DEEP-RX FOR HYPERSPECTRAL ANOMALY DETECTION

Hyperspectral anomaly detection is a widely studied topic that has garnered significant attention in recent years. However, designing effective nonlinear detectors remains a challenge for many traditional methods. To address this issue, we propose the integration of a variational autoencoder (VAE) in this paper. The VAE enables efficient feature extraction from hyperspectral images (HSIs) by mapping inputs to latent variables that follow a Gaussian distribution. The resulting latent representations are subsequently passed to the Reed-Xiaoli (RX) detector to obtain the final detection results. Through extensive testing on three real datasets, the detection results demonstrate the superiority of our proposed method.