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太空物体的识别和分类来自高光谱材料分析

Massimiliano Vasile¹, Lewis Walker², Andrew Campbell³

¹Mechanical and Aerospace Engineering, University of Strathclyde, James Weir Building, 75 Montrose Street, Glasgow, G1 1XJ, UK. massimiliano.vasile@strath.ac.uk.

Scientific reports

|January 18, 2024

概括

这项研究引入了一条管道,用于识别使用高光谱成像和机器学习的空间物体材料. 它准确地对物体进行分类,即使有经过气候变化的材料或不完整的光谱库.

科学领域:

太空科学太空科学
材料科学材料科学材料科学
计算机科学计算机科学

背景情况:

准确识别太空物体对于轨道碎片管理和太空局势意识至关重要.
超光谱成像为材料特征提供了详细的光谱特征.
当前的方法可能会与材料降解和不完整的光谱库作斗争.

研究的目的:

开发和验证数据处理管道,用于分析未知空间物体的材料组成.
在非理想条件下评估材料识别技术的稳定性.
根据检测到的材料组成,对空间物体进行分类.

主要方法:

使用单像素图像中的超光谱特征进行材料分析.
采用机器学习和最小正方形光谱匹配来进行材料识别.
应用监督机器学习用于基于材料检测的对象分类.
调查物质气候变化和不完整的光谱库的影响.

主要成果:

证明了从超光谱数据中确定材料组成的能力.
评估了对耐候材料的分类算法的性能.
评估了培训库中缺少光谱数据的影响.
介绍了空间物体识别和分类的初步结果.

结论:

拟议的管道有效地从超光谱签名中提取物质信息.
这些方法表现出对材料化和图书馆缺陷等挑战的弹性.
这种方法促进了空间物体的识别和分类,提高了空间局势意识.

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