Publicación: AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification
Fecha
2023
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info:eu-repo/semantics/restrictedAccess
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IEEE
Resumen
Convolutional models have provided outstanding performance in the analysis of hyperspectral images (HSIs). These architectures are carefully designed to extract intricate information from nonlinear features for classification tasks. Notwithstanding their results, model architectures are manually engineered and further optimized for generalized feature extraction. In general terms, deep architectures are time-consuming for complex scenarios, since they require fine-tuning. Neural architecture search (NAS) has emerged as a suitable approach to tackle this shortcoming. In parallel, modern attention-based methods have boosted the recognition of sophisticated features. The search for optimal neural architectures combined with attention procedures motivates the development of this work. This article develops a new method to automatically design and optimize convolutional neural networks (CNNs) for HSI classification using channel-based attention mechanisms. Specifically, 1-D and spectral–spatial (3-D) classifiers are considered to handle the large amount of information contained in HSIs from different perspectives. Furthermore, the proposed automatic attention-based CNN ( AAtt-CNN ) method meets the requirement to lower the large computational overheads associated with architectural search. It is compared with current state-of-the-art (SOTA) classifiers. Our experiments, conducted using a wide range of HSI images, demonstrate that AAtt-CNN succeeds in finding optimal architectures for classification, leading to SOTA results.
Descripción
The registered version of this article, first published in “IEEE Transactions on Geoscience and Remote Sensing, 61, 2023", is available online at the publisher's website: IEEE, https://doi.org/10.1109/TGRS.2023.3272639
La versión registrada de este artículo, publicado por primera vez en “IEEE Transactions on Geoscience and Remote Sensing, 61, 2023", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/TGRS.2023.3272639
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Citación
Mercedes E Paoletti, Sergio Moreno-Álvarez, Yu Xue, Juan M Haut, Antonio Plaza. "AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification". IEEE Transactions on Geoscience and Remote Sensing, 61, 03 May 2023, 1-18.
Centro
Facultades y escuelas::E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos