A weighted multiple-feature fusion classifier for hyperspectral images with limited training samples
In this paper, a novel weighted multiple-feature classifier based on sparse representation and locally dictionary collaborative representation (WMSLC) is put forward to improve the limited training samples’ hyperspectral image classification performance. The WMSLC method mainly includes the followin...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-01-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/22797254.2018.1529543 |