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首页> 外文期刊>Geocarto international >Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka, Southern India using hyperspectral remote sensing data
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Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka, Southern India using hyperspectral remote sensing data

机译:使用高光谱遥感数据的Karnataka Gadag Schist Belt(GSB)改变矿物的空间分布

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Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4-1.0m) and SWIR regions (2.0-2.4m). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4-1.0-m and 2.0-2.3-m wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6-0.7m, 0.86 and at 0.9m in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33m in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33-1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.
机译:使用Hyperion 2013的Hyperion数据进行岩石和土壤中改变矿物质的空间分布。通过强调VNIR(0.4-1.0M)和SWIR地区(2.0-2.4)加工整个光谱范围(2.0-2.4 m)。处理方法包括使用分析光谱装置光谱辐射计的光谱超电平校正,最小噪声分数转换,光谱特征拟合(SAM)的最小噪声分数转换,光谱特征拟合(SAM)。通过研究0.4-1.0-M和2.0-2.3-m波长区域之间的吸收带,共分析了总共155个带识别和映射主要改变的矿物质。由于过渡元件中的晶体场效应的电荷转移,在VNIR区域中,最重要的和诊断光谱吸收特征在VNIR区域中发生在0.6-0.7m,0.86和0.9m处,而在2.1,2.2,2.25和2.33m附近吸收SWIR地区与碳矿物(Al-OH,Fe-OH和Mg-OH)中的弯曲和拉伸的弯曲和拉伸有关,特别是在粘土矿物中。实施SAM和SFF技术以识别存在的矿物质。 SAM和SFF分配了0.33-1的分数,其中值1表示精确的矿物型。然而,将EndMember Spectra与美国地质调查和John Hopkins大学光谱文库进行比较,用于矿物和土壤。五个矿物质,即高岭石-5,高岭土-2,葡萄酒,哈米麦木,kaosmec和一块土壤,即灰棕壤壤土。灰褐色壤土和kaosmec已被映射为GSB的土壤和岩石中存在的主要风化/改变产品。接下来是哈米热和高岭石。然后将SAM分类器应用于Hyperion图像以产生矿物地图。该地区的主导岩性包括灰色瓦克,赤毛石和花岗岩球茎。

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