Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images
Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based ro...
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| Published in | ISPRS journal of photogrammetry and remote sensing Vol. 138; pp. 151 - 163 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.04.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0924-2716 1872-8235 |
| DOI | 10.1016/j.isprsjprs.2017.11.014 |
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| Abstract | Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. Based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects’ descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images. |
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| AbstractList | Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. Based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects’ descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images. |
| Author | Malihi, Shirin Hahn, Michael Maboudi, Mehdi Amini, Jalal |
| Author_xml | – sequence: 1 givenname: Mehdi orcidid: 0000-0003-3367-2404 surname: Maboudi fullname: Maboudi, Mehdi email: m.maboudi@tu-bs.de organization: Institute of Geodesy and Photogrammetry, Technical University of Braunschweig, Germany – sequence: 2 givenname: Jalal surname: Amini fullname: Amini, Jalal organization: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran – sequence: 3 givenname: Shirin surname: Malihi fullname: Malihi, Shirin organization: Faculty of Geodesy and Geomatics Engineering, Khaje Nasir Toosi University of Technology, Tehran, Iran – sequence: 4 givenname: Michael orcidid: 0000-0002-7809-1796 surname: Hahn fullname: Hahn, Michael organization: Faculty of Geomatics, Computer Science and Mathematics, University of Applied Sciences, Stuttgart, Germany |
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| Keywords | Ant colony optimization Road extraction Object based image analysis Fuzzy logic system |
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| SubjectTerms | algorithms ant colonies Ant colony optimization automation fuzzy logic Fuzzy logic system image analysis Object based image analysis remote sensing Road extraction satellites system optimization transportation uncertainty |
| Title | Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images |
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