期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 56, 期 10, 页码 5843-5849出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2826842
关键词
Least squares (LS); low geometrically decorrelated PS (LGD-PS); low temporally decorrelated-PS (LTD-PS); similar time-series interferometric phase (STIP) pixel
类别
资金
- NRDMS, DST Project, Multi-Sensor Approach for Landslide Monitoring [NRDMS/11/3050/014]
- NRDMS, DST Project, Landslide Inventory Preparation Using Multi-Temporal SAR Interferometry (InSAR) [NRDMS/02/37/016]
Time-series interferometric synthetic aperture radar (InSAR) methods are developed in recent times to retrieve deformation signal from two types of radar targets, i.e., persistent scatterers (PSs) having stable phase history and distributed scatterers (DS) with moderate temporal coherence. Recent algorithms (SqueeSAR, CAESAR, and PD-PSInSAR) claim to jointly process PS and DS. SqueeSAR algorithm applies a threshold based on the statistically homogenous pixels (SHP), which prevents only the PS pixels having single-bounce scatterer from inclusion in DS processing, but the remaining types of PS pixels cannot be preserved. The included PS pixels lose their coherent information during phase history optimization in DS processing. In this paper, a new measure, the similar time-series interferometric phase (STIP) pixel is used for the identification of brotherhood pixels, which improves the classification of interferometric pixels and coherence estimation. The application of STIP instead of SHP significantly improves the phase optimization efficiency of DS pixels while retaining valuable information of PS more effectively. Furthermore, to optimize the interferometric phase of DS pixels, a least squares model is developed, which is more time efficient as compared to the Maximum likelihood estimators of the conventional DS processing techniques. Results on SAR data acquired over specific sites of Uttarakhand, India, have shown superior performance of the proposed methodology over the standalone PS-InSAR, SqueeSAR, and PD-SAR.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据