An Improved Closed-Form Multi-Baseline Phase Unwrapping Algorithm

Multi-baseline phase unwrapping (PU) is an extension of single-baseline PU. Its accuracy directly affects the reliability of results in engineering tasks, such as InSAR topographic mapping and geological hazard monitoring, in complex scenarios. Meanwhile, its efficiency determines the timeliness of data delivery in emergency scenarios. The cluster-analysis (CA)-based algorithm represents a significant advancement in multi-baseline PU algorithms, wherein a strategy for pixel clustering and uniform PU is introduced. However, in the CA algorithm, phase noise degrades pixel clustering performance, leading to deviations in the determination of intercept centerlines and ultimately errors in ambiguity number search. In addition, the computational complexity is increased by the search for intercept peaks and ambiguity numbers. To address these limitations and ensure that accuracy and efficiency requirements are met in practical applications, an improved closed-form multi-baseline PU algorithm is proposed in this article. Compared with conventional CA algorithms, this algorithm offers the following four improvements. First, differential phase processing is introduced into the algorithm, which not only mitigates the impact of phase noise on pixel clustering but also provides new inputs for subsequent ambiguity-number solution. Secondly, a novel method for calculating the theoretical intercept is proposed, which depends solely on the external reference DEM and the ambiguity height. Thirdly, to eliminate the need for peak-intercept search and to suppress error propagation from incorrect intercepts, an intercept filtering method is introduced into the algorithm. In this method, a categorized filtering of actual intercepts for all pixels is performed. Fourthly, to address the phase-noise sensitivity and low efficiency in ambiguity-number search, the algorithm proposes a closed-form ambiguity-number solution method based on the Chinese Remainder Theorem (CRT). In this method, calculation accuracy can be ensured and solution efficiency improved by constructing and solving CRT equation groups with filtered error-free intercepts as remainders. The aforementioned four points are not independent of each other, but are strongly logically dependent and correlated. The effectiveness of the proposed algorithm is validated through one simulated data experiment and two real data experiments. The proposed algorithm achieves improvements in accuracy and efficiency across the three datasets. In terms of accuracy, the RMSE is reduced by at least 11.52%, while the PUSR increases by at least 1.36%. In terms of efficiency, runtime is shortened by at least 29.75%.