Comparative Evaluation Threshold Parameters of Spectral Angle Mapper (SAM) for Mapping of Chhabadiya Talc Minerals, Jahajpur, Bhilwara, India using Hyperion hyperspectral Remote Sensing Data

Mahesh Kumar Tripathi, H. Govil, P. Diwan
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引用次数: 3

Abstract

The significant capability of synoptic coverage of satellite remote sensing data at the time of advent providing accurate and immediate valuable information on various aspects. The progressive development of remote sensing increases its capability to identify and map the precious and valuable materials and minerals. Spectral Angle Mapper (SAM) is classifiers of supervised classification for mapping and classification. The characteristic of SAM is based on similarity between image spectra and reference spectra on the behalf of tolerance level of specified maximum angle of threshold. In this research work SAM algorithms applied for mapping of Chhabadiya talc mineral. In this research work SAM algorithms defines the applicability of similarity of angle and value of threshold parameters of spectral angle which show capability to interpret or map the maximum and minimum abundance of talc minerals. For this research work Hyperion hyperspectral remote sensing data used for study of applicability and efficiency of SAM algorithms for mineral mapping. The quality and abundance of minerals completely depend on the minimum degree of SAM threshold parameter. Maximum spectral angle shows maximum mapping area with minimum similarity, but low spectral angle shows small and more abundant mapping area with maximum similarity in the hyperspectral image. Conclusion of this research work verify the identification of minerals are depend on the spectral and spectral characteristics of hyperspectral remote sensing data and mapping with qualitative abundance of minerals depend on the lower value of spectral angle and threshold of SAM algorithms or maximum similarity of spectra.
利用Hyperion高光谱遥感数据对印度贾哈吉普尔Chhabadiya滑石矿物进行光谱角成像仪(SAM)比较评价阈值参数
卫星遥感数据在出现时的天气覆盖的重要能力提供了关于各个方面的准确和即时的有价值的信息。遥感技术的逐步发展提高了其识别和绘制珍贵和有价值的材料和矿物的能力。光谱角映射器(SAM)是一种用于制图和分类的监督分类分类器。SAM的特征是基于图像光谱与参考光谱之间的相似性,代表指定的最大阈值角度的公差水平。本研究将SAM算法应用于Chhabadiya滑石矿的填图。在本研究中,SAM算法定义了角度相似性和光谱角度阈值参数值的适用性,显示了对滑石矿物最大丰度和最小丰度的解释或映射能力。本研究工作利用Hyperion高光谱遥感数据,研究了SAM算法在矿产填图中的适用性和效率。矿物的质量和丰度完全取决于SAM阈值参数的最小程度。在高光谱图像中,最大光谱角显示出最大相似度和最小相似度的成图区域,而低光谱角显示出最小相似度和更丰富的小成图区域。本研究工作的结论验证了矿物的识别依赖于高光谱遥感数据的光谱和光谱特征,而矿物的定性丰度则依赖于SAM算法的光谱角和阈值的较低值或光谱的最大相似性。
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