Saliency-Driven Target Detection Based On Common Visual Feature Clustering For Multiple Sar Images

Saliency detection is a newly emerging tool to extract target in image processing. However, due to the loss of color in synthetic aperture radar (SAR) images, the detection result using the traditional saliency analysis is not satisfying. Therefore, a new saliency-driven target detection model based on common visual feature clustering is introduced for multiple SAR images. Firstly, Markov Random Field is applied to extract intra-image saliency map. Secondly, intensity, texture and curve features are extracted from multiple SAR images as common visual features, which can effectively compensate for the lack of color information. And then fuzzy c-means is employed to construct interimage saliency map. Finally, an effective fusion strategy is used to combine the intra-image saliency map with the interimage saliency map to obtain the final common saliency map. The experimental results demonstrate that the proposed model outperforms most the state-of-the-art saliency detection models.