Lightweight Autonomous Autoencoders for Timely Hyperspectral Anomaly Detection

Autoencoders (AEs) have attracted significant attention for hyperspectral anomaly detection (HAD) in remote sensing applications due to their ability to unveil small, unique objects scattered across large geographical regions in an unsupervised manner. However, the training and inference processes of AEs are computationally demanding, posing challenges for efficient HAD in resource-constrained onboard applications. Various optimization techniques and parallel computing approaches have been proposed to alleviate the computational burden and enhance the feasibility of AEs for real-time applications in HAD. In this letter, we first present an efficient lightweight autonomous autoencoder (LAutoAE) that addresses the computational challenges of the autonomous hyperspectral anomaly detection autoencoder (AUTO-AD) while maintaining a similar anomaly detection accuracy. To further enhance the accuracy, we introduce LAutoAE+, which integrates kernel principal component analysis (KPCA)-based preprocessing methods with the LAutoAE. Experiments on diverse datasets demonstrate that the proposed LAutoAE and LAutoAE+ achieve comparable or superior detection performance compared with conventional Auto-AD, while also achieving reductions of 87% and 89.4%, respectively, in the number of learnable parameters.