Visual attention based model for target detection in large-field images

It is of great significance to rapidly detect targets in large-field remote sensing images, with limited computation resources. Employing relative achievements of visual attention in perception psychology, this paper proposes a hierarchical attention based model for target detection. Specifically, at the preattention stage, before getting salient regions, a fast computational approach is applied to build a saliency map. After that, the focus of attention (FOA) can be quickly obtained to indicate the salient objects. Then, at the attention stage, under the FOA guidance, the high-level visual features of the region of interest are extracted in parallel. Finally, at the post-attention stage, by integrating these parallel and independent visual attributes, a decision-template based classifier fusion strategy is proposed to discriminate the task-related targets from the other extracted salient objects. For comparison, experiments on ship detection are done for validating the effectiveness and feasibility of the proposed model.