Multibaseline Phase Unwrapping Research Articles

Refined Two-Stage Programming Approach to Multibaseline Phase Unwrapping via Gradient Regularization and Quality-Guided Triangulation

Refined Two-Stage Programming Approach to Multibaseline Phase Unwrapping via Gradient Regularization and Quality-Guided Triangulation

An Improved Multi-Baseline Phase Unwrapping Method for GB-InSAR

Ground-based interferometric synthetic aperture radar (GB-InSAR) technology can be applied to generate a digital elevation model (DEM) with high spatial resolution and high accuracy. Phase unwrapping is a critical procedure, and unwrapping errors cannot be effectively avoided in the interferometric measurements of terrains with discontinuous heights. In this paper, an improved multi-baseline phase unwrapping (MB PU) method for GB-InSAR is proposed. This method combines the advantages of the cluster-analysis-based MB PU algorithm and the minimum cost flow (MCF) method. A cluster-analysis-based MB PU algorithm (CA-based MB PU) is firstly utilized to unwrap the clustered pixels with high phase quality. Under the topological constraints of a triangulation network, the connectivity graph of any non-clustered pixel is established with its adjacent unwrapped cluster pixels. Then, the absolute phase of these non-clustered pixels can be identified using the MCF method. Additionally, a spatial-distribution-based denoising algorithm is utilized to denoise the data in order to further improve the accuracy of the phase unwrapping. The DEM generated by one GB-InSAR is compared with that generated by light detection and ranging (LiDAR). Both simulated and experimental datasets are utilized to verify the effectiveness and robustness of this improved method.

CANet: An Unsupervised Deep Convolutional Neural Network for Efficient Cluster-Analysis-Based Multibaseline InSAR Phase Unwrapping

Multibaseline (MB) phase unwrapping (PU) is a vital processing procedure for MB synthetic aperture radar interferometry (InSAR) signal processing and can improve the traditional InSAR by changing the ill-posed problem to the well-posed problem. The existing research has shown that the MB PU problem can be successfully converted into an unsupervised cluster analysis problem. Using the high feature descriptiveness of the deep learning technique, an unsupervised deep convolutional neural network, referred to as CANet, is proposed to cluster all the pixels into different groups according to the input’s recognizable pattern of the ambiguity number of the MB interferometric phase. Subsequently, we extend our previous two-stage programming-based MB processing approach (TSPA) to processing MB PU on a sparse irregular network, which is established from the clustering result of CANet. Both theoretical analysis and experimental results show that the proposed method is an effective MB PU method, and its execution time is drastically lower than those of many classical MB PU methods.

Comparative Study of DEM Reconstruction Accuracy Between Single- and Multibaseline InSAR Phase Unwrapping

Phase unwrapping (PU) is a key processing step in interferometric synthetic aperture radar (InSAR). To date, a number of skillful single-baseline (SB) and multibaseline (MB) PU methods exhibiting different advantages have been proposed. However, as the basic principles of SB and MB PUs are essentially different, it is difficult to effectively and systematically compare the performance of SB and MB PUs, despite the knowledge that this type of analysis is important for allowing the ever-increasing number of InSAR practitioners to choose a suitable approach for practical applications and to optimally plan future InSAR satellite missions. Recently, the framework of the two-stage programming approach (TSPA) was proposed, and this allows for the majority of the existing SB PU methods to be transplanted into the MB domain, to allow practitioners to feasibly and comprehensively compare SB and MB PU methodologies. In this study, the digital elevation model (DEM) reconstruction accuracy is compared between the classical SB PU methods and their corresponding TSPA-framework-based MB PU methods using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L^{p}$ </tex-math></inline-formula> -norm model. Interestingly, we observed that although the number of PU residues in the MB case is larger than that in the SB case, the MB PU performance is better. The reason for this is that the type of MB residue is typically dipole, so the average length of the required MB branch-cut is shorter than that of SB. It is also demonstrated that the TSPA framework can effectively improve the PU accuracy of many existing SB PU methods when the number of input interferograms is sufficient.

Cluster Correction for Cluster Analysis-Based Multibaseline InSAR Phase Unwrapping

In recent years, multibaseline phase unwrapping (MBPU) has been widely studied, but it is still suffering from the problem of low noise robustness. In view of this, two cluster correction methods for the cluster-analysis (CA)-based MBPU algorithms are presented to improve the accuracy and effectiveness of the algorithm. In the first method, a suitable box centered on each pixel is selected and the cluster with the highest frequency in the box is taken as the corrected cluster to which the central pixel belongs; In the second method, all pixels are divided into core pixels and noncore pixels according to the predefined density, and then the cluster of the noncore pixels is corrected to the one that appears most frequently in the selected box centered on the noncore pixels. Experiments on both simulated and real datasets confirm the effectiveness of the proposed cluster correction methods.

