A semi-supervised, cluster-ensemble based method for the classification of multi/hyperspectral images is presented.Spectral clustering is a graph theory based clustering algorithm taking similarity as the basis, and has become increasingly popular in recent years.It can deal with arbitrary distribution of dataset but with a drawback for being sensitive to the scaling parameters.Cluster ensemble techniques are effective in improving both the robustness and the stability of the single clustering algorithm.Cluster ensemble also has a character of good robustness and generalization ability.The processing method in this paper utilizes the merits of cluster ensemble and develops a consensus function based spectral clustering algorthm.The clustering components are generated by spectral clustering.The affinity matrix is generated by computing the SAM between different datapoints.The Nystrm method is used to to speed up the classification process.Thus semi-supervised classification to multi/hyperspectral remote sensed data is completed.Experiments show that the method presented here has an excellent classificaation result for both multispectral and hyperspectral remote sensed dataset.