Method for detecting anomaly of hyperspectral image

The method discloses a method for detecting anomaly of a hyperspectral image, which can give consideration to advantages of an anomaly detecting method by global and local background modeling. The method comprises the following steps of: projecting a hyperspectral image to orthogonal subspaces of the background based on singular value decomposition so as to obtain a residual image including noise and anomaly; on the basis, introducing a space rank depth for local background modeling and dividing the residual image into two sample sets of noise background and potential anomaly; and finally, carrying out global background modeling by virtue of a multielement gaussian model, calculating the mahalanobis distance of various samples in the potential anomaly set, and comparing the distance with a threshold value to achieve anomaly detection, thus finally generating a binary mapping image capable of reflecting anomaly position coordinates and distribution regulation.