Supervised method for hyperspectral image camouflage target detection

Aiming at camouflage target detection problem, a supervised method for hyperspectral image camouflage target detection was proposed. The plant camouflage targets were taken as study objects, and then based on the spectral characteristics analysis of camouflage materials and plants, camouflage materials and plant's spectral differences were magnified through spectrum rearrangement, spectral derivative and spectrum difference enhancement. Then, principal components analysis(PCA) was used for dimensionality reduction, thus a detection method for big camouflage target in hyperspectral image was realized. The experimental result shows that the method outperforms weighted correlation matric-constrained energy minimization(WCM-CEM) and unsupervised target generation process-orthogonal subspace projection(UTGP-OSP) both in the detection time and detection result.