Target Detection of Hyperspectral Image Based on Convolutional Neural Networks

Convolutional neural networks (CNN) has been applied in image classification and target detection successfully, however, it is rarely introduced to the field of hyperspectral image (HSI) target detection. Therefore, in this paper, a hyperspectral image (HSI) target detection method based on CNN is proposed. Firstly, the raw HSI data is preprocessed and the spectral information could be obtained. Secondly, to extract the feature information, a CNN is trained and the parameters of the network are adjusted according to a HSI. Finally, the targets will be calibrated according to the extracted features. To estimate the target detection performance of the proposed method, deep belief network (DBN) and SVM methods are compared in the experiment of the real world AVIRIS HSI experiment. Numerical results show that the proposed method has promising prospect in the field of HSI target detection.