# Is it feasible to perform PCA on large size images prior to classification?

I have a dataset composed of $$970$$ images with size $$256\times256$$, so I have a data matrix $$X \in \mathbb{R}^{970\times65536}$$. My idea is to compute the PCA transformation in the training phase considering the whole data-set and then transform the single images before sending them to a classification algorithm (e.g., SVM or a DNN).

The problem is that the eigenvalue decomposition requires is it practically infeasible with a covariance matrix $$C \in \mathbb{R}^{256^2\times256^2}$$. Is it still possible to use PCA in this situation?