January 1, 2025IEEE Transactions on Image Processing

Uncertainty Quantification for Semi-Supervised Object Detection in Remote Sensing Images

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Authors

XYXi Yang PLPenghui Li QZQiubai Zhou

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Implication

Uncertainty quantification demonstrates improved alignment of bounding boxes in remote sensing images, indicating better accuracy in classification and regression tasks.

Key Points

Uncertainty quantification enhances the alignment of bounding boxes with ground truth in remote sensing imagery.
Dynamic uncertainty weighting calculates class uncertainty to prioritize instances, improving object detection accuracy.
Analysis on the DOTA dataset reveals improved detection performance, leading to significant advancements over conventional methods.
The approach emphasizes the importance of using unlabeled images to increase detection efficiency in challenging remote sensing scenarios.

Cite This Study

Yang et al. (2025) studied this question.

synapsesocial.com/papers/6925572cc0ce034ddc35a60f https://doi.org/10.1109/tip.2025.3629033

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Implication

Uncertainty quantification demonstrates improved alignment of bounding boxes in remote sensing images, indicating better accuracy in classification and regression tasks.

Uncertainty Quantification for Semi-Supervised Object Detection in Remote Sensing Images

Discussion

Member takes

Implication

Key Points

Cite This Study

Uncertainty Quantification for Semi-Supervised Object Detection in Remote Sensing Images

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider