Vehicle change detection from aerial imagery using detection response maps

Abstract

Image change detection has long been used to detect significant events in aerial imagery, such as the arrival or departure of vehicles. Usually only the underlying structural changes are of interest, particularly for movable objects, and the challenge is to differentiate the changes of intelligence value (change detections) from incidental appearance changes (false detections). However, existing methods for automated change detection continue to be challenged by nuisance variations in operating conditions such as sensor (camera exposure, camera viewpoints), targets (occlusions, type), and the environment (illumination, shadows, weather, seasons). To overcome these problems, we propose a novel vehicle change detection method based on the detection response maps (DRM). The detector serves as an advanced filter that normalizes the images being compared specifically for object level change detection (OLCD). In contrast to current methods that compare pixel intensities, the proposed DRM-OLCD method is more robust to nuisance changes and variations in image appearance. We demonstrate object-level change detection for vehicle appearing and disappearing in electro-optical (EO) visual imagery.