Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation

Jaeho Moon* 1     Jeonghwan Yun* 1     Jaehyun Kim* 1     Jaehyup Lee† 2     Munchurl Kim† 1
*Co-first authors (equal contribution)
†Co-Corresponding authors
1Korea Advanced Institute of Science and Technology, South Korea       
2Kyungpook National University, South Korea
Paper arXiv Code (To be released)

Overview of our Data Augmentation and Knowledge Transfer (DAKTer) strategy.

Abstract

Oil spills pose severe environmental risks, making early detection crucial for effective response and mitigation. As Synthetic Aperture Radar (SAR) images operate under all-weather conditions, SAR-based oil spill segmentation enables fast and robust monitoring. However, when using deep learning models, SAR oil spill segmentation often struggles in training due to the scarcity of labeled data.

To address this limitation, we propose a diffusion-based data augmentation with knowledge transfer (DAKTer) strategy. Our DAKTer strategy enables a diffusion model to generate SAR oil spill images along with soft label pairs, which offer richer class probability distributions than segmentation masks (i.e. hard labels). Also, for reliable joint generation of high-quality SAR images and well-aligned soft labels, we introduce an SNR-based balancing factor aligning the noise corruption process of both modalilties in diffusion models. By leveraging the generated SAR images and soft labels, a student segmentation model can learn robust feature representations without teacher models trained for the same task, improving its ability to segment oil spill regions.

Extensive experiments demonstrate that our DAKTer strategy effectively transfers the knowledge of per-pixel class probabilities to the student segmentation model to distinguish the oil spill regions from other look-alike regions in the SAR images. Our DAKTer strategy boosts various segmentation models to achieve superior performance with large margins compared to other generative data augmentation methods.

Quantitative Comparison


Quantitative comparison of the qualitiy of generated SAR images and segmentation performance improvement against other generative data augmentation methods.

Qaultitative Comparison of Generation Results


Data generation results: Qualitative comparison of SAR images and corresponding segmentation masks generated by (a) SemGAN, (b) DDPM, (c) SatSynth, and (d) Our DAKTer, compared to (e) samples from the original OSD dataset

BibTeX

@misc{moon2024dakddataaugmentationknowledge,
            title={DAKD: Data Augmentation and Knowledge Distillation using Diffusion Models for SAR Oil Spill Segmentation}, 
            author={Jaeho Moon and Jeonghwan Yun and Jaehyun Kim and Jaehyup Lee and Munchurl Kim},
            year={2024},
            eprint={2412.08116},
            archivePrefix={arXiv},
            primaryClass={cs.CV},
            url={https://arxiv.org/abs/2412.08116}, 
      }