Weighted sparse graph based dimensionality reduction for hyperspectral images

Wei He, Hongyan Zhang, Liangpei Zhang, Wilfried Philips, Wenzhi Liao, Alejandro Frery (Editor)

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)
39 Downloads (Pure)


Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. In this letter, we propose a weighted sparse graph based DR (WSGDR) method for HSIs. Instead of only exploring the locality structure (as in neighborhood preserving embedding) or the linearity structure (as in SGE) of the HSI data, the proposed method couples the locality and linearity properties of HSI data together in a unified framework for the DR of HSIs. The proposed method was tested on two widely used HSI data sets, and the results suggest that the locality and linearity are complementary properties for HSIs. In addition, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods.
Original languageEnglish
Pages (from-to)686-690
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number5
Publication statusPublished - 18 Mar 2016


  • sparse graph embedding
  • dimensionality reduction
  • nearest neighbor graph
  • hyperspectral image
  • weighted sparse coding
  • discriminant analysis
  • feature extraction


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