Automated target detection and discrimination using constrained kurtosis maximization
Automated target detection and discrimination using constrained kurtosis maximization
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DOI：
10.1109/lgrs.2007.907300
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发表时间：
2008-01-01
影响因子：
4.8
通讯作者：
Kopriva, Ivica
中科院分区：
文献类型：
--
作者：
Du, Qian;Kopriva, Ivica
在没有先验信息的情况下利用高光谱图像是一个挑战。在这种情况下，无监督目标检测成为一个异常检测问题。我们提出了一种有效的基于归一化第四中心矩的目标检测和识别算法，称为峰度，它可以测量分布的平坦度。高光谱图像中的小目标构成了分布的尾部，从而使其更重。高斯分布完全由前两阶统计量决定，峰度为零。因此，峰度测量分布与背景的偏差，适用于异常/目标检测。当对要最大化的峰度施加适当的不等式约束时，所得到的约束峰度最大化（CKM）算法能够快速检测到具有多个投影的小目标。与广泛使用的无约束峰度最大化算法（即快速独立分量分析）相比，CKM算法可以检测到投射较少的小目标，并且检测率略高。
Exploiting hyperspectral imagery without prior information is a challenge. Under this circumstance, unsupervised target detection becomes an anomaly detection problem. We propose an effective algorithm for target detection and discrimination based on the normalized fourth central moment named kurtosis, which can measure the flatness of a distribution. Small targets in hyperspectral imagery contribute to the tail of a distribution, thus making it heavier. The Gaussian distribution is completely determined by the first two order statistics and has zero kurtosis. Consequently, kurtosis measures the deviation of a distribution from the background and is suitable for anomaly/target detection. When imposing appropriate inequality constraints on the kurtosis to be maximized, the resulting constrained kurtosis maximization (CKM) algorithm will be able to quickly detect small targets with several projections. Compared to the widely used unconstrained kurtosis maximization algorithm, i.e., fast independent component analysis, the CKM algorithm may detect small targets with fewer projections and yield a slightly higher detection rate.