Building detection in remote sensing images using a digital surface model
https://doi.org/10.23947/2587-8999-2017-2-185-193
Abstract
The paper considers an approach to detection of buildings and structures in the satellite imagery. The proposed method performs the extraction of high objects in a digital surface model and then improves the recognition accuracy using the segmentation of spectral information. The results of the quality comparison of the proposed approach with using different image segmentation algorithms are presented.
Supporting agencies: The research is done with the support from the comprehensive program of the FSR UrB, Project No. 15-16-1-14.
About the Authors
Alexandra Valerievna Dunaeva
N.N. Krasovskii Institute of Mathematics and Mechanics of the Ural Branch of the Russian Academy of Sciences
(S. Kovalevskaya street 16,Yekaterinburg, Russia, 620990);
Ural Federal University named after the first President of Russia B.N. Yeltsin
(Mira street 19, Yekaterinburg, Russia, 620002)
Russian Federation
Russian Federation
Dunaeva Alexandra Valerievna, mathematician, Department of Applied Management Problems, N.N. Krasovskii Institute of Mathematics and Mechanics of the Ural Branch of the Russian Academy of Sciences (S. Kovalevskaya street 16,Yekaterinburg, Russia, 620990); assistant, Department of High-Performance Computer Technologies, Ural Federal University named after the first President of Russia B.N. Yeltsin (Mira street 19, Yekaterinburg, Russia, 620002)
Fedor Andreevich Kornilov
N.N. Krasovskii Institute of Mathematics and Mechanics of the Ural Branch of the Russian Academy of Sciences
(S. Kovalevskaya street 16, Yekaterinburg, Russia, 620990)
Russian Federation
Russian Federation
Kornilov Fedor Andreevich, researcher, Department of Applied Problems of Management, N.N. Krasovskii Institute of Mathematics and Mechanics of the Ural Branch of the Russian Academy of Sciences (S. Kovalevskaya street 16, Yekaterinburg, Russia, 620990), candidate of Physico-Mathematical Sciences
References
1. Singhal S., Radhika S. Automatic Detection of Buildings from Aerial Images Using Color Invariant Features and Canny Edge Detection. International Journal of Engineering Trends and Technology (IJETT) 11 (8). pp. 393-396, 2014.
2. Ghafarian S., Ghafarian S. Automatic building detection based on supervised classification using high resolution Google Earth images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40 (3). pp 101-106. 2014.
3. A. van Etten. Building Extraction with YOLT2 and SpaceNet Data. URL: https://medium.com/the-downlinq/building-extraction-with-yolt2-and-spacenet-data-a926f9ffac4f
4. V.B. Kostousov, D.S. Perevalov, F.A. Kornilov. Digital terrain model generation from sattelite stereoscopic data // Materials of the XXX conference of the memory of the outstanding designer of gyroscopic instruments N.N. Ostryakova. – St. Petersburg: Concern CSRI Elektropribor, JSC. 2016. pp. 382-388.
5. D. Comaniciu, P. Meer. Mean shift: A robust approach to feature space analysis // IEEE TPAMI. May 2002. Vol. 24. No. 5. pp. 603-619.
6. Cour T., Benezit F., and Shi, J. Spectral segmentation with multiscale graphs decomposition // IEEE TPAMI. August 2000. Vol. 22. No. 8. pp. 888-905.
7. M. Galun, E. Sharon, R. Basri, A. Brandt. Texture segmentation by multiscale aggregation of filter responses and shape elements // ICCV. Vol. 2. 2003.pp. 716-723.
8. P. Arbelaez, M. Maire, C. Fowlkes, J. Malik. Contour Detection and Hierarchical Image Segmentation // IEEE TPAMI, Vol. 33. No. 5. 2011. pp. 898-916.
9. Arbelaez P., Pont-Tuset J., Barron J.T., Marques F., Malik J. Multiscale Combinatorial Grouping // IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2014. pp. 328-335.
10. K.K. Maninis, J. Pont-Tuset, P. Arbeláez, L. Van Gool. Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks // IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). Issue 99. 2017. pp. 1-14.
11. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results // URL: http://www.pascalnetwork.org/challenges/VOC/voc2012/workshop/index.html
12. A.V. Maryanova. The investigation of the quality of image segmentation algorithms depending on the size of objects in the image // Proceedings of the 46th International Youth School-Conference «Actual problems of mathematics and its applications», Ekaterinburg. 2015. pp. 129-134.
13. V.V. Kozoderov, Т.V. Kondranin. Methods for assessing the state of soil and vegetation cover from optical remote sensing systems. Moscow, MIPT. 2008. 222 p.
Review
For citations:
Dunaeva A.V., Kornilov F.A. Building detection in remote sensing images using a digital surface model. Computational Mathematics and Information Technologies. 2017;1(2). https://doi.org/10.23947/2587-8999-2017-2-185-193
Views:
125
Email this article 