基于目标多特征的SAR影像舰船检测优化算法

Optimization Algorithm of Ship Detection Based on Multi-feature in SAR Images

针对传统的舰船检测算法无法有效避免旁瓣效应对结果的影响,及多考虑舰船与背景之间的灰度对比度而未充分利用SAR影像上目标对象的几何特征造成检测精度较低的问题,提出了一种基于舰船多特征的目标检测算法。该方法利用方位角估算法与逐步逼近法剔除旁瓣效应对计算目标对象几何特征(面积、长宽比和矩形度)及灰度对比度特征的影响,利用变异系数法赋予4个特征不同的权重,计算出目标对象的置信度,选取最佳置信阈值,剔除非目标对象,优化检测结果。利用Sentinel-1影像数据对算法进行了验证,并将其与双参数CFAR算法和KSW双阈值算法进行了对比实验。实验结果表明:对于3张背景复杂度不同的影像,所提出的算法质量因子均超过了0.7且耗时最短,同时对于背景较为复杂的影像仍能保持较好的检测性能。

In view of that the traditional ship detection algorithms cannot effectively avoid the influence of the side lobe effect on results,which mostly consider the gray contrast between the ship and the background.The geometric characteristics of the target object on the SAR images are not fully utilized,and the detection accuracies are low,therefore a target detection algorithm based on the ship’s multi-features is proposed.The azimuth estimation method and the stepwise approximation method are used to eliminate the influence of the side lobe effect on the geometric characteristics(area,aspect ratio and rectangularity)and gray contrast,and then the variance coefficient method is used to distribute different weight for the four features to calculate the confidence.By determining the best confidence threshold to remove the non-target objects among the candidate targets and optimize the detection results,this paper uses Sentinel-1 images to verify the algorithm,the two-parameter CFAR algorithm and the KSW double-threshold algorithm are used as comparative experiments.The experimental results show that for three images with different background complexities,the quality factor of the proposed algorithm exceeds 0.7 with the minimum calculation time,and it maintains optimal detection performance for images with complex background.