Progressive line processing of global and local real-time anomaly detection in hyperspectral images

Abstract

Hyperspectral imaging, which is characterized by its abundant spectral and spatial information, can effectively identify and detect ground objects. In order to detect moving targets and relieve the stress of big data storage, real-time processing of anomaly detection is greatly desired. This paper investigates both global and local real-time implementations of the most widely used RX detector in a line-by-line fashion. Firstly, global and local causal frameworks are designed to meet the causality, which is one requirement of real-time character. Secondly, taking advantage of the Woodbury matrix identity, recursive update equations of the inverse covariance matrix and background data estimate mean are derived, thereby achieving very low computational complexity. As for local real-time architecture, multiple local semi-windows are designed to simultaneously detect all pixels of a data line. This designation has an advantage that it is very beneficial for the implementation of real-time anomaly detection on graphics processing units. The proposed global and local real-time strategies have been deeply analyzed summarizing that the computational complexity is greatly reduced under the comparable detection accuracy. This is finally validated by experimental results.