This paper presents a spectral clustering algorithm based image segmentation to correctly and automatically determine the number of classes. Firstly

the weighted matrix and the normalized Laplacians matrix are established with the similarity graph corresponding to a given image. Then

the eigenvectors corresponding to the smaller eigenvalues of the normalized Laplacians matrix are calculated to generate eigenvectors matrix and the pixel feature points set is constructed by means of treating each line of the eigenvectors as a different data point. Secondly

when the Laplacians matrix is in different approximate block diagonal structure

the proposed algorithm exploits the clustering property of the pixel feature points belonging to the same class and calculates the corresponding clustering degree of the different number of segmentation classes by defining the index of clustering degree. Finally

when the clustering degree is the last one to have a greater degree of jumping

the number of the segmentation classes is selected as the number of classes in this paper. The FCM algorithm is used to partition the pixel feature points set corresponding to the number of classes selected to realize the image segmentation. Synthesized and real remote sensing images are used for testing the proposed algorithm. The results show that the proposed algorithm can identify the number of classes in an image correctly.