Learning Attribute Representations for Remote Sensing Ship Category Classification

Object category classification in remote sensing applications usually relies on exemplar-based training. The latter is achieved by modeling the intricate relationships between visual features and their corresponding object categories. However, these models might fail when applied to fine-grained object classification problems especially when training examples are scarce and when objects exhibit complex visual appearances and strong variability. In this paper, we introduce a framework dedicated to object category classification in the context of scarce datasets. Our method builds discriminative mid-level image representations (also referred to as attributes) by learning a nonlinear mapping between the input image features and the attribute space. Moreover, we also enforce these learned attributes to be highly discriminative and easy to predict. We compare our proposed framework to existing attribute and related dictionary-based methods and apply it to two challenging tasks with scarce datasets: binary ship classification on synthetic aperture radar images and multiclass ship category recognition on optical images. These experiments show that our proposed framework is indeed highly effective and generalizes well despite the scarcity of training data.