Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 12, Issue 12, Pages 2438-2442Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2015.2482520
Keywords
Hyperspectral imagery (HSI) classification; linear support vector machine; spectral-spatial feature learning; stacked sparse autoencoder (SSAE)
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Funding
- National Natural Science Foundation of China [41301453, 51479215]
- National 973 Plan of China [2012CB719903]
- China Postdoctoral Science Foundation [2013M530361, 2014T70790]
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In this letter, different from traditional methods using original spectral features or handcraft spectral-spatial features, we propose to adaptively learn a suitable feature representation from unlabeled data. This is achieved by learning a feature mapping function based on stacked sparse autoencoder. Considering that hyperspectral imagery (HSI) is intrinsically defined in both the spectral and spatial domains, we further establish two variants of feature learning procedures for sparse spectral feature learning and multiscale spatial feature learning. Finally, we embed the learned spectral-spatial feature into a linear support vector machine for classification. Experiments on two hyperspectral images indicate the following: 1) the learned spectral-spatial feature representation is more discriminative for HSI classification compared to previously hand-engineered spectral-spatial features, especially when the training data are limited and 2) the learned features appear not to be specific to a particular image but general in that they are applicable to multiple related images (e.g., images acquired by the same sensor but varying with location or time).
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