Multi-view clustering-based time series empirical tropospheric delay correction

Abstract

Tropospheric delays (TDs) still hinder the millimeter-scale measurement accuracy of interferometric synthetic aperture radar (InSAR). Toward higher accuracy, this letter presents a new time series TDs correction method. The rationale behind the proposed method is that multi-view clustering (MvC) is introduced to identify the spatiotemporal TDs behaviors, particularly, in which the one-pass multi-view clustering (OPMC) algorithm is employed to perform window segmentation rather than sticking to the commonly used boxcar windows. Next, a phase-elevation network correction model in each cluster is constructed by fully considering the spatiotemporal phase information. Besides, an iterative weighted scheme is designed to further enhance the robustness of the estimated model parameters. The Sentinel-1 datasets covering the southwest mountainous area, China, confirm the effectiveness of the new method.