Information-theoretic assessment of optical remote-sensing imagery

This work focuses on estimating the information conveyed to a user by multi-band remotely sensed optical data, either multi-spectral or hyper-spectral. A trade-off 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 of the data. 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 radiance data. An entropy model of the image source is defined and, once the standard deviation of the noise, assumed to be Gaussian, has been preliminary measured, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results of mutual information assessment are reported and discussed on Landsat TM data and on AVIRIS data.