Comparing multispectral image fusion methods for a target detection task

Abstract

Image fusion has gained importance with the advances in multispectral imaging. We examine four different fusion methods by comparing human observers' target detection performance with the resultant fused images. Three experiments with 89 participants were conducted. In the first experiment, images with multiple targets were presented to the participants. Quantitative measurements of participants' hit accuracy and reaction time were measured. In the second experiment, we implemented an approach that has not been generally used in the context of image fusion evaluation: we used the paired-comparison technique to qualitatively assess and scale the subjective value of the fusion methods. In the third experiment, participants' eye movements were recorded as the participants searched for targets. We introduce a novel method to compensate for eye-tracker precision limitations and to enable analysis of eye movement data of different image samples even for detection tasks with small targets. Results indicated that the false color and principal components fusion methods showed the best results over all experiments.