Site change detection and object recognition using thermophysical affine invariants from infrared imagery

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 imagery. These features are called thermophysical invariants and depend primarily on the material composition of the object. We use this approach for identifying objects or changes in scenes viewed by downward looking infrared images. 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.