Target detection is an important application in 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 like 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 itera...