Classification of terrestrial impact craters based on morphometric parameters using remote sensing data: a case study of Jeokjung-Chogye impact crater, South Korea

This study aims to develop an automated impact crater classification machine learning (ML) method based on the morphometric parameters extracted from SRTM DEM. The training and testing dataset comprises data from 52 confirmed, well preserved, and moderately eroded impact craters and a recently discovered impact crater in Korea, Jeokjung Chogye Basin (JCB). The morphometric parameters including rim diameter, floor diameter, and wall width of complex craters and simple craters were tested by Mann Whitney U test and One Sample Wilcoxon signed rank test. The tests showed that those parameters can statistically separate the two types of craters. The Random Forest model classified them with an accuracy of 88.6