Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Object-Based Road Extraction from VHR Satellite Image Using Improved Ant Colony Optimization

개선된 개미 군집 최적화를 이용한 고해상도 위성영상에서의 객체 기반 도로 추출

  • 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)
  • Received : 2019.05.09
  • Accepted : 2019.05.24
  • Published : 2019.06.30
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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.

도로 정보는 교통, 도시 계획, 지도 갱신, 위치기반서비스 그리고 GIS (Geographic Information System) 데이터 구축 등에 활용되는 중요한 기초 공간정보 자료이다. 따라서 정확한 도로 정보를 획득하고 이를 갱신하는 것은 다양한 공간정보 산업에 중요한 역할을 수행할 수 있다. 본 연구에서는 고해상도 위성영상에서 객체 기반의 도로 추출 기법으로 최근 소개된 개미 군집 최적화(ACO: Ant Colony Optimization)의 한계점을 분석하고 이를 개선하고자 하였다. 객체 기반의 ACO 도로 추출은 도로의 분광 및 형상 정보를 모두 활용하여 효과적으로 도로 추출을 수행할 수 있으나 객체 서술자 정보에 의존적이며 서술자 계산 시 사용자의 개입이 필요하다. 또한, 최적화 반복 종료 시점의 설정이 모호하다는 단점이 존재한다. 따라서 본 연구에서는 이를 개선하기 위해 기존 서술자의 한계를 보완하는 서술자와 최적화 반복 종료기준을 제안하였다. 제안된 방법은 기존의 알고리즘보다 52.51%의 완성도(completeness), 6.12%의 정확도(correctness), 51.53%의 품질(quality) 향상을 나타내었다.

Keywords

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Fig. 1. Flow chart of road extraction process

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Fig. 2. Computation of direction vector between two road objects (a) Direction vectors for each objects (b) Direction vector between two road objects

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Fig. 3. Ant colony optimization flow chart (a) Maboudi et al. (2017) (b) Proposed

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Fig. 4. Road candidates designation (a) Heuristic information map (b) Road candidates

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Fig. 5. Study area (a) VHR image (b) Ground truth

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Fig. 6. Road extraction results (a),(d) Random color-coded segmentation results (b),(e) ACO road extraction results (c),(f) Final road centerlines superimposed on the satellite images

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Fig. 7. Road extraction failure area

Table 1. Road characteristics

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Table 2. Object descriptors and corresponding road characteristics

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Table 3. Qualitative assessment of road extraction result

