The anomaly detection performance of kernel RX is low as a result of the complexity of spectral and spatial distributions of hyperspectral images.Aiming at the problem,this paper proposes an algorithm of band subsets anomaly detection for hyperspectral image based on fourth order cumulant.Firstly,the algorithm divides the original hyperspectral image into subsets in low dimensions according to the correlation coefficient between spectral bands.Then,the error image data are achieved after background interferences are suppressed for subset-bands using the orthogonal sub-spaces constructed by principal component analysis.Based on the data,the feature information of all band subsets is extracted by using the principal component analysis,which makes the information of anomaly target concentrated on the previous bands.At last,the optimal band subsets are achieved by fourth order cumulant of principal component in band subsets,and the anomaly detection is carried out combined with the kernel RX.The results show that the proposed algorithm has higher precision and lower false alarm probability,and it greatly outperforms the classical kernel RX algorithm.