Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

A Brief Survey of Building Extraction Techniques of Remote Sensing Images

Author : Sure.Venkata.Padmavathi Devi 1 Dr.D.Murugan 2

Date of Publication :14th March 2019

Abstract: This paper aims to provide a review of automatic building extraction techniques from remote sensing images. The building extraction technique is used for detecting and separating buildings from other land cover classes. In the past few years, researchers have proposed a number of building extraction methods. This paper commonly divides these methods into four main categories: template matching-based methods, knowledge-based methods, OBIA-based methods, and machine learning-based methods and provide a detailed survey of these methods. This paper also discusses the challenges and demerits of these methods to give a clear idea about these existing approaches. So this survey will be beneficial for the researchers to have a better understanding of this building extraction field.

Reference :

    1. E Li, S Xu, W Meng, X Zhang, Building extraction from remotely sensed images by integrating saliency cue, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 3 (2017): 906- 919.
    2. LI Jiménez, J Plaza, A Plaza, Efficient implementation of a morphological index for building/shadow extraction from remotely sensed images, the Journal of Supercomputing 73, no. 1 (2017): 482- 494.
    3. W Yuan, J Li, L Zhang, A new building extraction postprocessing framework for high-spatial-resolution remote-sensing imagery X Huang, xIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 2 (2017): 654- 668..
    4. W Zhai, H Shen, C Huang, W Pei, Building earthquake damage information extraction from a single post-earthquake image, Remote Sensing 8, no. 3 (2016): 171.
    5. J Yuan, arXiv Automatic building extraction in aerial scenes using convolutional networks, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40, no. 3 (2014): 101.
    6. N Shrivastava, PK Rai, Remote-sensing the urban area: Automatic building extraction based on multiresolution segmentation and classification,2015 - pdf.semantic cscholar.org
    7. TT Ngo, C Collet, V Mazet, Automatic rectangular building detection from VHR aerial imagery using shadow and image segmentation, IEEE Transactions on Geoscience and Remote Sensing 53, no. 6 (2015): 3325-3337.
    8. J Wang, X Yang, X Qin, X Ye, An efficient approach for automatic rectangular building extraction from very high-resolution optical satellite imagery, ISPRS Journal of Photogrammetry and Remote Sensing 63, no. 3 (2008): 365-381.
    9. J Han, D Zhang, G Cheng, L Guo, Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning, 2015 IEEE International Conference on, pp. 1483- 1487.
    10. S Ghaffarian, Automatic building detection based on supervised classification using high-resolution Google Earth images, International Conference on Advances in Geographic Information Systems, pp. 271-280. ACM, 2014
    11. Lefèvre, S., Weber, J., Sheeren, D., 2007. Automatic building extraction in VHR images using advanced morphological operators. In: Proc. Urban Remote Sens. Joint Event, pp. 1–5.
    12. Stankov, K., He, D.-C., 2013. Building detection in very high spatial resolution multispectral images using the hit-or-miss transform. IEEE Geosci. Remote Sens. Lett. 10, 86–90.
    13. Stankov, K., He, D.-C., 2014. Detection of buildings in multispectral very high spatial resolution images using the percentage occupancy hit-or-miss transform. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7, 4069–4080.
    14. Akçay, H.G., Aksoy, S., 2010. Building detection using directional spatial constraints. In: Proc. IEEE Int. Geosci. Remote Sens. Sympos., pp. 1932–1935
    15. Haala, N., Brenner, C., 1999. Extraction of buildings and trees in urban environments. ISPRS J. Photogramm. Remote Sens. 54, 130–137.
    16. Hofmann, A.D., Maas, H.-G., Streilein, A., 2002. Knowledge-based building detection based on laser scanner data and topographic map information. Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci. 34, 169–174.
    17. Irvin, R.B., McKeown, D.M., 1989. Methods for exploiting the relationship between buildings and their shadows in aerial imagery. IEEE Trans. Syst., Man, Cybern. 19, 1564–1575.
