Multiscale Sensor Fusion for Remote Sensing Imagery

Mr. K. Clint Slatton
Center for Space Research and
Dept. of Electrical and Computer Engineering
The University of Texas at Austin

Friday, October 13th, 3:00 PM, ENS 602

slatton@ece.utexas.edu

Abstract

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, an inverse problem is solved 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. By incorporating sparse LIDAR observations with the INSAR inversion results, the estimates of ground elevations and vegetation heights can be improved. We combine the two data types using a multiresolution Kalman filter approach, which provides the estimates and estimate errors 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. Novel aspects of this work include combining physical modeling with multiscale estimation to accommodate nonlinear measurement-state relationships and improving estimates of ground elevations and vegetation heights for remote sensing applications.

Biography

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.

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