Parallel hyperspectral image compression using iterative error analysis on graphics processing units

In this paper, we develop a new parallel implementation of the iterative error analysis (IEA) algorithm for lossy hyperspectral image compression on graphics processing units (GPUs), an inexpensive parallel computing platform that has recently become very popular in hyperspectral imaging applications. The proposed GPU implementation is tested on several different architectures from NVidia, the main GPU vendor worldwide, and is shown to exhibit real-time performance in the analysis of AVIRIS data sets. The GPU implementation of the IEA represents a step forward towards real-time onboard (lossy) compression of hyperspectral data where the quality of the compression can be also adjusted in real-time.