Bingcai Zhang BAE Systems San Diego, CA 92127 Bingcai.zhang@BAESystems.com
Introduction It is a trivial task for a five-year-old child to recognize and name an object such as a car, house or building. However, it is a challenging software problem to identify and label these same objects automatically in a digital image. Geospatial information technology such as digital photogrammetry can answer the where question accurately. The next breakthrough may be the what question, which is to identify and label objects automatically in digital imagery. Automatic 3-D building extraction from digital imagery is considered the Holy Grail in photogrammetry. It is very difficult to automatically extract buildings from images using only their radiometric properties. 2
Introduction continued In the past three decades, many algorithms have been developed to extract 3-D buildings from a very specific set of digital images Until now, there has not been a commercial software package that can reliably do this We show the results of automatically identifying objects such as houses and buildings, including the relationships between DSM accuracy and post spacing, and size requirements for automatically extracting and labeling objects 3
Radiometry vs. 3-D shapes Six different building colors and patterns Very difficult to extract buildings based on radiometric properties only Terrain shaded relief of digital surface model generated by NGATE The locations and approximate shapes of the buildings are obvious 4
Next-Generation Photogrammetry Automation System The two most important technologies in modern photogrammetry are sensor modeling and automation. Sensor modeling provides accurate measurements and automation for terrain generation, while 3-D building extraction increases productivity. 5
Technical approach Automatic transformation from LIDAR point cloud to bare-earth model Bare-Earth Profile Bare-Earth Morphology Bare-Earth Histogram Bare-Earth Dense Tree Canopy Identify and group 3-D object points into regions Separate buildings and houses from trees Trace region boundaries Regularize and simplify boundary polygons Construct complex roofs 6
Sample results: Case study one LIDAR data with a post spacing of 0.2 meters was converted to a GRID format with a post spacing of 0.1 meters The following building parameters were used: 1. Minimum height 2 meters 2. Minimum width 5 meters 3. Maximum width 200 meters 4. Roof detail 0.4 meters 5. Enforce building squaring on AFE transforms a GRID DSM into a GRID DEM using parameters 1, 2 and 3 as the first step 59 buildings and 13 trees extracted 7
Sample results: Case study one continued 8
Sample results: Case study one continued A complex building, with more than 50 sides, automatically extracted by AFE from LIDAR 9
Sample results: Case study one continued To determine the accuracy of automatically extracted building boundaries, we compare segment deltas of the same building boundary extracted by a human operator using photogrammetric stereo images. The stereo images have a pixel resolution of GSD 0.07 meters. Therefore, we can consider the manually extracted building boundary as ground truth for our accuracy analysis. The RMSE is about 0.2 meters or one post spacing. 3-D slant 2-D XY Elevation length delta length delta delta Max 0.910 0.830 0.900 Mean 0.276 0.215 0.080 RMSE 0.394 0.236 0.222 10
Sample Results: Case study two LIDAR data provided by USC s Integrated Media Systems Center (IMSC) with average post spacing of 0.4 meters 138 million posts covering a relatively flat area with many trees The following building parameters were used: 1. Minimum height 2 meters 2. Minimum width 3 meters 3. Maximum width 300 meters 4. Roof detail of 0.4 meters 5. Enforce building squaring on 2464 buildings and houses extracted 5164 trees extracted. 1.2 hours to complete (four 3 GHZ CPUs with debugging code) 11
Sample Results: Case study two continued Terrain shaded relief of 24.8 square kilometers 12
Sample Results: Case study two continued 13
Sample results: Case study two continued 14
Sample Results: Case study two continued 15
Sample results: Case study two continued 16
3-D flythrough 17
Potential applications Data fusion between LIDAR and other types of data such as EO images Registering existing 3-D site models from EO images to LIDAR 3-D site models When LIDAR is much more accurate, this improves the EO sensor parameters Robotics and UAV/UGV applications Make it real-time to navigate a robot Fighting vehicle (navigate and recognize target) 18
Conclusions Automatically identifying, extracting, and labeling 3-D buildings from dense digital surface models by using their invariant 3-D properties can be achieved to a production level capability. Unlike automatic terrain generation systems, which are very mature and have been used widely for two decades, the commercial production level automatic 3-D building extraction system is in its infancy. Our research and development indicates that we can automatically label 3- D buildings by applying advanced stereo image matching technology and LIDAR, automatic bare-earth transformation from DSM, and 3-D buildings invariant 3-D properties. For precise GIS cartographic mapping, manual digitizing should still be used. For fast and affordable 3-D modeling, simulation, and visualization, where accuracy is not as important as affordability and speed, automation technology, such as AFE, may soon gain user acceptance. 19
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