| Issue |
E3S Web Conf.
Volume 333, 2021
Regional Problems of Earth Remote Sensing (RPERS 2021)
|
|
|---|---|---|
| Article Number | 01010 | |
| Number of page(s) | 5 | |
| Section | Models and Methods of Remote Sensing Data Processing | |
| DOI | https://doi.org/10.1051/e3sconf/202133301010 | |
| 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: drusin@sfu-kras.ru
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.