Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 56, Issue 9, Pages 5408-5423Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2815613
Keywords
Convolutional neural network (CNN); deep learning; hyperspectral image
Categories
Funding
- Shenzhen Science and Technology Program [JCYJ20160330163900579, JCYJ20170413105929681, JCYJ20170811160212033]
- RGC of the Hong Kong SAR [CityU 11502115, CityU 11525716]
- National Natural Science Foundation of China (NSFC) Basic Research Program [71671155]
- Shenzhen Municipal Science and Technology Innovation Fund [JCYJ20160229165300897]
- CityU Shenzhen Research Institute
- NSFC [61562027]
- Education Department of Jiangxi Province [GJJ170413]
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Deep learning has achieved great successes in conventional computer vision tasks. In this paper, we exploit deep learning techniques to address the hyperspectral image classification problem. In contrast to conventional computer vision tasks that only examine the spatial context, our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification. In particular, we advocate four new deep learning models, namely, 2-D convolutional neural network (2-D-CNN), 3-D-CNN, recurrent 2-D CNN (R-2-D-CNN), and recurrent 3-D-CNN (R-3-D-CNN) for hyperspectral image classification. We conducted rigorous experiments based on six publicly available data sets. Through a comparative evaluation with other state-of-the-art methods, our experimental results confirm the superiority of the proposed deep learning models, especially the R-3-D-CNN and the R-2-D-CNN deep learning models.
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