A Closed-Form Robust Cluster-Analysis-Based Multibaseline InSAR Phase Unwrapping and Filtering Algorithm With Optimal Baseline Combination Analysis

Phase unwrapping (PU) and phase filtering are the key procedures for the interferometric synthetic aperture radar (InSAR) technology. As one of the most popular multibaseline PU (MBPU) algorithms, the cluster-analysis (CA)-based MBPU algorithm still has some problems that need to be improved. To begin with, the cluster ambiguity vector is obtained by searching the nearest integer point to the cluster centerline with known slope and intercept in the search space. It will be time-consuming and inconvenient when the number of baselines or the search space is too large. In addition, they do not have the capacity of phase filtering. Moreover, they do not consider the impact of different baseline combinations on the performance of the CA-based MBPU algorithm. For these reasons, a novel CA-based MBPU and filtering (MBPUF) algorithm is proposed in this article. The main contributions of this article are that it gives the closed-form solving formulas of the cluster ambiguity vector to improve the efficiency of the CA-based MBPU algorithm, proposes a novel MB InSAR phase-filtering strategy that makes the CA-based MBPU algorithm capable of solving the phase-discontinuity problem and improving the height-reconstruction accuracy simultaneously, and utilizes the optimal baseline combination to improve the robustness of the CA-based MBPU algorithm. Theoretical analysis and experiments on both simulated and real MB InSAR data sets show the effectiveness and robustness of the proposed closed-form robust CA-based MBPUF algorithm.

Extended Phase Unwrapping Max-Flow/Min-Cut Algorithm for Multibaseline SAR Interferograms Using a Two-Stage Programming Approach.

Multi-baseline (MB) phase unwrapping (PU) is a key step of MB synthetic aperture radar (SAR) interferometry (InSAR). Compared with the traditional single-baseline (SB) PU, MB PU is applicable to the area where topography varies violently without obeying the phase continuity assumption. A two-stage programming MB PU approach (TSPA) proposed by H. Yu. builds the link between SB and MB PUs, so many existing classical SB PU methods can be transplanted into the MB domain. In this paper, an extended PU max-flow/min-cut (PUMA) algorithm for MB InSAR using the TSPA, referred to as TSPA-PUMA, is proposed, consisting of a two-stage programming procedure. In stage 1, phase gradients are estimated based on Chinese remainder theorem (CRT). In stage 2, a Markov random field (MRF) model of PUMA is designed for modeling local contextual dependence based on the phase gradients obtained by stage 1. Subsequently, the energy of the MRF model is minimized by graph cuts techniques. The experiment results illustrate that the TSPA-PUMA method can drastically enhance the accuracy of the original PUMA method in the rugged area, and is more efficient than the original TSPA method. In addition, the noise robustness of TSPA-PUMA can be improved through adding more interferograms with different baseline lengths.

Large-Scale Multibaseline Phase Unwrapping: Interferogram Segmentation Based on Multibaseline Envelope-Sparsity Theorem

Multibaseline (MB) phase unwrapping (PU) is a critical processing step for the MB synthetic aperture radar interferometry (InSAR). Compared with the traditional single-baseline (SB) PU, MB PU has wider application scope on the study area with strong phase variation, because it can overcome the limitation of the Itoh condition. Since most of the MB PU methods need to process multiple interferograms simultaneously, the size of the input interferograms will pose unique challenges when it exceeds the limit of computational capabilities. Until now, the research achievements related to large-scale (LS) MB PU have been quite limited. To deal with such case, we propose a technique for applying the two-stage programming-based MB PU method (TSPA) proposed by Yu and Lan to the LS MB InSAR data set in this paper. To be specific, the MB $L^\kappa $ -norm envelope-sparsity theorem is proposed and proved first, which gives a sufficient condition to exactly guarantee the consistency between local and global TSPA solutions. Afterward, based on the MB $L^\kappa $ -norm envelope-sparsity theorem, we put forward an interferogram tiling strategy, whereby each LS interferogram in the input MB InSAR data set is partitioned into a set of several smaller sub-interferograms that can be unwrapped individually by TSPA in parallel or in series. Both theoretical analysis and experimental results show that the proposed tiling strategy is effective for the LS MB PU problem.

