Research progress on anomaly target detection algorithms for hyperspectral imagery

Hyperspectral image (HSI) data plays an important role in remote sensing images, and researchers have done a great deal of work to understand and recognize HSI. Among them, hyperspectral image-anomaly target detection (HSI-ATD) has numerous applications in both national defense, military, and civilian fields. In particular, because anomaly target detection does not require any prior information, it has become one of the key technologies and research hotspots in HSI processing and information extraction. Through systematic sorting and analysis, this paper summarizes the existing anomaly target detection algorithms in detail, and evaluates the key issues involved in HSI-ATD, future technological development directions (such as deep learning, multimodal, and real-time processing, etc), as well as the problems existing in the algorithms. Additionally, some innovative viewpoints and predictions on future research trends are proposed.