Comparing multispectral image fusion methods for a target detection task

With the advance in multispectral imaging, image fusion has emerged as a new and important research area. Many studies have examined human performance with specific fusion methods, over the individual input bands; yet few comparison studies have been conducted to examine which fusion method is preferable over another. This paper presents four different fusion methods, Average, false color (FC), principal component analysis (PCA), or edge enhancement (EE), for multispectral imaging and their impact on human observers' performance. In our experiment, images with multiple targets were presented to 56 participants performing a target detection task. Quantitative measurements of participants' hit accuracy and reaction time were measured. Results yielded an overall superior performance in target detection with the false color and principal components analysis compared with the Average and edge enhancement fusion methods.