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Real-time lossy compression of hyperspectral images using iterative error analysis on graphics processing units

Hyperspectral image compression is an important task in remotely sensed Earth Observation as the dimensionality 
of this kind of image data is ever increasing. This requires on-board compression in order to optimize the 
donwlink connection when sending the data to Earth. A successful algorithm to perform lossy compression of 
remotely sensed hyperspectral data is the iterative error analysis (IEA) algorithm, which applies an iterative 
process which allows controlling the amount of information loss and compression ratio depending on the number 
of iterations. This algorithm, which is based on spectral unmixing concepts, can be computationally expensive 
for hyperspectral images with high dimensionality. In this paper, we develop a new parallel implementation of 
the IEA algorithm for hyperspectral image compression on graphics processing units (GPUs). The proposed 
implementation is tested on several different GPUs from NVidia, and is shown to exhibit real-time performance 
in the analysis of an Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) data sets collected over different 
locations. The proposed algorithm and its parallel GPU implementation represent a significant advance towards 
real-time onboard (lossy) compression of hyperspectral data where the quality of the compression can be also 
adjusted in real-time.