论文标题
半监督域的适应性，用于从卫星图像中对道路的语义分割
Semi-Supervised Domain Adaptation for Semantic Segmentation of Roads from Satellite Images
论文作者
论文摘要
本文介绍了一种半监督分割方法的初步发现，该方法用于从Sattelite图像中提取道路。人工神经网络和图像分割方法是从卫星图像中提取道路数据的最成功方法之一。但是，这些模型需要来自不同地区的大量培训数据才能达到高精度。如果这些数据需要更具数量或质量，则是一种标准方法，可以通过从不同来源获得的注释数据转移知识来训练深层神经网络。这项研究提出了一种通过半监督学习方法执行路径分割的方法。已经提出了一种基于伪标记和最低类混淆方法的半监督场适应方法，并且已经观察到它可以提高目标数据集中的性能。
This paper presents the preliminary findings of a semi-supervised segmentation method for extracting roads from sattelite images. Artificial Neural Networks and image segmentation methods are among the most successful methods for extracting road data from satellite images. However, these models require large amounts of training data from different regions to achieve high accuracy rates. In cases where this data needs to be of more quantity or quality, it is a standard method to train deep neural networks by transferring knowledge from annotated data obtained from different sources. This study proposes a method that performs path segmentation with semi-supervised learning methods. A semi-supervised field adaptation method based on pseudo-labeling and Minimum Class Confusion method has been proposed, and it has been observed to increase performance in targeted datasets.
