高光谱图像波段子集模糊积分融合异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题，该文给出一种基于高相关性波段子集分割的模糊积分低概率目标检测融合算法。依据高光谱图像数据的波段相关性将原始高光谱数据分割为若干连续波段子集；利用非参核密度估计得到原假设下各波段子集数据RX检测器输出的概率密度函数，构造出非参隶属度映射函数；利用数据光谱维的特征值定义目标信号噪声能量比(TNER)，衡量各波段子集信源检测结果的重要程度；最后，通过Sugeno模糊积分实现波段子集检测结果的决策级融合。使用可见光/近红外波段OMIS-I高光谱图像进行了实验，实验结果证明了算法的有效性。

Anomaly Target Detection in Hyperspectral Imagery Based on Band Subset Fusion by Fuzzy Integral

An anomaly target detection method based on the high correlation band subsets and fuzzy integral fusion is presented to deal with detecting unknown target in unknown background for hyperspectral imagery. Original hyperspectral data is divided into several continuous band subsets according to the high correlation within the subset. Applying nonparametric kernel density estimation to the RX detector output of each subset to obtain its probability density function (pdf), and a nonparametric fuzzy membership function is constructed; based on the eigenvalues in spectral dimension, a target signal-noise-ratio is defined to measure the degree of importance of detection result from each subset; finally, decision fusion is implemented through Sugeno fuzzy integral method. Experiments on visible/near-infrared OMIS-I hyperspectral imagery justify the effectiveness of the algorithm.