Lossless compression of hyperspectral imagery: a real time approach

We present an algorithm for hyperspectral image compression that uses linear prediction in the spectral domain. In particular, we use a least squares optimized linear prediction method with spatial and spectral support. The performance of the predictor is competitive with the state of the art, even when the size of the prediction context is kept to a minimum; therefore the proposed method is suitable to spacecraft on-board implementation, where limited hardware and low power consumption are key requirements. With one band look-ahead capability, the overall compression of the proposed algorithm improves significantly with marginal usage of additional memory. Experiments on data cubes acquired by the NASA JPL's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are presented. In the second part of the paper, we revised some on-going research that aims at coupling linear prediction with polynomial fitting, exponential fitting or interpolation. Current simulations show that further improvement is possible. Furthermore, the two tier prediction allows progressive encoding and decoding. This research is promising, but still in a preliminary stage.