The UMass ASCENDER System

Robert T. Collins, Edward M. Riseman, Allen R. Hanson, Howard Schultz, Christopher O. Jaynes, Frank Stolle, Xiaoguang Wang, Yong-Qing Cheng

The UMass Ascender system has been designed to automatically populate a site model with buildings extracted from multiple, overlapping images taken from a variety of viewpoints and at different times of day. Automated construction and management of 3D geometric site models enables efficient exploitation of the tremendous volume of information collected daily by national sensors. Civilian benefits of this technology are also numerous, including automated cartography, land-use surveying and urban planning. Some sample building models automatically generated by the Ascender system are shown below.

Ascender's design philosophy incorporates several key ideas. First, 3D building reconstruction is based on geometric features that remain stable under a wide range of viewing and lighting conditions. Second, rigorous photogrammetric camera models are used to describe the relationship between pixels in an image and 3D locations in the scene, so that diverse sensor characteristics and viewpoints can be effectively exploited. Third, information is fused across multiple images for increased accuracy and reliability. Finally, known geometric constraints are applied whenever possible to increase the efficiency and reliability of the reconstruction process.

Ascender Version 1.0

In April 1995, Version 1.0 of the Ascender system was delivered to Lockheed-Martin for testing on classified imagery and for integration into the RADIUS Testbed System. At the same time, an informal transfer was made to the National Exploitation Laboratory (NEL) for familiarization and additional testing. Version 1.0 extracts flat-roofed rectilinear building models by applying the following algorithmic steps:

Current Work on Ascender

UMass is currently developing more general building extraction strategies that combine a wider range of 2D and 3D information to reliably extract many common building types, including multi-level flat roofs, peaked-roof buildings, curved-roof Quonset huts or hangars, and buildings with complex roof structures containing gables, slanted dormers or spires.

One building detection extension that has proven very effective is to directly fuse 2D rooftop polygon hypotheses with high-resolution Digital Elevation Map (DEM) data in order to estimate various classes of parametrically modeled 3D rooftop surfaces. The DEM data can be produced from a pair of overlapping images by area-based correlation matching along epipolar lines, or can be gathered from IFSAR sensors. Pixels within each detected roof polygon are backprojected onto the DEM data to determine a set of sampled 3D points, to which a parametric surface is fit using robust statistical estimation techniques. A sample DEM before and after refinement via parametric surface fitting is shown below.


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