Information mining from human visual reasoning about multi-temporal, high-resolution satellite imagery
Matthew N. Klaric, Blake John Anderson, Chi‐Ren Shyu · International Journal of Image and Data Fusion · 2012
Over time experts in image analysis learn to use visual cues to extract large amount of information from an image in a short period of time in comparison to novice viewers. This tacit knowledge of image search strategies develops through many years of experience. Efficient interpretation appears to be a gut instinct of analysts, and this ability is difficult to verbalise or teach to the next generation of analysts. To bridge the gap between experts and novices, we propose a method to attempt to uncover visual strategies in geospatial imagery through eye tracking. Going beyond typical feature-based and classification approaches, our research fuses measures of visual attention with image-based features to derive rules through associative mining. We study how the cognitive processing of an image relates to image content. This research may open the door to a better understanding of human reasoning about the analysis of multi-temporal VHR satellite imagery.