Local receptive field constrained stacked sparse autoencoder for classification of hyperspectral images
As a competitive machine learning algorithm, the stacked sparse autoencoder (SSA) has achieved outstanding popularity in exploiting high-level features for classification of hyperspectral images (HSIs). In general, in the SSA architecture, the nodes between adjacent layers are fully connected and ne...
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| Published in: | Journal of the Optical Society of America. A, Optics, image science, and vision Vol. 34; no. 6; p. 1011 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
01.06.2017
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| ISSN: | 1520-8532, 1520-8532 |
| Online Access: | Get more information |
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