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

This work focuses on reliably estimating the information conveyed to a user by multi-spectral and hyper-spectral image data. Goal is establishing the extent to which an increase in spectral resolution can increase the amount of usable information. Actually a trade off exists between spatial and spectral resolution, due to physical constraints of sensors imaging with a prefixed SNR. After reporting about some methods developed for unsupervisedly estimating the variance of the noise introduced by multi-spectral imagers, an original and effective data de-correlation algorithm designed for lossless compression of multi/hyper-spectral data is reviewed. Data compression can be adopted to measure the useful information content of multi-spectral data. In fact, the bit rate achieved by the compression process takes into account both of the entropy of the so called "observation" noise (i.e. information regarded as statistical uncertainty, but whose relevance to a user is zero), and of the intrinsic information of hypothetically noise-free data, By defining a suitable model, once the standard deviation of the observation noise has been preliminarily estimated, the code rate may be utilised to yield an estimate of the true information content of the multi-spectral source, that is of one band of the multi-spectral image arranged in a causal sequence in which the previous bands are known. Results show that the information content of multi-spectral Landsat TM images is superior to that of hyper-spectral AVIRIS images, notwithstanding the latter are recorded with a 12bit word length vs. the 8bit of the former.