Iterative enhanced multivariance products representation for effective compression of hyperspectral images

Süha Tuna, Behçet Uğur Töreyin, Metin Demiralp, Jinchang Ren, Huimin Zhao, Stephen Marshall

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Effective compression of hyperspectral images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy hyperspectral image compression method based on Enhanced Multivariance Products Representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyperspectral images. In addition to these, an efficient variety of EMPR is also introduced in the paper, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak-signal-to-noise-ratio values and improved classification accuracy are achieved from EMPR based methods.
Original languageEnglish
Number of pages16
JournalIEEE Transactions on Geoscience and Remote Sensing
Early online date16 Nov 2020
Publication statusE-pub ahead of print - 16 Nov 2020


  • hyperspectral images
  • classification accuracy
  • enhanced multivariance products representation
  • lossy compression

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