Abstract
Cubature Kalman filter phase unwrapping (CKFPU) is an effective algorithm in unwrapping the interferograms. The local phase slope estimation is a key factor that affects the unwrapped accuracy. However, the estimation accuracy of local phase slop is relatively low in high noisy and dense stripes areas, which usually leads to the unsatisfactory unwrapped results. In order to effectively solve this issue, the rewrapped map of the unwrapped phase (obtained by CKFPU algorithm), which is a filtered interferogram with clearer fringes and more detailed information, is proposed in this paper to improve the phase slope estimation. In order to solve the problem of imprecise error variance for the new phase slope estimation, an adaptive factor is introduced into the CKFPU algorithm to increase the stability and reliability of the phase unwrapping algorithm. The proposed method is compared with the standard CKFPU algorithm using both simulated and real data. The experimental results validate the feasibility and superiority of the proposed method for processing those high noise dense fringe interferograms.
Highlights
Phase unwrapping is one of the most critical steps in InSAR data processing and its precision directly affects the precision of elevation or deformation measurements [1,2,3,4]
Noise filtering is necessary before unwrapping the noisy interferograms. e second type is filter-based unwrapping methods, such as extended Kalman filter phase unwrapping (EKFPU), unscented Kalman filter phase unwrapping (UKFPU), and cubature Kalman filter phase unwrapping (CKFPU) [14,15,16,17,18,19,20,21,22,23,24,25,26,27]
In order to avoid this problem, an adaptive factor is added to the CKFPU model to increase the stability and performance of the algorithm. e effectiveness of the proposed algorithm is validated by experimental results using both simulated and real data
Summary
Phase unwrapping is one of the most critical steps in InSAR data processing and its precision directly affects the precision of elevation or deformation measurements [1,2,3,4]. E first type is to calculate the unwrapped phase based on path-following or the minimum norms’ theory, such as branch-cut, region-growing, minimum discontinuity, minimum cost flow (MCF) networks, weighted leastsquares, and unweighted least-squares [5,6,7,8,9,10,11,12,13] For these methods, noise filtering is necessary before unwrapping the noisy interferograms (usually called prefiltering). Several scholars have made numerous improvements, especially in the mathematical model for observation equation (sine and cosine function) and the stochastic model [16,17,18,19,20,21,22,23,24,25,26,27] For the former, the algorithms have been improved in accuracy from the nonlinear function perspective, such as UKFPU, CKFPU, and relative algorithms [20, 22,23,24,25,26,27]. The improvement is mainly reflected in the statistical model
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