A genetic algorithm approach to optimal spatial sampling of hyperspectral data for target tracking

Abstract

Hyperspectral imagery (HSI) data has proven useful for discriminating targets, however the relatively slow speed at which HSI data is gathered for an entire frame reduces the usefulness of fusing this information with grayscale video. A new sensor under development has the ability to provide HSI data for a limited number of pixels while providing grayscale video for the remainder of the pixels. The HSI data is co-registered with the grayscale video and is available for each frame. This paper explores the exploitation of this new sensor for target tracking. The primary challenge of exploiting this new sensor is to determine where the gathering of HSI data will be the most useful. We wish to optimize the selection of pixels for which we will gather HSI data. We refer to this as spatial sampling. It is proposed that spatial sampling be solved using a utility function where pixels receive a value based on their nearness to a target of interest (TOI). The TOIs are determined from the tracking algorithm providing a close coupling of the tracking and the sensor control. The relative importance or weighting of the different types of TOI will be accomplished by a genetic algorithm. Tracking performance of the spatially sampled tracker is compared to both tracking with no HSI data and although physically unrealizable, tracking with complete HSI data to demonstrate its effectiveness within the upper and lower bounds.