Traffic carrying flammable, corrosive, poisonous, and radioactive materials continues to increase in proportion with the growth in their production and consumption. The sustained risk of accidental releases of such hazardous materials poses serious threats to public safety. The early detection of spills will potentially save lives, protect the environment, and thwart the need for expensive clean up campaigns. Ground patrols and terrestrial sensing equipment cannot scale cost-effectively to cover the entire transportation network. Remote sensing with existing airborne and spaceborne platforms has the capacity to monitor vast areas regularly but often lack the spatial resolution necessary for high accuracy detections. The emergence of unmanned aircraft systems with lightweight hyperspectral image sensors enables a resolution agile approach that can adapt both spatial and spectral resolutions in real-time. Equipment operators can exploit such a capability to enhance the resolution of potential target materials detected within a larger fieldof- view to verify their identification or to perform further inspections. However, the complexity of algorithms available to classify hyperspectral scenes limits the potential for real-time target detection to support rapid decision-making. This research introduces and benchmarks the performance of a low-complexity method of hyperspectral image classification. The hybrid supervised-unsupervised technique approaches the performance of prevailing methods that are at least 30-fold more computationally complex.