Automated spatiotemporal change detection in digital aerial imagery

Handling change within integrated geospatial environments is a challenge of dual nature. It comprises automatic change detection, and the fundamental issue of modeling/representing change. In this paper we present a novel approach for automated change detection which allows us to handle change more efficiently than commonly available approaches. More specifically, we focus on the detection of building boundary changes within a spatiotemporal GIS environment. We have developed a novel approach, as an extension of least-squares based matching. Previous spatial states of an object are compared to its current representation in a digital image, and decisions are automatically made as to whether or not change at the outline has occurred. Older object information is used to produce templates for comparison with the representation of the same object in a newer image. Semantic information extracted through an analysis of template edge geometry, and estimates of accuracy are used to enhance our method. This template matching approach allows us to integrate in a single operation object extraction from digital imagery with change detection. By decomposing a complete outline into smaller elements and applying template matching along these locations we are able to detect precisely even small changes in building outlines. In this paper we present an overview of our approach, theoretical models, certain implementation issues like template selection and weight coefficient assignment, and experimental results.