Optimal Baseline Design for Multibaseline InSAR Phase Unwrapping

Multibaseline (MB) synthetic aperture radar interferometry (InSAR) has the potential to improve the traditional single-baseline InSAR from the ill-posed problem to the well-posed problem. It is because MB InSAR phase unwrapping (PU) can take advantage of the baseline diversity to significantly increase the ambiguity intervals of interferometric phases, so it completely overcomes the limitation of the phase continuity assumption. However, due to the mathematical foundation of most of the MB PU methods, i.e., the Chinese remainder theorem (CRT), has poor measurement bias robustness (measurement bias could be caused by surface deformation, atmospheric artifact, and phase noise), CRT is sensitive to the normal baseline length. In other words, even if we choose the same CRT-based MB PU method, different system baseline lengths could result in different PU performances. Therefore, how to choose the baseline lengths to optimize CRT performance is crucial to all the CRT-based MB PU methods. In this paper, a nonlinear mixed-integer programming-based baseline design criterion (referred to NIP criterion) is proposed to maximize the measurement bias tolerance of the CRT-based MB PU. The optimality condition of the NIP criterion quantitatively provides important instructions for the CRT-based MB PU applications and offers significant guidance in the development of the practical MB InSAR system. The experiment results are shown to verify the effectiveness of the NIP criterion by using three representative CRT-based MB PU methods.

An extended PUMA algorithm for multibaseline InSAR DEM reconstruction

ABSTRACT Phase unwrapping (PU) is one of the key processes in reconstructing the digital elevation model (DEM) of a scene from its corresponding interferometric synthetic aperture radar (InSAR) data. In contrast to traditional single-baseline PU, the multibaseline PU does not require phase continuity, which is useful when reconstructing the DEM in cases where the topography varies sharply. However, the performance of the multibaseline PU is directly related to the noise level, whereas the single-baseline PU algorithm has favourable noise robustness since it is based on the global wrapped phase information, e.g. the PU-max-flow (PUMA) algorithm. To improve the noise robustness of the multibaseline unwrapping process, we extend the single-baseline PUMA algorithm in this paper to a multibaseline domain, referred to as the multibaseline PUMA algorithm, which allows the unwrapping of multibaseline interferograms to generate the DEM. The proposed algorithm is less influenced by the phase continuity assumption by taking advantage of the multibaseline diversity and improves the noise robustness by using the global wrapped information from both single- and multibaseline domains. The performance of the proposed algorithm is tested on both simulated and real InSAR data experiments to demonstrate the effectiveness and noise robustness of the proposed algorithm.

Refined Two-Stage Programming-Based Multi-Baseline Phase Unwrapping Approach Using Local Plane Model

The problem of phase unwrapping (PU) in synthetic aperture radar (SAR) interferometry (InSAR) is caused by the measured range differences being ambiguous with the wavelength. Therefore, multi-baseline (MB) is a key processing step of MB InSAR. Compared with the traditional single-baseline (SB) PU, MB PU is advantageous in solving steep terrain due to its ability to break through the constraint of the phase continuity assumption. However, the accuracy of most of the existing MB PU methods is still limited to its mathematical foundation, i.e., the Chinese remainder theorem (CRT) is too sensitive to measurement bias. To solve this issue, this paper presents a refined algorithm based on the two-stage programming MB PU approach (TSPA) proposed by H. Yu. The significant advantage of the refined TSPA method (abbreviated as LPM-TSPA) is that it improves the performance of stage 1 of TSPA through assuming terrain height surface in the neighborhood pixels can be approximated by a plane to combine more information of the interferometric phase in the local region to estimate the ambiguity number gradient. The experiment results indicate that the LPM-TSPA method can significantly improve the accuracy of the MB PU solution.

Refined Two-Stage Programming Approach of Phase Unwrapping for Multi-Baseline SAR Interferograms Using the Unscented Kalman Filter

Phase unwrapping (PU) represents a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (InSAR) data. Compared with single-baseline (SB) PU, multi-baseline (MB) PU can resolve the phase discontinuities caused by noise and phase layover induced by terrain undulations. However, the MB PU performance is limited primarily by its poor robustness to measurement bias and noise. To address this problem, we propose a refined 2-D MB PU method based on the two-stage programming approach (TSPA). The proposed method uses the unscented Kalman filter (UKF) to improve the performance of the second stage of the original TSPA method. Specifically, the proposed method maintains the first stage of the TSPA to estimate the range and azimuth gradients between neighbouring pixels. Then, median filtering is slightly used to reduce the effects of the noise gradients on the estimated phase gradients. Finally, the UKF model is used to unwrap the interferometric phases using an efficient quality-guided strategy based on heap-sort. This paper is the first to integrate the UKF into the TSPA framework. The proposed method is validated using bistatic and monostatic MB InSAR datasets, and the experimental results show that the proposed method is effective for MB PU problems.

