Inferring Titan's surface features by means of Bayesian inversion algorithm applied to radar data

Details Inferring Titan's surface features by means of Bayesian inversion algorithm applied to radar data Inferring Titan's surface features by means of Bayesian inversion algorithm applied to radar data

Nov 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007spie.6746e...6v&link_type=abstract

SAR Image Analysis, Modeling, and Techniques IX. Edited by Notarnicola, Claudia; Posa, Francesco. Proceedings of the SPIE, Volum

Mathematics

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Scientific paper

During the first two years of the Cassini mission, a great amount of data dealing with Titan's surface has been collected. The analysis derived from the SAR imagery reflects the complex Titan's surface morphology with peculiar features such as: dark and bright areas (Ta, T3), periodic structure ("sand dunes") and, above all, lake-like features, firstly observed during the T16 flyby on 22 July 2006 and good candidates to be filled with liquid hydrocarbons. In this paper the modeling description of lakes is addressed by means of a double layer model. Subsequently this model is introduced into a Bayesian framework for the purpose of inferring the likely ranges of some lake parameter and in particular of the optical thickness of the hypothesized liquid hydrocarbons layer. The main idea is to use the information contained in the parameter probability density function, which describes how probability is distributed among the different values of parameters according to the various scenarios considered. The analysis has been carried out on lakes and surrounding areas detected on flybys T16, T19, T25 and has given plausible hypothesis on the lake composition and optical depth. Furthermore a first attempt has been made to exploit information from radiometric data. The typical inverse relationship between radar and radiometric data has been verified on some regions of interest chosen on the T25 flyby. This investigation may be used in a context of radar and radiometric data fusion to extract information on the optical thickness of lakes and other surface features.

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