Comparative Efficiency Analysis for Various Neuroarchitectures for Semantic Segmentation of Images in Remote Sensing Applications

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.