Interest in aerial image analysis has increased owing to recent developments in and availability of aerial imaging technologies, like UAVs (Unmanned aerial vehicles), as well as a growing need for autonomous surveillance systems.
Variant illumination, intensity noise, and different viewpoints are among the main challenges to overcome in order to determine changes in aerial images. In this paper, it is presented a robust method for change detection in aerial images.
To accomplish this, the method extracts three-dimensional (3D) features for segmentation of objects above a defined reference surface at each instant. The acquired 3D feature maps, with two measurements, are then used to determine changes in a scene over time.
In addition, the important parameters that affect measurement, such as the camera’s sampling rate, image resolution, the height of the drone, and the pixel’s height information, are investigated through a mathematical model. To exhibit its applicability, the proposed method has been evaluated on aerial images of various real-world locations and the results are promising.
The performance indicates the robustness of the method in addressing the problems of conventional change detection methods, such as intensity differences and shadows.
