Robust long-wave infrared hyperspectral change detection

In this paper, 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 two strategies to mitigate this effect. In the first, pre-processing via traditional temperature-emissivity separation (TES) yields approximately temperature-invariant emissivity vectors for use in change detection. In the second, we adopt a minimax approach that minimizes the maximal spectral deviation between measurements. While more computationally demanding, the second approach eliminates spectral density assumptions in traditional TES and provides superior change detection performance. Examples on synthetic and measured data quantify computational complexity and detection performance.