han, lirongPaoletti, Mercedes EugeniaMoreno Álvarez, SergioHaut, Juan M.Pastor Vargas, RafaelPlaza, Antonio2024-11-202024-11-202024L. Han, M. E. Paoletti, S. Moreno-Álvarez, J. M. Haut, R. Pastor-Vargas and A. Plaza, "Hashing for Retrieving Long-Tailed Distributed Remote Sensing Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024, Art no. 5608914, doi: 10.1109/TGRS.2024.33606210196-2892, eISSN: 1558-0644https://doi.org/10.1109/TGRS.2024.3360621https://hdl.handle.net/20.500.14468/24441The registered version of this article, first published in “IEEE Transactions on Geoscience and Remote Sensing, vol. 62, 2024", is available online at the publisher's website: IEEE, https://doi.org/10.1109/TGRS.2024.3360621 La versión registrada de este artículo, publicado por primera vez en “Computers in Human Behavior, 2021, vol. 115", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/TGRS.2024.3360621The widespread availability of remotely sensed datasets establishes a cornerstone for comprehensive image retrieval within the realm of remote sensing (RS). In response, the investigation into hashing-driven retrieval methods garners significance, enabling proficient image acquisition within such extensive data magnitudes. Nevertheless, the used datasets in practical applications are invariably less desirable and with long-tailed distribution. The primary hurdle pertains to the substantial discrepancy in class volumes. Moreover, commonly utilized RS datasets for hashing tasks encompass approximately two–three dozen classes. However, real-world datasets exhibit a randomized number of classes, introducing a challenging variability. This article proposes a new centripetal intensive attention hashing (CIAH) mechanism based on intensive attention features for long-tailed distribution RS image retrieval. Specifically, an intensive attention module (IAM) is adopted to enhance the significant features to facilitate the subsequent generation of representative hash codes. Furthermore, to deal with the inherent imbalance of long-tailed distributed datasets, the utilization of a centripetal loss function is introduced. This endeavor constitutes the inaugural effort toward long-tailed distributed RS image retrieval. In pursuit of this objective, a collection of long-tail datasets is meticulously curated using four widely recognized RS datasets, subsequently disseminated as benchmark datasets. The selected fundamental datasets contain 7, 25, 38, and 45 land-use classes to mimic different real RS datasets. Conducted experiments demonstrate that the proposed methodology attains a performance benchmark that surpasses currently existing methodologies.eninfo:eu-repo/semantics/restrictedAccess12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 InformáticaartículoCodesFeature extractionImage retrievalTailRemote sensingHeadEurope