Classifying ships in satellite or aerial images is a challenging problem in remote sensing imagery. This task requires data-hungry learning algorithms (in particular deep models) that build discriminative representations which capture highly variable ship categories. As labeled training data are scarce and expensive, ship category recognition should also rely on abundant unlabeled data in order to enhance its effectiveness. In this paper, we introduce a novel representation learning algorithm based on semi-supervised attributes. Our method allows us to learn deep and discriminative image characteristics shared among different categories while taking into account both labeled and unlabeled data. We demonstrate the effectiveness of this method on the challenging ship category recognition problem and we show its out-performance with respect to related baselines, especially under the regime of scarce labeled data and abundant unlabeled ones.