Coregistration of image pairs, DEM refinement and evaluation for SAR interferometry

Zhengxiao Li, Purdue University

Abstract

Two critical procedures in Synthetic Aperture Radar (SAR) Interferometric (InSAR) processing were studied: SAR image coregistration and InSAR DEM refinement. Two pairs of ERS-1/2 SAR tandem data, representing diverse terrain types and different baselines, were used in this research. The commonly used traditional SAR image coregistration algorithms were addressed and tested; the computationally intensive algorithms were examined; the results from those algorithms were compared, through the experiments carried out on real data. The results showed that the magnitude component had better performance compared to complex data for computing cross-correlation function. For fine coregistration, oversampling the cross-correlation function was more efficient than oversampling original SAR images and a factor of 10 was appropriate as the oversampling rate. A particular 4-parameter transformation was sufficient for subpixel coregistration of ERS SAR tandem data. The traditional resampling algorithms, nearest neighbor, bilinear, and cubic convolution, were tested and compared to the computationally intensive sinc interpolators with varied lengths. The most efficient sinc length was not always the longer one. The 2D sinc interpolation with windowing and modulation demonstrated the power of frequency preservation, but no evidence showed that the sinc produced better coherence than the common algorithms. The final InSAR DEM accuracy should be the ultimate standard for evaluating the optimal coregistration approaches. To generate an accurate digital elevation model (DEM), InSAR processing requires precise orbit data, which is not always available. An alternate approach is to apply quality ground control points (GCPs) into the InSAR processing, which is also difficult. A method is presented to align and register, by a variety of techniques, including least squares, an InSAR DEM generated from SAR images without precise orbit or baseline information and without GCPs, to an existing coarse reference DEM for refinement. The results showed this method achieved a comparable or even better accuracy than applying GCPs into InSAR processing. It was also found that an existing DEM with lower spatial resolution than the InSAR DEM could be a good reference for this alignment and registration. In this research, an InSAR DEM was aligned and registered to SRTM 3 Arc Second data, a global reference DEM. The "truth" DEM used for accuracy evaluation was a higher accuracy DEM from aerial imagery with post spacing of 1.5 meters and vertical accuracy of 1.8 meters.

Degree

Ph.D.

Advisors

Bethel, Purdue University.

Subject Area

Physical geography|Geophysics|Civil engineering

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