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Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning
Open AccessArticle

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

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 582; https://doi.org/10.3390/rs12030582
Received: 11 December 2019 / Revised: 8 February 2020 / Accepted: 8 February 2020 / Published: 10 February 2020
(This article belongs to the Special Issue Deep Learning and Feature Mining for Hyperspectral Imagery)
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 Full-Text
Keywords: hyperspectral image classification; deep learning; channel-wise attention mechanism; spatial-wise attention mechanism hyperspectral image classification; deep learning; channel-wise attention mechanism; spatial-wise attention mechanism
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MDPI and ACS Style

Li, R.; Zheng, S.; Duan, C.; Yang, Y.; Wang, X. Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network. Remote Sens. 2020, 12, 582.

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