Interferometric phase reconstruction using simplified coherence network

Interferometric time-series analysis techniques, which extend the traditional differential radar interferometry, have demonstrated a strong capability for monitoring ground surface displacement. Such techniques are able to obtain the temporal evolution of ground deformation within millimeter accuracy by using a stack of synthetic aperture radar (SAR) images. In order to minimize decorrelation between stacked SAR images, the phase reconstruction technique has been developed recently. The main idea of this technique is to reform phase observations along a SAR stack by taking advantage of a maximum likelihood estimator which is defined on the coherence matrix estimated from each target. However, the phase value of a coherence matrix element might be considerably biased when its corresponding coherence is low. In this case, it will turn to an outlying sample affecting the corresponding phase reconstruction process. In order to avoid this problem, a new approach is developed in this paper. This approach considers a coherence matrix element to be an arc in a network. A so-called simplified coherence network (SCN) is constructed to decrease the negative impact of outlying samples. Moreover, a pointed iterative strategy is designed to resolve the transformed phase reconstruction problem defined on a SCN. For validation purposes, the proposed method is applied to 29 real SAR images. The results demonstrate that the proposed method has an excellent computational efficiency and could obtain more reliable phase reconstruction solutions compared to the traditional method using phase triangulation algorithm.