Effects of spatial network on time series InSAR phase unwrapping: take the Delaunay and Dijkstra networks for example

doi:10.11947/j.AGCS.2022.20200469

Abstract

Abstract: In time series InSAR phase unwrapping, coherent pixels are required to be fully connected by a spatial network and then ambiguity can be estimated. During the network generation, Delaunay triangulation has been an invariable choice for InSAR community, but its network configuration easily contains the edge of high phase gradient, which leads to the violation of phase continuity assumption. Given that whether the geometry of Delaunay is suitable for phase unwrapping or not is rarely discussed, in this paper we quantitatively discussed the performance of Delaunay triangulation in phase unwrapping and we also proposed a new network through introducing the graph theory to improve the unwrapping accuracy. Experimental results validate that the proposed method can better satisfy the phase continuity assumption, and is superior to the Delaunay network for a reduction of ~33% unclosed interferogram triangle loops. Compared to the previous researches focused on unwrapping methods and the improvement of objective function, this study reveals the importance of the improvement of spatial network to the time series phase unwrapping.

Key words: time series, phase unwrapping, Dijkstra, spatial network, InSAR, phase continuity assumption

CLC Number:

P237

MA Zhangfeng, JIANG Mi, LI Guihua, HUANG Teng. Effects of spatial network on time series InSAR phase unwrapping: take the Delaunay and Dijkstra networks for example[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 248-257.

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