Unsupervised Automatic Building Extraction Using Active Contour Model on Unregistered Optical Imagery and Airborne LiDAR Data
This addresses building extraction for photogrammetry and remote sensing applications, but it is incremental as it builds on existing snake models.
The paper tackles the challenging problem of automatic building extraction from urban scenes by proposing an unsupervised active contour model that uses optical imagery and unregistered airborne LiDAR data, achieving high overall accuracy without manual inputs.
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.