Tridiagonal Folmat Enhanced Multivariance Products Representation Based Hyperspectral Data Compression

Zeynep Gundogar*, Behcet Ugur Toreyin, Metin Demiralp

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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 develop ed and continues to be improved in scientific literature. In this paper, we propose a recently developed lossy compression method whi ch is called tridiagonal folded matrix enhanced multivariance prod ucts representation (TFEMPR). This is a specific multidimensional array decomposition method using a new mathematical concept called 'folded matrix' and provides binary decomposi tion for multidimensional arrays. Beside the method a comparati ve analysis of compression algorithms is presented in this paper by means of compression performances. Compression performance of TFEMPR is compared with the state-art-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.

Original languageEnglish
Article number8410370
Pages (from-to)3272-3278
Number of pages7
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number9
Publication statusPublished - Sept 2018

Bibliographical note

Publisher Copyright:
© 2008-2012 IEEE.


Manuscript received April 12, 2018; revised May 29, 2018; accepted June 21, 2018. Date of publication July 11, 2018; date of current version September 5, 2018. This work was supported by the Scientific and Technical Research Council of Turkey under National Young Researchers Career Development Program (3501 TUBITAK CAREER) under Grant 114E200. (Corresponding author: Zeynep Gündog˘ar.) Z. Gündog˘ar and M. Demiralp are with the Department of Computational Science & Engineering, Institute of Informatics, ˙stanbul Technical University, ˙stanbul 34467, Turkey (e-mail:, [email protected]; metin.demiralp@

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu


    • Approximation methods
    • data compression
    • decomposition and factorization methods
    • hyperspectral imaging


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