Skip to Main Content
Radar sounders operating on satellite platforms (e.g., radar sounding missions at Mars) provide a huge amount of data that currently are mostly analyzed by means of manual investigations. This calls for the development of novel techniques for the automatic extraction of information from sounder signals that could greatly support the scientific community. Such a topic has not been addressed sufficiently in the literature. This paper provides a contribution to fill this gap by presenting both 1) a study of the theoretical statistical properties of radar sounder signals, and 2) two novel techniques for the automatic analysis of sounder radargrams. The main goal of the study is the identification of statistical distributions that can accurately model the amplitude fluctuations of different subsurface targets. This is fundamental for the understanding of signal properties and for the definition of automatic data analysis techniques. The results of such a study drive the development of two novel techniques for 1) the generation of subsurface feature maps, and 2) the automatic detection of the deepest scattering areas visible in the radargrams. The former produces for each radargram a map showing which areas have high probability to contain relevant subsurface features. The latter exploits a region-growing approach properly defined for the analysis of radargrams to identify and compose the basal scattering areas. Experimental results obtained on Shallow Radar data acquired on Mars confirm the effectiveness of the proposed techniques.