A new phase unwrapping method for cross-track interferometric synthetic aperture radar systems. (December 2022)

• Record Type: Journal Article
• Authors: Darang, Razieh; Nasri, Saeed; Zeinali, Mansoor
• Is Part Of: Measurement. Volume 205(2023)
• Publication Date: 2022-12
• Subjects: Interferometric Synthetic Aperture Radar (InSAR) -- Phase unwrapping -- Path-following; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112142
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The problem of phase unwrapping in InSAR processing for digital elevation model (DEM) extraction was considered in this paper. We solve the resulting optimization problem using a fast and efficient approach to optimize submodular convex Markov random field functions. The proposed method's performance is examined using several numerical studies compared to its relevant competitors. Here, the mean square error of elevation estimation is considered as the performance metric. Numerical simulations have confirmed the efficiency of the proposed algorithm in terms of MSE of elevation estimation. Abstract: One of the essential applications of interferometric synthetic aperture radar (InSAR) systems is to estimate the ground surface height. To this end, the altitude of each pixel in the region is estimated according to the phase difference of the two received signals by two sensors. However, the sensor has an ambiguity in the relative terrain altitude due to the 2π cyclic nature of the interferometric phase, which leads to a significant error in altitude estimation. In this paper, by focusing on the phase unwrapping issue in InSAR processing, an efficient iterative approach to minimize the phase difference error is presented. For this purpose, first, according to the InSAR imaging geometry, the interferometric phase relationships are described. Second, based on the least-squares approach, the phase unwrapping problem is expressed as an optimization issue with a specific cost function. Third, the cost function is converted to a submodular convex Markov random field (MRF) function. Finally, the resulting optimization problem is solved by using a fast and efficient approach to optimize submodular convex MRF functions. The proposed method's performance is examined using several numerical studies compared to its relevant competitors. The mean square error (MSE) of elevation estimation is considered the performance metric. Numerical simulations have confirmed the efficiency of the proposed algorithm in terms of MSE of elevation estimation. As seen, the outperformance of the proposed method with respect to its best competitor is at least 2 times (3 dB). The operation times of various methods are also compared as a measure of their convergence rate. Based on simulation results, the convergence rate of the proposed algorithm is more than 50 times better than that of its closest competitor.