TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification

• Published in: Multimedia tools and applications Vol. 80; no. 7; pp. 11291 - 11312
• Main Authors: Cai, Weiwei, Liu, Botao, Wei, Zhanguo, Li, Meilin, Kan, Jiangming
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Deep learning; Experiments; Feature selection; Hyperspectral imaging; Image classification; Mining; Multimedia; Multimedia Information Systems; Neural networks; Remote sensing; Semantics; Special Purpose and Application-Based Systems; Triple-attention mechanism; Hyperspectral image; Bi-directional long-short term memory networks; Residual and dense networks

Each sample in the hyperspectral remote sensing image has high-dimensional features and contains rich spatial and spectral information, which greatly increases the difficulty of feature selection and mining. In view of these difficulties, we propose a novel Triple-attention Guided Residual Dense and...