The most commonly used smoothing algorithms for complex data processing are low pass filters. Unfortunately, an
undesired side effect of the aforementioned techniques is the blurring of scene discontinuities in the interferogram. For
Digital Surface Map (DSM) extraction and subsidence measurement, the smoothing of the scene discontinuities can
cause inaccuracy in the final product. Our goal is to perform spatially non-uniform smoothing to overcome the
aforementioned disadvantages. We achieve this by using an Anisotropic Non-Linear Diffuser (ANDI). Here, in this
paper we will show the utility of ANDI filtering on simulated and actual Interferometric Synthetic Aperture Radar
(IFSAR) data for preprocessing, subsidence measurement and DSM extraction to overcome the difficulties of typical
filters. We also compare the results of the ANDI filter with a wavelet filter. Finally, we detail some of our results of the
New Orleans IFSAR research project with Canadian Space Agency, NASA, and USGS. The Harris LiteSiteTM Urban
3D Modeling software is used to illustrate some of the results of our RADARSAT-1 processing.
The most commonly used smoothing algorithms for complex data processing are blurring functions (i.e., Hanning,
Taylor weighting, Gaussian, etc.). Unfortunately, the filters so designed blur the edges in a Synthetic Aperture Radar
(SAR) scene, reduce the accuracy of features, and blur the fringe lines in an interferogram. For the Digital Surface Map
(DSM) extraction, the blurring of these fringe lines causes inaccuracies in the height of the unwrapped terrain surface.
Our goal here is to perform spatially non-uniform smoothing to overcome the above mentioned disadvantages. This is
achieved by using a Complex Anisotropic Non-Linear Diffuser (CANDI) filter that is a spatially varying. In particular,
an appropriate choice of the convection function in the CANDI filter is able to accomplish the non-uniform smoothing.
This boundary sharpening intra-region smoothing filter acts on interferometric SAR (IFSAR) data with noise to produce
an interferogram with significantly reduced noise contents and desirable local smoothing. Results of CANDI filtering
will be discussed and compared with those obtained by using the standard filters on simulated data.
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