Electronic Thesis and Dissertation Repository

Degree

Master of Science

Program

Planetary Science

Supervisor

Dr. Jinfei Wang, Dr. Phil Stooke

Abstract

Impact craters are used as subjects for the remote study of a wide variety of surface and subsurface processes throughout the solar system. Their populations and shape characteristics are collected, often manually, and analysed by a large community of planetary scientists. This research investigates the application of automated methods for both the detection and characterization of impact craters on the Moon and Mars, using machine learning techniques and digital elevation data collected by orbital spacecraft. We begin by first assessing the effect of lunar terrain type variation on automated crater detection results. Next, we develop a novel automated crater degradation classification system for martian complex craters using polynomial profile approximation. This work identifies that surface age estimations and crater statistics acquired through automatic crater detection are influenced by terrain type, with unique detection error responses. Additionally, we demonstrate an objective system that can be used to automate the classification of crater degradation states, and identify some potential areas of improvement for such a system.


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