Target temperature estimation from thermal infrared (TIR) imagery is a complex task that becomes increasingly
more difficult as the target size approaches the size of a projected pixel. At that point the assumption of pixel
homogeneity is invalid as the radiance value recorded at the sensor is the result of energy contributions from
the target material and any other background material that falls within a pixel boundary. More often than not,
thermal infrared pixels are heterogeneous and therefore subpixel temperature extraction becomes an important
capability. Typical subpixel estimation approaches make use of data from multispectral or hyperspectral sensors.
These technologies are expensive and data collected by a multispectral or hyperspectral thermal imagery might
not be readily available for a target of interest.
A methodology has been developed to retrieve the temperature of an object that is smaller than a projected
pixel of a single-band TIR image using physics-based modeling. The process can be broken into two distinct
pieces. In the first part, the Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool will be used
to replicate a collected TIR image based on parameter estimates from the collected image. This is done many
times to build a multi-dimensional lookup table (LUT). For the second part, a regression model is built from
the data in the LUT and is used to perform the temperature retrieval. The results presented are from synthetic
imagery.
KEYWORDS: Sensors, Digital imaging, Radiometry, Remote sensing, Hyperspectral imaging, Point spread functions, Systems modeling, Mathematical modeling, Chemical elements, Image resolution
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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