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A Novel Mixed-Norm Multibaseline Phase-Unwrapping Algorithm Based on Linear Programming
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DOI：10.1109/LGRS.2014.2381666.png)
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The multibaseline phase unwrapping (PU) of L-1-norm can be efficiently solved using linear programming. However, the huge memory requirement of linear programming limits its application in multibaseline PU for large-scale data. In order to reduce the required memory when linear programming is performed, a novel mixed-norm multibaseline PU algorithm is proposed in this letter, which is regarded as an approximation of the L-1-norm method. In this method, an L-infinity-norm cost function is employed to substitute for that of the L-1-norm, i.e., it takes the optimization which is aimed to minimize the maximum component of the optimization variable as the representation of the one that minimizes the absolute sum of L-1-norm. Consequently, the cost function in the proposed method changes to be an L-1-norm plus an L-infinity-norm. Compared with the traditional L-1-norm method, the size of the optimization variable in the proposed method is generally reduced by about one-seventh. Therefore, it is logical that less memory is needed in the proposed algorithm. The effectiveness of the proposed algorithm is validated via a simulated and real repeat-pass interferometric-synthetic-aperture-radar data set.
Keyword:
Linear programming
mixed-norm
multibaseline
phase unwrapping (PU)
synthetic aperture radar interferometry
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