Delineation and geometric modeling of road networks |
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| Authors: | Charalambos Poullis Suya You |
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| Institution: | 1. Department of Information Technology, Easwari Engineering College, Chennai, Tamil Nadu, India;2. Department of Information Science and Technology, Anna University, College of Engineering, Guindy, Chennai, Tamil Nadu, India;1. Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur 721302, India;2. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India;1. College of Electronic Science, National University of Defense Technology, Changsha, China;2. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China;1. FEI Visualization Sciences Group, 11 Greenway Plaza, Houston, TX 77046, USA;2. Old Dominion University, Department of Modeling, Simulation, and Visualization Engineering, 1113 Engineering and Computer Science Building, Norfolk, VA 23529, USA |
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| Abstract: | In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification.Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies.Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments.Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations. |
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