Convolutional Neural Network (CNN) has shown great potential for structural information extraction, however, Synthetic Aperture Radar (SAR) image interpretation via CNN, is still a challenging task due to the pixel- to- pixel variation nature of SAR signal. In this paper, a pattern strengthened deep model for SAR image classification is presented. The main idea underlying this method is to strengthen (or sharpen) the salient patterns contained in SAR image while attenuate the random variation, so as to leverage the enormous potential of deep model. To achieve this goal, max pooling is elaborately designed to form a pattern strengthened deep model. The experimental results on two SAR datasets show that the proposed method increases the classification accuracy by 4.91% and 4.64% compared with the typical CNN, and indicate our innovative attempt is promising for SAR image classification in the case of limited training data.