Assessment of noise variance and information content of multi-/hyper-spectral imagery

We focus on reliably estimating the information conveyed to a user by multispectral 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 trade-off exists between spatial and spectral resolution, due to physical constraints of sensors imaging with a prefixed SNR. After describing some methods developed for automatically estimating the variance of the noise introduced by multispectral imagers, lossless data compression is exploited to measure the useful information content of the multispectral data. 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, whose relevance is null to a user, and the intrinsic information of hypothetically noise free multispectral data. An entropic model of the image source is defined and, once the standard deviation of the noise, assumed to be white and Gaussian, 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 of both noise and information assessment are reported and discussed on synthetic noisy images and on Landsat thematic mapper (TM) data.