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Application of convolutional neural networks for thematic mapping of time sequences of satellite multispectral images


V.E. Dementiev – Ph.D.(Eng.), Associate Professor, Department «Telecommunications», Ulyanovsk State Technical University
D.S. Kondratiev – Post-graduate Student, Department «Telecommunications», Ulyanovsk State Technical University
A.G. Frankel – Post-graduate Student, Department «Telecommunications», Ulyanovsk State Technical University

One of the fundamental steps in the processing of remote sensing imagery is segmentation. The use of known segmentation algorithms for thematic mapping of satellite imagery leads to significant errors due to two reasons. Firstly, the known algorithms are mostly unable to take into account the fact that the satellite image is a multi-zone image. Secondly, the existing approaches do not allow processing the time sequence of such images. The aim of this paper is to overcome these disadvantages by modifying neural network segmentation procedures and classifying multidimensional data.
In this paper, we consider a modification of the convolutional UNET network, based on co-processing and the current multizone image and archival processing results obtained in previous moments. It is shown that this modification allows to improve the quality of treatment by 10−12% and in a significant number of cases to achieve near-pixel accuracy. Compared to the existing algorithms, the proposed approach requires relatively small computational costs and can be implemented in standard image processing packages.

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  7. Dstl Satellite Imagery Feature Detection. URL =
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