Presentation + Paper
7 June 2024 Recognizing surface and subsurface objects based on dynamic responses in infrared imagery
Author Affiliations +
Abstract
A physics-based approach to detecting and classifying surface and sub-surface objects in longwave (thermal) infrared imagery is described. The main premise is to associate a heat capacity and effective depth with each voxel (or segment) in the image. An energy budget for the voxel then leads to a linear, first-order differential equation, in which the temperature is forced by fluxes in and out of the voxel (shortwave solar radiation, longwave radiation, sensible and latent turbulent heat exchanges with the atmosphere), while relaxing towards an equilibrium temperature determined by a weighted mean of the air and ground temperatures. Next, it is shown how this simplified model can be incorporated into maximum-likelihood and Bayesian classifiers to distinguish buried objects from their surroundings. In particular, a version of the Bayesian classifier is formulated that leverages the differing amplitude and phase response of a buried object over the diurnal cycle. These classifiers will be tested on experimental data in future work.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
D. Keith Wilson, Sora C. Haley, Max E. Krackow, Sophia P. Bragdon, Vuong H. Truong, Jay L. Clausen, and Megan I. Bishop "Recognizing surface and subsurface objects based on dynamic responses in infrared imagery", Proc. SPIE 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII, 130570T (7 June 2024); https://doi.org/10.1117/12.3013859
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KEYWORDS
Covariance matrices

Air temperature

Infrared radiation

Heat flux

Covariance

Infrared imaging

Control systems

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