Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey

The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of 94 confirmed and well-preserved terrestrial impact craters, 104 volcanic calderas, and 124 valleys were extracted from real-world surface features. For craters with different sizes, eight optimal scale parameters from 80 to 3000 have been identified using multi-resolution segmentation, where the scale parameters have a positive correlation (R-2 = 0.78) with the diameters of craters. The object-oriented random forest approach classified the tested impact craters, volcanic calderas, and valleys with an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The investigated terrestrial impact craters, in general, have relatively lower rim circularity, higher length-to-width ratio, and lower relief, slope, and elevation than volcanic calderas. The topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters. The excavation and ejection by initial impact and rebound of excavated materials contribute to low elevation. The post-impact deformation, including inward collapse and slump of unstable rims, weathering, erosion, and sediment deposition, further reduces elevation and relief and modifies shapes resulting in lower circularity and higher length-to-width ratio. Due to the resolution limitation of the source DEM data and the number of real-world samples, the model has only been validated for craters of 0.88 to 100 km in diameter, which can be generalized to explore undiscovered terrestrial impact craters using cloud computing with global datasets provided by platforms such as Google Earth Engine and Microsoft Planetary Computer.