Proc. IEEE Southwest Symposium on Image Analysis and Interpretation April 7-9, 2002, pp. 72-76, Santa Fe, NM.

Sensitivity Analysis of a Spatially-Adaptive Estimator for Data Fusion

K. Clint Slatton1,3, Melba M. Crawford2,3, and Brian L. Evans1

(1) Department of Electrical and Computer Engineering, Engineering Science Building, The University of Texas at Austin, Austin, TX 78712-1084
slatton@ece.utexas.edu - bevans@ece.utexas.edu

(2) Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712
crawford@csr.utexas.edu

(3) The Center for Space Research, The University of Texas at Austin, 3925 W. Braker Ln., Suite 200, Austin, TX 78759 USA

Paper - Poster

We analyze the parametric sensitivity of a spatially-adaptive multiscale data fusion method. The fusion problem is formulated as a recursive estimation problem in scale and space using a set of 1-D Kalman filters. The overall filter accommodates data acquired at different resolutions and missing data. The filter approaches optimal performance for data with spatially-varying statistics by adaptively updating the filter parameters using the innovation-correlation method. The contribution of this paper is the determination of the estimation error sensitivity to the process noise and measurement noise variances.

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Last Updated 01/30/03.