Active machine learning approach for crater detection from planetary imagery and digital elevation models

Abstract

Craters are dominant geomorphological features on the surfaces of the moon, Mars, and other planets. The distribution of craters provides valuable information on the planetary surface geology. Machine learning is a widely used approach to detect craters on planetary surface data. A critical step in machine learning is the determination of training samples. In previous studies, the training samples were mainly selected manually, which usually leads to insufficient numbers due to the high cost and unfavorable quality. Surface imagery and digital elevation models (DEMs) are now commonly available for planetary surfaces; this offers new opportunities for crater detection with better performance. This paper presents a novel active machine learning approach, in which the imagery and DEMs covering the same region are used for collecting training samples with more automation and better performance. In the training process, the approach actively asks for annotations for the 2-D features derived from imagery with inputs from 3-D features derived from the DEMs. Thus, the training pool can be updated accordingly, and the model can be retrained. This process can be conducted several times to obtain training samples in sufficient number and of favorable quality, from which a classifier with better performance can be generated, and it can then be used for automatic crater detection in other regions. The proposed approach highlights two advantages: 1) automatic generation of a large number of high-quality training samples and 2) prioritization of training samples near the classification boundary so as to learn more quickly. Two sets of test data on the moon and Mars were used for the experimental validation. The performance of the proposed approach was superior to that of a regular machine learning method.