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Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM

  • role： First author第一作者
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

  • Email：15911157479@163.com
  • Introduction：井然，1991年生，男，博士研究生，研究方向为遥感技术及地学应用。E-mail: 15911157479@163.com
JING Ran123,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

  • Email：gongzhn@163.com
  • Introduction：宫兆宁，1976年生，女，副教授，研究方向为遥感技术及地学分析应用。E-mail：gongzhn@163.com
GONG Zhaoning123*,  
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

ZHU Wending123,  
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

GUAN Hongliang123,  
  • Affiliation：

    College of Resources Environment & Tourism, Capital Normal University, Beijing 100048, China

    Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China

    Key Laboratory of Resources Environment and GIS of Beijing Municipal，Beijing 100048，China

ZHAO Wenji123,  
  • Affiliation：

    Satellite Surveying and Mapping Application Center , State Bureau of Surveying and Mapping , Beijing 100048, China

ZHANG Tao4

résumé

The extraction of buildings from remote sensing imagery has an important application value. However, high-resolution images contain detailed information and complex features that hinder the difficulty of building extraction process.To address this problem, we propose a building extraction method of building extraction based on multi-scale SLIC-GMRF and FCNSVM that demonstrates an improved ability of extracting buildings from high-resolution remote sensing images to some extent. First, a multi-scale SLIC-GMRF segmentation algorithm is applied to determine the initial building area, and then the advantages of the FCN neural network in semantic segmentation are utilized to extract the building features. Second, the extracted building features are used to train an SVM classifier to refine the building extraction results of building.The results of three control experiments and two comparative tests reveal that the SLIC segmentation algorithm affects the initial segmentation results, the SVM classifier affects the extraction of building details, and the FCN features influence the performance of the SVM classifier. The precision rate, recall rate, and quality index of the proposed method are all better than the compared methods.The following conclusions can be drawn from the experimental results. For the study area with clear features and minimal obstructions, the proposed method can effectively extract buildings from an image. This method can also obtain ideal results for areas with a complex distribution of buildings can also get ideal results.

mots-clés

remote sensing;building extraction;image segmentation;FCN neural network;SVM;high-resolution remote sensing image

