Aiming at the interference of close outliers and the complexity of background features

a new anomaly detection method based on separable local projection in hyperspectral imagery is proposed. After normalizing the data

the test pixel spectrum is selected as the reference spectrum to build the target subspace. Then

the background pixels in close areas are projected onto this subspace and the formula of the abnormal degree is acquired through the modulus of the projection vectors. Finally

comparing the anomalies with the main surface features

parts of the false alarms are eliminated. The experiments were conducted on HyMap hyperspectral data and the results show that the proposed algorithm overcomes the impact of background complexity and interference pixels

especially in situations where the interference pixels and test pixel are in different classes.