Adaptive Multiscale Estimation for Fusing Image Data

Mr. K. Clint Slatton

Electrical and Computer Engineering
The University of Texas at Austin
Austin, TX

Monday, December 3rd, 1:00 PM, ENS 637

slatton@sol.csr.utexas.edu

Abstract

Interferometric synthetic aperture radar (INSAR) data are fused with laser altimeter (LIDAR) data to produce improved estimates of bare-surface topography and vegetation heights for remote sensing applications. The data from both sensors are first transformed into estimates of surface elevations and vegetation heights to obtain linear measurement-state relations. For the INSAR data, this is accomplished by inverting an electromagnetic scattering model. A spatially-adaptive multiscale estimation framework is developed to combine the data, which were acquired at different resolutions. The estimation is performed in scale and space via a set of Kalman filters, and yields better error characteristics than the nonadaptive multiscale filter.

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. His research interests include multiscale statistical signal processing, data fusion, and electromagnetic scattering. He has published one IEEE journal paper and several conference papers. Mr. Slatton earned B.S. (1993) and M.S. (1997) degrees in aerospace engineering and a M.S. (1999) degree in electrical engineering from the University of Texas at Austin. He has worked at the Jet Propulsion Laboratory (JPL) on processing algorithms for polarimetric and interferometric synthetic aperture radar (SAR) data. He is also a recipient of a NASA Graduate Student Research Program (GSRP) Fellowship.

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