基于向量机的红外小目标检测技术研究

针对红外序列图像中弱小目标检测问题，提出了用最小二乘向量机对原始红外图像中每一像素的局部区域作灰度曲面最佳拟合，在拟合曲面上进行灰度极大值像素点位置估计，实现目标的粗定位。真正的目标取决于中心点的灰度是否高于其邻域的平均灰度。并以径向基核函数为例推导出了极值点估计所需的二阶方向导数算子。对模拟和实际图像进行了小目标检测的实验验证。结果表明，基于支持向量机的小目标检测算法具有较强的适应性。

SVM-based infrared small target detection

Because of the influence of nature meteorological condition,background environment and the structure of target,the detection of weak and small targets in infrared image is one of the difficulties among image targets detection and identification.A new small target detection method is presented,in which the infrared image intensity surface is well fitted by the least square support vector machine(LS-SVM),and then the maximum extremum points are detected based on the fitted intensity surface.It is decided whether the extremum point is a target position or not by comparing the gray level of the point with the average gray intensity of its local area.The second order directional derivative operators are de-duced from the LS-SVM with the radial basis kernel function,as an example.The computer small target detection experiments are carried out for the real and simulated images.Experimental results demonstrate that the proposed algorithm is robust and efficient.