Hyperspectral Anomaly Detection with CNN-Based VAE and RX Algorithm

Hyperspectral imaging (HSI) offers rich spectral and spatial information, making anomaly detection in hyperspectral images a critical and widely applied task. While combining Variational Autoencoders (VAE) with the Reed-Xiaoli (RX) algorithm has shown advantages, existing methods often rely on individual pixels without fully exploiting spatial information. In this work, we propose a CNN-based VAE framework that extracts latent representations from multi-scale data cubes, which are then analyzed by the RX algorithm to detect anomalies. This method effectively incorporates both spectral and spatial information, addressing the limitations of existing detectors.