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Deep shared proxy construction hashing for cross-modal remote sensing image fast target retrieval

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dc.contributor.authorhan, lirong
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorHaut, Juan M.
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.date.accessioned2024-11-20T10:54:59Z
dc.date.available2024-11-20T10:54:59Z
dc.date.issued2024
dc.descriptionThe registered version of this article, first published in “ISPRS Journal of Photogrammetry and Remote Sensing, vol 218, 2024", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.isprsjprs.2024.10.004 La versión registrada de este artículo, publicado por primera vez en “ISPRS Journal of Photogrammetry and Remote Sensing, vol 218, 2024", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.isprsjprs.2024.10.004
dc.description.abstractThe diversity of remote sensing (RS) image modalities has expanded alongside advancements in RS technologies. A plethora of optical, multispectral, and hyperspectral RS images offer rich geographic class information. The ability to swiftly access multiple RS image modalities is crucial for fully harnessing the potential of RS imagery. In this work, an innovative method, called Deep Shared Proxy Construction Hashing (DSPCH), is introduced for cross-modal hyperspectral scene target retrieval using accessible RS images such as optical and sketch. Initially, a shared proxy hash code is generated in the hash space for each land use class. Subsequently, an end-to-end deep hash network is built to generate hash codes for hyperspectral pixels and accessible RS images. Furthermore, a proxy hash loss function is designed to optimize the proposed deep hashing network, aiming to generate hash codes that closely resemble the corresponding proxy hash code. Finally, two benchmark datasets are established for cross-modal hyperspectral and accessible RS image retrieval, allowing us to conduct extensive experiments with these datasets. Our experimental results validate that the novel DSPCH method can efficiently and effectively achieve RS image cross-modal target retrieval, opening up new avenues in the field of cross-modal RS image retrievalen
dc.description.versionversión publicada
dc.identifier.citationLirong Han, Mercedes E. Paoletti, Sergio Moreno-Álvarez, Juan M. Haut, Antonio Plaza, Deep shared proxy construction hashing for cross-modal remote sensing image fast target retrieval, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 218, Part B, 2024, Pages 44-56, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.10.004
dc.identifier.doihttps://doi.org/10.1016/j.isprsjprs.2024.10.004
dc.identifier.issn0924-2716
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24449
dc.journal.titleISPRS Journal of Photogrammetry and Remote Sensing
dc.journal.volume218
dc.language.isoen
dc.page.final56
dc.page.initial44
dc.publisherELSEVIER
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
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.keywordsRemote sensing image retrievalen
dc.subject.keywordsDeep learningen
dc.subject.keywordsHash-based retrieval methodsen
dc.subject.keywordsCross-modal target retrievalen
dc.subject.keywordsShared proxy constructionen
dc.titleDeep shared proxy construction hashing for cross-modal remote sensing image fast target retrievalen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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