Abstract

The problem of image understanding currently attracts considerable attention of researchers, since its solution is critically important for a significant number of applied problems. Among the most critical components of this problem is the semantic segmentation of images, which provides a classification of objects on the image at the pixel level. One of the applied problems in which semantic segmentation is an essential element of the process of solving them is the information support of the behavior control systems for robotic UAVs. Among the various types of images that are used to solve such problems, it should be noted images obtained by remote sensing of the Earth’s surface. A significant number of variants of neuroarchitectures based on convolutional neural networks have been proposed to solve the semantic image segmentation problem, However, for various reasons, not all of them are suitable for working with images 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 concerning this task is carried out.

中文翻译：

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遥感应用中图像语义分割的各种神经体系结构的比较效率分析

摘要

图像理解的问题目前引起了研究者的极大关注，因为其解决方案对于大量应用问题至关重要。该问题最关键的组成部分是图像的语义分割，它在像素级别提供了图像上对象的分类。语义分段是解决它们的过程中必不可少的应用问题之一是机器人无人机行为控制系统的信息支持。在用于解决此类问题的各种图像中，应注意通过遥感地球表面获得的图像。已经提出了基于卷积神经网络的大量神经体系结构变体来解决语义图像分割问题，但是，由于各种原因，并非所有这些都适合使用遥感技术获得的地球表面图像。确定了可能适合解决地球表面图像的语义分割问题的神经体系结构，并对其有效性进行了比较分析。