Ship classification using synthetic aperture radar (SAR) imagery is a challenge problem in maritime surveillance. Because of the scale limitation of ship targets in SAR image, convolutional neural networks (CNNs) can not achieve similar performance as for natural image classification. In this paper, we propose a joint CNNs framework for small-scale ship targets classification in SAR image, where a generator and a classifier are jointly connected. The generator can reconstruct the small-scale low-resolution (LR) images to large-scale super-resolution (SR) images, and the classifier is used for ship classification. A novel joint loss optimization strategy is introduced to solve the problem, where an MSE-based content loss is employed to generate high quality SR images, and a classification loss is applied to enable the generator and the classifier to be trained in a joint way. Experiments are conducted to demonstrate the superior performance of our proposed method, as compared with the state-of-the-art methods.