Fusion of Optical and Radar Images for the Enhancement of Different Surface Features
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 different land cover classes. 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. Of these methods, the best result is obtained by the use of the wavelet-based fusion. Overall, the research indicates that multisource data sets can significantly improve the interpretation and analysis of land cover types.