Multi-sensor datasets-based optimal integration of spectral, textural, and morphological characteristics of rocks for lithological classification using machine learning models-Reference-Cited by-同舟云学术

Multi-sensor datasets-based optimal integration of spectral, textural, and morphological characteristics of rocks for lithological classification using machine learning models

Published:2021-05-24 Issue: Volume: Page:1-29
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Kumar Chandan¹^ORCID,Chatterjee Snehamoy¹^ORCID,Oommen Thomas¹^ORCID,Guha Arindam²^ORCID,Mukherjee Abhijeet³

Affiliation:

1. Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, USA

2. National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India

3. National Mineral Development Corporation, Hyderabad, India

Funder

National Aeronautics and Space Administration

Fall 2020 finishing fellowship of Michigan Technological University.

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.1920632

Reference98 articles.

1. Support vector machine for multi-classification of mineral prospectivity areas

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4. Feature Selection and Classification of Hyperspectral Images With Support Vector Machines

5. Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

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