Spatial vector filtering to reduce noise in interferometric phase images

Abstract

and Cooperative Research Centre for Sensor Signal and Information Processing Ph: $61 7 3365 3697, Fax: +61 7 3365 3684,Abstract-Spatial filtering to reduce noise is an important process to improve phase measurements in interferometric imaging. Currently these techniques are predominately based on filtering the interferometric phase. However the amplitude, which is ignored or only used to a minimal extent in conventional filtering, holds useful information about the nature of the phase measurement. When combining multilook images, adding the full complex vectors produces a least mean square estimate of the interferometric phase. Utilising this principle, a new spatial filtering technique is introduced. In this technique, a compromise is suggested where the square-root of the interferometric amplitude is taken prior to summing the spatially weighted vectors. To deduce the weights for the filtering function it was necessary to utilise a Monte-Carlo approach, which calculates the filter weights via an iterative minimisation procedure. The main application for this filter is to improve the quality of displacement maps obtained by a ground based interferometric radar system that monitors rock slope movement in a mining environment. For this application high SNR is expected, however, due to speckle, large errors can stdl be present in the image. The filter is designed to reduce these errors. Using simulated data and C-Band ERS images, the new spatial vector filter is shown to produce improved phase measurements compared with conventional filtering techniques.

A modification to the Goldstein radar interferogram filter

“…The quality of DTM and displacement maps can be improved by many methods at different processing levels. One of them is filtering of the interferometric phase, as demonstrated in [6]- [10]. However, while filtering reduces noise in the interferogram, it does not necessarily enhance or recover the signal.…”

Comparisons of Speckle Noise Filtering Methods on Interferometric Synthetic Aperture Radar Images

“…The quality of DEM can be improved by many methods at different processing levels, and one of them is filtering of the interferometric phase. At the earliest, there are several interferogram filtering methods as demonstrated: Goldstein filter [10], Lee filter [11], rotating kernel transformation [12], median filtering [13], spatial vector filtering [14], etc. shows that Then, [15] presents a modification to the adaptive Goldstein filter to make parameter alpha dependent on coherence, [16,17] propose modifications to the Lee adaptive complex filter with adjusted directional windows aim tocut losses of signal and reduce noise.…”

Pyramid non-local mean filter for interferometric phase denoising

“…Because of the same principle, all of the techniques of InSAR family have similar processing flow among which interferometric phase filtering is an important step significantly influencing the performance of following steps. According to the characteristics of interferogram, many filters have been developed [5,6,7]. However, these filters all have a same drawback that some detail structures like the edge of interferogram fringes will be wiped off.…”