Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficien...

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Bibliographic Details
Main Authors: Nan Lin, Jiawei Fu, Ranzhe Jiang, Genjun Li, Qian Yang
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Remote Sensing
Subjects:
hyperspectral
lithology classification
XGBoost
feature selection
Online Access:https://www.mdpi.com/2072-4292/15/15/3764

Internet

https://www.mdpi.com/2072-4292/15/15/3764

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