利用二阶方向导数极大值检测红外小目标

为提高复杂环境下红外小目标的检测率，提出了基于二阶方向导数极大值的红外小目标检测算法。该算法首先对二阶方向导数的性质进行了分析，对极大值进行阈值翻转操作，将背景中的平坦成分和边缘成分剔除。接着，根据小面模型对背景进行预测，并以预测误差为权值进一步增强小目标区域。以上2个步骤的计算可通过4个卷积实现，加快了检测速度。最后，对少量候选小目标计算局部对比度，降低了虚警率。实验结果表明：该检测算法在6种复杂背景下平均信杂比增益为78.413 0，平均背景抑制因子为35.079 6，具有较强的鲁棒性和较高的检测率。

Infrared Small-target Detection Using The Maximum of Second-order Directional Derivative

In order to improve the detection rate of infrared small-target in complex environment, a infrared small-target detection algorithm based on the maximum of second-order directional derivative was proposed. Firstly, the properties of second-order directional derivative were analyzed, meanwhile, the flat component and edge of back-ground were removed by threshold and flip operations of the maximum. Then, the background was predicted based on facet model and further enhanced the small-target by prediction error as weight. The above two steps can be achieved by four convolutions and the detection speed was accelerated. At last, the local contrast of candidate small-targets was calculated to reduce the false alarm rate. The experimental results show that the average signal to clutter ratio gain is 78. 413 0 and the average background suppression factor is 35. 079 6 in 6 kinds of complex background. The proposed detection algorithm has stronger robustness and higher detection rate.