Mapping of Aluminous Rich Laterite Depositions through Hyper Spectral Remote Sensing

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ISSN Print: 2156-8359
ISSN Online: 2156-8367
www.scirp.org/journal/ijg
E-mail: ijg@scirp.org

"Mapping of Aluminous Rich Laterite Depositions through Hyper Spectral Remote Sensing"
written by M. J. Ratnakanth Babu, E. N. Dhanamjaya Rao, Lalitha Kallempudi, Das Iswar Chandra,
published by International Journal of Geosciences, Vol.9 No.2, 2018

has been cited by the following article(s):

[1]	Few-Shot Hyperspectral Image Classification Based on Cross-Domain Spectral Semantic Relation Transformer
	2023 IEEE International …, 2023

[2]	A Comparative Analysis of Laterite Index and a Novel Laterite Index to Improve Detection of Lateritic Bauxite in the Eastern Ghats of Odisha, India using Sentinel 2A …
	2023 6th International Conference …, 2023

[3]	Using reflectance spectroscopy and Advanced Spaceborne Thermal Emission and Reflection Radiometer data to identify bauxite deposits in vicinity of Az Zabirah …
	2021

[4]	Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation
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[5]	MAPPING OF BAUXITE ORE USING REMOTE SENSING AND GIS
	2018

[6]	SPATIAL TECHNOLOGY FOR MAPPING MINERAL RESOURCES: A
	2018

[1]	Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data Remote Sensing, 2024 DOI:10.3390/rs16132295

[2]	Few-Shot Hyperspectral Image Classification Based on Cross-Domain Spectral Semantic Relation Transformer 2023 IEEE International Conference on Image Processing (ICIP), 2023 DOI:10.1109/ICIP49359.2023.10222564

[3]	Using reflectance spectroscopy and Advanced Spaceborne Thermal Emission and Reflection Radiometer data to identify bauxite deposits in vicinity of Az Zabirah, northern Saudi Arabia Arabian Journal of Geosciences, 2021 DOI:10.1007/s12517-021-07155-7

[4]	Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation Remote Sensing, 2018 DOI:10.3390/rs10111675

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