Journal
REMOTE SENSING
Volume 12, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/rs12030582
Keywords
hyperspectral image classification; deep learning; channel-wise attention mechanism; spatial-wise attention mechanism
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Funding
- National Natural Science Foundations of China [41671452]
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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.
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