Content Controlled Spectral Indices for Detection of Hydrothermal Alteration Minerals Based on Machine Learning and Lasso-Logistic Regression Analysis

Content Controlled Spectral Indices for Detection of Hydrothermal Alteration Minerals Based on Machine Learning and Lasso-Logistic Regression Analysis

This article introduced the quantity controlled spectral indices working at mineral contents higher than 5 wt.% for detection of sericite, chlorite, and pyrophyllite, which are the representative alteration minerals of phyllic, propylitic, and advanced argillic hydrothermal alterations. T...

Full description

Bibliographic Details
Main Authors: Kyuhun Shim, Jaehyung Yu, Lei Wang, Sangin Lee, Sang-Mo Koh, Bum Han Lee
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Chlorite
hydrothermal alteration minerals
least absolute shrinkage and selection operator (LASSO) logistic regression
pyrophyllite
quantity controlled indices
random forest (RF)
Online Access:https://ieeexplore.ieee.org/document/9489372/

Internet

https://ieeexplore.ieee.org/document/9489372/

Similar Items