Traditionally, synthetic imagery has been constructed to simulate
images captured with low resolution, nadir-viewing sensors.
Advances in sensor design have driven a need to simulate scenes
not only at higher resolutions but also from oblique view angles.
The primary efforts of this research include: real image capture,
scene construction and modeling, and validation of the synthetic
imagery in the reflective portion of the spectrum. High resolution
imagery was collected of an area named MicroScene at the Rochester
Institute of Technology using the Chester F. Carlson Center for
Imaging Science's MISI and WASP sensors using an oblique view
angle. Three Humvees, the primary targets, were placed in the
scene under three different levels of concealment. Following the
collection, a synthetic replica of the scene was constructed and
then rendered with the Digital Imaging and Remote Sensing Image
Generation (DIRSIG) model configured to recreate the scene both
spatially and spectrally based on actual sensor characteristics.
Finally, a validation of the synthetic imagery against the real
images of MicroScene was accomplished using a combination of
qualitative analysis, Gaussian maximum likelihood classification,
and the RX algorithm. The model was updated following each
validation using a cyclical development approach. The purpose of
this research is to provide a level of confidence in the synthetic
imagery produced by DIRSIG so that it can be used to train and
develop algorithms for real world concealed target detection.