Impact of Multispectral Image Fusion Methods on Visual Search Behavior

Image fusion has gained importance in various target-based tasks with the advance of multispectral imaging. In this study, we examine three different fusion methods by comparing human observers' target detection performance with the resultant fused images. In the experiment reported here, participants' eye movements were recorded as they 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 attracted observer attention; a result which we contend shows superiority of those fusion methods over simple averaging.