Site change detection for RADIUS using thermophysical algebraic invariants

Nagaraj Nandhakumar; Johnathan D. Michel; D. Gregory Arnold; Vincent J. Velten; George A. Tsihrintzis

doi:10.1117/12.233057

19 February 1996 Site change detection for RADIUS using thermophysical algebraic invariants

Nagaraj Nandhakumar, Johnathan D. Michel, D. Gregory Arnold, Vincent J. Velten, George A. Tsihrintzis

Proceedings Volume 2645, 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation; (1996) https://doi.org/10.1117/12.233057
Event: 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation, 1995, Washington, DC, United States

Abstract

Research on the formulation of invariant features for model-based object recognition has mostly been concerned with geometric constructs either of the object or in the imaging process. We describe a new method that identifies invariant features computed from long wave infrared (LWIR) imagery. These features are called thermophysical invariants and depend primarily on the material composition of the object. Features are defined that are functions of only the thermophysical properties of the imaged materials. A physics-based model is derived from the principle of conservation of energy applied at the surface of the imaged regions. A linear form of the model is used to derive features that remain constant despite changes in scene parameters/driving conditions. Simulated and real imagery, as well as ground truth thermo-couple measurements were used to test the behavior of such features. A method of change detection in outdoor scenes is investigated. The invariants are used to detect when a hypothesized material no longer exists at a given location. For example, one can detect when a patch of clay/gravel has been replaced with concrete at a given site. This formulation yields promising results, but it can produce large values outside a normally small range. Therefore, we adopt a new feature classification algorithm based on the theories of symmetric alpha- stable (S(alpha) S) distributions. We show that symmetric, alpha-stable distributions model the thermophysical invariant data much better than the Gaussian model and suggest a classifier with superior performance.

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Nagaraj Nandhakumar, Johnathan D. Michel, D. Gregory Arnold, Vincent J. Velten, and George A. Tsihrintzis "Site change detection for RADIUS using thermophysical algebraic invariants", Proc. SPIE 2645, 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation, (19 February 1996); https://doi.org/10.1117/12.233057

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KEYWORDS

Long wavelength infrared

Wind energy

Data modeling

Object recognition

Solar energy

Aluminum

Thermography

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