Multi-Objective Support Vector Regression Reduces Systematic Error in Moderate Resolution Maps of Tree Species Abundance

Multi-Objective Support Vector Regression Reduces Systematic Error in Moderate Resolution Maps of Tree Species Abundance

When forest conditions are mapped from empirical models, uncertainty in remotely sensed predictor variables can cause the systematic overestimation of low values, underestimation of high values, and suppression of variability. This regression dilution or attenuation bias is a well-recognized problem...

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Bibliographic Details
Main Authors: Kasey Legaard, Erin Simons-Legaard, Aaron Weiskittel
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Remote Sensing
Subjects:
support vector machines
genetic algorithm
multi-objective optimization
Pareto optimization
regression
regression dilution bias
Online Access:https://www.mdpi.com/2072-4292/12/11/1739

Internet

https://www.mdpi.com/2072-4292/12/11/1739

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