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

Abstract

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.

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

“…In this case, TSPA-PUMA cannot obtain the correct PU result where the fringe is polluted by high noise. Unlike [24,25] who apply the filter-based methods to suppress the influence of incorrect phase gradients obtained by stage 1 of TSPA-PUMA, in this paper, we utilize MB InSAR dataset with more interferograms to remove the phase gradient errors. Major parameters of the MB InSAR system are the same as that used in experiment 1 which is listed in Table 1, but this time, eight interferograms with different baseline lengths are used to perform the TSPA-PUMA method (baseline lengths are 70 m, 150 m, 330 m, 471 m, 550 m, 631 m, 753 m and 831 m, respectively).…”

“…Under this condition, the incorrect phase gradient information obtained in stage 1 will reduce the accuracy of final PU result directly. Unlike [24,25] both using filtering-based methods to alleviate the effects of the phase noise on the estimated phase gradients, in this paper, we resist the influence of the noise in stage 1 of TSPA-PUMA through using the MB InSAR dataset with different baseline lengths. To be specific, the more interferograms are involved to estimate the phase gradients based on the CRT formulation, the higher accuracy on ambiguity number gradient estimation will be obtained (it is because that more observed samples of interferometric phases from different interferograms with different baseline lengths are involved, more phase noise can be ignored).…”

“…More than that, some studies indicate that the divide-and-conquer criterion can be used to further reduce the computational and peak memory consumption of the MCF model [22,23]. To further improve the noise robustness of stage 1 of TSPA, [24] proposed a local phase model, which assumes terrain height surface in the neighborhood pixels can be approximated by a plane. Furthermore, [25] used the unscented Kalman filter (UKF) to improve the performance of the stage 2 of TSPA reducing the effect of the noise gradient on the PU results.…”

Performance Improvement for SAR Tomography Based on Local Plane Model

“…In this study, we analyze the performance of a tomographic method that exploits a parametric model of the unknown ground surface profile to improve the scatterer detection rate and reconstruction accuracy. In particular, a local plane (LP) model of the surface, which was already adopted to improve the performance of phase unwrapping (PU) for SAR interferometric processing [30]- [31], is proposed.…”

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

“…H. Yu [12] proposed a groundbreaking MBPU framework that can introduce an SBPU model to an MBPU method, which has important implications for MBPU research. Y. Lan [24] proposed a refined two-stage programming-based (TSPA) MB approach using a local plane model. In summary, MBPU can obtain better unwraped results, even in areas with complex terrain, but its robustness and efficiency still need further improvement.…”