An improved double-branch network for estimation of crater ages based on semi-supervised learning and multi-source Lunar data

While various methods have been developed to estimate the age of impact craters, such as the crater size frequency distribution and morphology methods. Accurately and efficiently estimating the ages of lunar craters using traditional techniques is challenging due to their complex morphology and large number. As a result, the accuracy of age estimation algorithms for meteorite craters based on deep learning is restricted by factors such as a scarcity of age-labeled data and the complex morphology of these craters. To address these issues, this paper presents an enhanced double-branch network for estimating crater ages via semi-supervised learning and multi-source lunar data. The algorithm consists of three steps: semi-supervised training data augmentation, adaptive two-branch feature extraction, and a two-stage crater age classification process. The effectiveness of the improved approach was validated through ablation experiments, resulting in an overall accuracy (OA) of 83.7% on the test set of meteorite craters. This is 5.2% higher than the accuracy achieved by the previous deep learning method.