Experimental Approach to Camouflaged Target Detection and Camouflage Evaluation

This work discusses three individual camouflage experiments from a drone-based hyperspectral measurement campaign conducted in 2021. The experiments were designed to provide insight into different scenarios of camouflage classification and detection of camouflaged objects. The first experiment demonstrates an approach to detect different objects under camouflage using spectral unmixing. The second experiment presents the performance of commonly used hyperspectral classifiers for camouflage detection with respect to natural illumination changes throughout the day. Finally, the third experiment evaluates the effect of moisture on camouflage detection. For all experiments, we discuss the conditions under which hyperspectral data together with established detection and classification approaches can be used to robustly locate camouflage nets, and when detection is impaired.