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Multisensor Classification of Wetland Environments Using Airborne Multispectral and SAR Data

Michael R. Ricard, Amy L. Neuenschwander,
Melba M. Crawford, and James C. Gibeaut

Center for Space Research, University of Texas at Austin
3925 W. Braker Ln., Suite 200, Austin, TX 78759-5321
Bureau of Economic Geology, University of Texas at Austin,
Campus Mail Code: E0630, Austin, TX 78712

This work was supported in part by the NASA Topography and Surface Change Program (Grant NAG5-2954) and by the NASA National Space Grant Consortium (Grant NGT40003).


Near concurrent airborne data were acquired over the wetlands of the Bolivar Peninsula on the Texas coast by the NASA/JPL AIRSAR (June 28, 1996) and NASA/Stennis Space Center Calibrated Airborne Multispectral Scanner (CAMS) (July 3, 1996), both at 4m spatial resolution. Several approaches which utilize information from both sensors are investigated for classifying the landcover in these data sets. Differences in statistical characteristics of the data necessitate individual parametric models for observations from each sensor, so data are initially classified separately, then a final classification is obtained by combining results from the statistical models using different multisensor integration techniques. These integrated results are compared to single-sensor classification results, as well as to a multisensor classification based on artificial neural networks.


Last Modified: Tue July 13, 1999