结合多尺度视觉显著性的舰船目标检测

光学遥感图像海面舰船目标检测易受云雾,海岛,海杂波等复杂背景的干扰。本文提出了一种适用于复杂背景的舰船目标检测方法。首先为了克服目标尺度多变问题,利用视觉显著性生成多尺度显著图,然后使用基尼指数自适应选择最优显著图。考虑到全局阈值分割算法带来的漏检测问题,提出一种新的方案来分离目标和背景像素点。利用图像膨胀原理获取显著图的局部极大值点,然后使用k-means算法判断极大值点属于目标像素点还是背景像素点。接着对目标点邻近区域进行精细分割。最后引入基于径向梯度变换的旋转不变特征来进一步剔除虚警。实验结果表明,该算法能够成功检测出不同尺寸和方向的舰船目标,有效克服复杂背景的干扰。算法检测正确率93%,虚警率4%,优于其他舰船检测方法。

Ship detection based on the multi-scale visual saliency model

Ship detection from optical remote sensing images is easily disturbed by complex backgrounds, such as clouds, islands, and sea clutter. In this paper, a novel ship detection method was proposed to solve these problems. First, to solve the change in target size, multi-scale saliency maps were generated using a spectral residual visual saliency model, and the optimal saliency map was adaptively selected using the Gini index. Further, considering the problem of missing detection caused by the threshold segmentation, a two-stage segmentation method was proposed to separate the target and background pixels. The local maximums of the saliency map were then obtained using image expansion, and the k-means algorithm was adopted to determine whether each local maximum belongs to the target pixel or background pixel. The accurate candidate locations were obtained using the local threshold segmentation. Finally, the rotation invariant feature based on the radial gradient transform was introduced to further eliminate false alarm. The experimental results show that the proposed detection method can successfully detect ship targets of different sizes and directions and effectively overcome complex background interference. Additionally, the detection accuracy is 93%, and the false alarm rate is 4%, which are better than other saliency-based ship detection methods.