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arXiv 1803.02192astro-ph.EP
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通过深度学习识别月球陨石坑

Lunar Crater Identification via Deep Learning

Ari Silburt, Mohamad Ali-Dib, Chenchong Zhu, Alan Jackson, Diana Valencia, Yevgeni Kissin, Daniel Tamayo, Kristen Menou

摘要:

月球和其他天体上的陨石坑计数对于限制太阳系的动力学历史至关重要。这传统上是通过视觉检查图像来完成的,因此限制了检索的范围、效率和/或准确性。在本文中,我们证明了使用卷积神经网络(CNN)来确定月球数字高程图(DEM)中陨石坑的位置和大小的可行性。我们从人类生成的测试集中恢复了92%的陨石坑,几乎是陨石坑检测总数的两倍。在这些新的陨石坑中,15%的直径小于地面实况数据集中的最小陨石坑尺寸。我们的中位数分数经度,纬度和半径误差为11%或更少,与人类生成的数据集吻合良好。通过对361个新陨石坑的人工检查,我们估计新陨石坑的假阳性率为11%。此外,我们的月球训练的CNN在水星的DEM图像上测试时表现良好,在每张地图上检测到大部分陨石坑。我们的研究结果表明,深度学习将成为快速自动提取各种太阳系天体上陨石坑的有用工具。我们在https://github.com/silburt/DeepMoon.git和https://doi.org/10.5281/zenodo.1133969上公开提供我们的代码和数据。

英文摘要:

Crater counting on the Moon and other bodies is crucial to constrain the dynamical history of the Solar System. This has traditionally been done by visual inspection of images, thus limiting the scope, efficiency, and/or accuracy of retrieval. In this paper we demonstrate the viability of using convolutional neural networks (CNNs) to determine the positions and sizes of craters from Lunar digital elevation maps (DEMs). We recover 92% of craters from the human-generated test set and almost double the total number of crater detections. Of these new craters, 15% are smaller in diameter than the minimum crater size in the ground-truth dataset. Our median fractional longitude, latitude and radius errors are 11% or less, representing good agreement with the human-generated datasets. From a manual inspection of 361 new craters we estimate the false positive rate of new craters to be 11%. Moreover, our Moon-trained CNN performs well when tested on DEM images of Mercury, detecting a large fraction of craters in each map. Our results suggest that deep learning will be a useful tool for rapidly and automatically extracting craters on various Solar System bodies. We make our code and data publicly available at https://github.com/silburt/DeepMoon.git and https://doi.org/10.5281/zenodo.1133969 .

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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