Subjective Saliency Model Driven by Multi-Cues Stimulus for Airport Detection

Traditional saliency models designed for natural scene images usually use human visual characteristics to detect the target, but those salient areas in the remote sensing images (RSIs) may not be the targets we are really interested in. Taking both remote sensing image attributes and airport characteristics into account, we put forward a subjective saliency model driven by multi-cues stimulus for the airport's detection (MCS-SSM). Different from traditional saliency models, this model mainly relies on the subjective target detection task to find specific target area eliminating disturbance from other salient targets. Based on the low-level features, we train an LDA classifier by only small target samples and then build an object feature map. In the meantime, the shape information based on line density is extracted to get a shape map. Depending on the fusion result of two saliency maps, we optimize the subjective saliency map with SVM classifier. Moreover, MST density map is generated to suppress background and highlight interesting airport regions in the subjective saliency map. Consequently, MCS-SSM can respectively take the target, the background, and the detection task as multiple cues to quickly locate interest airport targets in RSI with a large cover area. MCS-SSM breaks through the limitations on color, texture, and other low-level characteristics compared with traditional saliency models, which are more targeted to detect the specific targets. The extensive experimental results demonstrate that the proposed MCS-SSM outperforms nine state-of-the-art saliency models. Besides, it has a higher detection rate and better effective performance than other three airport detection approaches.