• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.311-316
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• 2006

Despite geospatial information systems are widely used in many different fields as a powerful tool for spatial analysis and decision-making, their capabilities to handle realistic 3-D urban environment are very limited. The objective of this work is to integrate the recent developments in 3-D modeling and visualization into GIS to enhance its 3-D capabilities. To achieve a photorealistic view, building models are collected from a pair of aerial stereo images. Roof and wall textures are respectively obtained from ortho-rectified aerial image and ground photography. This study is implemented by using ArcGIS as the work platform and ArcObjects and Visual Basic as development tools. Presented in this paper are 3-D geometric modeling and its data structure, texture creation and its association with the geometric model. As the results, photorealistic views of Purdue University campus are created and rendered with ArcScene.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.4
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• pp.301-308
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• 2003

This study describes the methodology that improves the accuracy of the 3D object-space data extracted from IKONOS satellite images by improving the accuracy of a RPC(Rational Polynomial Coefficient) model. For this purpose, we developed the algorithm to adjust a RPC model, and could improve the accuracy of a RPC model with this algorithm and geographically well-distributed GCPs(Ground Control Points). Furthermore, when a RPC model was adjusted with this algorithm, the effects of geographic distribution and the number of GCPs on the accuracy of the adjusted RPC model was tested. The results showed that the accuracy of the adjusted RPC model is affected more by the distribution of GCPs than by the number of GCPs. On the basis of this result, the algorithm using pseudo_GCPs was developed to improve the accuracy of a RPC model in case the distribution of GCPs was poor and the number of GCPs was not enough to adjust the RPC model. So, even if poorly distributed GCPs were used, the geographically adjusted RPC model could be obtained by using pseudo_GCPs. The less the pseudo_GCPs were used -that is, GCPs were more weighted than pseudo_GCPs in the observation matrix-, the more accurate the adjusted RPC model could be obtained, Finally, to test the validity of these algorithms developed in this study, we extracted 3D object-space coordinates using RPC models adjusted with these algorithms and a stereo pair of IKONOS satellite images, and tested the accuracy of these. The results showed that 3D object-space coordinates extracted from the adjusted RPC models was more accurate than those extracted from original RPC models. This result proves the effectiveness of the algorithms developed in this study.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.121-130
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• 2011

This paper highlights the reconstruction of the rectilinear type of 3D rooftop model from satellite image data using centroid neural network. The main idea of the proposed 3D reconstruction method is based on the grouping of 3D line segments. 3D lines are extracted by 2D lines and DEM (Digital Elevation Map) data evaluated from a pair of stereo images. Our grouping process consists of two steps. We carry out the first grouping process to group fragmented or duplicated 3D lines into the principal 3D lines, which can be used to construct the rooftop model, and construct the groups of lines that are parallel each other in the second step. From the grouping result, 3D rooftop models are reconstructed by the final clustering process. High-resolution IKONOS images are utilized for the experiments. The experimental result's indicate that the reconstructed building models almost reflect the actual position and shape of buildings in a precise manner, and that the proposed approach can be efficiently applied to building reconstruction problem from high-resolution satellite images of an urban area.