Introducing An Efficient Set of High Spatial Resolution Images of Urban Areas to Evaluate Building Detection Algorithms

Document Type : Research Paper

Authors

1 University of Tehran, College of Engineering, Faculty of Surveying and Geoinformation Engineering, Remote Sensing Department

2 University of Isfahan

Abstract

The present work aims to introduce an efficient set of high spatial resolution (HSR) images to evaluate building detection algorithms more fairly. The introduced images are chosen from two recent HSR sensors (QuickBird and GeoEye-1) and based on several challenges of urban areas encountered in building detection such as diversity in building density, building dissociation, building shape, building size, building alignment, building roof color, building height, and imaging angle. To practically examine the proposed dataset, three-building detection algorithms with different strategies are employed. The results imply the proposed dataset can be helpful to more fairly evaluate each algorithm, so that it indicates where the algorithm can be efficient and successful and where may be encountered with the problems in detecting buildings in urban areas. 

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References

[1]      M. Bouziani, K. Goita and D-C. He, "Rule-based classification of a very high resolution image in an urban environment using multispectral segmentation guided by cartographic data," IEEE Trans. Geosci. Remote Sens. vol. 48, no. 8, pp. 3198–3211, Aug. 2010.
[2]      K. Khoshelham, C. Nardinocchi, E. Frontoni, A. Mancini, and P. Zingaretti, "Performance evaluation of automated approaches to building detection in multi-source aerial data," ISPRS J. Photogramm. Remote Sens. vol. 65, pp. 123–133, 2010.
[3]      I. Khosravi, M. Momeni, and M. Rahnemoonfar, "Performance evaluation of object-based and pixel-based building detection algorithms from very high spatial resolution imagery," Photogramm. Eng. Remote Sens. vol. 80, no. 5, pp. 519–523, June 2014.
[4]      X. Hunag, and L. Zhang, "A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery," Photogramm. Eng. Remote Sens. vol. 77, no. 7, pp. 721–732, July 2011.
[5]      X. Hunag, and L. Zhang, "Morphological building/shadow index for building extraction from high-resolution imagery over urban areas," IEEE J. Selected Topics in Applied Earth Observations and Remote Sens. vol. 5, no. 1, pp. 161–172, Feb. 2012.
[6]      O. Aytekin, A. Erener, I. Ulusoy, and HSB. Duzgun, "Unsupervised building detection in complex urban environments from multispectral satellite imagery," Int. J. Remote Sens. vol. 33, no. 7, pp. 2152–2177, April 2012.
[7]      H. Taubenbock, T. Esch, M. Wurm, A. Roth and S. Dech, "Object-based feature extraction using high spatial resolution satellite data of urban area," J. Spatial Sci. vol. 55, no. 1, pp. 117–132, June 2010.
[8]      M. Ghanea, P. Moallem, and M. Momeni, "Automatic building extraction in dense urban areas through GeoEye multispectral imagery," Int. J. Remote Sens. vol. 35, no. 13, pp. 5094–5119, 2014.
[9]      X. Jin, and C.H. Davis, "Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual and spectral information," EURASIP J. Applied Signal Processing, vol. 14, pp. 2196–2206, 2005.
[10]   I. Sebari and D-C. He, "Automatic fuzzy object-based analysis of VHSR images for urban objects extraction," ISPRS J. Photogramm. Remote Sens. vol. 79, pp. 171–184, 2013.
[11]   S.W. Myint, P. Gober, A. Brazel, S. Grossman-Clarke and Q. Weng, "Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery," Remote Sens. Environ. vol.115, no. 5, pp. 1145–1161, 2011.
[12]   B. Salehi, Y. Zhang, M. Zhong and V. Dey, "Object-based classification of urban areas using VHR imagery and height points ancillary data," Remote Sens. vol. 4, pp. 2256–2276, 2012.
[13]   X. Hunag, and L. Zhang, "An SVM ensemble approach combining spectral, structural and semantic features for the classification of high resolution remotely sensed imagery," IEEE Trans. Geosci. Remote Sens. vol. 51, no. 1, pp. 257–272, Jan. 2013.
[14]   T. Thuy Vu, "Building extraction from high-resolution satellite images for tsunami early damage estimation," Applied Geomatics, vol. 3, no. 2, pp. 75–81, June 2011.
Modeling and Simulation in Electrical and Electronics Engineering
Volume 1, Issue 1
May 2021
Pages 43-48

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History

  • Receive Date: 07 September 2015
  • Revise Date: 01 May 2015
  • Accept Date: 27 January 2015
  • First Publish Date: 01 May 2021

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