Abstract
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.
Highlights
This paper presents the results of textural segmentation of satellite images with spatial resolution
The task was set to create a machine-learning model based on convolutional neural networks
Architecture Xception is a convolutional neural network that is capable of working with a small amount of training data for segmentation tasks
Summary
This paper presents the results of textural segmentation of satellite images with spatial resolution
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