Quality evaluation of Hyper-spectral image data acquired by push-broom sensors

This work focuses on evaluating quality and estimating the information of multi-dimensional signals and in particular of hyperspectral remote sensing image data. Lossless data compression is exploited to measure the information content of the data. In fact, the bit-rate achieved by the reversible compression process takes into account both the contribution of the noise, whose relevance is null to a user, and the information of hypothetically noise-free data. The parametric model of the noise has been preliminary estimated. Since we want to know what is the amount of information without the observation noise, an entropy model of the image source is defined and such a model is inverted. Results are reported and discussed on hyper-spectral data acquired by the CHRIS and VIRS imaging spectrometers.