Feature-aided tracking via synthetic hyperspectral imagery

Abstract

Hyperspectral imaging (HSI) feature-aided tracking (FAT) is an emerging area of research, employing HSI instruments and exploitation techniques with the goal to track moving objects within challenging environments and across frequent ambiguities. A series of studies have been conducted to demonstrate HSI-FAT with contemporary and novel HSI instruments. Synthesized HSI data have been the key enabler to this effort. Capabilities have been evaluated with synthetic models of low-cost, off-the-shelf sensors such as a video-rate liquid crystal tunable filter, as well as sophisticated emerging sensor concepts such as microelectromechanical-adapted systems. A suite of end-to-end synthetic experiments have been conducted, which include high-fidelity moving-target urban vignettes, synthetic hyperspectral rendering, and full image-chain treatment of the various sensor models. Corresponding algorithm development has focused on motion segmentation, spectral feature modeling, classification, fused kinematic/spectral association, and adaptive sensor feedback/ control.