Recognition of Urban Transport Infrastructure Objects Via Hyperspectral Images

摘要

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.