Various different methods to perform multi-spectral image fusion have been suggested, mostly on the pixel level.
However, the jury is still out on the benefits of a fused image compared to its source images. We present here a new
multi-spectral image fusion method, multi-spectral segmentation fusion (MSSF), which uses a feature level processing
paradigm. To test our method, we compared human observer performance in an experiment using MSSF against two
established methods: Averaging and Principle Components Analysis (PCA), and against its two source bands, visible and
infrared. The task that we studied was: target detection in the cluttered environment. MSSF proved superior to the other
fusion methods. Based on these findings, current speculation about the circumstances in which multi-spectral image
fusion in general and specific fusion methods in particular would be superior to using the original image sources can be
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.
With the advance in multispectral imaging, the use of image fusion has emerged as a new and important research area.
Many studies have considered the advantages of specific fusion methods over the individual input bands in terms of
human performance, yet few comparison studies have been conducted to determine which fusion method is preferable to
another. This paper examines four different fusion methods, and compares human performance of observers viewing
fused images in a target detection task. In the presented 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. Results indicated that the false color and average methods showed
the best results.
Target acquisition tasks are often augmented by automated aids to advise human observers during the detection process. The question arises whether these automated aids, by nature imperfect, should be implemented under all conditions. This study examines the efficacy of an imperfect automated aid in human target acquisition performance under conditions of high mental workload. Human observers performed a target acquisition task aided by an automated aid (cuer) of varying accuracy concurrently with a military tracking task. Results indicate that the automated aid can improve performance over the unaided condition when it is highly reliable. Evidence of dependence on the system by the observers was found. The thesis here is that the condition of high mental workload induced greater acceptance of and reliance on the automated aid by the observers, which manifested in the highly significant cue dependence results. Another element found in the observers' interaction with the automated aid, which could be a consequence of overreliance, was a high perceived reliability of the cuer, which led observers to respond more confidently to cued targets. These results can help increase the understanding of human behavior vis-à-vis working with automated aids when under conditions of high mental workload.