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Assessment of Multi-Sensor Neural Image Fusion and Fused Data Mining for Land Cover Classification

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3 Author(s)
M. Pugh ; Air Force Research Laboratory, Information Directorate, Rome, NY, U.S.A. mark.pugh@rl.af.mil ; A. Waxman ; D. Fay

Recent studies suggest that the combination of imagery from earth observation satellites with complementary spectral, spatial, and temporal information may provide improved land cover classification performance. This paper assesses the benefits of new biologically-based image fusion and fused data mining methods for improving discrimination between spectrally-similar land cover classes using multi-spectral, multi-sensor, and multi-temporal imagery. For this investigation multi-season Landsat and Radarsat imagery of a forest region in central New York State was processed using opponent-band image fusion, multi-scale visual texture and contour enhancement, and the fuzzy ARTMAP neural classifier. These methods are shown to enable identification of sub-categories of land cover and provide improved classification accuracy compared to traditional statistical methods

Published in:

2006 9th International Conference on Information Fusion

Date of Conference:

10-13 July 2006