Deep Learning for Unsupervised Denoising of Radar Sounder Data

Analyzing radar sounder (RS) profiles allows the retrieval of critical information on subsurface geology. However, radar-grams suffer from several noise contributions, adversely affecting the data quality and reliability. In the remote sensing literature, there are no methods for denoising radargrams, and those for optical data denoising and SAR and GPR data de-speckling are based on assumptions that are not valid in the RS domain. This paper analyses the statistical distributions of the noisy contributions in radargrams at different levels of processing. It proposes a novel method to denoise complex raw radargrams using a generative network (diffusion probabilistic model) that learns the noise statistical properties. The model is iteratively trained to learn the information loss as a function of the noise level increment in the data. By reversing the process, the network estimates the noise statistical properties and denoises unseen radargrams. The method has been successfully validated on the raw Experiment Data Record (EDR) radargrams of Mars that the Shallow Radar (SHARAD) acquired.