Numerous methods exist to perform hyperspectral target detection. Application of these algorithms often re-quires the data to be atmospherically corrected. Detection for longwave infrared data typically requires surfacetemperature estimates as well. This work compares the relative robustness of various target detection algorithmswith respect to atmospheric compensation and target temperature uncertainty. Specifically, the adaptive co-herence estimator and spectral matched filter will be compared with subspace detectors for various methods ofatmospheric compensation and temperature-emissivity separation. Comparison is performed using both daytimeand nighttime longwave infrared hyperspectral data collected at various altitudes for various target materials.
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