Recognition of Urban Transport Infrastructure Objects Via Hyperspectral Images

Oleg Saprykin, Alexander Fedoseev, Tatyana Mikheeva

2016

Abstract

Actualization of vector maps of the urban transport infrastructure, including street and road network, in conditions of constant changes is a resource-consuming task and it requires the automation of the process. The article considers the solving of problem of transport infrastructure objects recognition in hyperspectral images by deep convolutional neural networks. The hyperspectral images from different sources are considered for solving the problem. We propose a new approach to the formation of receptive fields of convolutional neural networks: the receptive field covers several pixels, but the depth of the colour channels is limited. In the proposed approach the receptive field moves in three dimensions in two spatial dimensions and in spectral channels dimension. It gives the ability to recognize the transport infrastructure objects by spatial patterns and spectrum.

References

  1. Cavalli, R., Fusilli, L., Pascucci, S., Piguatti, S. and Santini, F. (2008). Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy), Sensors, 8(5), pp. 3299-3320.
  2. Chandra, A.M. (2008). Remote Sensing and Geografical Information Systems, Technosphera, Moscow.
  3. Chang, C.I. (2000). An information-Theoretic Approach to Spectral Variability, Similarity, and Discrimination for Hyperspectral Image Analysis. Information Theory, IEEE Transactions on Information Theory, 46(5), pp. 1927-1932.
  4. Gomez, R.B. (2002). Hyperspectral imaging: a useful technology for transportation analysis. Optical Engineering, 41(9), pp. 2137-2143.
  5. Gorban, A., Kegl, B, Wunsch, D. and Zinovyev A. (2008). Principal Manifolds for Data Visualisation and Dimension Reduction, Lecture notes in computational science and engineering, Springer, Berlin - Heidelberg - New York.
  6. Gualtieri, J.A. and Cromp, R.F. (1999). Support vector machines for hyperspectral remote sensing classification, available at: http://ntrs.nasa.gov/archive /nasa/casi.ntrs.nasa.gov/19990021532.pdf (accessed 5 January 2016).
  7. Herold, M., Roberts, D., Smadi, O and Noronha, V. (2004a). Road condition mapping with hyperspectral remote sensing. Available at: http://www.geogr.unijena.de/c5hema/urbanspec/av04_roadmapping_herol detal.pdf (accessed 5 January 2016).
  8. Herold, M., Gardner, M, Noronha, V. and Roberts, V. (2004b). Spectrometry and hyperspectral remote sensing of urban road infrastructure. Available at: http://www.eo.uni-jena.de/c5hema/pub/rse04_herold etal.pdf (accessed 5 January 2016).
  9. Hu, W., Huang, Y., Wei, L., Zhang, F., and Li, H. (2015). Deep Convolutional Neural Networks for Hyperspectral Image Classification. Journal of Sensors, vol. 2015, Article ID 258619, 12 pages.
  10. Keshava, N. (2003). Survey of Spectral Unmixing Algorithms. Lincoln Laboratory Journal, 14(1), pp. 55-78.
  11. Krizhevsky, A., Sutskever, I. and Geoffrey E.H. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25. Curran Associates, Inc. Pp. 1097-1105. Available at: http://papers.nips.cc/paper/48 24-imagenet-classification-with-deep-convolutional-ne ural-networks.pdf.
  12. Kukharenko, B.G. (2013). Algorithms of analysis of hyperspectral images components, Supplement of Journal “Information technologies”, No. 6, - 32 p.
  13. Mei, A., Salvatori, R., Fiore, N., Allegrini, A. and D'Andrea (2014). A Integration of field and laboratory spectral data with multi-resolution remote sensed imagery for asphalt surface differentiation. Remote sensing, Vol. 6, pp. 2765-2781.
  14. Mikheeva, T.I. and Fedoseev, ?.?. (2014). Clusterization of Hyperspectral Data of Transport Infrastructure Objects Monitoring. Reporter of Samara Scientific Center of Russian Academy of Sciences, Vol. 16 No. 4 (2), pp. 435-442.
  15. Miraliakbari, A. and Hahn, M (2014). Development of multi-sensor system for road condition mapping. The International archives of the photogrammetry, remote sensing and spatial information, Vol. XL-1, pp. 265- 272.
  16. Resende, M., Bernucci, L. and Quintanilha, J. (2014). Monitoring the condition of roads pavement surfaces: proposal of methodology using hyperspectral images, Journal of Transport Literature, 8(2), pp. 201-220.
  17. Ratle, F., Camps-Valls, G. and Weston, J. (2010). Semisupervised neural networks for efficient hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 48(5), pp. 2271- 2282.
  18. Robila, S. (2005). Investigation of Spectral Screening Techniques for Independent Component Analysis Based Hyperspectral Image Processing. Available at: http ://www.cs.uno.edu/stefan/ (accessed 5 January 2016).
  19. Rodarmel, C. and Shan, J. (2002). Principal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62(2), pp. 115-122.
  20. Saprykin, O. and Saprykina, O. (2015). Multilevel Modelling of Urban Transport Infrastructure. In Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS-2015), Portugal, Lisbon: SCITEPRESS, pp. 78-82.
  21. Schott, J. (2007). Remote sensing: the image chain approach, 2nd ed., Oxford University Press, USA.
  22. Schowengerdt, R.A. (2010). Remote Sensing: Methods and Models for Image Processing. Technosphera, Moscow.
  23. Simonyan, K. and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR. abs/1409.1556.
  24. Yuanliu, X., Runsheng, W. and Shengwen, L. (2007). Atmospheric correction of hyperspectral data using MODTRAN model. Proceedings of 16th National Symposium on Remote Sensing of China, 7 pages.
  25. Zhuravel, J.N. and Fedoseev, A.A. (2013). Specificity of Hyperspectral Remote Sensing Data Processing for the Tasks of Environment Monitoring, Computer Optics, Vol. 37 No. 4. - pp. 471-476.
  26. Wei, J., Zhou, G., Zheng, Z. (2009). Survey and analysis of land satellite remote sensing applied in highway transportations infrastructure engineering. Available at: http://www.asprs.org/a/publications/proceedings/ba ltimore09/0102.pdf (accessed 5 January 2016).
Download


Paper Citation


in Harvard Style

Saprykin O., Fedoseev A. and Mikheeva T. (2016). Recognition of Urban Transport Infrastructure Objects Via Hyperspectral Images . In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-185-4, pages 203-208. DOI: 10.5220/0005901902030208


in Bibtex Style

@conference{vehits16,
author={Oleg Saprykin and Alexander Fedoseev and Tatyana Mikheeva},
title={Recognition of Urban Transport Infrastructure Objects Via Hyperspectral Images},
booktitle={Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2016},
pages={203-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005901902030208},
isbn={978-989-758-185-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Recognition of Urban Transport Infrastructure Objects Via Hyperspectral Images
SN - 978-989-758-185-4
AU - Saprykin O.
AU - Fedoseev A.
AU - Mikheeva T.
PY - 2016
SP - 203
EP - 208
DO - 10.5220/0005901902030208