Application of random set-based clustering to landmine detection with hyperspectral imagery

We apply a population-based classifier to Long Wave HyperSpectral Imagery (LWHSI) for the purposes of landmine detection. In LWHSI, there are many environmental factors that are correlated with groups of samples (pixels in an image) in sample populations (individual images). These factors greatly affect samples’ values making it difficult for standard classification models to perform well on a consistent basis. Population-based classifiers capture information correlated with sample populations. We perform classification experiments over a range of LWHSI imagery and compare results between the population-based classifier and standard kNN. After analysis, we show that the use of population-correlated information in LWHSI greatly improves classification results and consistency.