A Detail-Preservation Method of Deep Learning One-Step Phase Unwrapping

Abstract

Phase unwrapping is essential in interferometric synthetic aperture radar (InSAR) data processing. Currently, deep learning is widely used in the phase unwrapping process. For instance, the one-step phase unwrapping method is excellent because of its strong noise adaptability. The method treats the unwrapping process as a regression problem, which uses the l1 or l2 loss function to constrain the reconstructed phase to be close to the ground truth of the absolute phase. However, no matter whether the l1 or l2 loss function is used, the result may lack details in texture, and the details cannot be well preserved. This is because the l1 or l2 smoothens the output greatly, and the texture detail loss is not intentionally considered. Due to the noise of the wrapped phase, there is speckle noise in the valley part of the unwrapping result. To solve these problems, we study the generative adversarial network (GAN) with mixed loss functions. The texture details are preserved with the trained GAN, and the speckle noise in the valley is significantly reduced.