Target detection is an important research direction in the hyperspectral field. Hyperspectral target detection aims to distinguish pixels as background or target according to the spectral characteristics of the target. Several detection algorithms have been proposed in the past few decades. However, the complexity of background samples in hyperspectral images and the limited number of target samples lead to considerable challenges in detection algorithms. A hyperspectral target detection algorithm based on background reconstruction is proposed in this paper. Taking advantage of the large proportion of background samples in hyperspectral images, the self-representation model of the background samples is trained, and then the background is reconstructed. Simultaneously, the constrained energy minimization is used to detect the residual image, and the reconstructed background is used for the calculation of the correlation matrix. Therefore, the target sample is not involved in the calculation to affect the response energy of the target sample, and the detection accuracy is improved. Results on real hyperspectral image data are better than those of comparison experiments, which verify the effectiveness of this method.Obtaining numerous training sets of artificially labeled hyperspectral data is difficult. Therefore, using limited samples to train deep neural networks is the largest difficulty in applying deep learning to hyperspectral target detection. When calculating the average output energy of the background, the calculation of the correlation matrix of all samples is used. Therefore, the target pixel also participates in the calculation, causing a certain degree of damage to the target spectrum. The background is used as a training sample to train the entire network to solve the above problems, and the reconstructed background is utilized for constrained energy minimization detection to reduce the impact on the target spectrum during the detection process.This paper proposes a hyperspectral target detection based on constrained energy minimization variational autoencoder. First, the image is roughly detected to obtain the training background sample. The background sample then is inputted into the variational autoencoder for training. The network introduces a constraint energy minimization regularization to remove the characteristics of the target sample and help the reconstructed sample retain only the background information. The 3D residual is acquired by calculating the difference between the original image and the reconstructed background. Thus, the constraint energy minimization is used to detect the residual. The background correlation matrix is employed in the detection process to replace the residual correlation matrix. Finally, the final detection result is obtained by weighting.Compared with other comparative experiments, the proposed method achieved good detection results. The AUC table shows that most of the six hyperspectral datasets performed better than the comparison experiments, and most of the AUC values reached more than 99%. The detection map reveals that the target part is well detected. A close ROC curve to the upper left corner yields satisfactory effects. The curve of the proposed method performs well compared with other methods.Overall, a hyperspectral target detection based on constrained energy minimization variational autoencoder is proposed. The algorithm utilizes the characteristics of the large distribution of background pixels in hyperspectral images. First, the coarse detection is used to obtain the background samples for training. The variational autoencoder then trains the background self-representation model and reconstructs its background. A constraint energy minimization regularization is introduced to help the reconstructed samples retain only the background information. Simultaneously, when using the constraint energy minimization to detect the residual image, the background correlation matrix contributes to the calculation to prevent the participation of non-background pixels in the calculation and lose the target signal output. Experimental results on real hyperspectral datasets show that the algorithm outperforms other comparative experimental results. However, the model is highly dependent on the effect of background reconstruction. If the effect of background reconstruction is superior, then the detection rate will be high. Therefore, improving the stability of background reconstruction in the future is necessary.

hyperspectral;target detection;background reconstruction;constrained energy minimization;correlation matrix