The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W1, 183–190, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W1-183-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W1, 183–190, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W1-183-2017

  25 Jul 2017

25 Jul 2017

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

J. Yang and Z. Kang J. Yang and Z. Kang
  • Department of Remote Sensing and Geo-Information Engineering, School of Land Science and Technology, University of Geosciences, Xueyuan Road, Beijing, 100083 CN

Keywords: Bayesian Network, CCD Images, Digital Elevation Model, Impact Crater Recognition, Data Integration

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.