MPDA: Multivariate Probability Distribution Autoencoder for Hyperspectral Anomaly Detection

In recent years, the significant success of deep learning (DL) in computer vision has contributed to its continuous development in the field of hyperspectral image (HSI) anomaly detection (AD). However, in practical applications, HSI-AD based on DL faces many challenges due to the inability to effectively acquire training samples and predict the types of anomaly targets. This makes it a challenging task, especially for AD in complex scenes. In this article, we propose an unsupervised DL framework for HSI-AD based on the multivariate probability distribution autoencoder (MPDA). First, to explore the distribution characteristics of high-dimensional data, we use the probability density histogram to statistically distribute the frequencies of each interval adaptively, dividing the HSI and obtaining an AD-guided image through the designed grid structure. Second, we propose an unsupervised multilayer autoencoder network based on energy-weighted skip connections. By coupling the detection-guided image in the network, we achieve reverse-guided module reconstruction, weakening the feature representation of anomalous in the reconstructed information and enhancing the separability of targets. Finally, we model the reconstructed error images using the multivariate skewed t-distribution based on data distribution characteristics to obtain the final AD map. Through the comparative experiments with other innovative AD algorithms on authentic HSI datasets captured in five different scenarios, the proposed algorithm demonstrates strong generalization and detection capabilities. The source code of the MPDA will be public at https://github.com/muzhenhuam/MPDA/tree/master.