Multi-scale Based Approach for Crater Detection on Lunar Surface using Clustering algorithm.

This paper introduced automated detection and analysis of lunar craters are crucial for advancing lunar research and facilitating mission planning. This study introduces a comprehensive multi-scale approach for crater detection on the lunar surface using LROC (Lunar Reconnaissance Orbiter Camera) Dataset, enabling the identification of craters across varying scales and enhancing overall detection accuracy and efficiency. The process involves preprocessing lunar imagery to enhance features and delineate potential crater regions, utilizing edge detection algorithms to extract crater boundaries. Clustering techniques are then applied to group similar edge points and isolate potential crater candidates. A thresholding mechanism based on statistical edge intensity analysis refines the detection process. To address multiple detections of the same crater at slightly different diameters, radius criteria are implemented, yielding high diameter accuracy of 84% for mare Imbrium (test site 1) and 80% for mare Nubium (test site 2). Subsequently, a depth-diameter analysis validates crater-like characteristics by combining depth measurements with crater diameter estimation, showcasing the authenticity of potential craters. we compare our diameter and depth with different reference papers and we get high accuracy of R2 =0.99 then other methods. The results highlight the potential of this multi-scale approach for automated lunar crater detection, providing deeper insights into the Moon's surface morphology and history for future lunar exploration missions. [ABSTRACT FROM AUTHOR]