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

Remote sensing (Basel, Switzerland), 2020-02, Vol.12 (3), p.582 [Peer Reviewed Journal]

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Title:
Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network
Author: Li, Rui ; Zheng, Shunyi ; Duan, Chenxi ; Yang, Yang ; Wang, Xiqi
Subjects: channel-wise attention mechanism ; deep learning ; hyperspectral image classification ; spatial-wise attention mechanism
Is Part Of: Remote sensing (Basel, Switzerland), 2020-02, Vol.12 (3), p.582
Description: 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.
Publisher: MDPI AG
Language: English
Identifier: ISSN: 2072-4292
EISSN: 2072-4292
DOI: 10.3390/rs12030582
Source: ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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