Adaptive Selective Learning for Automatic Identification of Sub-Kilometer Craters

Counting craters is a fundamental task of planetary science, because it provides the only tool for measuring relative ages of planetary surfaces. However, advances in surveying craters present in data gathered by planetary probes have not kept up with advances in data collection. It becomes extremely challenging to automatically count a very large number of small, sub-kilometer size craters in a deluge of high resolution planetary images. In this paper, we combine active learning with semi-supervised learning to build an adaptive learning system to automatically detect craters from high resolution panchromatic planetary images. We propose an adaptive selective algorithm to iteratively enrich an original small training set, using unlabeled test set without additional human labeling effort, to detect craters from a large volume of images. We propose three strategies to improve detection accuracy by integrating classification with exploration on unlabeled samples. The Majority Vote Strategy is used to automatically obtain class labels by exploiting unlabelled samples. The De-Mixed Strategy is used on instance filtering to obtain reliable samples. The Active Stability Strategy is used to obtain an appropriate class distribution in the constructed training set by detecting unstable classes. By using those three strategies, we actively select test instances from test images into an existing small initial training set while rebuilding the classifier in the mean time. Our proposed algorithms are empirically evaluated on a large high resolution Martian image, exhibiting a heavily cratered Martian terrain characterized by heterogeneous surface morphology. The experimental results demonstrate that the proposed approach achieves a higher accuracy than other existing approaches to a large extent. Keywords-pattern recognition, active learning, semi-supervised learning, crater detection, Mars 1