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Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

1
Robotics and internet of things Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
2
Research Laboratory Smart Electricity & ICT, SEICT, LR18ES44, National Engineering School of Carthage, University of Carthage, Carthage 1054, Tunisia
3
Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
4
Prince Sultan University, Saudi Arabia/Gaitech Robotics, China/CISTER, INESC-TEC, ISEP, Polytechnic Institute of Porto, 4200-465 Porto, Portugal
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1369; https://doi.org/10.3390/rs11111369
Received: 25 April 2019 / Revised: 30 May 2019 / Accepted: 1 June 2019 / Published: 7 June 2019
(This article belongs to the Special Issue Convolutional Neural Networks Applications in Remote Sensing)
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Abstract

Segmenting aerial images is of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pretrained segmentation model to survey a new city that is not included in the training set significantly decreases accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We designed an algorithm that reduces the domain shift impact using generative adversarial networks (GANs). In the experiments, we tested the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves overall accuracy from 35% to 52% when passing from the Potsdam domain (considered as source domain) to the Vaihingen domain (considered as target domain). In addition, the method allows efficiently recovering the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%. View Full-Text
Keywords: convolutional neural networks; semantic segmentation; aerial imagery; domain adaptation; gener ative adversarial networks convolutional neural networks; semantic segmentation; aerial imagery; domain adaptation; gener ative adversarial networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Benjdira, B.; Bazi, Y.; Koubaa, A.; Ouni, K. Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images. Remote Sens. 2019, 11, 1369.

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