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4th Workshop on Remote Sensing and Geology Proceedings preprint EnGeoMAP - a geological mapping tool applied to the EnMAP mission

机译:第四届遥感与地质学报研讨会预印版EnGeoMAP-一种应用于EnMAP任务的地质制图工具

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摘要

Hyperspectral imaging spectroscopy offers a broad range of spatial applications that are primarily based on the foregoing identification of surface cover materials. In this context the future hyperspectral sensor EnMAP will provide a new standard of highly qualitative imaging spectroscopy data from space that allows a spatiotemporal monitoring of surface materials. The high SNR of EnMAP offers the possibility to differentiate and to identify minerals that are showing characteristic absorption features as a 30m x 30m spatial mixture in the visible, the near infrared and the short wave infrared range (0.4 -2.5 urn). For this purpose spectral mixture analysis (SMA) approaches are traditionally used. However, these approaches lack in transferability, repeatability and inclusion of sensor characteristics. Additionally, they rely on image based and randomly detected endmembers as well as on in-situ or laboratory spectra that are not spatially stable in case of an image based extraction and assumed to be spectrally pure. In this work, a new framework is proposed that addresses these limitations considering the EnMAP sensor characteristics. It is named as EnMAP Geological Mapper - EnGeoMAP. It consists of several new and adapted approaches to identify spectrally homogeneous regions. In parallel, minerals are identified and semi-quantified by a sensor related and knowledge based fitting approach. Supplementary outputs are abundance, classification, homogeneity and uncertainty maps. First results show that the proposed approach offers 100% repeatability and gains an identification error for minerals of about 2 % on average for different studies.
机译:高光谱成像光谱学提供了广泛的空间应用,这些应用主要基于前面对表面覆盖材料的识别。在这种情况下,未来的高光谱传感器EnMAP将提供一种来自太空的高定性成像光谱数据的新标准,从而可以对表面材料进行时空监视。 EnMAP的高信噪比提供了区分和识别在30%x 30m可见,近红外和短波红外范围(0.4 -2.5 um)范围内具有30m x 30m空间混合特征的矿物的可能性。为此,传统上使用光谱混合分析(SMA)方法。然而,这些方法缺乏可传递性,可重复性和传感器特性的包含。此外,它们还依赖于基于图像的,随机检测的末端成员,以及依赖于原位或实验室光谱的原位或实验室光谱,这些光谱在基于图像的提取情况下在空间上不稳定,并且假定为光谱纯净。在这项工作中,提出了一个新的框架来解决考虑到EnMAP传感器特性的这些限制。它被命名为EnMAP地质映射器-EnGeoMAP。它由几种新的,经过改进的方法组成,可以识别光谱上均一的区域。同时,通过与传感器相关和基于知识的拟合方法来识别和半定量矿物。补充输出是丰度,分类,同质性和不确定性图。初步结果表明,所提出的方法可提供100%的可重复性,并且在不同研究中对矿物的识别误差平均约为2%。

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  • 来源
    《EARSeL newsletter》 |2012年第90期|p.17-22|共6页
  • 作者单位

    Helmholtz Centre Potsdam-GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany;

    Helmholtz Centre Potsdam-GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany;

    Helmholtz Centre Potsdam-GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany;

    Helmholtz Centre Potsdam-GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany;

    Helmholtz Centre Potsdam-GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing, Potsdam, Germany;

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