基于背景判别与邻域补偿的高光谱异常检测

为克服高光谱局部异常检测算子背景虚警严重,探测效果不佳等问题,提出了基于核光谱角背景判别与邻域补偿的异常检测算法。算法从背景像元的筛选和探测结果的补偿两个角度提高像元探测精度,在背景像元的处理方面,提出了一种基于核光谱角距离相似度的背景像元筛选算法,将光谱分辨性能更强的核光谱角引入背景差异性判别过程,准确可靠地实现局部背景像元的筛选和优化;同时,针对异常检测算子探测精度不高等问题,引入邻域加权的空谱联合补偿机制,并提出基于核光谱角距离相似度的动态模板卷积补偿算法,显著增强了背景与目标的可分性。在与RX、LRX、KRX和CRD等异常检测算法的对比中发现,该算法表现出较强的探测性能,在抑制虚警和提高探测精度等方面达到了不错的效果。

Hyperspectral anomaly detection based on background determination and neighborhood compensation

There are serious false alarm and poor detection performance of the local anomaly detection operator for hyperspectral image. To solve these problems,one kind of improved anomaly detection algorithmbased on background discrimination and neighborhood compensation by kernel spectral angle is proposed.The background pixels screening and detecting results compensation are taken into account.In the termof background pixel processing, thealgorithm based on kernel spectral Angle distance similitude is proposed.The kernel spectral angle with stronger spectral resolution is introduced into the background difference discrimination process. In this way,the optimization of local background pixel accurately and reliably is realized.Meanwhile, to solve the problem of low detection accuracy, the joint compensation mechanism of space spectral characteristics is introduced into neighborhood weighting.The dynamic template convolution compensation algorithm based on kernel spectral Angle distance similarity is proposed, which significantly enhances the separability of background and target. Compared with other abnormal detection algorithms (e.g., RX, LRX, KRX and CRD), the proposed algorithm shows strong detection performance and achieves good effectiveness in suppressing false alarms and improving detection accuracy.