A Multi Stage Approach for Urban Building Extraction from Remote Sensing Satellite Images

  • Authors

    • VSSN Gopala Krishna Pendyala
    • Hemantha Kumar Kalluri
    • C.V. Rao
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.24.21864
  • The most important parameter for urban information system is the building information which is represented by the geographic location of the buildings as well as the area, perimeter, density, inter building distances. This data is integrated with demographic data for various applications. High resolution Remote sensing images are widely used as primary data for automatic extraction of building information. Many researchers have developed different methods for maximizing the detection percentage with minimum errors. This paper analyzes the primary data available for researchers, deriving the secondary information and utilizing it effectively. Case studies by various researchers were analyzed and a methodology has been outlined using their experiences, which is expected to be more efficient and reduced errors.

  • References

    1. [1] Carmen Radut, Ramona Chitu (2009), Geographical Information Systems and Urban Management. Theoretical and Empirical Researches in Urban Management.

      [2] Itzhak Benenson, Shai Aronovich, Saar Noam (2005), Let’s talk objects: generic methodology for urban high-resolution simulation. Computers, Environment and Urban Systems 29 425–453.

      [3] Mahak, Khurana., Vaishali, Wadhwa.(2015), Automatic Building Detection Using Modified Grab Cut Algorithm from High Resolution Satellite Image. International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 8.

      [4] Topan, H., Oruc, M., Jacobsen, K. (2009), Potential of manual and automatic feature extraction from high resolution space images in mountainous urban areas. ISPRS Hannover Workshop, www.isprs.org/proceedings /XXXVIII/ 1_4_7-W5.

      [5] VSSN Gopala Krishna Pendyala, Hemantha Kumar Kalluri, V Raghu Venkataramanand C V Rao (2018), Comparative study of Automatic Urban Building Extraction Methods from Remote Sensing Data. Advs in Intelligent Syst., Computing, Vol. 815, in press.

      [6] Małgorzata, Verőné Wojtaszek.(2015), Opportunities of object-based image analysis for detecting urban environment. presentation at IGIT, Internet resource, www.geo.info.hu.

      [7] Huan, Ni., Xiangguo, Lin., Jixian, Zhang.(2017), Classification of ALS Point Cloud with ImprovedPoint Cloud Segmentation and Random Forests. Remote Sensing

      [8] “Satellite Mission Database†Internet Resource https://earth.esa.int/web/eoportal/satellite-missions 2018.

      [9] Internet Resource “https://www.satimagingcorp.com/satellite-sensors/geoeye-2â€, 2018

      [10] Ibrahim, F. Shaker., Amr, Abd-Elrahman., Ahmed, K. Abdel-Gawad., Mohamed, A. Sherief.(2011), Building Extraction from High Resolution Space Images in High Density Residential Areas in the Great Cairo Region. Remote Sesiing.

      [11] K Jacobsen (2008), Satellite Image Orientation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B1. Beijing.

      [12] Shan, J. and A. Sampath (2005), Urban DEM Generation from Raw Lidar Data: A Labeling Algorithm and its Performance. International Journal of Remote Sensing 71, pp. 217-222.

      [13] Meng, X., L. Wang, J. L. Silván-Cárdenas and N. Currit (2009), A multi-directional ground filtering algorithm for airborne LIDAR. ISPRS Journal of Photogrammetry and Remote Sensing 64(1), pp. 117-124.

      [14] Sefercik, U G., Karakis, S., Atalay, C., Yigit, I., Gokmen, U. (2017), Novel fusionapproach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000. Geocarto International.

      [15] Demir, N, Poli, D, Baltsavias, E.(2008), Extraction of Buildings and Trees Using Image and LiDAR data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII. Part B4. Beijing.

      [16] Benarchid, O., Raissouni, N., El Adib, S., Abbous, A., Azyat, A., Ben Achhab, N., Lahraoua, M., Chahboun, A. (2013), Building Extraction using Object-Based Classification and Shadow Information in Very High Resolution Multispectral Images, a Case Study: Tetuan, Morocco. Canadian Journal on Image Processing and Computer Vision Vol. 4 No. 1.

      [17] Txomin, Hermosilla., Luis, A Ruiz., Jorge A Recio., Javier, Estornell.(2011), Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data. Remote Sensing.

      [18] Jiang, N., Zhang, J X., Li, H T., Lin, X G. (2008),Semi-Automatic Building Extraction from High Resolution Imagery Based on Segmentation. International Workshop on Earth Observation and Remote Sensing Applications, Beijing.

      [19] Huan, Ni., Xiangguo, Lin., Jixian, Zhang. (2017), Classification of ALS Point Cloud with ImprovedPoint Cloud Segmentation and Random Forests. Remote Sensing.

      [20] Bittnera, K., Cuia, S., Reinartz, P. (2017), Building Extraction from Remote Sensing Data Using Fully Convolution Networks. ISPRS Workshop, Hannover.

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  • How to Cite

    Pendyala, V. G. K., Kalluri, H. K., & Rao, C. (2018). A Multi Stage Approach for Urban Building Extraction from Remote Sensing Satellite Images. International Journal of Engineering & Technology, 7(4.24), 95-99. https://doi.org/10.14419/ijet.v7i4.24.21864