There are two main topics to be discuss in this paper

pixel-level image fusion based on Principle Component Analysis（PCA）

and feature-level image fusion based on Dempster-Shafer evidence theory.In pixel-level case

the SAR image at HV polarization is relatively sensitive to the vegetation canopy.We combined the HV polarization information from SAR and spectral characteristic from SPOT images in an effort to enhance land cover classification.Before the fusion process

wavelet transform was first applied to denoise the SAR image which suffers from speckle contamination due to coherent process.PCA method is then used to fuse the SPOT and SAR images.In doing so

the PC-1 component is replaced by SAR image（approximation image

after wavelet transform） and then the inverse transform is followed.At last

the maximum likelihood classifier was used for both SPOT-XS images and fusion images.In feature-level case

fully polarization information from SAR is used to combine with spectral characteristic from SPOT images

mainly to enhance land cover classification.We first denoise the SAR image by Lee filter.Next

the maximum likelihood classifier based on different distribution was used for SAR and SPOT images（Based on Wishart distribution and multivariate Gaussian distribution respectively）

to extract the conditional probability of each pixel for each class.Dempster-Shafer evidence theory is then applied

to combine the classified results of SAR and SPOT data.Experimental results show that the classification accuracy is dramatically improved by making use of the proposed methods.Data fusion can take advantage of the use of complementary information to obtain a better overall accuracy than using single data source only.