  Automatic extraction of buildings in urban scenes has become a subject of
growing interest in the domain of photogrammetry and remote sensing,
particularly with the emergence of LiDAR systems since mid-1990s. However, in
reality, this task is still very challenging due to the complexity of building
size and shapes, as well as its surrounding environment. Active contour model,
colloquially called snake model, which has been extensively used in many
applications in computer vision and image processing, is also applied to
extract buildings from aerial/satellite imagery. Motivated by the limitations
of existing snake models addressing to the building extraction, this paper
presents an unsupervised and fully automatic snake model to extract buildings
using optical imagery and an unregistered airborne LiDAR dataset, without
manual initial points or training data. The proposed method is shown to be
capable of extracting buildings with varying color from complex environments,
and yielding high overall accuracy.
