Hyperspectral target detection based on transform domain adaptive constrained energy minimization

Traditional hyperspectral target detection methods use spectral domain information for target recognition. Although it can effectively retain intrinsic characteristics of substances, targets in homogeneous regions still cannot be effectively recognized. By projecting the spectral domain features on the transform domain to increase the separability of background and target, fractional domain-based revised constrained energy minimization detector is proposed. Firstly, the fractional Fourier transform is adopted to project the original spectral information into the fractional domain for improving the separability of background and target. Then, a newly revised constrained energy minimization detector is performed, where sliding double window strategy is used to make the best of the local spatial statistical characteristics of testing pixel. In order to make the best of inner window information, the mean value of Pearson correlation coefficient is measured between prior target pixel and testing pixel associated with its four neighborhood pixels. Extensive experiments for four real hyperspectral scenes indicate that the performance of the proposed algorithm is excellent when compared with other related detectors.

Automated summary

This algorithm improves the separability of background and target by projecting the original spectral information into the fractional domain using fractional Fourier transform. By utilizing a revised constrained energy minimization detector and sliding double window strategy, it effectively leverages the local spatial statistical characteristics of testing pixel for target recognition, demonstrating excellent performance compared to other detectors in four real hyperspectral scenes.