Statistical models for physically derived target sub-spaces
Abstract
Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmo-
spherically compensated imagery. Rather than compensate the imagery, a more recent approach uses physical
models to generate target sub-spaces. These radiance sub-spaces can then be used in an appropriate detection
scheme to identify potential targets.
The generation of these sub-spaces involves some a priori knowledge of data acquisition parameters, scene and
atmospheric conditions, and possible calibration errors. Variation is allowed in the model since some parameters
are di±cult to know accurately. Each vector in the subspace is the result of a MODTRAN simulation coupled
with a physical model. Generation of large target spaces can be computationally burdensome. This paper
explores the use of statistical methods to describe such target spaces. The statistically modeled spaces can then
be used to generate arbitrary radiance vectors to form a sub-space. Statistically modeled target sub-spaces,
using limited training samples, were found to accurately resemble MODTRAN derived radiance vectors.