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Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification

1
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
2
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1307; https://doi.org/10.3390/rs11111307
Received: 8 May 2019 / Revised: 25 May 2019 / Accepted: 27 May 2019 / Published: 1 June 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
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Abstract

Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method. View Full-Text
Keywords: hyperspectral image classification; spectral-spatial feature fusion; channel-wise attention; spatial-wise attention hyperspectral image classification; spectral-spatial feature fusion; channel-wise attention; spatial-wise attention
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Ma, W.; Yang, Q.; Wu, Y.; Zhao, W.; Zhang, X. Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification. Remote Sens. 2019, 11, 1307.

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