Change Detection in Overhead Imagery Using Neural Networks
Applied Intelligence
Integrating Geometric and Photometric Information for Image Retrieval
Shape, Contour and Grouping in Computer Vision
Robust estimation of 3-D line segments from satellite images for model building and change detection
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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Many applications require detecting structural changes in a scene over a period of time. Comparing intensity values of successive images is not effective as such changes don't necessarily reflect actual changes at a site but might be caused by changes in the view point, illumination and seasons. We take the approach of comparing a 3-D model of the site, prepared from previous images, with new images to infer significant changes. This task is difficult as the images and the models have very different levels of abstract representations. Our approach consists of several steps: registering a site model to a new image, model validation to confirm the presence of model objects in the image; structural change detection seeks to resolve matching problems and indicate possibly changed structures; and finally updating models to reflect the changes. Our system is able to detect missing (or mis-modeled) buildings, changes in model dimensions, and new buildings under some conditions.