Mr. K. Clint Slatton
Department of Electrical and Computer Engineering
The University of Texas at Austin
Friday, January 26th, 3:00 PM, ENS 302
Interferometric synthetic aperture radar (INSAR) sensors have been used extensively to map topography. Accuracies are limited over vegetated regions, however, because the observations are not measurements of true surface topography. The measurements correspond to a height above the true surface that depends on both the sensor and the vegetation. Laser altimeter (LIDAR) systems, conversely, can map topography over smaller areas very accurately. In order to determine surface elevations and vegetation heights from dual-baseline INSAR data, we solve an inverse problem for INSAR scattering. To keep the inverse problem well-posed, a simplified scattering model is used, which can lead to large uncertainties in the height estimates. LIDAR observations that are acquired over specific regions of interest are combined with the INSAR inversion results to improve the estimates of ground elevations and vegetation heights. We combine the two data types using a multiresolution Kalman filter approach, which provides the estimates and estimate uncertainties at each pixel. Combining data from the two sensors provides estimates that are more accurate than those obtained from INSAR alone, yet have dense and extensive coverage, which are difficult to obtain with LIDAR.
The standard Kalman formulation only provides optimal estimates (in the mean squared sense) when there is perfect a priori knowledge of the state and measurement models. Statistically self-similar processes, such as 1/f noise, have been used to describe the evolution of natural phenomena, such as topography, through scale. Simple 1/f behavior across scales is an idealization though, and does not account for spatial variability at a given scale. If errors exist in the assumed process or measurement noise variances, the computed estimate uncertainty will be understated. We investigate a 1-D adaptive estimation approach and it's generalization to the multiresolution case to reduce errors in the process model.
The approach is based on the correlation-innovations method.
K. Clint Slatton is a Ph.D. student in the Department of Electrical and Computer Engineering at The University of Texas at Austin. He is a member of the graduate technical staff at the University's Center for Space Research and Laboratory for Image and Video Engineering. His research interests include multi-dimensional statistical signal processing, data fusion, and electromagnetic scattering. For his dissertation, he is applying these topics to solve an inverse problem in remote sensing. Data from airborne radar and laser altimeter sensors are optimally combined using a multiscale approach, and then terrain topography and vegetation heights are estimated using constrained nonlinear optimization. Other interests include synthetic aperture radar processing and environmental remote sensing. Mr. Slatton has published several refereed conference papers in these fields. His B.S. (1993) and M.S. (1997) degrees in aerospace engineering are from the University of Texas at Austin. In 1999, he earned a M.S degree in electrical engineering. During 1994, he worked as a graduate co-op student at the Jet Propulsion Laboratory (JPL) on the fabrication and testing of an experimental Ka-band communications satellite. In 1998, he returned to JPL to work on processing algorithms for polarimetric and interferometric AIRSAR data. He is a student member of IEEE and a member of Sigma Xi, Tau Beta Pi, and Sigma Gamma Tau technical honor societies. In 1993, he received an undergraduate fellowship from the California Institute of Technology. He is a recipient of a University of Texas Continuing Fellowship (1998) and Texas Space Grant Consortium Fellowships (1998, 2000). In 1999, he was awarded a NASA Graduate Student Research Program (GSRP) Fellowship. The GSRP fellowship was renewed in 2000.
A list of Telecommunications and Signal Processing Seminars is available at from the ECE department Web pages under "Seminars". The Web address for the Telecommunications and Signal Processing Seminars is http://signal.ece.utexas.edu/seminars