Statistics – Applications
Scientific paper
Sep 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004esasp.553e...2a&link_type=abstract
Proceedings of ESA-EUSC 2004 - Theory and Applications of Knowledge-Driven Image Information Mining with Focus on Earth Observa
Statistics
Applications
Scientific paper
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.
Aiazzi Bruno
Alparone Luciano
Baronti Stefano
Lastri Cinzia
Santurri L.
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