Hyperspectral Anomaly Detection Based on Graph Regularized Variational Autoencoder

Abstract

Nowadays, deep learning can play an important role in addressing the issue of hyperspectral anomaly detection (HAD). In order to further utilize the spatial information in hyperspectral images (HSIs), an anomaly detection method for HSIs is presented based on graph regularized variational autoencoder (GRVAE). Firstly, the proposed method uses the superpixel segmentation algorithm to segment the hyperspectral image (HSI) and constructs an adjacency matrix to evaluate the similarity between pixels. Secondly, a variational autoencoder is used to reconstruct the spectral vector of the HSI, and meanwhile, the spatial similarity of the image is shared in the feature space through the graph regularization term. Finally, the reconstructed background and the original input are used to obtain the spectral error map, and then the attribute filtering is used to further refine the detection results. Performed on four data sets of abnormal target data with different shapes and different background complexity, the experiments show that the method has promising anomaly detection performance.