E3S Web Conf.
Volume 333, 2021Regional Problems of Earth Remote Sensing (RPERS 2021)
|Number of page(s)||5|
|Section||Models and Methods of Remote Sensing Data Processing|
|Published online||21 December 2021|
Using deep learning algorithms for texture segmentation of ultra-high resolution satellite images
1 Deep Learning Laboratory, Siberian Federal University, 660041 Krasnoyarsk, Russia
2 Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, 119991 Moscow, Russia
* Corresponding author: firstname.lastname@example.org
This paper presents the results of textural segmentation of satellite images with spatial resolution <1 m using U-Net convolutional neural networks. To conduct numerical experiments, a panchromatic image of the WorldView-2 test site on the territory of the Bronnitsky Forestry (Moscow region) used. The possibilities of automating the selection of neural network parameters based on genetic algorithms investigated. The proposed method makes it possible to effectively segment the main types of natural and man-made objects, as well as to distinguish structural classes of woodlands.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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