How do human detect targets of remote sensing images with visual attention?

Human visual attention is the basis of target recognition, change detection and classification in remote sensing images. However, the human visual attention of remote sensing images during target detection remains uninvestigated. In this study, we simultaneously collected eye tracking and electroencephalography (EEG) data of 40 experts during target detection in 1000 remote sensing images. We quantified their attention in different phases of target detection (i.e., target search, selection, and identification) from their eye movements and brain activities. The eye-tracking results indicated that hue and lightness were crucial visual features that guided visual search for remote sensing images. The size of the target also affected the allocation of human attention resources. Particularly, participants tended to miss the targets that were smaller than 3.9 % of the whole image area. The fixation event-related potentials (FRPs) in temporal and parietal brain regions further distinguished the attention of targets and distractors, which could be attributed to the process of memory and processing. Our findings offer new visual and neural evidence for human attention in target detection of remote sensing images, which not only contribute to the performance of attention-based algorithms in remote sensing interpretation, but also provide an empirical basis for guiding human-machine intelligent fusion.