Constrained Energy Minimization with a DNN Detector

The inherent spectral variability in hyperspectral images, the noise, and other factors bring difficulties to traditional detectors to separate the target and background by using linear decision boundaries. In this paper, by generalizing the classical constrained energy minimization (CEM) method, and considering the feature auto-extraction ability of deep neural networks (DNN), we propose a nonlinear detector based on semi-supervised learning (named deepCEM). This approach designs a deep neural network structure to provide a specific form of the nonlinear detector and trains the DNN model with knowledge of target spectra and unlabeled samples. Experiments performed on several hyperspectral data sets show that the proposed method performs better than other state-of-the-art methods.