Hybrid Compression of Hyperspectral Images Based on PCA With Pre-Encoding Discriminant Information

Chulhee Lee, Sungwook Youn, Taeuk Jeong, Eunjae Lee, Joan Serra-Sagristà

Research output: Contribution to journalArticleResearchpeer-review

29 Citations (Scopus)


© 2004-2012 IEEE. It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.
Original languageEnglish
Article number7063895
Pages (from-to)1491-1495
JournalIEEE Geoscience and Remote Sensing Letters
Issue number7
Publication statusPublished - 1 Jul 2015


  • Compression
  • discriminant information
  • feature images
  • hyperspectral images
  • principal component analysis (PCA)
  • residual images


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