An InSAR Ionospheric Correction Method Based on Variance Component Estimation With Integration of MAI and RSS Measurements

Abstract

Ionospheric phase delay can seriously affect the low-frequency spaceborne Synthetic Aperture Radar (SAR) systems. Ionospheric correction is, therefore, vital to improving the measurement accuracy of Interferometric SAR (InSAR). InSAR ionospheric correction relies mainly on two kinds of approaches, i.e., one uses SAR azimuth offsets measures, and the other achieves through a Range Split-Spectrum (RSS) technique. However, the two approaches have different spatial sensitivities to the ionosphere, but they can complement each other. In this study, we present an integration InSAR ionospheric correction method. This method uses the Helmert Variance Component Estimation (H-VCE) to reasonably allocate the weights for the ionospheric measurements obtained from the azimuth offset-based and RSS techniques to improve the ionospheric correction performance. The azimuth offset can be derived from a pixel offset-tracking procedure or multiple-aperture InSAR (MAI). We demonstrated the proposed method on 28 MHz FBS mode ALOS-1 PALSAR images acquired before and after the Wenchuan earthquake in Sichuan Province, China, 2008. This study also applied the proposed method to 14 MHz FBD mode ALOS-1 PALSAR images with no significant surface deformation and high coherence in Chile. After the ionospheric correction with the proposed method, we found that the ionospheric errors in the Wenchuan and Chile cases have varied from -31.0 to 21.1 cm and -4.7 to 93.9 cm, respectively. The results show that the proposed method can effectively remove the long-wavelength ionospheric delay. Our study reveals that this method can also alleviate the influence of local ionospheric disturbance simultaneously.

Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain

“…Moreover, there exists another non-linear function behind the interval [−σ n , σ n ] to tune the larger wavelet components. The results show that the proposed enhanced AGGD-oriented function is a promising method in de-noising the satellite remote sensing images [52,53]. For future work, to achieve better results, we will deal with utilizing some optimization algorithms [54] and also will focus on the non-linear part behind the interval, in which we will use a new type of non-linear data-driven function to be connected to the AGGD function in the interval [−σ n , σ n ].…”

Calculating of Taftan Volcano Displacement Using PSI Technique and Sentinel 1 Images

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