Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 14, Issue 12, Pages 2355-2359Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2764915
Keywords
Convolutional neural network (CNN); deep learning; feature extraction (FE); Gabor filtering; hyperspectral images (HSIs)
Categories
Funding
- National Natural Science Foundation of China [61771171]
- State Key Laboratory of Frozen Soil Engineering [SKLFSE201614]
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Recently, the capability of deep learning-based approaches, especially deep convolutional neural networks (CNNs), has been investigated for hyperspectral remote sensing feature extraction (FE) and classification. Due to the large number of learnable parameters in convolutional filters, lots of training samples are needed in deep CNNs to avoid the overfitting problem. On the other hand, Gabor filtering can effectively extract spatial information including edges and textures, which may reduce the FE burden of the CNNs. In this letter, in order to make the most of deep CNN and Gabor filtering, a new strategy, which combines Gabor filters with convolutional filters, is proposed for hyperspectral image classification to mitigate the problem of overfitting. The obtained results reveal that the proposed model provides competitive results in terms of classification accuracy, especially when only a limited number of training samples are available.
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