Information-theoretic assessment of imaging systems via data compression

This work focuses on estimating the information conveyed to a user by either multispectral or hyperspectral image data. The goal is establishing the extent to which an increase in spectral resolution can increase the amount of usable information. As a matter of fact, a tradeoff exists between spatial and spectral resolution, due to physical constraints of sensors imaging with a prefixed SNR. Lossless data compression is exploited to measure the useful information content. In fact, the bit rate achieved by the reversible compression process takes into account both the contribution of the observation noise i.e., information regarded as statistical uncertainty, the relevance of which is null to a user, and the intrinsic information of hypothetically noise-free data. An entropic model of the image source is defined and, once the standard deviation of the noise, assumed to be Gaussian and possibly nonwhite, has been preliminarily estimated, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results both of noise and of information assessment are reported and discussed on synthetic noisy images, on Landsat TM data, and on AVIRIS data.