自适应阈值的视觉注意模型SAR舰船检测算法

Ship Detection Algorithm Based on Adaptive Threshold of Visual Attention Model in Synthetic Aperture Radar Image

为了解决SAR图像基于人类视觉注意模型舰船检测算法中需要人工确定经验阈值的问题,提出一种自适应阈值的视觉注意模型SAR舰船检测算法。引入最大类间方差(OTSU)法确定自适应阈值进行图像初分割,再应用视觉注意模型得到视觉显著图,最终根据显著图的统计特性进行自适应阈值分割检测出舰船目标。该算法相对于已有的视觉注意模型舰船检测算法自动化程度更高,与视觉注意模型舰船检测算法以及目前普遍使用的双参数CFAR、K-CFAR、KSW双阈值算法同时处理3种星载SAR数据——ENVISAT ASAR(25 m)、Sentinel-1(10m)和Cosmo-SkyMed(3m),进行对比分析实验,实验结果证明该算法简单、准确、高效。

In the SAR image, ship detection algorithm based on the human visual attention model needs to manually determine the experience threshold. In order to solve this problem,ship detection algorithm in synthetic aperture radar imagery based on adaptive threshold of visual attention model is proposed. Firstly, the maximum between class variance method, i. e. OTSU method,is introduced to determine the adaptive threshold for image segmentation. Then, a visual saliency map is obtained by using the visual attention model. Finally, according to the statistical characteristics of the saliency map, the adaptive threshold segmentation method is used to detect the ship targets. Compared with the existing visual attention model, the proposed ship detection algorithm has higher automation degree. The proposed algorithm, the visual attention model of ship detection algorithm,and the commonly used algorithm of two parameters CFAR,K-CFAR and KSW double threshold algorithm are used to deal with three kinds of spaceborne SAR data, which are ENVISAT ASAR （25 m）, Sentinel-1 （10 m）and Cosmo-SkyMed （3 m）simultaneously. The comparative analysis experiment is then carried out. The experimental results show that the proposed algorithm is simple, accurate and efficient.