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An Improved Phase Filter for Differential SAR Interferometry Based on an Iterative Method

delete2018-08-01
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A
Alejandro Mestre-Quereda
J
Juan M. López‐Sánchez *
J
J. Selva
P
Pablo J. González
DOI:10.1109/TGRS.2018.2820725delete
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摘要

摘要

En 中文
Phase quality is a key element in the analysis of the deformation of the Earth's surface carried out with differential synthetic aperture radar interferometry. Various decorrelation sources may degrade the surface deformation estimates, and thus, phase filters are needed for this kind of application. The wellknown Goldstein filter is the most widely used due to its simple implementation and computational efficiency. In the past years, improved filters have been proposed, which are based on this filter but introduce variations in the data processing. The effectiveness of these filters mostly depends on the size of the filtering window, the weight of the smoothed spectrum, and the kernel used to filter the spectrum. In this paper, we evaluate the performance of four of these filters and present a new method that outperforms all of them. The proposed filter is based on an iterative method in which the original phase is denoised progressively with adaptive filtering windows of different sizes. The effectiveness of the filter is controlled by the interferometric coherence, a direct indicator of the phase quality. Moreover, we introduce some modifications regarding the processing of the power spectrum. Specifically, we propose to smooth the original phase using a new filter which is based on a Chebyshev interpolation scheme. The performance of the new filter has been tested on both simulated and real interferograms, acquired by RADARSAT-2 and the Uninhabited Aerial Vehicle Synthetic Aperture Radar, which mapped two different geological events that caused surface deformation.
Keyword:
Differential synthetic aperture radar interferometry (InSAR)
Goldstein filter
phase noise filtering
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期刊

IEEE Transactions on Geoscience and Remote Sensing 封面图
IEEE Transactions on Geoscience and Remote Sensing
IF:
8.6
论文数:
2.2W
被引数:
10.7W

机构

U
universitat d'alacant
学者数:
6.9K
论文数: 7.0K
被引数: 12
U
University of Liverpool
学者数:
2.8W
论文数: 2.5W
被引数: 3.5W
引用论文

引用论文

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