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http://dx.doi.org/10.7848/ksgpc.2019.37.3.109

Object-Based Road Extraction from VHR Satellite Image Using Improved Ant Colony Optimization  

Kim, Han Sae (Dept. of Civil and Environmental Engineering, Seoul National University)
Choi, Kang Hyeok (Dept. of Civil and Environmental Engineering, Myongji University)
Kim, Yong Il (Dept. of Civil and Environmental Engineering, Seoul National University)
Kim, Duk-Jin (School of Earth and Environmental Sciences, Seoul National University)
Jeong, Jae Joon (Dept. of Geography. Sungshin Women's University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.37, no.3, 2019 , pp. 109-118 More about this Journal
Abstract
Road information is one of the most significant geospatial data for applications such as transportation, city planning, map generation, LBS (Location-Based Service), and GIS (Geographic Information System) database updates. Robust technologies to acquire and update accurate road information can contribute significantly to geospatial industries. In this study, we analyze the limitations of ACO (Ant Colony Optimization) road extraction, which is a recently introduced object-based road extraction method using high-resolution satellite images. Object-based ACO road extraction can efficiently extract road areas using both spectral and morphological information. This method, however, is highly dependent on object descriptor information and requires manual designations of descriptors. Moreover, reasonable iteration closing point needs to be specified. In this study, we perform improved ACO road extraction on VHR (Very High Resolution) optical satellite image by proposing an optimization stopping criteria and descriptors that complements the limitations of the existing method. The proposed method revealed 52.51% completeness, 6.12% correctness, and a 51.53% quality improvement over the existing algorithm.
Keywords
Object-Based; Road Extraction; Ant Colony Optimization; VHR Satellite Images;
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