Enhancing Crop Type Mapping in Data-Scarce Regions Through Transfer Learning: A Case Study of the Hexi Corridor

Enhancing Crop Type Mapping in Data-Scarce Regions Through Transfer Learning: A Case Study of the Hexi Corridor

Timely and accurate crop mapping is crucial for providing essential data support for agricultural production management. Reliable ground truth samples form the foundation for crop mapping using remote sensing imagery, a task that presents significant challenges in regions with limited sample availab...

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Bibliographic Details
Main Authors: Jingjing Mai, Qisheng Feng, Shuai Fu, Ruijing Wang, Shuhui Zhang, Ruoqi Zhang, Tiangang Liang
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Remote Sensing
Subjects:
transfer learning
crop mapping
CDL
random forest
XGBoost
TrAdaBoost
Online Access:https://www.mdpi.com/2072-4292/17/9/1494
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