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
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-10188-x

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 BiLSTM networks(TARDB-Net) to reduce redundant features while increasing feature fusion capabilities, which ultimately improves the ability to classify hyperspectral images. First, a novel Triple-attention mechanism is proposed to assign different weights to each feature. Then, the residual network is used to perform the residual operation on the features, and the initial features of the multiple residual blocks and the generated deep residual features are intensively fused, retaining a host number of prior features. And use the bidirectional long short-term memory network to integrate the contextual semantics of deep fusion features. Finally, the classification task is completed by Softmax classifier. Experiments on three hyperspectral datasets—Indian Pines, University of Pavia, and Salinas—show that under 10% of the training samples, the overall accuracy of our method is 87%, 96% and 96%, which is superior to several well-known methods.