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

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 375 ◽  
Author(s):  
Lifan Zhou ◽  
Yang Lan ◽  
Yu Xia ◽  
Shengrong Gong
Keyword(s):  
Chinese Remainder Theorem ◽  
Phase Unwrapping ◽  
Sar Interferometry ◽  
Noise Robustness ◽  
Programming Approach ◽  
Two Stage ◽  
Extended Phase ◽  
Contextual Dependence ◽  
Stage 1 ◽  
Min Cut

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.

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

2019 ◽  
Vol 11 (5) ◽  
pp. 491 ◽  
Author(s):  
Yang Lan ◽  
Hanwen Yu ◽  
Mengdao Xing
Keyword(s):  
Chinese Remainder Theorem ◽  
Phase Unwrapping ◽  
Sar Interferometry ◽  
Two Stage ◽  
Baseline Phase ◽  
Continuity Assumption ◽  
Local Plane ◽  
Steep Terrain ◽  
Single Baseline ◽  
Stage 1

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.

scholarly journals Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach

2020 ◽  
Vol 10 (9) ◽  
pp. 3139
Author(s):  
YanDong Gao ◽  
XinMing Tang ◽  
Tao Li ◽  
QianFu Chen ◽  
Xiang Zhang ◽  
...  
Keyword(s):  
Estimation Method ◽  
Phase Unwrapping ◽  
Noise Robustness ◽  
Programming Approach ◽  
Bayesian Filtering ◽  
Two Stage ◽  
Baseline Phase ◽  
Large Gradient ◽  
Digital Elevation ◽  
Single Baseline

Phase unwrapping (PU) has been a key step in the processing of interferometric synthetic aperture radar (InSAR) data, and its processing accuracy will directly affect the reconstruction results of digital elevation models (DEMs). The traditional single-baseline (SB) PU must be calculated under continuity assumptions. However, multi-baseline (MB) PU can get rid of the limitation of continuity assumption, so reasonable results can be obtained in regions with large gradient changes. However, the poor noise robustness of MBPU has always been a key problem. To address this issue, we transplant three Bayesian filtering methods with a two-stage programming approach (TSPA), and propose corresponding MBPU models. First, we propose a gradient-estimation method based on the first step of TSPA, and then the corresponding PU model is determined according to different Bayesian filtering. Finally, the wrapped phase can be obtained by unwrapping, one by one, using an effective quality map based on heapsort. These methods can improve the robustness of the MBPU methods. More significantly, this paper establishes a novel TSPA-based Bayesian filtering MBPU framework for the first time. This is of great significance for broadening the research of MBPU. The proposed methods experiments on simulated and real MB InSAR datasets. From the results, we can see that the TSPA-based Bayesian filtering MBPU framework can significantly improve the robustness of the MBPU method.

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

2019 ◽  
Vol 11 (2) ◽  
pp. 199 ◽  
Author(s):  
Yandong Gao ◽  
Shubi Zhang ◽  
Tao Li ◽  
Qianfu Chen ◽  
Xiang Zhang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Surface Deformation ◽  
Unscented Kalman Filter ◽  
Phase Unwrapping ◽  
Programming Approach ◽  
Interferometric Synthetic Aperture Radar ◽  
Two Stage ◽  
Second Stage ◽  
Digital Elevation ◽  
Single Baseline

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 two-stage design optimization framework for multifield origami-inspired structures

2021 ◽  
pp. 1045389X2110116
Author(s):  
Wei Zhang ◽  
Saad Ahmed ◽  
Jonathan Hong ◽  
Zoubeida Ounaies ◽  
Mary Frecker
Keyword(s):  
Case Studies ◽  
Computing Time ◽  
Computational Cost ◽  
High Fidelity ◽  
Fe Model ◽  
Two Stage ◽  
Baseline Design ◽  
Optimization Framework ◽  
Highly Nonlinear ◽  
Stage 1

Different types of active materials have been used to actuate origami-inspired self-folding structures. To model the highly nonlinear deformation and material responses, as well as the coupled field equations and boundary conditions of such structures, high-fidelity models such as finite element (FE) models are needed but usually computationally expensive, which makes optimization intractable. In this paper, a computationally efficient two-stage optimization framework is developed as a systematic method for the multi-objective designs of such multifield self-folding structures where the deformations are concentrated in crease-like areas, active and passive materials are assumed to behave linearly, and low- and high-fidelity models of the structures can be developed. In Stage 1, low-fidelity models are used to determine the topology of the structure. At the end of Stage 1, a distance measure [Formula: see text] is applied as the metric to determine the best design, which then serves as the baseline design in Stage 2. In Stage 2, designs are further optimized from the baseline design with greatly reduced computing time compared to a full FEA-based topology optimization. The design framework is first described in a general formulation. To demonstrate its efficacy, this framework is implemented in two case studies, namely, a three-finger soft gripper actuated using a PVDF-based terpolymer, and a 3D multifield example actuated using both the terpolymer and a magneto-active elastomer, where the key steps are elaborated in detail, including the variable filter, metrics to select the best design, determination of design domains, and material conversion methods from low- to high-fidelity models. In this paper, analytical models and rigid body dynamic models are developed as the low-fidelity models for the terpolymer- and MAE-based actuations, respectively, and the FE model of the MAE-based actuation is generalized from previous work. Additional generalizable techniques to further reduce the computational cost are elaborated. As a result, designs with better overall performance than the baseline design were achieved at the end of Stage 2 with computing times of 15 days for the gripper and 9 days for the multifield example, which would rather be over 3 and 2 months for full FEA-based optimizations, respectively. Tradeoffs between the competing design objectives were achieved. In both case studies, the efficacy and computational efficiency of the two-stage optimization framework are successfully demonstrated.

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