash code production. Lastly, a three-component loss function is developed to train the entire model. Comprehensive experiments are conducted on two common RS datasets: UCMerced and AID. The experimental results validate that the proposed method has competitive performance against other RS image hashing methods.en
dc.description.versionversión publicada
dc.identifier.citationL. Han, M. E. Paoletti, S. Moreno-Álvarez, J. M. Haut and A. J. Plaza, "Deep Robust Hashing Using Self-Distillation for Remote Sensing Image Retrieval," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 7111-7114, doi: 10.1109/IGARSS53475.2024.10641118
dc.identifier.doihttps://doi.org/10.1109/IGARSS53475.2024.10641118
dc.identifier.isbn979-8-3503-6032-5
dc.identifier.issn2153-6996, eISSN: 2153-7003
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24462
dc.language.isoen
dc.publisherIEEE
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.congressInternational Geoscience and Remote Sensing Symposium (IGARSS), IGARSS 2024
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsTrainingen
dc.subject.keywordsCodesen
dc.subject.keywordsImage retrievalen
dc.subject.keywordsProductionen
dc.subject.keywordsSensorsen
dc.subject.keywordsRemote sensingen
dc.titleDeep Robust Hashing Using Self-Distillation for Remote Sensing Image Retrievalen
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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