Anomaly Detection in Hyperspectral Image Using 3D-Convolutional Variational Autoencoder

Anomaly detection (AD) has become a hot topic in hyperspectral image (HSI) analysis. Anomalies are samples that are significantly different from the surrounding background in space or spectrum. However, the rich spectral-spatial features in HSI are not fully discovered by most traditional AD methods. In this paper, a 3D-convolutional Variational Au-toencoder (3D-CVAE) based AD method is proposed to make full use of the spectral-spatial information. The spectral-spatial features are extracted by the 3D-CVAE encoder and the background is reconstructed using these features through 3D-CVAE decoder. The residual between the original input and the reconstructed background contains the anomalies which can be easily detected by the Reed-Xiaoli(RX) detector in the residual. Experimental results on two HSI datasets demonstrate the advantage of the proposed method.