Sub-pixel radiometry: a three-part study in generating synthetic imagery that incorporates sub-pixel variation

Sarah Paul; Adam A. Goodenough; Scott D. Brown; Carl Salvaggio

doi:10.1117/12.850180

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12 May 2010 Sub-pixel radiometry: a three-part study in generating synthetic imagery that incorporates sub-pixel variation

Sarah Paul, Adam A. Goodenough, Scott D. Brown, Carl Salvaggio

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Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76950N (2010) https://doi.org/10.1117/12.850180
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

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 algorithms.

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Sarah Paul, Adam A. Goodenough, Scott D. Brown, and Carl Salvaggio "Sub-pixel radiometry: a three-part study in generating synthetic imagery that incorporates sub-pixel variation", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76950N (12 May 2010); https://doi.org/10.1117/12.850180

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