Hyperspectral Image Classification Using Spectral-Spatial Features With Informative Samples
This paper proposes a new active-learning approach for multi-feature hyperspectral image classification. First, the extended multi-attribute morphological profiles (EMAPs) are introduced as features into the classifier of the multinomial logistic regression (MLR). Second, discontinuity preserving re...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8640040/ |