Automatic segmentation of radar data from the Chang’E-4 mission using unsupervised machine learning: A data-driven interpretation approach - Giannakis, Iraklis; McDonald, Ciaran; Feng, Jianqing; Zhou, Feng; Su, Yan; Martin-Torres, Javier; Zorzano, Maria-Paz; Warren, Craig; Giannopoulos, Antonios; Leontidis, Georgios (Icarus (New York, N.Y. 1962), 2024)
Chang’E-3, Chang’E-4 and Chang’E-5 were the first planetary missions with in-situ ground-penetrating radar (GPR) in their scientific payloads. Apart from the Chang’E missions, in-situ GPR has also been used in the Martian missions Tianwen-1 and Perseverance, establishing GPR as a mainstream and pivotal tool in the new era of planetary exploration. Despite its widespread use in planetary science, interpreting GPR data from the Chang’E-4 mission is still challenging, with varying and ambiguous interpretations offered by different researchers. This is primarily due to the complexity and heterogeneity of the Lunar subsurface, which results to a difficult to interpret radagram with low signal-to-clutter ratio. Clutter masks potential targets and makes interpretation a laborious and highly subjective process. To tackle this, we interpret the GPR data from Chang’E-4 mission using a novel processing paradigm based on un-supervised machine learning. A set of statistical attributes is initially calculated locally for different sliding windows in the investigated radagram. Subsequently, these statistical features are clustered into groups using un-supervised machine learning. The segmented radagram highlights boundaries between different formations, and reveals hidden structures previously unseen using typical processing approaches. The proposed framework is fully automatic and objective, facilitating processing, and increasing the overall reliability of the final interpretation. •Introducing a novel radar segmentation scheme based on unsupervised machine learning.•Use the segmentation scheme to cluster the radar data from the Chang’E-4 mission.•Identify previously unseen formations in the Von Kármán using solely data-driven interpretation tools.