基于多检测器最大熵融合的多通道光谱图像异常检测

提出了一种多检测器最大熵融合的多通道光谱图像异常检测算法.选择多个不同的异常检测器，并利用自适应窗宽非参核密度估计方法估计其各自的输出分布，保留了多通道光谱图像数据的“长尾”特性，且避免了先验模型假设带来的模型误差.将各原始检测器的输出投影到具有标准正态边缘分布的变换空间中，利用变换空间中模型化的最大熵融合规则实现多检测器的决策级最优概率融合.在原数据空间通过似然函数的检验完成多通道光谱图像的目标检测.利用机载EPS-A航拍多通道光谱图像进行了实验，实验结果表明了算法的有效性.

Anomaly Detection in Multi-Band Spectral Imagery Based on Multiple-Detector Maximum Entropy Fusion

Anomaly detection in multi-band spectral imagery using single detector has great deficiency due to the model limitation,thus a multiple-detector fusion algorithm is proposed in this paper for this problem.Several different anomaly detectors are selected for obtaining pilot detection results,then a nonparametric method called Kernel Density Estimation(KDE) with bandwidth adjusted adaptively is used to estimate the Probability Density Function(PDF) statistics of the output of each individual detector,which preserves the long-tail behavior of multi-band spectral imagery to avoid the model error.The obtained probabilistic information are then transformed to a space with standard Gaussian marginal distribution,in which optimal probabilistic fusion of multi-detector on the decision level is accomplished utilizing a modeled joint distribution under maximum entropy principle.Target detection is finally achieved by likelihood function test in the original data space.Experimental results on EPS-A aerial multi-band spectral imagery demonstrate the effectiveness of the proposed algorithm.