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Temperature Robust Longwave Infrared Hyperspectral Change Detection, Nicholas A. Durkee

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In this thesis, we develop and evaluate change detection algorithms for longwave infrared (LWIR) hyperspectral imagery. Because measured radiance in the LWIR domain depends on unknown surface temperature, care must be taken to prevent false alarms resulting from in-scene temperature differences that appear as material changes. We consider fewer variables. Examples using synthetic and measured data quantify the computational complexity of the proposed methods and demonstrate orders of magnitude reduction in false alarm rates relative to existing methods. Four strategies to mitigate this effect. In the first, pre-processing via traditional temperatureemissivity separation yields approximately temperature-invariant emissivity vectors for use in …