The Virtual Targets Center (VTC) is a strategic alliance between the Targets Management Office within the US Army Simulation, Training, and Instrumentation Command (STRICOM) and the Systems Simulation and Development Directorate within the US Army Aviation and Missile Command (AMCOM). This center reduces duplication of effort by making DoD owned geometry models available for reutilization and supports the modeling and simulation community by redistributing or creating geometry models in formats applicable to a wide range of simulation activities. In addition to these activites, the VTC is developing methods and tools to enhance existing target models. A new software simulation exists at the VTC to automatically create facet models of camouflage netting by considering the netting as a 2D membrane that balances internal tensional stresses and the external force of gravity by assuming a minimum energy configuration - accurately replicating the draping of real netting. The geometric information of this virtual camouflage netting is exported to a file in a format commonly used for three-dimensional modeling, thereby making it available to workers in signature prediction and visualization.
Multispectral sensing with acoustic-optical tunable filters (AOTFs) offers several advantages for the automated recognition of targets and classification of terrain features. High spectral resolution, real-time selection of the wavelength, and dual polarization are among the chief advantages. AOTFs employed in an imaging sensor usually involve a shifting of the spatial image on the focal plane as the wavelength is sampled. This results in a misregistered data hypercube where selected spectral images are not aligned spatially. This can severely limit the sensor's application if not accounted for and rectified. An edged-based routine operating on data taken with the Real-Time Multispectral Sensor (RTMS), an imaging AOTF sensor produced by the Jet Propulsion Laboratory, will be described and demonstrated in this paper. The method is completely general and is capable of removing misregistration for any reason (e.g. platform jitter) not only for AOTF-induced misregistration. This paper will also provide examples of image classification within several scenes collected by RTMS during tower data collection. The basis for the classification is spectral and/or polarization characteristics of the targets and scenes.