A Novel Method for Deformation Estimation Based on Multibaseline InSAR Phase Unwrapping

The three-pass differential synthetic aperture radar interferometry (DInSAR) is one of the approaches in radar interferometry applications for measuring the deformation of the earth surface. The conventional three-pass DInSAR needs a successful single-baseline (SB) phase unwrapping (PU) procedure on each interferogram to ensure accurate deformation monitoring. Because of the limitation of the phase continuity assumption, the SB PU becomes a challenging processing step when the study area has strong phase variation. In this paper, the multibaseline (MB) InSAR PU methodology, which can eliminate the phase continuity assumption by means of the baseline diversity, is transplanted into the conventional three-pass DInSAR domain. Based on MB PU, a novel terrain deformation estimation approach is developed. Both theoretical analysis and experimental results demonstrate that the proposed method is an effective surface deformation estimation method.

A Multibaseline InSAR Phase Unwrapping Method Using Designed Optimal Baselines Obtained by Motion Compensation Algorithm

Multibaseline (MB) interferometric synthetic aperture radar phase unwrapping (PU) has the advantage of using the baseline diversity to increase the ambiguity intervals of the interferometric phases and overcome the limitation of the phase continuity assumption. One major challenge of the MB PU is that the performance of PU greatly depends on the baseline configuration. In this letter, the motion compensation algorithm is used to modify the baselines of the interferograms. Therefore, optimal baseline configuration can be used in the MB PU to improve the PU performance. Real ALOS/PALSAR data over Himalayan mountain area have been used to verify the performance of the proposed method. The experimental results show that the MB PU with modified baselines becomes more reliable.

A Refined Cluster-Analysis-Based Multibaseline Phase-Unwrapping Algorithm

As is well known, multibaseline phase unwrapping (PU) is put forward to overcome single-baseline PU in discontinuous-terrain-height estimation. This letter presents a refined algorithm based on the cluster analysis (CA)-based noise-robust efficient multibaseline PU algorithm proposed by H. Yu. The basic idea is to combine multiple interferometric synthetic aperture radar interferograms with different baseline lengths by a linear combination. The new interferograms after the linear combination increase the ambiguity heights. The number of resulting groups on the envelope of the intercept histogram is decreased and the distance between different intercept groups is widened. Compared with the conventional CA method, the significant advantage of the refined CA (RCA) algorithm is that it improves noise robustness when the intercept groups are densely distributed. The proposed RCA algorithm is validated using the simulated interferometric data. The results demonstrate that the noise robustness performance is better than that of the CA method.

Robust Two-Dimensional Phase Unwrapping for Multibaseline SAR Interferograms: A Two-Stage Programming Approach

Multibaseline 2-D phase unwrapping (PU) is a critical step for the multibaseline synthetic aperture radar interferometry. Compared with the single-baseline PU, the multibaseline PU does not need to obey the phase continuity assumption, i.e., it is applicable to the terrain with the violent change. However, the performance of the multibaseline PU is directly related to noise level. In order to improve the noise robustness of the multibaseline PU, in this paper, we transplant the framework of the single-baseline PU into the multibaseline PU and propose a two-stage programming approach, referred to as TSPA, which makes use of the gradient information of the interferogram similar to how the conventional single-baseline PU method does. Fortunately, although the proposed method belongs to the integer programming (usually, the integer programming is an NP-hard problem which is hard to solve), the constraint of the optimization model of the TSPA method is unimodular, so it can be efficiently solved. Furthermore, interestingly, some useful and important concepts of the single-baseline PU, for example, residue and branch cut, are also transplanted into the multibaseline PU in this paper, and we discuss the potential of extending most of the representative single-baseline PU methods into the multibaseline domain as well. Finally, the experiment results show the effectiveness and noise robustness of the TSPA multibaseline PU method.

