Tridiagonal Folmat Enhanced Multivariance Products Representation Based Hyperspectral Data Compression

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.