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ASTROSENSE: applying astrophysics algorithms to remote sensing data

ASTROSENSE：将天体物理学算法应用于遥感数据

基本信息

批准号：
ST/R005265/1
负责人：
James Geach
金额：
$ 38.4万
依托单位：
University of Hertfordshire
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ST%2FR005265%2F1
关键词：
ASTROSENSE applying astrophysics algorithms remote

项目摘要

Machine learning is a computational data analysis technique that offers tremendous benefits over traditional methods. In particular, algorithms can be developed that can automatically identify objects or features of interest in digital imaging. Some machine learning algorithms require extensive training through labelled examples, but unsupervised algorithms can learn from the data itself, requiring no pre-labelled training set. This makes such algorithms incredibly versatile and can be easily applied to many different types of imaging. In principle, the algorithm's performance should improve over time as it 'experiences' more examples of input data. We are developing just an unsupervised machine learning algorithm for use in large-scale astronomical surveys that can also be applied in other 'remote sensing' data, such as underwater sonar imaging of the sea bed and aerial/satellite imagery. Such an algorithm can, for example, help determine the local terrain and identify hazards in complex, changing environments that could be missed by a human inspector. This could feed into AI-assisted navigation units in autonomous vehicles for example. Our goal in this project is to develop a versatile, robust algorithm that can be deployed in a variety of practical areas, with a view to performing real-time image classification and analysis on input data, both from astrophysics and 'real-world' industrial sectors.

机器学习是一种计算数据分析技术，与传统方法相比具有巨大的优势。特别地，可以开发可以自动识别数字成像中感兴趣的对象或特征的算法。一些机器学习算法需要通过标记的示例进行广泛的训练，但无监督算法可以从数据本身学习，不需要预先标记的训练集。这使得这种算法非常通用，可以很容易地应用于许多不同类型的成像。原则上，算法的性能应该随着时间的推移而提高，因为它“经历”了更多的输入数据示例。我们正在开发一种用于大规模天文调查的无监督机器学习算法，该算法也可以应用于其他“遥感”数据，例如海底水下声纳成像和航空/卫星图像。例如，这种算法可以帮助确定当地地形，并识别人类检查员可能错过的复杂变化环境中的危险。例如，这可以输入自动驾驶汽车中的人工智能辅助导航单元。我们在这个项目中的目标是开发一个通用的，强大的算法，可以部署在各种实际领域，以期执行实时图像分类和分析的输入数据，无论是从天体物理学和“现实世界”的工业部门。