Hierarchical Suppression Method for Hyperspectral Target Detection

Target detection is an important application in the hyperspectral image processing field, and several detection algorithms have been proposed in the past decades. Some traditional detectors are built based on the statistical information of the target and background spectra, and their performances tend to be affected by the spectral quality. Some previous methods cope with this problem by refining the target spectra to make the detector robust. In this paper, instead of doing similar to this, we propose a new hierarchical method to suppress the backgrounds while preserving the target spectra, with the purpose of boosting the performance of traditional hyperspectral target detector. The proposed method consists of different layers of classical constrained energy minimization (CEM) detectors. In each layer of detection, the CEM's output of each spectrum is transformed by a nonlinear suppression function and then considered as a coefficient to impose on this spectrum for the next round of iteration. To our knowledge, such hierarchical structure is proposed for the first time. Theoretically, we prove the convergence of the proposed algorithm, and we also give a theoretical explanation on why we can obtain the gradually increasing detection performance through the hierarchical suppression process. Experimental results on two real hyperspectral images and one synthetic image suggest that our method significantly improves the performance of the original CEM detection algorithm and also outperforms other classical and recently proposed hyperspectral target detection algorithms.