A Novel Mixed-Norm Multibaseline Phase-Unwrapping Algorithm Based on Linear Programming

The multibaseline phase unwrapping (PU) of $L^{1} $ -norm can be efficiently solved using linear programming. However, the huge memory requirement of linear programming limits its application in multibaseline PU for large-scale data. In order to reduce the required memory when linear programming is performed, a novel mixed-norm multibaseline PU algorithm is proposed in this letter, which is regarded as an approximation of the $L^1$ -norm method. In this method, an $L^{\infty} $ -norm cost function is employed to substitute for that of the $L^1$ -norm, i.e., it takes the optimization which is aimed to minimize the maximum component of the optimization variable as the representation of the one that minimizes the absolute sum of $L^1$ -norm. Consequently, the cost function in the proposed method changes to be an $L^1$ -norm plus an $L^{\infty}$ -norm. Compared with the traditional $L^1$ -norm method, the size of the optimization variable in the proposed method is generally reduced by about one-seventh. Therefore, it is logical that less memory is needed in the proposed algorithm. The effectiveness of the proposed algorithm is validated via a simulated and real repeat-pass interferometric-synthetic-aperture-radar data set.

A Cluster-Analysis-Based Noise-Robust Phase-Unwrapping Algorithm for Multibaseline Interferograms

Two-dimensional phase unwrapping (PU) is a key step of synthetic aperture radar interferometry (InSAR). Moreover, the conventional single-baseline PU method is restricted to the phase continuity assumption, so it cannot work correctly in the case that phase jumps between adjacent pixels are larger than π. To effectively solve this problem, multibaseline PU is put forward. The performance of conventional multibaseline PU methods is directly related to the noise level. In order to improve noise robustness, a cluster analysis (CA) based noise-robust PU algorithm for multibaseline interferograms (CANOPUS) is proposed in this paper, which is the extension and improvement of the CA-based efficient multibaseline PU algorithm proposed by H. Yu. For the sake of overcoming the disadvantages of the CA method, the dimension of the recognizable mathematical pattern is expanded. Under this condition, due to the density discrimination in spatial space, different clusters are able to be distinguished by the density-based clustering algorithm, and clusters are regarded as a set of density-connected patterns. Compared with the conventional CA method, the significant advantage of the new algorithm is that it improves noise robustness. What is more, the proposed algorithm runs in linear time. From the experiment results, it can be seen that the proposed method may be effectively applied to multibaseline InSAR data sets.

Moving target detection and location based on millimeter-wave InISAR imaging

介绍了基于运动补偿的运动目标ISAR成像方法，将雷达成像的概念引入到低信噪比运动目标探测的过程中，在成像的同时获取目标的径向和横向运动速度，采用多基线图像干涉处理的方法完成运动目标的定位和横向尺寸估计.建立了运动目标成像几何模型，给出了多天线时分复用接收机的通道间信号补偿方法，以及多基线相位解缠实现不模糊干涉测角定位的方法，分析了信噪比对图像干涉定位精度的影响.利用仿真数据和实际数据验证了本方法的有效性.;An inverse synthetic aperture radar (ISAR) imaging method based on motion compensation of a moving target was presented. The concept of radar imaging was applied to the process of moving target detection with low signal to noise ratio(SNR). The lateral and radial velocity of the target can be acquired by means of imaging processing. The location of the moving target and estimation of its crosssectional area could be fulfilled using the multibaseline image interferometric method. The geometric model for imaging moving target was established. A method for the compensation of signals from channels of multi antenna time division multiplexing (TDM) receiver was proposed. The unambiguous angle orientation based on multibaseline phase unwrapping was presented. The influence of the image SNR on interferometric orientation accuracy was analyzed. The validity of the proposed method was testified by both simulation and an example of its application.

A Cluster-Analysis-Based Efficient Multibaseline Phase-Unwrapping Algorithm

Multibaseline phase unwrapping is a critical processing procedure of multibaseline synthetic aperture radar interferometry (InSAR). It has an advantage over single-baseline phase unwrapping in discontinuous-terrain-height estimation. In this paper, the pixels' combination information of multiple InSAR interferograms with different baseline lengths is deeply investigated, and the term ambiguity vector is proposed to represent a pixel's ambiguity number of multiple interferograms. It is also revealed that pixels with the same ambiguity vector have an exclusive pattern among them. A fast cluster-analysis (CA)-based method is proposed for multibaseline phase unwrapping. In this method, all pixels are first clustered into different groups according to their patterns, and then, information of the cluster center is used to unwrap the phases of pixels group by group. Simulation results are shown to verify the effectiveness, efficiency, and noise robustness of CA multibaseline phase-unwrapping method.