Publicación: Deep Robust Hashing Using Self-Distillation for Remote Sensing Image Retrieval
No hay miniatura disponible
Fecha
2024
Editor/a
Director/a
Tutor/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
info:eu-repo/semantics/restrictedAccess
Título de la revista
ISSN de la revista
Título del volumen
Editor
IEEE
Resumen
This 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.
Descripción
The 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
Categorías UNESCO
Palabras clave
Training, Codes, Image retrieval, Production, Sensors, Remote sensing
Citación
L. 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
Centro
Facultades y escuelas::E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos