The fusion of multi-source data is one of the most promising techniques for improved classification of remote sensing
images. This paper utilized the images by fusing the high-resolution optical and SAR images for land use classification.
The high-resolution optical and SAR images present the land features for many aspects and supplement information to
each other because of their different imaging modes and wavebands, so classical methods such as Hue - Intensity
-Saturation(HIS) transform based, Brovey(color normalized),Principal Component Substitution(PCS) approaches and
wavelet-based method are used in fusing process, then the fused results been evaluated through the calculation of some
quantitative index(Mean grey value, Standard error, Entropy, Definition). The wavelet-based fused image shows better
effect than others. A MLC(maximum likelihood classification) method was employed to the wavelet-based fused
image ,Classification accuracy was assessed using high-resolution aerial orthophotos. The overall accuracy for six
classes(Settlement, River, Agricultural field, Country road, Tree nurseries, Bare land) was found to be 94.36% with
kappa coefficient of 0.92.