Complexity-aware algorithm architecture for real-time enhancement of local anomalies in hyperspectral images

Abstract

Anomaly detection (AD) from remotely sensed multi-hyperspectral images is a powerful tool in many applications, such as strategic surveillance and search and rescue operations. In a typical operational scenario, an airborne hyperspectral sensor searches a wide area to identify regions that may contain potential targets. These regions typically cue higher spatial-resolution sensors to provide target recognition and identification. While this procedure is mostly automated, an on-board operator is generally assigned to examine in real time the AD output and select the regions of interest to be sent for cueing. Real-time enhancement of local anomalies in images of the over flown scene can be presented to the operator to facilitate the decision-making process. Within this framework, one of the ultimate research interests is undoubtedly the design of complexity-aware AD algorithm architectures capable of assuring real-time or nearly real-time in-flight processing and prompt decision making. Among the different AD algorithms developed, this work focuses on those AD algorithms aimed at detecting small rare objects that are anomalous with respect to their local background. One of such algorithms, called RX algorithm, is based on a local Gaussian assumption for background and locally estimates its parameters from each pixel local neighborhood. RX has been recognized to be the benchmark AD algorithm for detecting local anomalies in multi-hyperspectral images. RX decision rule has been employed to develop computationally efficient algorithms tested in real-time operating systems. These algorithms rely upon a recursive block-based parameter estimation procedure that makes their processing and, in turn, their detection performance differ from those of original RX. In this paper, a complexity-aware algorithm architecture fully adaptable to real-time processing is presented that allows the computational load to be reduced with respect to original RX, while strictly following its original formulation and thus assuring the same detection performance. An experimental study is presented that analyzes in detail the complexity reduction, in terms of number of elementary operations, offered by the proposed architecture with respect to original RX. A real hyperspectral image of a scene with deployed targets has been employed to perform a case-study analysis of the complexity reduction to be experienced in different operational scenarios. The real data are also adopted to illustrate a possible strategy for on-board line-by-line enhanced visualization of anomalies for decision support.