Semisupervised Dimension Reduction Based on Pairwise Constraint Propagation for Hyperspectral Images

Weibao Du, Meng Lv, Qiuling Hou, Ling Jing*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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 languageEnglish
Article number7725986
Pages (from-to)1880-1884
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume13
Issue number12
DOIs
Publication statusPublished - Dec 2016
Externally publishedYes

Keywords

  • Dimension reduction (DR)
  • hyperspectral images (HSIs)
  • locality preserving projection
  • pairwise constraint propagation
  • semisupervised learning

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