Presenting an extended evaluation framework for building detection algorithms using high spatial resolution images

Document Type : Original Article

Authors

Department of Surveying Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran

Abstract

This paper aims to provide an extended evaluation framework for building detection algorithms using a diverse set of High Spatial Resolution (HSR) images. The HSR images utilized in this paper were chosen from different places and different sensors, and based on several important challenges in an urban area such as building alignment, density, shape, size, color, height, and imaging angle. The classical evaluation metrics such as detection rate, reliability, false positive rate, and overall accuracy only demonstrate the performance evaluation of an algorithm in relation to the buildings and cannot interpret the mentioned challenges. The extended evaluation framework proposed in this paper composed several extended metrics for performance evaluation of building detection algorithms in relation to these challenges in addition to the classical metrics. The paper intends to declare that the success or failure metrics of a building detection algorithm can have more varieties. In fact, a building detection algorithm may be successful at one or several metrics, whilst it may be unsuccessful at the other metrics.

Keywords

References

Aytekın, Ö., Erener, A., Ulusoy, İ., & Düzgün, Ş. (2012). Unsupervised building detection in complex urban environments from multispectral satellite imagery. International Journal of Remote Sensing, 33(7), 2152-2177.
Baatz, M. (2000). Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte geographische informationsverarbeitung, 12-23.
Benediktsson, J. A., Pesaresi, M., & Amason, K. (2003). Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing, 41(9), 1940-1949.
Bouziani, M., Goita, K., & He, D. C. (2010). Rule-based classification of a very high resolution image in an urban environment using multispectral segmentation guided by cartographic data. IEEE Transactions on Geoscience and Remote Sensing, 48(8), 3198-3211.
Dalla Mura, M., Benediktsson, J. A., Waske, B., & Bruzzone, L. (2010). Morphological attribute profiles for the analysis of very high resolution images. IEEE Transactions on Geoscience and Remote Sensing, 48(10), 3747-3762.
eCognition Developer 8.7.2 User Guide. 2012
Ghanea, M., Moallem, P., & Momeni, M. (2014). Automatic building extraction in dense urban areas through GeoEye multispectral imagery. International journal of remote sensing, 35(13), 5094-5119.
Gonzalez R.C, Woods R.E, Eddins S.L. Digital Image Processing Using MATLAB, 2nd ed. Prentice-Hall, Inc, 2004.
Hester, D. B., Cakir, H. I., Nelson, S. A., & Khorram, S. (2008). Per-pixel classification of high spatial resolution satellite imagery for urban land-cover mapping. Photogrammetric Engineering & Remote Sensing, 74(4), 463-471.
Lu, Y. H., Trinder, J. C., & Kubik, K. (2006). Automatic building detection using the Dempster-Shafer algorithm. Photogrammetric Engineering & Remote Sensing, 72(4), 395-403.
Huang, X., & Zhang, L. (2011). A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery. Photogrammetric Engineering & Remote Sensing, 77(7), 721-732.
Huang, X., & Zhang, L. (2013). An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE transactions on geoscience and remote sensing, 51(1), 257-272.
Huang, X., Zhang, L., & Li, P. (2008). Classification of very high spatial resolution imagery based on the fusion of edge and multispectral information. Photogrammetric Engineering & Remote Sensing, 74(12), 1585-1596. Hunag X, Zhang L. 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, vol. 5, no. 1, pp. 161–172, February 2012.
Jin, X., & Davis, C. H. (2005). Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information. EURASIP Journal on Advances in Signal Processing, 2005(14), 745309.
Khoshelham, K., Nardinocchi, C., Frontoni, E., Mancini, A., & Zingaretti, P. (2010). Performance evaluation of automated approaches to building detection in multi-source aerial data. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 123-133.
Khosravi, I., Momeni, M., & Rahnemoonfar, M. (2014). Performance evaluation of object-based and pixel-based building detection algorithms from very high spatial resolution imagery. Photogrammetric Engineering & Remote Sensing, 80(6), 519-528.
Meng, X., Currit, N., Wang, L., & Yang, X. (2012). Detect residential buildings from lidar and aerial photographs through object-oriented land-use classification. Photogrammetric Engineering & Remote Sensing, 78(1), 35-44.
Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote sensing of environment, 115(5), 1145-1161.
Salehi, B., Zhang, Y., Zhong, M., & Dey, V. (2012). Object-based classification of urban areas using VHR imagery and height points ancillary data. Remote Sensing, 4(8), 2256-2276.
Sebari, I., & He, D. C. (2013). Automatic fuzzy object-based analysis of VHSR images for urban objects extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 171-184.
Taubenböck, H., Esch, T., Wurm, M., Roth, A., & Dech, S. (2010). Object-based feature extraction using high spatial resolution satellite data of urban areas. Journal of Spatial Science, 55(1), 117-132.
Zhang, L., Huang, X., Huang, B., & Li, P. (2006). A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(10), 2950-2961.
Earth Observation and Geomatics Engineering
Volume 1, Issue 1
June 2017
Pages 26-35

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