A BAYESIAN NETWORK FRAMEWORK FOR AUTOMATIC DETECTION OF LUNAR IMPACT CRATERS BASED ON OPTICAL IMAGES AND DEM DATA

Abstract. Impact craters are among the most noticeable geo-morphological features on the planetary surface and yield significant information on terrain evolution and the history of the solar system. Thus, the recognition of lunar impact craters is an important branch of modern planetary studies. To address problems associated with the insufficient and inaccurate detection of lunar impact craters, this paper extends the strategy that integrates multi-source data and proposes a Bayesian Network (BN) framework for the automatic recognition of impact craters that is based on CCD stereo camera images and associated Digital Elevation Model (DEM) data. The method uses the SVM model to fit the probability distribution of the impact craters in the feature space. SVM model, whose output is used as the intermediate posterior probability, is embedded in the Bayesian network as a node, and the final posterior probability is obtained by integration under the Bayesian network. We validated our proposed framework with both CCD stereo camera images acquired by the Chang’e-2 satellite and DEM data acquired by Lunar Reconnaissance Orbiter (LRO). Experimental results demonstrate that the proposed framework can provide a very high level of accuracy in the recognition phase. Moreover, the results showed a significant improvement in the detection rate, particularly for the detection of sub-kilometer craters, compared with previous approaches.