Name
Abstract | ||
|---|---|---|
We present a model-based approach to the automatic detection and reconstruction of buildings from aerial imagery. Buildings are first segmented from the scene in an optical image followed by a reconstruction process that makes use of a corresponding digital elevation map (DEM). Initially, each segmented DEM region likely to contain a building rooftop is indexed into a database of parameterized surface models that represent different building shape classes such as peaked, flat, or curved roofs. Given a set of indexed models, each is fit to the elevation data using a robust iterative procedure that determines the precise position and shape of the building rooftop. The indexed model that converges to the data with the lowest residual fit error is then added to the scene by extruding the fit rooftop surfaces to a local ground plane.The approach is based on the observation that a significant amount of rooftop variation can be modeled as the union of a small set of parameterized models and their combinations. By first recognizing the rooftop as one of the several potential rooftop shapes and fitting only these surfaces, the technique remains robust while still capable of reconstructing a wide variety of building types. In contrast to earlier approaches that presuppose a particular class of rooftops to be reconstructed (e.g., flat roofs), the algorithm is capable of reconstructing a variety of building types including peaked, flat, multi-level flat, and curved surfaces. The approach is evaluated on two datasets. Recognition rates for the different building rooftop classes and reconstruction accuracy are reported. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S1077-3142(03)00027-4 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
potential rooftop shape,fit rooftop surface,model-based approach,rooftop variation,lowest residual fit error,earlier approach,different building shape class,multiple aerial image,flat roof,different building rooftop class,building rooftop,indexation,optical imaging | Computer vision,Residual,Optical image,Parameterized complexity,Digital elevation map,Artificial intelligence,Elevation,Small set,Aerial imagery,Mathematics | Journal |
Volume | Issue | ISSN |
90 | 1 | Computer Vision and Image Understanding |
Citations | PageRank | References |
21 | 1.03 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Jaynes | 1 | 245 | 20.92 |
| E. M. Riseman | 2 | 1402 | 458.95 |
| Allen Hanson | 3 | 211 | 33.75 |