Two-dimensional phase unwrapping (2-D PU) is one of the key processes in reconstructing the topography or displacement of the Earth surface from its interferometric synthetic aperture radar (InSAR) data. Estimating the absolute phase gradient information is an unavoidable step utilized by almost all the 2-D PU methods. Traditionally, the gradient estimation step relies on the phase continuity assumption, which requests that the observed area has spatial continuity. However, the abrupt topographic changes and system noise usually results in the failure of the phase continuity assumption in reality. Under this condition, it is difficult for the traditional 2-D PU to provide the correct absolute phase over the area with abrupt interferometric fringe change or with strong system noise. To solve the issue, we propose a novel deep convolutional neural network (DCNN), abbreviated as PGNet, to estimate the phase gradient information instead of the phase continuity assumption in this article. The major advantage of PGNet lies in its deep architecture to learn the characteristics of phase gradients from enormous training images with different noise levels and topographic features. Subsequently, the L¹-norm objective function is used to minimize the difference between unwrapped phase gradients and the gradients estimated by PGNet for obtaining the final PU result. Taking the phase gradient pattern of the TerraSAR-X-TanDEM-X interferogram as the learning object, experimental results demonstrate the absolute phase gradient estimated by PGNet is more credible than that from the phase continuity assumption such that the corresponding PU result outperforms those obtained by the traditional 2-D PU methods.

Interferometric synthetic aperture radar (InSAR) is a radar technique widely used in geodesy and remote sensing applications, e.g., topography reconstruction and subsidence estimation. Phase unwrapping (PU) is one of the key procedures of InSAR signal processing. Artificial intelligence (AI) techniques have proven to be potentially powerful in many fields and have been introduced into the PU domain, achieving superior performance. In this article, we provide a comprehensive overview of AI-based PU techniques in InSAR. We survey the AI-based single-baseline (SB) PU methods and then review the AI techniques related to multibaseline (MB) PU. In addition, we show several experimental examples of these methods, from both simulated and real InSAR data sets, which gives readers an overview of AI-based PU processing's potential and limitations. It is our hope that this article will provide researchers with guidelines and inspiration to further enhance the development of AI-based PU.

Two-dimensional phase unwrapping (PU) is a classical ill-posed problem in synthetic aperture radar interferometry (InSAR). The traditional algorithmic model-based 2-D PU methods are limited by the Itoh condition, which is from the PU researchers’ experience and has critical challenges under strong phase noises or violent phase changes. Recently, advanced learning-based 2-D PU methods could break through the limitation of the Itoh condition owing to their data-driven frameworks, offering promising results in terms of both the speed and accuracy. The one-step learning-based PU method, as one of the representatives, retrieves the unwrapped phase directly from the wrapped phase through regression. However, the main disadvantage of one-step learning-based PU is that it usually blurs the output unwrapped phase due to its $L_{2}$ loss, that is, it cannot guarantee the congruency between the rewrapped interferometric fringes of the PU solution and the input interferogram. To solve this problem, we propose a one-step 2-D PU method based on the conditional generative adversarial network (referred to as PU-GAN), which treats 2-D PU as an image-to-image translation problem. The generator in PU-GAN can be trained to generate the unwrapped phase through minimizing a $L_{1}$ -norm loss based on a U-Net architecture, while simultaneously the corresponding discriminator can learn an adversarial loss by a structure of Patch-GAN that tries to classify if the output unwrapped phase image is real or fake. Both a theoretical analysis and the experimental results show that the proposed method outperforms the representative algorithmic model-based and learning-based 2-D PU methods.

With improvements in synthetic aperture radar (SAR) image resolutions and multitemporal SAR interferometry (MTInSAR) algorithms, it becomes feasible to generate 3D point clouds with millimeter deformation accuracy to monitor urban infrastructure health in detail. This article reviews the progress and challenges of MT interferometric SAR (InSAR) techniques for infrastructure deformation monitoring, with an emphasis on refinement performance. After a brief overview of literature statistics, we examine characteristics of the main MTInSAR algorithms. We demonstrate performance refinement in terms of point density, height accuracy, and deformation accuracy, using a comparative study of the reclaimed Hong Kong International Airport (HKIA). A series of typical infrastructure dynamic behaviors in urban areas revealed by MTInSAR is then illustrated. This article also describes key issues that remain to be addressed to facilitate operational fine surveillance. Finally, we go beyond MTInSAR to illustrate intelligent infrastructure diagnosis systems applicable to city-scale and even infrastructural component-scale analysis. It is our expectation that this review will provide a reference and guidance for future research and applications of MTInSAR in urban infrastructure monitoring.

Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the traditional 2-D PU is an ill-posed inverse problem, which means that regardless of how skillful the PU algorithm designer is, it is impossible to design an algorithm that can correctly process all the 2-D PU situations, i.e., we can only design the best PU algorithm in the statistical sense. Therefore, accumulating PU processing experience from different study cases is important for PU algorithm design. Currently, the deep learning (DL) technique provides a potential framework to accumulate processing experience, and a flood of valuable data coming from different InSAR sensors provides the ability to enable the learning-based PU technique outside the traditional model-based technique. In this article, we transform the 2-D PU problem into a learnable image semantic segmentation problem and propose a DL-based branch-cut deployment method (abbreviated as BCNet). To start, we propose the optimal branch-cut connection criterion (referred to as OPT-BC) with the reference unwrapped phase given. Next, using the relationship between the residue and branch-cut as the learning objective, BCNet is trained using the samples provided by OPT-BC to produce the branch-cut result. Finally, the traditional branch-cut method is utilized to perform the postprocessing procedure to obtain the final PU result. The experimental results demonstrate that the proposed BCNet-based PU method is a near-real-time 2-D PU algorithm, and its accuracy outperforms the traditional model- and learning-based 2-D PU methods.