Hyperspectral Anomaly Detection With Guided Autoencoder

Abstract

Recently, autoencoder-based hyperspectral anomaly detection methods have demonstrated excellent performance on hyperspectral images (HSIs). The autoencoder (AE) can simultaneously reconstruct both the anomaly targets and background, but the lack of prior information limits ability to detect anomalies. This study proposes a novel hyperspectral anomaly detection method based on a guided AE to reduce the feature representation for anomaly targets. First, a multi-layer AE network with skip connections is proposed to fully extract the abundant latent features from HSIs and enhance the expressive ability of the network. The reconstructed HSI can be obtained by the proposed AE network. Second, to suppress anomaly targets in the obtained reconstructed HSI and better represent background features, a guided module based on a guided image is added to the network to reduce the feature representation of anomaly targets by providing feedback information. Moreover, the guided image is calculated using a proposed spectral similarity method that uses the local spatial features of the HSI. Finally, we use the reconstruction error as a performance metric and compare the results of our proposed method with other state-of-the-art methods on six real-world HSIs. The results demonstrate the effectiveness and superiority of the proposed method.