A SAM weighted KEST algorithm based on kernel eigenspace separation transform (KEST) was proposed for anomaly detection in hyperspectral imaging. Weights are introduced for each sample in the difference correlation matrix (DCOR), and the input pixel neighbor surroundings. All samples were weighted according to the angle between the sample spectral vector and the centered vector in detection window to minimize the influence of anomalous data and outstand the contribution of principle component. In this way, DCOR represented the difference between target and background distribution much better. Experimental results indicate that the proposed method shows superior performance over the conventional anomaly detection algorithms and KEST.