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KeypointDiff: Keypoints-Guided Diffusion Model for Unpaired Object-Level SAR-to-Optical Aircraft Image Translation
Summary
This study introduces KeypointDiff, a novel model for translating Synthetic Aperture Radar (SAR) to optical images of aircraft. It enables accurate object-level translation without paired data, enhancing interpretation and detail recovery.
Area of Science:
- Computer Vision
- Remote Sensing
- Machine Learning
Background:
- Synthetic Aperture Radar (SAR) offers all-weather imaging but produces abstract visuals lacking detail for automated interpretation.
- Existing SAR-to-optical translation methods primarily focus on scenes, not objects, due to data scarcity and detail preservation challenges.
Purpose of the Study:
- To develop an object-level SAR-to-optical image translation method for unpaired aircraft targets.
- To enhance the fidelity of contours and textures in translated images for improved downstream tasks.
Main Methods:
- Proposed KeypointDiff, a keypoint-guided diffusion model using modality-agnostic structural anchors for unpaired data.
- Introduced a class-angle guidance module (CAGM) for integrating class and angle information.
- Employed detector-based supervision and visual consistency losses for improved detail quality.
Main Results:
- KeypointDiff achieved superior performance over existing methods in SAR-to-optical translation for aircraft.
- The model demonstrated effective pixel-level detail recovery and object-level translation.
- Achieved strong zero-shot generalization to new aircraft types.
Conclusions:
- KeypointDiff provides an efficient and effective solution for object-level SAR-to-optical translation.
- The method successfully addresses the challenges of unpaired data and detail preservation.
- The model shows significant practical applicability in remote sensing image analysis.