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References

  1. Baltsavias, E.P. (2004), Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 58, No. 3-4, pp. 129-151. https://doi.org/10.1016/j.isprsjprs.2003.09.002
  2. Bentabet, L., Jodouin, S., Ziou, D., and Vaillancourt, J. (2003), Road vectors update using SAR imagery: a snake-based method, IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 8, pp. 1785-1803. https://doi.org/10.1109/TGRS.2003.813850
  3. Blaschke, T. (2010), Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, No. 1, pp. 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
  4. Cheng, G., Wang, Y., Xu, S., Wang, H., Xiang, S., and Pan, C. (2017), Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network, IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 6, pp. 3322-3337. https://doi.org/10.1109/TGRS.2017.2669341
  5. Dal Poz, A.P., Gallis, R.A., da Silva, J.F., and Martins, E.F. (2012), Object-space road extraction in rural areas using stereoscopic aerial images, IEEE Geoscience and Remote Sensing Letters, Vol. 9, No. 4, pp. 654-658. https://doi.org/10.1109/LGRS.2011.2177438
  6. Das, S., Mirnalinee, T.T., and Varghese, K. (2011), Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 10, pp. 3906-3931. https://doi.org/10.1109/TGRS.2011.2136381
  7. Fauvel, M., Benediktsson, J. A., Chanussot, J., and Sveinsson, J.R. (2008), Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles, IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 11, pp. 3804-3814. https://doi.org/10.1109/TGRS.2008.922034
  8. Grote, A., Heipke, C., and Rottensteiner, F. (2012), Road network extraction in suburban areas, The Photogrammetric Record, Vol. 27, No. 137, pp. 8-28. https://doi.org/10.1111/j.1477-9730.2011.00670.x
  9. Hu, X. and Tao, C.V. (2005), A reliable and fast ribbon road detector using profile analysis and model-based verification, International Journal of Remote Sensing, Vol. 26, No. 5, pp. 887-902. https://doi.org/10.1080/0143116042000298243
  10. Huang, X., Zhang, L., and Li, P. (2007), Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery, IEEE Geoscience and Remote Sensing Letters, Vol. 4, No. 2, pp. 260-264. https://doi.org/10.1109/LGRS.2006.890540
  11. Huang, X. and Zhang, L. (2009), Road centreline extraction from high-resolution imagery based on multiscale structural features and support vector machines. International Journal of Remote Sensing, Vol. 30, No. 8, pp. 1977-1987. https://doi.org/10.1080/01431160802546837
  12. Kirthika, A. and Mookambiga, A. (2011), Automated road network extraction using artificial neural network, 2011 International Conference on Recent Trends in Information Technology (ICRTIT), IEEE, 3-5 June 2011, Chennai, Tamil Nadu, India, pp. 1061-1065.
  13. Katartzis, A., Sahli, H., Pizurica, V., and Cornelis, J. (2001), A model-based approach to the automatic extraction of linear features from airborne images, IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 9, pp. 2073-2079. https://doi.org/10.1109/36.951102
  14. Klang, D. (1998), Automatic detection of changes in road data bases using satellite imagery, International Archives of Photogrammetry and Remote Sensing, Vol. 32, No. 4, pp. 293-298.
  15. Li, M., Stein, A., Bijker, W., and Zhan, Q. (2016), Region-based urban road extraction from VHR satellite images using binary partition tree, International Journal of Applied Earth Observation and Geoinformation, Vol. 44, pp. 217-225. https://doi.org/10.1016/j.jag.2015.09.005
  16. Maboudi, M., Amini, J., Hahn, M., and Saati, M. (2017), Object-based road extraction from satellite images using ant colony optimization, International Journal of Remote Sensing, Vol. 38, No. 1, pp. 179-198. https://doi.org/10.1080/01431161.2016.1264026
  17. Maboudi, M., Amini, J., Malihi, S., and Hahn, M. (2018), Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 138, pp. 151-163. https://doi.org/10.1016/j.isprsjprs.2017.11.014
  18. Mayer, H., Hinz, S., Bacher, U., and Baltsavias, E. (2006), A test of automatic road extraction approaches, International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences, Vol. 36, No. 3, pp. 209-214.
  19. Mayer, H. and Steger, C. (1998), Scale-space events and their link to abstraction for road extraction, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 53, No. 2, pp. 62-75. https://doi.org/10.1016/S0924-2716(97)00040-3
  20. McKeown, D.M. and Denlinger, J.L. (1988), Cooperative methods for road tracking in aerial imagery, CVPR'88: The Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 5-9 June 1988, Ann Arbor, MI, USA, pp. 662-672.
  21. Miao, Z., Wang, B., Shi, W., and Zhang, H. (2014), A semi-automatic method for road centerline extraction from VHR images, IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 11, pp. 1856-1860. https://doi.org/10.1109/LGRS.2014.2312000
  22. Miao, Z., Shi, W., Gamba, P., and Li, Z. (2015), An object-based method for road network extraction in VHR satellite images, IEEE journal of selected topics in applied earth observations and remote sensing, Vol. 8, No. 10, pp. 4853-4862. https://doi.org/10.1109/JSTARS.2015.2443552
  23. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., and Weng, Q. (2011), Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sensing of Environment, Vol. 115, No. 5, pp. 1145-1161. https://doi.org/10.1016/j.rse.2010.12.017
  24. Tupin, F., Houshmand, B., and Datcu, M. (2002), Road detection in dense urban areas using SAR imagery and the usefulness of multiple views, IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 11, pp. 2405-2414. https://doi.org/10.1109/TGRS.2002.803732
  25. Wang, F. and Newkirk, R. (1988), A knowledge-based system for highway network extraction. IEEE Transactions on Geoscience and Remote sensing, Vol. 26, No. 5, pp. 525-531. https://doi.org/10.1109/36.7677
  26. Yager, N. and Sowmya, A. (2003), Support vector machines for road extraction from remotely sensed images, In International Conference on Computer Analysis of Images and Patterns, 25-27 August 2003, Springer, Berlin, Germany, Vol. 2756, pp. 285-292.
  27. Zhou, J., Bischof, W. F., and Caelli, T. (2006), Road tracking in aerial images based on human-computer interaction and Bayesian filtering, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 61, No. 2, pp. 108-124. https://doi.org/10.1016/j.isprsjprs.2006.09.002
  28. Zlotnick, A. and Carnine, P.D. (1993), Finding road seeds in aerial images, CVGIP: image understanding, Vol. 57, No. 2, pp. 243-260. https://doi.org/10.1006/ciun.1993.1016