Proc. IEEE International Geoscience and Remote Sensing Symposium, Sydney, Australia, July 9-13, 2001.

Multiscale Adaptive Estimation for Fusing Interferometric Radar and Laser Altimeter Data

K. Clint Slatton (1), Melba M. Crawford (1), and Brian L. Evans (2)

(1) The Center for Space Research, The University of Texas at Austin, 3925 W. Braker Lane, Suite 200, Austin, TX 78759 USA
slatton@csr.utexas.edu - crawford@csr.utexas.edu

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

Paper - Presentation (PowerPoint) - Presentation (PDF)

Interferometric synthetic aperture radar (INSAR) data are fused with laser altimeter (LIDAR) data to produce improved estimates of bare-surface topography and vegetation heights. The data from both sensors are first transformed into estimates of surface elevations and vegetation heights to obtain linear measurement-state relations. A spatially-adaptive multiscale estimation framework is then used to combine the data, which were acquired at different resolutions. The estimation is performed in scale and space via a set of Kalman filters. It yields better error characteristics than the nonadaptive multiscale filter and accommodates non-stationarity in the image data.

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