Publicación: Parameter-Free Attention Network for Spectral–Spatial Hyperspectral Image Classification
No hay miniatura disponible
Fecha
2023
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
Hyperspectral images (HSIs) comprise plenty of information in the spatial and spectral domain, which is highly beneficial for performing classification tasks in a very accurate way. Recently, attention mechanisms have been widely used in the HSI classification due to their ability to extract relevant spatial and spectral features. Notwithstanding their positive results, most of the attentional strategies usually introduce a significant number of parameters to be trained, making the models more complex and increasing the computational load. In this article, we develop a new parameter-free attention network for HSI classification. The main advantage of our model is that it does not add parameters to the original network (as opposed to other state-of-the-art approaches) while providing higher classification accuracies. Extensive experimental validations and quantitative comparisons are conducted—using different benchmark HSIs—to illustrate these advantages. The code is available on https://github.com/mhaut/Free2Resnet
Descripción
The registered version of this article, first published in “IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023", is available online at the publisher's website: IEEE, https://doi.org/10.1109/TGRS.2023.3295097
La versión registrada de este artículo, publicado por primera vez en “IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/TGRS.2023.3295097
Categorías UNESCO
Palabras clave
feature extraction, computational modeling, training, three-dimensional displays, hyperspectral imaging, convolution, task analysis
Citación
M. E. Paoletti et al., "Parameter-Free Attention Network for Spectral–Spatial Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-17, 2023, Art no. 5516817, doi: 10.1109/TGRS.2023.3295097
Centro
Facultades y escuelas::E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos