Geology is a science deal with the minerals and rocks majorly. Hyperspectral data is used in the geology domain for mineral identification and mapping. Because of its high spatial as well as high spectral resolution. The main objective is to identify the different minerals on the earth crust using different mineral mapping algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using AVIRIS-NG data and conclude the better algorithm for mineral mapping. For identification of a different kind of minerals, spectral analysis is performed. Imagery pixel spectrum is matched with predefined or unique (pure) spectra for the mineral detection. SAM algorithm mainly computes the angle between the pixel spectra and reference spectra. The SFF compares the continuum removed spectrum of the image with the continuum removed reference spectrum and operated the least square fitting. MTMF is a combination of two different algorithms and works on the MNF transformed data for estimation of abundance of minerals along with the identification of the false positive. MTMF perform well and gives better results for mineral mapping.
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Ronak Jain is presently pursuing PhD from Mohanlal Sukhadia University, Rajasthan. He received his M.Sc.(2015) & M.Sc. Tech.(2016) in Geology from MLSU and Post-Graduate Diploma in Geoinformatics (2017) from Indian Institute of Remote Sensing, ISRO and Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente, Netherlands.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Geology is a science deal with the minerals and rocks majorly. Hyperspectral data is used in the geology domain for mineral identification and mapping. Because of its high spatial as well as high spectral resolution. The main objective is to identify the different minerals on the earth crust using different mineral mapping algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using AVIRIS-NG data and conclude the better algorithm for mineral mapping. For identification of a different kind of minerals, spectral analysis is performed. Imagery pixel spectrum is matched with predefined or unique (pure) spectra for the mineral detection. SAM algorithm mainly computes the angle between the pixel spectra and reference spectra. The SFF compares the continuum removed spectrum of the image with the continuum removed reference spectrum and operated the least square fitting. MTMF is a combination of two different algorithms and works on the MNF transformed data for estimation of abundance of minerals along with the identification of the false positive. MTMF perform well and gives better results for mineral mapping. 72 pp. Englisch. Seller Inventory # 9786137344262
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jain RonakRonak Jain is presently pursuing PhD from Mohanlal Sukhadia University, Rajasthan. He received his M.Sc.(2015) & M.Sc. Tech.(2016) in Geology from MLSU and Post-Graduate Diploma in Geoinformatics (2017) from Indian Institut. Seller Inventory # 385845198
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Taschenbuch. Condition: Neu. Neuware -Geology is a science deal with the minerals and rocks majorly. Hyperspectral data is used in the geology domain for mineral identification and mapping. Because of its high spatial as well as high spectral resolution. The main objective is to identify the different minerals on the earth crust using different mineral mapping algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using AVIRIS-NG data and conclude the better algorithm for mineral mapping. For identification of a different kind of minerals, spectral analysis is performed. Imagery pixel spectrum is matched with predefined or unique (pure) spectra for the mineral detection. SAM algorithm mainly computes the angle between the pixel spectra and reference spectra. The SFF compares the continuum removed spectrum of the image with the continuum removed reference spectrum and operated the least square fitting. MTMF is a combination of two different algorithms and works on the MNF transformed data for estimation of abundance of minerals along with the identification of the false positive. MTMF perform well and gives better results for mineral mapping.Books on Demand GmbH, Überseering 33, 22297 Hamburg 72 pp. Englisch. Seller Inventory # 9786137344262
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Geology is a science deal with the minerals and rocks majorly. Hyperspectral data is used in the geology domain for mineral identification and mapping. Because of its high spatial as well as high spectral resolution. The main objective is to identify the different minerals on the earth crust using different mineral mapping algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using AVIRIS-NG data and conclude the better algorithm for mineral mapping. For identification of a different kind of minerals, spectral analysis is performed. Imagery pixel spectrum is matched with predefined or unique (pure) spectra for the mineral detection. SAM algorithm mainly computes the angle between the pixel spectra and reference spectra. The SFF compares the continuum removed spectrum of the image with the continuum removed reference spectrum and operated the least square fitting. MTMF is a combination of two different algorithms and works on the MNF transformed data for estimation of abundance of minerals along with the identification of the false positive. MTMF perform well and gives better results for mineral mapping. Seller Inventory # 9786137344262
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Taschenbuch. Condition: Neu. Study of Mineral Mapping Techniques using Airborne Hyperspectral Data | Exploring the potential of AVIRIS-NG for Mineral Identification | Ronak Jain (u. a.) | Taschenbuch | 72 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786137344262 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 111697658
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