Hyperspectral Image Compression Using Prediction-based Band Reordering Technique

Abstract

The hyperspectral image represents various spectral properties Because it consists of broad spectral information of ground materials that can be used for various applications, These images are collected as large amounts of data that must be processed and transmitted to the ground station. These acquired images contain redundant spectral information that has to be reduced in order to reduce transmission and storage capacity. This work focuses on preserving their quality while compressing them using band reordering techniques and prediction coding. This can be accomplished by preprocessing in which sub-bands are decomposed and bands are reordered into unsequenced compression can be accomplished through using the technique of linear prediction. The report discusses the Pavia University hyperspectral image data cube, which was acquired via a sensor known as a reflected optics system imaging spectrometer (ROSIS-3) over the city of Pavia, Italy.