Analysis of rock abundance on lunar surface from orbital and descent images using automatic rock detection

Abstract

Information on the distribution and abundance of rocks on the lunar surface is essential for understanding the characteristics of lunar regolith and for selecting suitable landing sites for lunar exploration missions. This paper first presents a novel automatic rock detection approach based on the gradient differences in illumination direction. Multiple-source images covering different regions of the lunar surface are then used for rock detection and rock abundance analysis, including the Chang'E-3 descent images (0.02–0.17 m/pixel) around the landing area, the Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera images (0.3–1.1 m/pixel) at the landing sites of the Chang'E-3, Luna 17, and Luna 23, and the LRO Narrow Angle Camera image mosaic (1.5 m/pixel) in the Oceanus Procellarum area. A rock abundance model using an exponential form is derived from the results of the abovementioned analysis to describe the overall cumulative fractional area of rocks versus their diameters. The rock detection results and the derived rock abundance estimated by the proposed model are compared with the ground measurements obtained using the rover images of the Chang'E-3 landing site and the LRO Diviner Radiometer-derived rock abundance. A comparison analysis indicates that the derived rock abundance model can feasibly represent the rock abundance in various situations on local and large scales. In general, a remarkable consistency is observed between our results and the results obtained using the ground measurements at the Chang'E-3 landing site and the orbit measurements from the Diviner radiometer, while our rock detection results and rock abundance model exhibit better performance in presenting detailed information in local areas. Further analyses on rock abundance and the crater morphology and other crater characteristics in the Oceanus Procellarum area indicate that it is feasible to use rock abundance information to estimate the surface maturity.