References

  1. 1.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P and Süsstrunk S. 2010. SLIC Superpixels. EPFL Technical Report no. 149300. EPFL
  2. 2.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P and Süsstrunk S. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11): 2274-2282
  3. 3.
    Benedek C, Shadaydeh M, Kato Z, Szirányi T and Zerubia J. 2015. Multilayer Markov random field models for change detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 107: 22-37
  4. 4.
    Bengio Y. 2012. Practical recommendations for gradient-based training of deep architectures//Montavon G, Orr G B and Müller K B, eds. Neural Networks: Tricks of the Trade. Berlin, Heidelberg: Springer: 437-478
  5. 5.
    Bengio Y, Courville A and Vincent P. 2013. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8): 1798-1828
  6. 6.
    Besag J. 1986. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. Series B: Methodological, 48(3): 259-302
  7. 7.
    Bouman C and Liu B. 1991. Multiple resolution segmentation of textured images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(2): 99-113
  8. 8.
    Bouman C A and Shapiro M. 1994. A multiscale random field model for bayesian image segmentation. IEEE Transactions on Image Processing, 3(2): 162-177
  9. 9.
    Bruzzone L and Prieto D F. 2000. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1171-1182
  10. 10.
    Canziani A, Paszke A and Culurciello E. 2016. An analysis of deep neural network models for practical applications//Proceedings of 2016 ICLR. San Juan: ICLR
  11. 11.
    Chang C C and Lin C J. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3): 27
  12. 12.
    Collobert R and Weston J. 2008. A unified architecture for natural language processing: deep neural networks with multitask learning//Proceedings of the 25th International Conference on Machine Learning. Helsinki, Finland: ACM: 160-167
  13. 13.
    Cortes C and Vapnik V. 1995. Support-vector networks. Machine Learning, 20(3): 273-297
  14. 14.
    Drăguţ L, Tiede D and Levick S R. 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871
  15. 15.
    Drăguţ L, Csillik O, Eisank C and Tiede D. 2014. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 119-127
  16. 16.
    Felzenszwalb P F and Huttenlocher D P. 2004. Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2): 167-181
  17. 17.
    Gao B C. 1996. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266
  18. 18.
    Geman S and Geman D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(6): 721-741
  19. 19.
    Gong M G, Yang H L and Zhang P Z. 2017. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS Journal of Photogrammetry and Remote Sensing, 129: 212-225
  20. 20.
    Grinias I, Panagiotakis C and Tziritas G. 2016. MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 122: 145-166
  21. 21.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, United States: IEEE: 770-778
  22. 22.
    Heipke C, Mayer H, Wiedemann C and Jamet O. 1997. Evaluation of automatic road extraction. International Archives of Photogrammetry and Remote Sensing, 32(3-2W3): 47-56
  23. 23.
    Hinton G, Li D, Dong Y, Dahl G E, Mohamed A R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T N and Kingsbury B. 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Processing Magazine, 29(6): 82-97
  24. 24.
    Huang X and Zhang L P. 2012. Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(1): 161-172
  25. 25.
    Jia Y Q, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S and Darrell T. 2014. Caffe: convolutional architecture for fast feature embedding//Proceedings of the 22nd ACM International Conference on Multimedia. Orlando: ACM: 675-678
  26. 26.
    Kaufman L and Rousseeuw P J. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley and Sons: 111-112
  27. 27.
    Krishnamachari S and Chellappa R. 1997. Multiresolution gauss-Markov random field models for texture segmentation. IEEE Transactions on Image Processing, 6(2): 251-267
  28. 28.
    Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe: ACM: 1097-1105
  29. 29.
    Laptev D, Savinov N, Buhmann J M and Pollefeys M. 2016. TI-POOLING: transformation-invariant pooling for feature learning in Convolutional Neural Networks//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE: 289-297
  30. 30.
    Levinshtein A, Stere A, Kutulakos K N, Fleet D J, Dickinson S J and Siddiqi K. 2009. TurboPixels: fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12): 2290-2297
  31. 31.
    Li S Z. 1994. Markov random field models in computer vision//Proceedings of the 3rd European Conference on Computer Vision. Stockholm, Sweden: Springer-Verlag: 361-370
  32. 32.
    Li X W and Wang Y T. 2013. Prospects on future developments of quantitative remote sensing. Acta Geographica Sinica, 68(9): 1163-1169
  33. 33.
    Liasis G and Stavrou S. 2016. Building extraction in satellite images using active contours and colour features. International Journal of Remote Sensing, 37(5): 1127-1153
  34. 34.
    Liu S Y and Deng W H. 2016. Very deep convolutional neural network based image classification using small training sample size//Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition. Kuala Lumpur, Malaysia: IEEE: 730-734
  35. 35.
    Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, Massachusetts: IEEE: 3431-3440
  36. 36.
    Mnih V. 2013. Machine Learning for Aerial Image Labeling. Toronto: University of Toronto
  37. 37.
    Moore A P, Prince S J D, Warrell J, Mohammed U and Jones G. 2008. Superpixel lattices//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK: IEEE: 1-8
  38. 38.
    Ren X F and Malik J. 2003. Learning a classification model for segmentation//Proceedings of the 9th IEEE International Conference on Computer Vision. Nice, France: IEEE: 10-17
  39. 39.
    Rignot E and Chellappa R. 1992. Segmentation of polarimetric synthetic aperture radar data. IEEE Transactions on Image Processing, 1(3): 281-300
  40. 40.
    Romero A, Gatta C and Camps-Valls G. 2016. Unsupervised deep feature extraction for remote sensing image classification.IEEE Transactions on Geoscience and Remote Sensing, 54(3):1349-1362
  41. 41.
    Rouse J W, Hass R H, Schell J A, Deering D W and Harlan J C. 1974. Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. NASA/GSFC Type III Final Report. Greenbelt, MD
  42. 42.
    Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z H, Karpathy A, Khosla A, Bernstein M, Berg A C and Li F F. 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3): 211-252
  43. 43.
    Saito S, Yamashita T and Aoki Y. 2016. Multiple object extraction from aerial imagery with convolutional neural networks. Journal of Imaging Science and Technology, 60(1): 10402-1-10402-9
  44. 44.
    Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R and LeCun Y. 2013. OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229
  45. 45.
    Sharma A, Liu X W, Yang X J and Shi D. 2017. A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95: 19-28
  46. 46.
    Shi J B and Malik J. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8): 888-905
  47. 47.
    Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition//Proceedings of 2015 ICLR. San Diego, California: [s.n.]: 1409-1556
  48. 48.
    Socher R, Lin C C Y, Ng A Y and Manning C D. 2011. Parsing natural scenes and natural language with recursive neural networks//Proceedings of the 28th International Conference on Machine Learning. Bellevue, USA: Omnipress: 129-136
  49. 49.
    Volpi M and Tuia D. 2017. Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(2): 881-893
  50. 50.
    Wickham H. 2011. The split-apply-combine strategy for data analysis. Journal of Statistical Software, 40(1): 1-29
  51. 51.
    Yang Y and Newsam S. 2010. Bag-of-visual-words and spatial extensions for land-use classification//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose, California: ACM: 270-279
  52. 52.
    Yu Q Y and Clausi D A. 2006. Filament preserving model (FPM) segmentation applied to SAR sea-ice imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(12): 3687-3694
  53. 53.
    Zhang H Y, Wang L W and Chen Y X. 2013. Research progress of probabilistic graphical models: a survey. Journal of Software, 24(11): 2476-2497

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