Improving phase unwrapping techniques by the use of local frequency estimates

Abstract

In multi-pass space-borne SAR interferometry, the two acquisitions often present low correlation levels and very noisy phase measurements which are incompatible with automatic phase unwrapping. Instead of dealing with many residues due to erroneous wrapped phase di erences, we propose to use the local frequency as measured by a spectral analysis algorithm presented in a previous paper 1]. For this purpose we present two conventional unwrapping algorithms, one local, the other global, that we revisit to bene t from the robust local frequency estimates. For a local approach based on path following techniques, we use the frequency estimates in a slope compensated lter which extend the complex averaging up to a su cient number of look to eliminate residues due to the noise. Then we connect residues due to non-interferometric features along mask components resulting from the detection of lay-overs and uncorrelated areas. For a global approach such as weighted least squares methods, we demonstrate that the use of noisy discrete phase gradient leads to a biased solution. To avoid this drawback, we propose to use the local frequency estimate and associated measure of con dence as phase gradient and weight. Results are presented on both topographic and di erential interferograms obtained from ERS-1 European radar satellite over various landscapes and over the displacement eld of the Landers 1992 earthquake.

DEM Error Retrieval by Analyzing Time Series of Differential Interferograms

“…Next, over each glacier, the three wrapped phase difference between differential interferograms are unwrapped [11].…”

High-Rise Building Layover Exploitation with Non-Local Frequency Estimation in SAR Interferograms

“…Current approaches, such as MUISC and maximum likelihood, basically use rectangular sliding windows to estimate the local frequency [13,14]. They also test a group of different subwindows to determine the best size of the window [12,15].…”

A Method of Filtering and Unwrapping Sar Interferometric Phase Based on Nonlinear Phase Model

“…Supposing that the interferometric phase over the filtering window is stationary and consistent and that the noise of each pixel is statistically independent, the larger the filtering window is, the better the filtering performance is. Spagnolini [7] and Trouvé [8,9] presented slope compensated filter method to achieve the phase samples over the filtering window to be stationary and consistent. It assumes that the phase can be approximated by a 2D single frequency signal over a window, then…”

“…These can be divided into two categories: spatial domain filter and transform domain filter. The spatial domain filter has median filter, mean filter, adaptive filter with directionally dependent windows [4], adaptive median filter [5], the zero intermediate frequency vector filter [6], slope compensated filter [7][8][9][10][11], and adaptive contoured window filter [12]. Due to the circular periodic feature of the interferometric fringes, the interferometric phases will be in complex exponential form in these filters.…”