Anterior|Siguiente

Road extraction method for high resolution optical remote sensing images with multiple feature constraints

  • role： First author第一作者
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

    Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China

  • Email：daijg03@163.com
  • Introduction：戴激光，1978年生，男，副教授，研究方向为遥感理论和应用。E-mail：daijg03@163.com
Jiguang DAI12,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Yang DU1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Xinxin FANG1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Yang WANG1,  
  • Affiliation：

    School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Zhipeng MIAO1

resumen

Based on the characteristics of road images, a road extraction method for high-resolution optical remote sensing images with multiple feature constraints is proposed in this paper. First, the road image features are analyzed. Second, the feasibility of extracting road images with different feature constraints is determined to emphasize the necessity of building a road feature detection model. This study provides specific methods and steps. First, an improved line segment secondary extraction model is presented based on road geometric characteristics. The model is composed of the aggregation constraint and line segment dynamic fitting. Meanwhile, a candidate road seed set is constructed based on the geometric relationship between the extracted maximum length of 100 lines and known road width. Second, road structure information is inputted. The road radiation characteristics of the candidate road seed set are determined through an entire evaluation, which is based on improved template matching in road value formula, and the overall matching step concrete is identified. Third, when the candidate road seed set cannot meet the requirements of radiation characteristics, road context features are used to evaluate the seeds. Therefore, a light vehicle detection model that includes vehicle image geometric parameter analysis, morphological processing, edge detection, vehicle closed radiation information analysis, and other steps is established. The detection result of light vehicles will be used as the one of the road’s context features. Fourth, the candidate seed points are verified according to the road topological feature. In this process, the road topological analysis model is constructed. The model uses the matching–tracking model and the contextual features to verify the road inspection points. The study proposes an improvement in the matching–tracking model and matching measure. Fifth, in road post-processing, the seed points of the extraction road are optimized, the false road seed points and the low precision road seed points are eliminated, and the road line fitting optimization method is proposed. This work starts by experimenting the secondary extraction method of the high-resolution remote sensing line segment. Results show that the secondary extraction line segment is relatively complete and that defining the geometric features of the road is easy. Then, an experiment of light vehicle extraction is performed by using the remotely sensed image to verify the feasibility of the proposed method. Furthermore, the results can be used to reflect motor vehicle road context characteristics. Different experiments are performed in different scenes and resolutions of high-resolution remote sensing images. In comparison with the mature software ERDAS and ECognition, the experimental results show that this method is relatively automated, efficient, and considerably suitable in performing road extraction in complex scenes with diversified road types and geometrical spectral noise.

palabra clave

information extraction;road seed point;multiple feature;constraint;road extraction;high resolution;optical

