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Nontronite mineral identification in nilgiri hills of tamil nadu using hyperspectral remote sensing

机译:泰米尔纳德尼尔尼尔尼尔尼尔尼尔尼尔尼尔山的非巨石矿物质识别使用高光谱遥感

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Hyperspectral Remote sensing is a tool to identify the minerals along with field investigation. Tamil Nadu has abundant minerals like 30% titanium, 52% molybdenum, 59% garnet, 69% dunite, 75% vermiculite and 81% lignite. To enhance the user and industry requirements, mineral extraction is required. To identify the minerals properly, sophisticated tools are required. Hyperspectral remote sensing provides continuous extraction of earth surface information in an accurate manner. Nontronite is an iron-rich mineral mainly available in Nilgiri hills, Tamil Nadu, India. Due to the large number of bands, hyperspectral data require various preprocessing steps such as bad bands removal, destriping, radiance conversion and atmospheric correction. The atmospheric correction is performed using FLAASH method. The spectral data reduction is carried out with minimum noise fraction (MNF) method. The spatial information is reduced using pixel purity index (PPI) with 10000 iterations. The selected end members are compared with spectral libraries like USGS, JPL, and JHU. In the Nontronite mineral gives the probability of 0.85. Finally the classification is accomplished using spectral angle mapper (SAM) method.
机译:高光谱遥感是一种识别矿物的工具以及现场调查。泰米尔纳德邦拥有丰富的矿物质,如30%钛,52%钼,59%石榴石,69%的Dunite,75%蛭石和81%的褐煤。为了提高用户和行业要求,需要矿物提取。要正确识别矿物质,需要复杂的工具。高光谱遥感可以准确的方式提供地面表面信息的连续提取。非巨石是一种富含铁的矿物,主要在印度泰米尔纳德邦尼尔吉里山。由于频带数量大,高光谱数据需要各种预处理步骤,如障碍频段去除,消除,辐射转换和大气校正。使用FLAASH方法进行大气校正。光谱数据减少以最小噪声分数(MNF)方法进行。使用10000次迭代的像素纯度索引(PPI)减少了空间信息。将所选终端成员与Quals,JPL和JHU等光谱库进行比较。在非巨石矿物中,给出了0.85的概率。最后,使用光谱角映射器(SAM)方法完成分类。

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