Semantic Segmentation for Multi-Scene Remote Sensing Images with Noisy Labels Based on Uncertainty Perception
Author:
Affiliation:
1. Beijing Normal University,School of Artificial Intelligence,Beijing,China,100875
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10445798/10445803/10447009.pdf?arnumber=10447009
Reference24 articles.
1. DA2Net: Distraction-Attention-Driven Adversarial Network for Robust Remote Sensing Image Scene Classification
2. APS-Net: An Adaptive Point Set Network for Optical Remote-Sensing Object Detection
3. FactSeg: Foreground Activation-Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery
4. A Self-Learning-Update CNN Model for Semantic Segmentation of Remote Sensing Images
5. Self-Supervised Learning With Data-Efficient Supervised Fine-Tuning for Crowd Counting
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