BUILDING EXTRACTION FROM VERY HIGH-RESOLUTION SATELLITE IMAGES FOR MAP UPDATING IN EGYPT | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 7, Volume 48, No 5, September and October 2020, Page 869-887 PDF (2.26 MB) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2020.115673 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mostafa H. Shoaib* 1; Yasser G Mostafa1; Yousef A. Abbas2 | ||||
| 1Civil Eng. Dept., Faculty of Engineering, Sohag University, Sohag, Egypt | ||||
| 2Civil Eng. Dept., Faculty of Engineering, Assuit University, Assuit, Egypt | ||||
| Abstract | ||||
| Robust building detection from satellite images has been a subject of interest for several decades. Very High Resolution (VHR) satellite images support the efficient extraction of manmade objects. The main aim of this paper is to present an approach for building extraction from VHR satellite images for map updating in Egypt. To achieve this aim, a comparison of pixel and object-based classification techniques has been applied. Then, different refinement processes based on shadow, context, shape, and Digital Surface Model (DSM) data are carried out. Two study areas from the VHR satellite images for Assuit and Sohag cities are used. A comparison of the classification techniques shows that the Maximum Likelihood Classifier (MLC) for pixel-based technique and Support Vector Machine (SVM) for object-based technique give the highest overall accuracy results. Refinement based on shadow, context, shape, and DSM information improves the overall accuracy with an average of 18%. Thus, the building extraction results can contribute significantly to update maps in Egypt. | ||||
| Keywords | ||||
| Building extraction; Pixel-based; Object-based; Classification; Accuracy assessment; Map updating | ||||
| References | ||||
|
[1] Wang, S., Hou, X., and Zhao, X. (2020). “Automatic Building Extraction From High-Resolution Aerial Imagery via Fully Convolutional Encoder-Decoder Network With Non-Local Block”. IEEE Access, 8, 7313-7322.
[2] Ghaffarian, S. (2014). “Automatic building detection based on supervised classification using high resolution Google Earth images”. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(3), 101.
[3] Belgiu, M., and Drǎguţ, L. (2014). “Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery”. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 67-75.
[4] Attarzadeh, R., and Momeni, M. (2012). “Object-based building extraction from high resolution satellite imager”. International Archives of the Photogrammetry”, Remote Sensing and Spatial Information Sciences, 39, B4.
[5] Shedlovska, Y. I., and Hnatushenko, V. V. (2018, April). “A very high resolution satellite imagery classification algorithm”. In 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) (pp. 654-657). IEEE.
[6] Elsharkawy, A., Elhabiby, M., and El-Sheimy, N. (2012). “New combined pixel/object-based technique for efficient urban classification using WorldView-2 data”. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, B7.
[7] Mostafa, F. A., Mostafa, Y., and Yousef, M. A. (2014). “The optimal method for classifying high resolution satellite images in Egypt environment”. Journal of Engineering Sciences Assiut University, vol. 42, no. 4, pp. 1106-1121.
[8] Jiang, N., Zhang, J. X., Li, H. T., and Lin, X. G. (2008). “Object-oriented building extraction by DSM and very high-resolution orthoimages”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 441-446.
[9] Manandhar, P., Aung, Z., and Marpu, P. R. (2017, July). “Segmentation based building detection in high resolution satellite images”. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3783-3786). IEEE.
[10] Turker, M., and San, K. (2010). “Building detection from pan-sharpened IKONOS imagery through support vector machines classification”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 28(8), 841-846.
[11] Benarchid, O., Raissouni, N., El Adib, S., Abbous, A., Azyat, A., Achhab, N. B., and 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, 4(1), 1-8.
[12] ŞENARAS, Ç. (2007). “An automated building extraction model using fuzzy K-NN classifier from monocular aerial images” Doctoral dissertation, middle east technical university.
[13] Thanh Noi, P., and Kappas, M. (2018). “Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery”. Sensors, 18(1), 18.
[14] Mondal, A., Kundu, S., Chandniha, S. K., Shukla, R., and Mishra, P. K. (2012). “Comparison of support vector machine and maximum likelihood classification technique using satellite imagery”. International Journal of Remote Sensing and GIS, 1(2), 116-123.
[15] Zeng, C. (2014). “Automated Building Information Extraction and Evaluation from High-resolution Remotely Sensed Data”. Doctoral dissertation, University of Western Ontario.
[16] Abd Elwahed, A., Farrag, A., and Mostafa, Y., (2011). “Evaluation of Information Content and Feature Extraction Capability from Egypt Sat-1 Images”. Journal of Engineering Sciences, Assiut University, 39(3), 529-538.
[17] Perumal, K., and Bhaskaran, R. (2010). “Supervised classification performance of multispectral images”. arXiv preprint arXiv:1002.4046.
[18] Darwish, A., Leukert, K., and Reinhardt, W. (2003, July). “Image segmentation for the purpose of object-based classification”. In International Geoscience and Remote Sensing Symposium (Vol. 3, pp. III-2039).
[19] Nady, B., Mostafa, Y., Abas, Y., and Enieb, M., 2020 “Using Of VHR Satellite Images for Road Network Extraction in Egypt” Journal of Engineering Sciences, Assiut University, Faculty of Engineering, Vol. 48, No. 1, pp. 20 -31.
[20] Stehman, S. V. (1997). “Selecting and interpreting measures of thematic classification accuracy”. Remote sensing of Environment, 62(1), 77-89.
[21] Mostafa, Y., and Abdelhafiz, A. (2017). “Accurate shadow detection from high-resolution satellite images”. IEEE Geoscience and Remote Sensing Letters, 14(4), 494-498.
[22] Mostafa, F. A., Mostafa, Y., Yousef, M., and Abas Y., (2014), “Using of high resolution satellite images for updating large scale mapping in Egypt”. J. Eng. Sci, 42, 1122-1137. | ||||
|
Statistics Article View: 386 PDF Download: 406 |
||||
