Evaluating the Efficiency of Multisensor Satellite Data Fusion Based on the Accuracy Level of Land Cover/Use Classification

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 pixel-level 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.