Tridiagonal Folmat Enhanced Multivariance Products Representation Based Hyperspectral Data Compression

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 11; no. 9; pp. 3272 - 3278
• Main Authors: Gundogar, Zeynep, Toreyin, Behcet Ugur, Demiralp, Metin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation methods; AVIRIS; Compression; Data; Data compression; Data processing; Decomposition; decomposition and factorization methods; Hyperspectral imaging; Image coding; Image quality; Image reconstruction; Imaging techniques; Mathematical analysis; Matrix decomposition; Matrix methods; Principal component analysis; Principal components analysis; Remote sensing; Representations; Satellites; Signal-to-noise ratio
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2018.2851368

Hyperspectral imaging features an important issue in remote sensing and applications. Requirement to collect high volumes of hyper spectral data in remote sensing algorithms poses a compression problem. 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 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 state-art-methods such as compressive -projection principal component analysis, matching pursuit 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 proposed technique in comparison to state-of-the-art hyperspectral data compression methods.