THE ANOMALY DETECTION EFFICIENCY OF KERNEL DENSITY ESTIMATION FUNCTIONS ON UAV IMAGES

Abstract

The anomalous pixel detection efficiency of algorithms on UAV images is represented by the two criteria: anomalous pixel detection effect (which uses the area under ROC curve for evaluation) and calculation time. A highly effective researcher-recommended technique for anomaly detection on UAV images is to apply Neyman-Pearson lemma by calculating Kernel Density Estimation (KDE) for background data and making decision therefrom. In this method, the selection of kernel function and bandwidth plays the determinant role in anomaly detection efficiency. However, there has not been any research that mentions this issue to date. Hereby, in this study, we evaluate anomaly detection efficiency on UAV images through a number of common kernel functions typically cited in researches on KDE, and follow up with making recommendations for appropriate uses of kernel functions. Experiments and evaluations are carried out on the sample data set photographed on varied terrain types and objects of interest.