An Empirical Study on Data Augmentation for Pixelwise Satellite Image Time-Series Classification and Cross-Year Adaptation
Satellite image time series (SITS) are widely used for land cover mapping and vegetation monitoring. Despite the success of deep learning methods in SITS classification, their performance strongly depends on large labeled datasets. Data augmentation is a cost-effective strategy to prevent deep learn...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10833777/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|