Using MRNet to Predict Lunar Rock Categories Detected by Chang'e 5 Probe

• URL: http://arxiv.org/abs/2502.09952v1
• Date: Fri, 14 Feb 2025 07:12:19 GMT
• Title: Using MRNet to Predict Lunar Rock Categories Detected by Chang'e 5 Probe
• Authors: Jin Cui, Yifei Zou, Siyuan Zhang,
• Abstract summary: The Oceanus Procellarum, chosen by Chang'e 5 mission, contains various kind of rock species. The data set contains 100 images, which randomly divided into training, validation and test set. In order to make full use of the global information in Moon images, this paper proposes the MRNet network architecture.
• Score: 11.35022157278453
• License:
• Abstract: China's Chang'e 5 mission has been a remarkable success, with the chang'e 5 lander traveling on the Oceanus Procellarum to collect images of the lunar surface. Over the past half century, people have brought back some lunar rock samples, but its quantity does not meet the need for research. Under current circumstances, people still mainly rely on the analysis of rocks on the lunar surface through the detection of lunar rover. The Oceanus Procellarum, chosen by Chang'e 5 mission, contains various kind of rock species. Therefore, we first applied to the National Astronomical Observatories of the China under the Chinese Academy of Sciences for the Navigation and Terrain Camera (NaTeCam) of the lunar surface image, and established a lunar surface rock image data set CE5ROCK. The data set contains 100 images, which randomly divided into training, validation and test set. Experimental results show that the identification accuracy testing on convolutional neural network (CNN) models like AlexNet or MobileNet is about to 40.0%. In order to make full use of the global information in Moon images, this paper proposes the MRNet (MoonRockNet) network architecture. The encoding structure of the network uses VGG16 for feature extraction, and the decoding part adds dilated convolution and commonly used U-Net structure on the original VGG16 decoding structure, which is more conducive to identify more refined but more sparsely distributed types of lunar rocks. We have conducted extensive experiments on the established CE5ROCK data set, and the experimental results show that MRNet can achieve more accurate rock type identification, and outperform other existing mainstream algorithms in the identification performance.

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