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

• URL: http://arxiv.org/abs/2212.13079v1
• Date: Mon, 26 Dec 2022 10:50:40 GMT
• Title: Semi-Supervised Domain Adaptation for Semantic Segmentation of Roads from Satellite Images
• Authors: Ahmet Alp Kindiroglu, Metehan Yal\c{c}{\i}n, Furkan Burak Ba\u{g}c{\i}, Mahiye Uluya\u{g}mur \"Ozt\"urk
• Abstract summary: This paper presents the preliminary findings of a semi-supervised segmentation method for extracting roads from sattelite images. A semi-supervised field adaptation method based on pseudo-labeling and Minimum Class Confusion has been proposed.
• Score: 1.1470070927586016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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