Open Access

ARTICLE

Frequency Domain Filtering SAR Interferometric Phase Noise Using the Amended Matrix Pencil Model

by Y,ong Gao¹, Shubi Zhang¹, Kefei Zhang², Shijin Li¹

1 School of Environment Science and Spatial Informatics, China University of Miningand Technology, Xuzhou, 221116, China.
2 SPACE Research Centre, School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Victoria, 3000, Australia.

* Corresponding Authors: Shubi Zhang. Email: .
  Kefei Zhang. Email: .

(This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)

Computer Modeling in Engineering & Sciences 2019, 119(2), 349-363. https://doi.org/10.32604/cmes.2019.03943

Abstract

Interferometric phase filtering is one of the key steps in interferometric synthetic aperture radar (InSAR/SAR). However, the ideal filtering results are difficult to obtain due to dense fringe and low coherence regions. Moreover, the InSAR/SAR data range is relatively large, so the efficiency of interferential phase filtering is one of the major problems. In this letter, we proposed an interferometric phase filtering method based on an amended matrix pencil and linear window mean filter. The combination of the matrix pencil and the linear mean filter are introduced to the interferometric phase filtering for the first time. First, the interferometric signal is analyzed, and the interferometric phase filtering is transformed into a local frequency estimation problem. Then, the local frequency is estimated using an amended matrix pencil at a window. The local frequency can represent terrain changes, thus suggesting that the frequency can be accurately estimated even in dense fringe regions. Finally, the local frequency is filtered by using a linear window mean filter, and the filtered phase is recovered. The proposed method is calculated by some matrices. Therefore, the computational complexity is reduced, and the efficiency of the interferometric phase filtering is improved. Experiments are conducted with simulated and real InSAR data. The proposed method exhibits a better filtering effect and an ideal efficiency as compared with the traditional filtering method.

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Citations