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Abstract

The term imagery fusion has been used to describe a variety of combining operations performed to increase the ground resolution of multispectral data. The objective of this study was to characterize and evaluate the impact of different pixellevel fusion methods on the accuracy level of land cover/use classification. Panchromatic FORMOSAT-2 data and multispectral SPOT-6 data were considered, and Brovey, Ehlers, and principal component analysis (PCA) algorithms were used as pixel-level fusion algorithms. The improvement in the accuracy of the fused images relative to the original images was determined. The land cover/use categories were classified into five groups by using a maximum likelihood algorithm. To verify and assess the accuracy of classification, training sites were selected for all land cover/use themes. The classification accuracy was calculated for all images by using error matrices. The greatest improvement in land cover/use classification was obtained by using the Brovey algorithm; the overall accuracy was 93.68% and the kappa coefficient was 0.9115. The next greatest improvement was obtained using the Ehlers algorithm, and the overall accuracy and kappa coefficient were 89.54% and 0.8620, respectively. Finally, the least accurate classification was obtained by using the PCA algorithm; the overall accuracy was 88.36% and the kappa coefficient was 0.8247. Comparing the fused images with the original images, the overall accuracy of 86.36% and kappa coefficient value of 0.8036, which were obtained for the original images, were used as benchmarks.

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