Training Sample Selection Based on SAR Images Quality Evaluation With Multi-Indicators Fusion
In recent years, with the development of artificial neural networks, efficiently training models for synthetic aperture radar (SAR) image classification tasks has garnered significant attention from researchers. Particularly when dealing with datasets containing a large number of redundant samples,...
Saved in:
| Main Authors: | Pengcheng Wang, Huanyu Liu, Junbao Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/sil2/1612434 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Angle-Controllable SAR Image Generation for Target Recognition with Few Samples
by: Xilin Wang, et al.
Published: (2025-03-01) -
A multi-scale enhanced feature fusion model for aircraft detection from SAR images
by: Guoqing Zhou, et al.
Published: (2025-08-01) -
SFDA-MEF: An Unsupervised Spacecraft Feature Deformable Alignment Network for Multi-Exposure Image Fusion
by: Qianwen Xiong, et al.
Published: (2025-01-01) -
Fusion PSPnet Image Segmentation Based Method for Multi-Focus Image Fusion
by: Jingchun Zhou, et al.
Published: (2019-01-01) -
Demand response potential evaluation based on feature fusion with expert knowledge and multi‐image
by: Jiale Liu, et al.
Published: (2024-12-01)