Journal
REMOTE SENSING
Volume 11, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/rs11020159
Keywords
hyperspectral image classification; spectral-spatial feature extraction; dense connectivity; attention mechanism
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Funding
- National Natural Science Foundation of China [61871460, 61876152]
- Foundation Project for Advanced Research Field [614023804016HK03002]
- Shaanxi International Scientific and Technological Cooperation Project [2017KW-006]
- National Key Laboratory of Science and Technology on Space Microwave [6142411040404]
- Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University [CX201816]
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Hyperspectral images (HSIs) data that is typically presented in 3-D format offers an opportunity for 3-D networks to extract spectral and spatial features simultaneously. In this paper, we propose a novel end-to-end 3-D dense convolutional network with spectral-wise attention mechanism (MSDN-SA) for HSI classification. The proposed MSDN-SA exploits 3-D dilated convolutions to simultaneously capture the spectral and spatial features at different scales, and densely connects all 3-D feature maps with each other. In addition, a spectral-wise attention mechanism is introduced to enhance the distinguishability of spectral features, which improves the classification performance of the trained models. Experimental results on three HSI datasets demonstrate that our MSDN-SA achieves competitive performance for HSI classification.
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