Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii

Abstract

Abstract Lunar swirls are recognized as broad, bright albedo features in various regions of the Moon. These features are often separated by dark off-swirl lanes or terminate against the dark background, such as lunar maria. Prior mapping of swirls has been done primarily by albedo contrast, which is prone to subjectivity. Closer examination of on-swirl areas shows that they are not uniform, making the boundary between on- and off-swirl difficult to map with certainty. We have applied machine learning techniques to address these issues by identifying the number of swirl units and then mapping them based on actual reflectance, or I/F data. Using LROC NAC paired stereo images that are converted to I/F reflectance at a range of incidence angles, we applied both unsupervised K-means clustering and supervised Maximum Likelihood Classification algorithms to classify and map portions of lunar swirls in Reiner Gamma and Mare Ingenii. Results show that the classification maps are a reasonable match to the representative albedos for the two study regions. A third transitionary swirl unit, termed diffuse-swirl, is present in both the maps and the cumulative distribution plots of the reflectance values. Overall, we find that the use of both algorithms provides independent confirmation of both the number and location of these units and their interrelation. More importantly, the algorithms remove mapping subjectivity by using quantitative information. The data and the statistics generated from the maps also have value in future studies by placing limits for categorizing swirl units in different regions on the Moon.