A novel approach to the automatic detection of subsurface features in planetary radar sounder signals

Nowadays, planetary radar sounding missions operating at Mars are providing a huge amount of data which are mostly analyzed by means of manual investigations. The development of techniques for the automatic extraction of information from radar sounder data is thus a crucial issue for a proper data exploitation from the scientific community. As an important step to the definition of automatic methods that can be implemented in a more general framework for the analysis of radar sounder data, in this paper we present a study of the statistical properties of radar sounder signals. The statistical distributions of the amplitude of the samples related to different subsurface targets are analyzed in order to define a suitable fitting model. Exploiting the results of such a study, we also propose a novel automatic technique capable to extract the deepest scattering area visible in the radargrams. The basal scattering area is composed using an advanced region-growing approach properly defined for the analysis of radargrams. The obtained regions are kept or discarded according to the statistical distribution of their samples. Experimental results obtained on data acquired by the Shallow Radar (SHARAD) on Mars confirm the effectiveness of the proposed approach.