Two-dimensional phase unwrapping method using a refined D-LinkNet-based unscented Kalman filter

Abstract

Two-dimensional phase unwrapping (2-D PU) has a strong influence on the accuracy of interferometric synthetic aperture radar (InSAR) data processing results. Phase gradient estimation (PGE) is one of the key steps in the processing of 2-D PU. Moreover, the accuracy of the PGE will directly affect the accuracy of the final PU result. The phase continuity assumption is an important prerequisite for the PGE of traditional 2-D PU methods. However, the accuracy of PGE is not ideal in areas with high-noise and large-gradient changes. To address this issue, in this article, we propose a 2-D PU method of an unscented Kalman filter (UKF) using a refined LinkNet with a pretrained encoder and dilated convolution (D-LinkNet). To the best of our knowledge, this article is the first time to combine deep-learning and UKF for 2-D PU. First of all, this article analyzes the distribution characteristics of different terrains. To ensure the accuracy of the training model, we use shuttle radar topography missions (SRTMs) with different terrains to obtain simulated learning training data. Then, the refined d-LinkNet method is used to accurately estimate the gradient ambiguity numbers and is combined with the small window median filter to obtain the vertical and horizontal gradients. Finally, the UKF model is used for 2-D PU. Experiments are conducted with simulated and TanDEM-X InSAR datasets. In addition, compared with the existing PGE methods and 2-D PU methods, the experimental results show that the proposed method can obtain more accurate results than the existing methods.