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Infrared ship detection based on latent low-rank decomposition and salient feature fusion

To improve the accuracy of infrared ship detection and more effective background suppression, we propose an infrared ship detection algorithm based on latent low-rank decomposition and salient feature fusion. First, to avoid the influence of the number of superpixel segmentation on the integrity of the GR saliency detection, multiple salient maps with different numbers of superpixels are taken large, and a single foreground coarse salient map of the ship target is obtained using a high-level central prior and graph regularization. Secondly, to suppress the noise influence and refine the foreground coarse saliency map, sparse salient features of the ship target image are extracted using a method based on latent low-rank decomposition. Then, the sparse and null salient features are fused with the feature values. Finally, the accurate ship target is segmented based on the OTSU algorithm. The subjective comparison experiments and quantitative analysis show that the infrared ship segmentation algorithm proposed in this paper has better performance in terms of accuracy and visual effect.