Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance

Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance

Imbalanced learning is a methodological challenge in remote sensing communities, especially in complex areas where the spectral similarity exists between land covers. Obtaining high-confidence classification results for imbalanced class issues is highly important in practice. In this paper, extreme...

Full description

Bibliographic Details
Main Authors: Fei Sun, Run Wang, Bo Wan, Yanjun Su, Qinghua Guo, Youxin Huang, Xincai Wu
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:ISPRS International Journal of Geo-Information
Subjects:
gradient boosting trees
margin
class imbalance
very-high resolution (VHR) remote sensing
land cover classification
disagreement performance
Online Access:https://www.mdpi.com/2220-9964/8/7/315

Internet

https://www.mdpi.com/2220-9964/8/7/315

Similar Items