Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

By Rui Li, Shunyi Zheng, Chenxi Duan, Yang Yang, Xiqi Wang. Remote Sensing, 2019

409 citations recorded by the source corpus.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

View Paper: https://www.semanticscholar.org/paper/35c8c3e72eb24227df7f5c07c136797656faae0b

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Citations in the corpus

CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification (2020, methodology): “…method: they are the deep CNN (DCNN) [25], the TCCNN [26], the MCCNN [31], the spectral–spatial residual network (SSRN) [33], the fast dense spectral–spatial convolutional (FDSSC) network [34], the hybrid spectral network (HybridSN) [27], and the double-branch dual-attention network (DBDA) [38].”
Multistage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images (2020, background): “The non-local module [6], dot-product-based attention for CV, has demonstrated its superiority on a wide range of vision tasks [5, 7].”