SELF-SUPERVISED LEARNING FOR INSAR PHASE AND COHERENCE ESTIMATION

This paper focuses on the estimation of interferometric SAR parameters, a step that precedes the entire interferometric processing chain to produce derived information such as digital elevation models and ground displacement. Deep learning, especially convolutional neural networks (CNN), has revolutionized image denoising and has recently received considerable attention. However, traditional supervised approaches require labeled images for training, which are generally unavailable or inaccurate, especially in remote sensing applications. To overcome this limitation, semiand self-supervised denoising approaches have recently been proposed. These can learn from exclusively noisy samples, which can be obtained from pairs of noisy images or from noisy values within the same image. In this paper, we build on the foundation of these self-supervised learning methods, in particular, we borrow concepts from the Noise2Void and Noise2Self approaches, which have already shown excellent performance in various image denoising tasks. We extend this method to address the challenges specific to InSAR phase and coherence estimation, where the complex-valued nature of SAR interferograms poses unique processing considerations.