A highly informative content makes visible and infrared images the most used remotely sensed data (generally speaking) in earth resource and environmental analysis. On the other hand, sensitivity to surface roughness, water content, and independence of weather conditions and sunlight are the features that justify the growing interest and use of microwave radar data. The previous considerations clearly indicate data fusion as a key point for remote-sensing image classification. In this paper, a knowledge-based system to exploit such numerous and diverse sources of information is proposed. The authors started with the problem of fusing Landsat- MSS and Seasat-SAR images for terrain classification in order to increase the reliability of results with respect to single-sensor analysis. A new approach to the fusion of 2-D images, called the 'region overlapping' technique, is employed, and its advantages for terrain classification are shown. Experimental results are presented and discussed to show the interest of the approach.
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