Sub-Pixel Radiometry: A three-part study in generating synthetic imagery that incorporates sub-pixel variation

摘要

A pixel represents the limit of spatial knowledge that can be represented in an image. It is represented as asingle (perhaps spectral) digital count value that represents the energy propagating from a spatial portion of ascene. In any captured image, that single value is the result of many factors including the composition of sceneoptical properties within the projected pixel, the characteristic point spread function (or, equivalently, modulationtransfer function) of the system, and the sensitivity of the detector element itself. This presentation examines theimportance 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, anestablished ray-tracing based synthetic modeling system whose approach to sub-pixel computations was updatedduring this study.The paper examines three aspects of sub-pixel variability of interest to the remote sensing community. The firststudy simply looks at sampling frequency relative to structural frequency in a scene and the effects of aliasingon an image. The second considers the task of modeling a sub-pixel target whose signature would be mixedwith background clutter, such as a small, hot target in a thermal image. The final study looks at capturingthe inherent spectral variation in a single class of material, such as grass in hyperspectral imagery. Througheach study we demonstrate in a quantitative fashion, the improved capabilities of DIRSIG's sub-pixel renderingalgorithms.