A Hierarchical Salient-Region Based Algorithm for Ship Detection in Remote Sensing Images

In this paper, we present a hierarchical salient-region based algorithm and apply it for automatic ship detection in remote sensing images. The novel framework breaks down the complex problem of scene analysis by hierarchical attention, in a computationally efficient manner, such that only the salient-regions which contain potential targets can be analyzed in detail. Firstly, a parallel method is adopted for crudely selecting saliency tiles from entire scene by using low-level feature extraction mechanisms, and then the Region-of-Interest (ROI) around each saliency object is taken out from the saliency tiles to pass to the further processing. Shape and texture features are extracted from the multiresource ROIs to describe more details for candidate targets respectively. Finally, Support Vector Machine (SVM) is applied for target validation. Experiments show the proposed algorithm achieves high probabilities of recall and correct detection, as well as the false alarms can be greatly diminished, with a reasonable time-consumption.