Semi-Supervised Domain Adaptation for Semantic Segmentation of Roads from Satellite Images

TL;DR

This paper introduces a semi-supervised domain adaptation approach for road segmentation in satellite images, leveraging pseudo-labeling and class confusion minimization to improve accuracy with limited labeled data.

Contribution

It proposes a novel semi-supervised field adaptation method combining pseudo-labeling and Minimum Class Confusion for improved road segmentation.

Findings

Performance increased on targeted datasets

Semi-supervised approach reduces need for extensive labeled data

Method outperforms traditional supervised models

Abstract

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.