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一种人工智能探测月相的方法:通过天文数据集成增强新月亮的识别能力

Murad Al-Rajab¹, Samia Loucif², Raed Abu Zitar³

¹College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.

Frontiers in artificial intelligence

|March 2, 2026

概括

人工智能 (AI) 通过轨道图像有效地检测新月新月. 机器学习模型,特别是随机森林 (RF) 和卷积神经网络 (CNN),显示出对月球观测的高精度和稳定性.

科学领域:

天文学和天体物理学.
人工智能和机器学习
图像处理和计算机视觉

背景情况:

月新月观测对于天文学,文化传统和宗教日历至关重要.
现有的基于地球的对月球相的成像,特别是新月亮,是有限的.
太空图像为持续的月球观测提供了一个潜在的解决方案.

研究的目的:

探索使用人工智能来检测和分析新月新月的可行性.
为此任务评估深度学习 (CNN) 和传统机器学习 (RF,SVM) 模型.
利用超过13年的月球侦察轨道器 (LRO) 数据.

主要方法:

将CNN,RF和SVM应用于轨道月球图像.
实现的图像预处理:灰度转换,自适应式直方图等级,降低噪声.
通过整合月球年龄数据来增强CNN;测试了与噪音和阻塞有关的模型稳定性.

主要成果:

所有的车型都实现了高性能 (约. 98%的准确性,精确性,回忆,F-分数).
射频和CNN模型的表现优于SVM,显示出优越的整体性能.
美国有线电视新闻网 (CNN) 对高斯噪声和高达50%的图像屏蔽表现出强大的稳定性.

结论:

人工智能技术,特别是CNN和RF技术,对于从轨道图像中检测新的月球新月亮非常有效.
美国有线电视新闻网 (CNN) 模型的稳定性表明,在现实世界的月球观测中具有实际应用性.
这项研究支持传统的新月识别,并有助于减少日历差异.

关键词:

天文图像天文图像天文图像深度学习是一种深度学习.月亮新月探测器月亮新月探测器月球周期的识别.机器学习是机器学习.新的新月的识别标识月球探测中的遮蔽.

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