References

  1. 1.
    Akinlar C and Topal C. 2011. EDLines: a real-time line segment detector with a false detection control. Pattern Recognition Letters, 32(13): 1633–1642
  2. 2.
    Alshehhi R and Marpu P R. 2017. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 245–260
  3. 3.
    Cao F Z, Xu Y B, Zhu B S and Li R S. 2015. Semi-automatic road centerline extraction from high-resolution remote sensing by image utilizing dynamic programming. Journal of Geomatics Science and Technology, 32(6): 615–618, 625 (
  4. 4.
    Cao F Z, Zhu S L, Zhu B S, Li R S and Meng W C. 2016. Tracking road centerlines from remotely sensed imagery using mean shift and Kalman filtering. Acta Geodaetica et Cartographica Sinica, 45(2): 205–212 (
  5. 5.
    Cao T Y and Shen L. 2015. High resolution RS image processing method for vehicles recognition and location according to the illumination model. Acta Geodaetica et Cartographica Sinica, 44(1): 39–45 (
  6. 6.
    Dai J G, Zhang L, Li J W and Fang X X. 2017a. A line extraction method for chain code tracking with phase verification. Acta Geodaetica et Cartographica Sinica, 46(2): 218–227 (
  7. 7.
    Dai J G, Zhang L, Zhu E Z, Liao J C, Fang X X and Li J W. 2017b. Principal line detection in remote sensing image. Journal of Remote Sensing, 21(2): 228–238 (
  8. 8.
    Fu G, Zhao H R, Li C and Shi L M. 2013. Road detection from optical remote sensing imagery using circular projection matching and tracking strategy. Journal of the Indian Society of Remote Sensing, 41(4): 819–831
  9. 9.
    Fu G, Zhao H R, Li C and Shi L M. 2014. A method by improved circular projection matching of tracking twisty road from remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 43(7): 724–730 (
  10. 10.
    Kumar M, Singh R K, Raju P L N and Krishnamurthy Y V N. 2014. Road network extraction from high resolution multispectral satellite imagery based on object oriented techniques. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-8: 107–110
  11. 11.
    Leitloff J, Hinz S and Stilla U. 2010. Vehicle detection in very high resolution satellite images of city areas. IEEE Transactions on Geoscience and Remote Sensing, 48(7): 2795–2806
  12. 12.
    Li H S, Huang P P and Su Y. 2015. A method for road extraction from remote sensing imagery. Remote Sensing for Land and Resources, 27(2): 56–62 (
  13. 13.
    Nakaguro Y, Makhanov S S and Dailey M N. 2011. Numerical experiments with cooperating multiple quadratic snakes for road extraction. International Journal of Geographical Information Science, 25(5): 765–783
  14. 14.
    Pudaruth S. 2016. Extraction of roads from remotely sensed images using a multi-angled template matching technique//Proceedings of the 3rd International Symposium on Computer Vision and the Internet. Jaipur, India: ACM: 21–29 [DOI: 10.1145/2983402.2983412]
  15. 15.
    Rajeswari M, Gurumurthy K S, Omkar S N, Senthilnath J and Reddy L P. 2011. Automatic road extraction using high resolution satellite images based on level set and mean shift methods//Proceedings of the 3rd International Conference on Electronics Computer Technology (ICECT). Kanyakumari, India: IEEE: 424–428 [DOI: 10.1109/ICECTECH.2011.5941731]
  16. 16.
    Raziq A, Xu A G and Li Y. 2016. Automatic extraction of urban road centerlines from high-resolution satellite imagery using automatic thresholding and morphological operation method. Journal of Geographic Information System, 8(4): 517–525
  17. 17.
    Sarkar S. 2003. An introduction to perceptual organization//Proceedings of 2003 International Conference on Integration of Knowledge Intensive Multi-Agent Systems. Cambridge: IEEE: 330–335 [DOI: 10.1109/KIMAS.2003.1245066]
  18. 18.
    Sengupta S K, Lopez A S, Brase J M and Paglieroni D W. 2004. Phase-based road detection in multi-source images//Proceedings of 2004 IEEE International Geoscience and Remote Sensing Symposium. Anchorage, AK: IEEE, 6: 3833–3836 [DOI: 10.1109/IGARSS.2004.1369959]
  19. 19.
    Steger C, Glock C, Eckstein W, Mayer H and Radig B. 1995. Model-based road extraction from images//Gruen A, Kuebler O and Agouris P. Automatic Extraction of Man-Made Objects from Aerial and Space Images. Basel: Birkhäuser: 275–284 [DOI: 10.1007/978-3-0348-9242-1_26]
  20. 20.
    Tan R L, Wan Y C, Yuan F and Li G. 2014. Semi-automatic road extraction of high resolution remote sensing images based on circular template. Bulletin of Surveying and Mapping(10): 63–66 (
  21. 21.
    von Gioi R G, Jakubowicz J, Morel J M and Randall G. 2010. LSD: a fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(4): 722–732
  22. 22.
    Vosselman G and de Knecht J. 1995. Road tracing by profile matching and kaiman filtering//Gruen A, Kuebler O, Agouris P. Automatic Extraction of Man-made Objects from Aerial and Space Images. Basel: Birkhäuser: 265–274
  23. 23.
    Wang F P, Wang W X, Xue B Y, Cao T and Gao T. 2017. Road extraction from high-spatial-resolution remote sensing image by combining GVF Snake with salient features. Acta Geodaetica et Cartographica Sinica, 46(12): 1978–1985 (
  24. 24.
    Wang L Z, Lin X G and Liang Y. 2017. Automatic extraction of main roads from high resolution remote sensing imagery based on perceptual organization. Science of Surveying and Mapping, 42(7): 127–131 (
  25. 25.
    Wiedemann C, Ebner H. 2000. Automatic completion and evaluation of road networks//International Archives of Photogrammetry and Remote Sensing. Amsterdam: International Archives of Photogrammetry and Remote Sensing, 33: 979–986
  26. 26.
    Wu X W and Xu H Q. 2010. Level set method major roads information extract from high-resolution remote-sensing imagery. Journal of Astronautics, 31(5): 1495–1502 (
  27. 27.
    Zhang J X, Lin X G, Liu Z J and Shen J. 2011. Semi-automatic road tracking by template matching and distance transformation in urban areas. International Journal of Remote Sensing, 32(23): 8331–8347

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website