Comparison of multisource image fusion methods and land cover classification
The aim of this study is to explore the performances of different data fusion techniques for the enhancement of urban features and evaluate the features obtained by the fusion techniques in terms of separation of urban land cover classes when multisource images are under consideration. For the data fusion, multiplicative method, Brovey transform, principal component analysis (PCA), Gram–Schmidt fusion, wavelet-based fusion and Elhers fusion are used and the results are compared.