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, cilt.11, ss.3272-3278, 2018 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 11 Konu: 9
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1109/jstars.2018.2851368
  • Dergi Adı: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
  • Sayfa Sayıları: ss.3272-3278

Özet

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.