Comparative Evaluation Threshold Parameters of Spectral Angle Mapper (SAM) for Mapping of Chhabadiya Talc Minerals, Jahajpur, Bhilwara, India using Hyperion hyperspectral Remote Sensing Data
{"title":"Comparative Evaluation Threshold Parameters of Spectral Angle Mapper (SAM) for Mapping of Chhabadiya Talc Minerals, Jahajpur, Bhilwara, India using Hyperion hyperspectral Remote Sensing Data","authors":"Mahesh Kumar Tripathi, H. Govil, P. Diwan","doi":"10.1109/ICCT46177.2019.8969015","DOIUrl":null,"url":null,"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.","PeriodicalId":118655,"journal":{"name":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCT46177.2019.8969015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.