基于支持向量机的红外小目标分割和聚类方法研究

目标图像的分割与聚类是检测和识别红外小目标过程的预处理部分。通过映射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,再通过聚类分析提取出可能的小目标。并以混合核函数为例导出了极值点估计所需的二阶方向导数算子。对模拟和实际的海空红外图像进行了实验验证。研究表明,基于支持向量机的小目标分割和聚类算法具有较强的适应性。

Research of SVM-Based Infrared Small Object Segmentation and Clustering Method

Segmentation and clustering of infrared small target images is considered in this paper, which is the preprocessing part of the detection and recognition of the moving small targets in an infrared image sequence. The infrared image intensity surface is well-fitted by the mapped least squares support vector machines (LS-SVM), and then the maximum extremum points are detected on the fitted intensity surface by convolving the image with the second order directional derivative operators deduced from LS-SVM with mixtures of radial basis function and polynomial kernels. The possible targets are extracted by the clustering analysis. The computer experiments are carried out for the real and simulated sky and sea-sky infrared images. The experimental results demonstrate the proposed approach is robust and efficient.