利用背景残差数据检测高光谱图像异常

针对高光谱图像微小目标检测中存在的严重背景干扰问题,提出了一种基于背景残差数据的非线性异常检测算法.首先利用提取的背景光谱端元对图像各像元进行光谱解混,实现了目标信息和复杂背景信息的分离;接着将含有丰富目标信息的解混残差数据非线性映射到高维特征空间,可以充分挖掘高光谱图像波段间隐含的非线性信息,并在特征空间利用RX算子完成目标的检测,从而在抑制大概率背景信息的基础上有效地利用了高光谱图像波段间的非线性统计特性.为了验证算法的有效性,利用真实的AVIRIS数据进行了实验研究,并与经典RX算法、未抑制背景的特征空间核RX算法的检测结果相比较,结果表明基于背景残差数据的检测算法具有良好的检测性能和较低的虚警,且运算复杂度较低.

DETECTING HYPERSPECTRAL ANOMALY BY USINGBACKGROUND RESIDUAL ERROR DATA

In order to overcome the serious background interferences for small target detection of hyperspectral imagery,a nonlinear anomaly detection algorithm based on background residual error data was proposed.After the background endmembers were extracted,spectral unmixing technique was applied to all mixed spectral pixels to separate target information from complicated background clutter.Then,the unmixing residual error data that included abundant target information was mapped into a high-dimensional feature space by a nonlinear mapping function.Nonlinear information between the spectral bands of hyperspectral imagery was exploited and the anomaly targets could be detected by using RX operator in the feature space.Thus,the nonlinear statistical characteristics between the hyperspectral bands were used effectively on the basis of suppressing the large probability background information.Numerical experiments were conducted on real AVIRIS data to validate the effectiveness of the proposed algorithm.The detection results were compared with those detected by the classical RX algorithm and KRX which did not suppress the background information.The results show that the proposed algorithm has better detection performance,lower false alarm probability and lower computational complexity than other detection algorithms.