Dimension reduction is one key step in statistical analysis for high dimensional data. When each observation is a matrix or a higher order tensor, the traditional approach is to vectorize the data before executing reduction algorithms. This approach often leads to an extremely high dimensional problem which comes along with intensive computations and inefficient estimations. High order SVD and Multilinear principal component analysis (MPCA) are thus proposed for tensor structure data. They reduce each mode space of the tensor separately and thus reduce the computations signicantly. One criticism to the new approach is that, unlike PCA, the projected data in the reduced space are still correlated. To this end, we propose a two stage dimension reduction method, called structure PCA (SPCA). SPCA employs MPCA on the tensor data first, and then applies PCA on the vectorized projected core scores from MPCA. A successful application of SPCA on a cryo-electron microscopy image data will also be presented.