Anomaly detection of hyperspectral image is one of the important research contents in processing onboard satellite.Based on the traditional RX algorithm, a hyperspectral image anomaly detection method is proposed by use of incremental learning and the hierarchical method.Incremental learning is used to update the detector model.When generating a new covariance matrix, there is no need to calculate the covariance matrix of all samples, which avoids repeated data calculating and inverse matrix solving.The hierarchical method is used to suppress the background and preserve target spectrum, which effectively improves the performance of hyperspectral image target detector.The experimental results show that: 1)Compared with SAM algorithm and the traditional RX algorithm, this algorithm has the highest detection probability, and its detection result is the closest to the ground target; and 2)The computation complexity of this algorithm is reduced by an order of magnitude, the running time is reduced by 0.215 s compared with SAM algorithm.Therefore, the anomaly detection algorithm proposed here has higher detection speed and occupies less onboard resources, which is superior to the traditional RX algorithm.