基于加权组合核RX算法异物检测及其参量选择

为了融合光谱形状差异信息和多项式核函数全局信息,充分挖掘地物特征,提高异常检测正确率,提出了一种加权组合核RX算法.该算法在高斯核函数的基础上,增加一个光谱角核函数.由于核函数参量和加权参量直接影响算法性能,分别采用了随机函数法、爬山法和粒子群算法对上述参量进行了选择.结果表明,在恒虚警率下使用粒子群算法进行参量设定得到的效果最好,且采用加权组合核函数RX算法得到的目标检测率为83.5%,相对于普通的核RX算法,正确率得到了提高.

Anomaly detection based weighted combination kernel RX algorithm and its parameter selection

In order to combine the spectral shape difference information and the polynomial kernel function global information, exploit the object feature fully and improve the accuracy of anomaly detection, anomaly detection method was proposed based on weighted combination kernel RX algorithm. A spectral angle kernel function was added to Gaussian kernel function in the anomaly detection method. Because the kernels' parameter and the weighting parameter will affect the efficiency of the algorithm, the random function selection, the hill climbing method and the particle swarm optimization algorithm were implemented for setting the above parameters. Experiment results show that at a constant false alarm rate, it is the best to set the parameters by means of the particle swarm algorithm. Target detection rate is 83.5% by using the weighted combination kernel RX algorithm, higher than that by means of the traditional kernel RX algorithm.