Abstract
In the last decade, computer vision algorithms, including those related to the problem of understanding images, have developed a lot. One of the tasks within the framework of this problem is semantic segmentation of images, which provides the classification of objects available in the image at the pixel level. This kind of segmentation is essential as a source of information for robotic UAV behavior control systems. One of the types of pictures that are used in this case is the images obtained by remote sensing of the earth’s surface. A significant number of various neuroarchitecture based on convolutional neural networks were proposed for solving problems of semantic segmentation of images. However, for some reasons, not all of them are suitable for working with pictures of the earth’s surface obtained using remote sensing. Neuroarchitectures that are potentially suitable for solving the problem of semantic segmentation of images of the earth’s surface are identified, a comparative analysis of their effectiveness as applied to this task is carried out.
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This research is supported by the Ministry of Science and Higher Education of the Russian Federation as Project No. 9.7170.2017/8.9.
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Igonin, D.M., Tiumentsev, Y.V. (2020). Semantic Segmentation of Images Obtained by Remote Sensing of the Earth. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_36
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DOI: https://doi.org/10.1007/978-3-030-30425-6_36
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