Abstract
This letter presents a semisupervised dimension reduction method based on pairwise constraint propagation (SSDR-PCP) for hyperspectral images (HSIs). SSDR-PCP first utilizes pairwise constraint propagation, which is based on the labeled samples and k-nearest neighbor graphs to obtain more similarity information. Then SSDR-PCP applies the obtained weak supervised information of the entire training data set to construct a new similarity matrix. At last, we embed the similarity matrix to local preserving projection to achieve dimension reduction by finding the optimal transformation matrix for HSIs. The experimental results demonstrate that SSDR-PCP achieves better performance than the previous methods on two HSIs.
Original language | English |
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Article number | 7725986 |
Pages (from-to) | 1880-1884 |
Number of pages | 5 |
Journal | IEEE Geoscience and Remote Sensing Letters |
Volume | 13 |
Issue number | 12 |
DOIs | |
Publication status | Published - Dec 2016 |
Externally published | Yes |
Keywords
- Dimension reduction (DR)
- hyperspectral images (HSIs)
- locality preserving projection
- pairwise constraint propagation
- semisupervised learning