Tridiagonal Folmat Enhanced Multivariance Products Representation Based Hyperspectral Data Compression


Gundogar Z., Töreyin B. U. , Demiralp M.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol.11, no.9, pp.3272-3278, 2018 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 9
  • Publication Date: 2018
  • Doi Number: 10.1109/jstars.2018.2851368
  • Journal Name: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.3272-3278
  • Keywords: Approximation methods, data compression, decomposition and factorization methods, hyperspectral imaging, PROJECTIONS

Abstract

Hyperspectral imaging features an important issue in remote sens ing and applications. Requirement to collect high volumes of hyper spectral data in remote sensing algorithms poses a compression prob lem. To this end, many techniques or algorithms have been developed and continues to be improved in scientific literature. In this paper, we propose a recently developed lossy compression method which is called tridiagonal folded matrix enhanced multivariance products representation (TFEMPR). This is a specific multidimensional array decomposition method using a new mathematical concept called "folded matrix" and provides binary decomposition for multidimensional arrays. Beside the method a comparative analysis of compression algorithms is presented in this paper by means of compression performances. Compression performance of TFEMPR is compared with the stateart-methods such as compressive -projection principal component analysis, matching pursu it and block compressed sensing algorithms, etc., via average peak signal-to-noise ratio. Experiments with AVIRIS data set indicate a superior reconstructed image quality for the propo sed technique in comparison to state-of-the-art hyperspectral data compression methods.