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

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.

The Search for Topographic Correlations within the Reiner Gamma Swirl

“…Next, we identify the location of on-swirl, off-swirl, and transitional diffuse-swirl units directly from the brightness variations in the spacecraft images. We apply two different ML algorithms to both classify and map the swirl units to ensure robust results (Chuang et al 2022). Using the mapped units, we then extract the elevation values from the slope-corrected and feature-masked topography for each unit.…”

“…Using surface reflectance image data undersampled to the same resolution as the topography, we apply two independent methods to identify and map swirl units within the Reiner Gamma study region. These methods were introduced previously by Chuang et al (2022), but here we discuss their application to this study. We note that no changes have been made to these methods in the time since the study by Chuang et al (2022).…”

“…We apply similar techniques for generating SPC data products and determine the statistical correlations as in Domingue et al (2022Domingue et al ( , 2023. However, in this study we have applied both supervised and unsupervised machine learning (ML) techniques, used by Chuang et al (2022) to classify and map swirl units. As the Reiner Gamma study region here is identical to that in Chuang et al (2022), we adopt the same unit maps from their study.…”

“…However, in this study we have applied both supervised and unsupervised machine learning (ML) techniques, used by Chuang et al (2022) to classify and map swirl units. As the Reiner Gamma study region here is identical to that in Chuang et al (2022), we adopt the same unit maps from their study.…”

“…The on-swirl area gradually reduces in albedo as it transitions to off-swirl. These transition areas were identified by Chuang et al (2022) and termed as "diffuse-swirl." Here, we examine the correlations between topography and the three swirl units (on-swirl, off-swirl, and diffuse-swirl) within the Reiner Gamma study region as mapped earlier by Chuang et al (2022).…”