Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 14, Issue 12, Pages 2250-2254Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2759168
Keywords
Hyperspectral image (HSI); pattern classification; semisupervised learning; stacked autoencoders (SAE)
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Funding
- National Natural Science Foundation of China [61401413, 41576011]
- Key Research and Development Program of Shandong Province [GG201703140154]
- State Key Laboratory of Applied Optics
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Hyperspectral image (HSI) classification is a popular yet challenging research topic in the remote sensing community. This letter attempts to encode both spectral and spatial information into deep features for HSI classification. We first propose a semisupervised method for training the stacked autoencoder to obtain discriminative deep features. A batch training scheme is introduced to constrain the label consistency on a neighborhood region. Second, a mean pooling procedure is suggested to further fuse the spectral and local spatial information for deep feature generation. The experimental results on two hyperspectral scenes show that the proposed method achieves promising classification performance.
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