The requirement for realistic simulation of military scenarios arises from a dearth of suitable and accessible measured data. Furthermore, measurement campaigns are restricted by the trial locality and availability of appropriate targets. Targets located in and around tree-lines are of particular interest, as they present scenarios that conventional broadband sensor systems find problematic. Utilising the spectral component of scenes, through the use multi- or hyperspectral technologies, can be beneficial in detecting these difficult targets.
In this paper we describe the use of a Monte Carlo ray-tracing model (FLIGHT) to simulate forest scenes. This model is capable of calculating the interesting BRDF properties specific to forests. Targets are also incorporated in these simulations, and we describe contrast discrimination of the target from the background. This technique has application for targets in deep hide as well as at the forest edge (i.e., in a tree-line).
Assessment methods that can be applied to simulated hyperspectral imagery are investigated, to determine how realistic these scenes are in comparison to measurement. This is of key importance in ensuring that simulated imagery, as well as measured data, can be used to assess algorithmic techniques to detect and discriminate targets. Statistical assessment measures are discussed that utilise the spatial and spectral properties of the image.