Comprehensive three dimensional change detection using volumetric appearance modeling

The problem of detecting changes in images taken by a stationary camera has been well studied and many algorithms now exist which perform robustly in real-world applications. However no comprehensive solution exists for the analogous “3-d” change detection problem, where the camera taking the images is allowed to move and the scene is not planar. This situation is common in a growing number of applications, especially in aerial surveillance where the recent explosion of available aerial imagery has made manual examination by analysts infeasible. This thesis presents the first comprehensive solution to the 3-d change detection problem, using a new framework called volumetric appearance modeling (VAM). This approach can manage the complications of unknown and sometimes changing world surfaces by maintaining a 3-d voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. These distributions are continuously updated as new images are received using an adaptive learning procedure. VAM is demonstrated to perform well on several aerial image sequences taken from helicopter from a wide range of viewpoints. The core VAM framework is extended to additionally deal with the variable lighting and haze conditions present in satellite imagery. The success of this complete framework is demonstrated on selected scenes of Baghdad, Iraq exhibiting a challenging range of viewpoints, lighting directions, and haze.