A pixel represents the limit of spatial knowledge that can be represented in an image. It is represented as a
single (perhaps spectral) digital count value that represents the energy propagating from a spatial portion of a
scene. In any captured image, that single value is the result of many factors including the composition of scene
optical properties within the projected pixel, the characteristic point spread function (or, equivalently, modulation
transfer function) of the system, and the sensitivity of the detector element itself. This presentation examines the
importance of sub-pixel variability in the context of generating synthetic imagery for remote sensing applications.
The study was performed using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool, an
established ray-tracing based synthetic modeling system whose approach to sub-pixel computations was updated
during this study.
The paper examines three aspects of sub-pixel variability of interest to the remote sensing community. The first
study simply looks at sampling frequency relative to structural frequency in a scene and the effects of aliasing
on an image. The second considers the task of modeling a sub-pixel target whose signature would be mixed
with background clutter, such as a small, hot target in a thermal image. The final study looks at capturing
the inherent spectral variation in a single class of material, such as grass in hyperspectral imagery. Through
each study we demonstrate in a quantitative fashion, the improved capabilities of DIRSIG's sub-pixel rendering