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Effect of Reduced Spatial Resolution on Mineral Mapping Using Imaging Spectrometry—Examples Using Hyperspectral Infrared Imager (HyspIRI)-Simulated Data

机译:降低的空间分辨率对使用成像光谱仪进行矿物制图的影响-以高光谱红外成像仪(HyspIRI)模拟数据为例

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The Hyperspectral Infrared Imager (HyspIRI) is a proposed NASA satellite remote sensing system combining a visible to shortwave infrared (VSWIR) imaging spectrometer with over 200 spectral bands between 0.38 and 2.5 μm and an 8-band thermal infrared (TIR) multispectral imager, both at 60 m spatial resolution. Short Wave Infrared (SWIR) (2.0–2.5 μm) simulation results are described here using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data in preparation for the future launch. The simulated data were used to assess the effect of the HyspIRI 60 m spatial resolution on the ability to identify and map minerals at hydrothermally altered and geothermal areas. Mineral maps produced using these data successfully detected and mapped a wide variety of characteristic minerals, including jarosite, alunite, kaolinite, dickite, muscovite-illite, montmorillonite, pyrophyllite, calcite, buddingtonite, and hydrothermal silica. Confusion matrix analysis of the datasets showed overall classification accuracy ranging from 70 to 92% for the 60 m HyspIRI simulated data relative to 15 m spatial resolution data. Classification accuracy was lower for similar minerals and smaller areas, which were not mapped well by the simulated 60 m HyspIRI data due to blending of similar signatures and spectral mixing with adjacent pixels. The simulations demonstrate that HyspIRI SWIR data, while somewhat limited by their relatively coarse spatial resolution, should still be useful for mapping hydrothermal/geothermal systems, and for many other geologic applications requiring mineral mapping.
机译:高光谱红外成像仪(HyspIRI)是一种拟议的NASA卫星遥感系统,结合了可见光到短波红外(VSWIR)成像光谱仪和200多个0.38至2.5μm光谱带以及8波段热红外(TIR)多光谱成像仪,两者在60 m空间分辨率下。此处使用机载可见/红外成像光谱仪(AVIRIS)数据介绍了短波红外(SWIR)(2.0–2.5μm)模拟结果,为将来的发射做准备。模拟数据用于评估HyspIRI 60 m空间分辨率对在热液蚀变和地热区域识别和测绘矿物的能力的影响。使用这些数据生成的矿物图成功地检测并绘制了各种特色矿物,包括黄钾铁矾,铝矾石,高岭石,地开石,白云母-伊利石,蒙脱石,叶蜡石,方解石,芽石和水热二氧化硅。数据集的混淆矩阵分析显示,相对于15 m空间分辨率数据,60 m HyspIRI模拟数据的总体分类精度为70%到92%。对于相似的矿物和较小的区域,分类精度较低,由于相似特征的混合以及与相邻像素的光谱混合,模拟的60 m HyspIRI数据无法很好地映射。模拟表明,HyspIRI SWIR数据虽然受到相对粗糙的空间分辨率的一定限制,但对于水热/地热系统的测绘以及需要矿物测绘的许多其他地质应用仍应有用。

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