SpecTf:变压器可以实现数据驱动的成像光谱检测云检测
Jake H Lee1, Michael Kiper1, David R Thompson1
1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109.
概括
一个新的深度学习模型,光谱变压器 (SpecTf),只使用光谱信息,准确地选地球观测数据中的云. 这一突破通过克服传统方法的局限性,加强了全球环境监测.
科学领域:
- 地球科学 地球科学 地球科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 可见短波红外 (VSWIR) 成像光谱仪为全球地球系统过程量化提供了巨大的潜力.
- 可靠的云选是一个关键的挑战,因为传统的空间和时间方法受到云的变化和时间覆盖范围的限制.
研究的目的:
- 为VSWIR成像光谱数据开发先进的云检测方法.
- 解决现有的云选技术的局限性,使用一种新的深度学习方法.
主要方法:
- 介绍了光谱变压器 (SpecTf),这是一个针对光谱学的深度学习架构.
- SpecTf仅使用光谱信息进行云检测,将光谱测量处理为序列.
- 该模型与地球表面矿物灰尘源InvesTigation (EMIT) 仪器的基线方法进行了评估.
主要成果:
- SpecTf显著优于目前的EMIT基线云选方法.
- SpecTf 实现了与其他机器学习方法可比的性能,参数显著减少.
- 该模型的注意力机制提供了可解释性,突出了物理上有意义的光谱特征.
结论:
- SpecTf为VSWIR成像光谱中的云选提供了强大而高效的解决方案.
- 该模型展示了跨仪器概括的潜力,使仪器不可知算法成为可能.
- 这项工作推进了使用遥感进行全球环境监测的自动化数据处理.
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