    18. Lin, C., Nevatia, R., 1998. Building detection and description from a single intensity image. Comput. Vis. Image Understand. 72, 101–121.
    19. Liow, Y.-T., Pavlidis, T., 1990. Use of shadows for extracting buildings in aerial images. Comput. Vis. Graph. Image Process. 49, 242–277
    20. McGlone, J.C., Shufelt, J., 1994. Projective and object space geometry for monocular building
    21. extraction. In: Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., pp. 54–61.
    22. Ok, A.O., 2013. Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS J. Photogramm. Remote Sens. 86, 21–40.
    23. Ok, A.O., Senaras, C., Yuksel, B., 2013. Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery. IEEE Trans. Geosci. Remote Sens. 51, 1701–1717.
    24. Peng, J., Liu, Y., 2005. Model and context-driven building extraction in dense urban aerial images. Int. J. Remote Sens. 26, 1289–1307.
    25. Shufelt, J.A., 1996. Exploiting photogrammetric methods for building extraction in aerial images. Int. Arch. Photogramm. Remote Sens. 31, B6.
    26. Stilla, U., Geibel, R., Jurkiewicz, K., 1997. Building reconstruction using different views and context knowledge. Int. Arch. Photogramm. Remote Sens. 32, 129– 136.
    27. Weidner, U., Förstner, W., 1995. Towards automatic building extraction from high resolution digital elevation models. ISPRS J. Photogramm. Remote Sens. 50, 38– 49
    28. Huertas, A., Nevatia, R., 1988. Detecting buildings in aerial images. Comput. Vis. Graph. Image Process. 41, 131–152.
    29. Bontemps, S., Bogaert, P., Titeux, N., Defourny, P., 2008. An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sens. Environ. 112, 3181–3191.
    30. Chen, G., Hay, G.J., 2012. Object-based change detection. Int. J. Remote Sens. 33, 4434–4457.
    31. Contreras, D., Blaschke, T., Tiede, D., Jilge, M., 2016. Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L‟Aquila (Italy). Cartogr. Geogr. Inform. Sci. 43, 115– 133.
    32. Dissanska, M., Bernier, M., Payette, S., 2009. Object-based classification of very high-resolution panchromatic images for evaluating recent change in the structure of patterned peatland. Can. J. Remote Sens. 35, 189–215.
    33. Doxani, G., Siachalou, S., Tsakiri-Strati, M., 2008. An object-oriented approach to urban land cover change detection. Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci. 37, 1655–1660.
    34. . Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D., 2013. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 80, 91–106
    35. Im, J., Jensen, J.R., Tullis, J.A., 2008. Object-based change detection using correlation image analysis and image segmentation. Int. J. Remote Sens. 29, 399–423.
    36. Nebiker, S., Lack, N., Deuber, M., 2014. Building change detection from historical aerial photographs using dense image matching and object-based image analysis. Remote Sens. 6, 8310–8336.
    37. Walter, V., 2004. Object-based classification of remote sensing data for change detection. ISPRS J. Photogramm. Remote Sens. 58, 225–238.
    38. Ari, C., Aksoy, S., 2014. Detection of compound structures using a Gaussian mixture model with spectral and spatial constraints. IEEE Trans. Geosci. Remote Sens. 52, 6627–6638.
    39. Lei, Z., Fang, T., Huo, H., Li, D., 2012. Rotationinvariant object detection of remotely sensed images based on texton forest and Hough voting. IEEE Trans. Geosci. Remote Sens. 50, 1206–1217.
    40. Li, E., Femiani, J., Xu, S., Zhang, X., Wonka, P., 2015a. Robust rooftop extraction from visible band images using higher order CRF. IEEE Trans. Geosci. Remote Sens. 53, 4483–4495.
    41. Senaras, C., Ozay, M., Yarman Vural, F.T., 2013. Building detection with decision fusion. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 6, 1295– 1304
    42. Wegner, J.D., Hänsch, R., Thiele, A., Soergel, U., 2011a. Building detection from one orthophoto and high-resolution I nSAR data using conditional random fields. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4, 83–91.
    43. Wegner, J.D., Soergel, U., Rosenhahn, B., 2011b. Segment-based building detection with conditional random fields. In: Proc. Joint Urb. Remote Sens. Event, pp. 205– 208

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