A Deep Learning-Based Solution to the Class Imbalance Problem in High-Resolution Land Cover Classification
Class imbalance (CI) poses a significant challenge in machine learning, characterized by a substantial disparity in sample sizes between majority and minority classes, leading to a pronounced “long-tail effect” in statistical distributions and subsequent inference processes. This issue is particular...
Saved in:
| Main Authors: | Pengdi Chen, Yong Liu, Yuanrui Ren, Baoan Zhang, Yuan Zhao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/11/1845 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review
by: Pengdi Chen, et al.
Published: (2025-01-01) -
Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance
by: Valerie Zermatten, et al.
Published: (2024-01-01) -
A context aware multiclass loss function for semantic segmentation with a focus on intricate areas and class imbalances
by: Zahra Ghanaei, et al.
Published: (2025-07-01) -
CSDNet: Context-Aware Segmentation of Disaster Aerial Imagery Using Detection-Guided Features and Lightweight Transformers
by: Ahcene Zetout, et al.
Published: (2025-07-01) -
MSHRNet: a multi-scale high-resolution network for land cover classification from high spatial resolution remote sensing images
by: Fang Chen, et al.
Published: (2025-08-01)