Recognition of Urban Transport Infrastructure Objects Via Hyperspectral Images

Oleg Saprykin, Alexander Fedoseev, Tatyana Mikheeva


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.


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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

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,},

in EndNote Style

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