Multibaseline Interferometric Phase Denoising Based On Kurtosis In the NSST Domain.

Yanfang Liu,Shiqiang Li,Heng Zhang

doi:10.3390/s20020551

Abstract

Interferometric phase filtering is a crucial step in multibaseline interferometric synthetic aperture radar (InSAR). Current multibaseline interferometric phase filtering methods mostly follow methods of single-baseline InSAR and do not bring its data superiority into full play. The joint filtering of multibaseline InSAR based on statistics is proposed in this paper. We study and analyze the fourth-order statistical quantity of interferometric phase: kurtosis. An empirical assumption that the kurtosis of interferograms with different baselines keeps constant is proposed and is named as the baseline-invariant property of kurtosis in this paper. Some numerical experiments and rational analyses confirm its validity and universality. The noise level estimation of nature images is extended to multibaseline InSAR by dint of the baseline-invariant property of kurtosis. A filtering method based on the non-subsampled shearlet transform (NSST) and Wiener filter with estimated noise variance is proposed then. Firstly, multi-scaled and multi-directional coefficients of interferograms are obtained by NSST. Secondly, the noise variance is represented as the solution of a constrained non-convex optimization problem. A pre-thresholded Wiener filtering with estimated noise variance is employed for shrinking or zeroing NSST coefficients. Finally, the inverse NSST is utilized to obtain the filtered interferograms. Experiments on simulated and real data show that the proposed method has excellent comprehensive performance and is superior to conventional single-baseline filtering methods.

Highlights

Interferometric synthetic aperture radar is an important extension of synthetic aperture radar (SAR), which is extensively adopted to topography surveying [1], surface deformation monitoring [2]and so forth
As a promotion of non-subsampled shearlet transform (NSST) filter, the developed method is compared with five state of the art single-baseline filters, including: Goldstein method, local frequency estimate (LFE) algorithm, optimal integration-based adaptive direction filter (OADF), iterative NL-interferometric SAR (InSAR) and InSAR-block-matching 3-D (BM3D)
The parameters of various filters are set as Goldstein: the filtering window size is 32 × 32, α equals 0.9; OADF: the filtering window size is 7 × 7; LFE: the local frequency estimation window and filtering window are set to 9 × 9; NL-InSAR: the iterative number is 10; InSAR-BM3D: the parameters are consistent with [15]; NSST: the decomposition scale equals 5

Summary

Introduction

Interferometric synthetic aperture radar is an important extension of synthetic aperture radar (SAR), which is extensively adopted to topography surveying [1], surface deformation monitoring [2]and so forth. Interferometric synthetic aperture radar is an important extension of synthetic aperture radar (SAR), which is extensively adopted to topography surveying [1], surface deformation monitoring [2]. Multi-baseline interferometry can comprehensively utilize the diversity of interferograms with different baselines in the same scene to effectively extract the height information of difficult topography, under the circumstance in which the interferometric phase does not satisfy phase continuity assumption [3]. The interferometric phase filtering is a critical step in multibaseline interferometric SAR (InSAR). The interferometric phase is contaminated by massively coherent noise brought from thermal noise decoherence, baseline decoherence, time decoherence and many other decoherent factors in practice [4]. The main motivation of the interferometric phase filtering is to eliminate noises as much as possible while preserving most of the detail information

Methods

Results

Conclusion