Semisupervised local discriminant analysis for feature extraction in hyperspectral images

Wenzhi Liao, Aleksandra Pizurica, Paul Scheunders, Wilfried Philips, Youguo Pi

Research output: Contribution to journalArticlepeer-review

168 Citations (Scopus)


We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods.
Original languageEnglish
Pages (from-to)184-198
Number of pages15
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number1
Publication statusPublished - 31 Jan 2013


  • feature extraction
  • hyperspectral remote sensing
  • semisupervised
  • classification


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