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.