Anomaly detection in hyperspectral imagery based on feature fusion of band subsets

Detecting camouflaged targets in an unknown environment presents a great challenge in hyperspectral image analysis since the prior knowledge about targets and background is not available. A nomaly detection method for hyperspectral imagery was proposed for this problem. Features were extracted from subband sets of hyperspectral imagery, then fusion algorithm for detection was implemented by D-S evidence reasoning while basic belief assignment function was constructed involving high-order moments of features. Theoretical analysis and results of experiment verify the effectiveness of the algorithm.