基于曲率的近地面红外小目标检测算法

Infrared Small Target Detection Method Based on Curvature near the Ground

针对在近地面场景下,红外图像的背景杂波较难抑制、红外小目标检测率较低的问题,提出了一种基于曲率的近地面红外小目标检测算法.将图像看作一张三维曲面,分析目标与背景在曲面形状上的差异,并使用曲率表征这种差异;利用Facet模型计算图像四个方向的一阶与二阶方向导数,对于一阶方向导数搜索其过零点区域,并利用过零点区域的二阶方向导数构建方向曲率图;融合四个方向的方向曲率图,得到最终的曲率图并进行自适应阈值分割实现目标检测.利用四组不同场景下的红外序列图像对算法进行验证,实验结果表明,本文提出的算法在信杂比与背景抑制因子方面都大于10,均高于其他算法,同时在低于6×10^(-4)的虚警率下就能达到100%的探测率,明显优于其他算法.

For the detection rate is low and clutter is difficult to suppress under the complex scenes,a novel method for infrared small target detection based on curvature near the ground was proposed.The infrared image was regarded as a three-dimensional surface,and the differences between the target and the background in the shape of the surface was analyzed which was represented by curvature in this paper.Then the Facet model is used to calculate the first-order and second-order directional derivatives of the four directions of the image.Search for the zero-crossing area of the first-order directional derivatives,and construct the directional curvature map by using the second-order directional derivatives of the zero-crossing areas.Finally,the directional curvature map in four directions are fused to obtain the final curvature map and the real target was achieved by an adaptive threshold segmentation directly.The algorithm is validated by infrared sequence images in four different scenes.The experimental results show that the algorithm proposed in this paper is greater than 10 in terms of signal-to-clutter ratio and background suppression factor,which are higher than other algorithms.And the detection rate of 100%can be achieved under the false alarm rate below 6×10-4,which is obviously superior to other algorithms.