A Novel Flexible Architecture Based on SAM for Automatic Exraction of Rampart Craters From Martian High Resolution Images

Extracting rampart crater ejecta blankets is crucial for understanding impact crater formation and material transport processes, offering key insights into the distribution of subsurface water and ice on Mars. However, traditional methods often fail to extract rampart crater ejecta blankets due to complex terrain, noise interference, and blurred boundaries. To overcome these challenges, we propose an Edge-Aware Segment Anything Model (SAM) Sputter Analysis (EASSA) framework for the rapid and accurate extraction of rampart crater ejecta contours. EASSA comprises three key components. First, Wiener filtering and multi-scale Retinex preprocessing are applied to suppress terrain noise and enhance the visual distinction between rampart features and the background. Second, a SAM-based unsupervised segmentation module is employed to automatically identify rampart crater boundaries. Finally, we refined the extracted edges by designing a contour optimization pipeline that applies classical image operators such as Sobel to enhance gradients, Suzuki–Abe to correct contours, and Douglas–Peucker to simplify and smooth shapes. To validate EASSA, we construct a multi-scale rampart crater dataset using Context Camera (CTX) (targeting craters <1 km) and THEMIS imagery (for craters >1 km) in the Chryse Planitia and Arabia Terra regions. Experimental results demonstrate that our method achieves a detection accuracy of 97.36%, a recall of 93.36%, and an IoU of 0.93, significantly outperforming the baseline SAM segmentation. Morphological analysis further reveals that rampart ejecta in both regions exhibit mobilities greater than 2, an average lobateness coefficient of 1.06, and relatively shallow excavation depths. Additionally, we observe that elevated terrains exhibit lower ejecta flow mobility under similar latitudinal conditions, while geomorphic evidence of past fluvial activity remains evident. These findings provide new insights into Martian subsurface water dynamics and the mobility characteristics of ejecta under varied geologic settings.