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

2019-05-08 16:35:02
Bilel Benjdira, Yakoub Bazi, Anis Koubaa, Kais Ouni

Abstract

Segmenting aerial images is being 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 pre-trained segmentation model to survey a new city that is not included in the training set significantly decreases the 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 design an algorithm that reduces the domain shift impact using Generative Adversarial Networks (GANs). In the experiments, we test the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves the overall accuracy from 35% to 52% when passing from Potsdam domain (considered as source domain) to Vaihingen domain (considered as target domain). In addition, the method allows recovering efficiently 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%.

Abstract (translated)

航拍图像的分割在城市区域的监视和场景理解中具有很大的潜力。它提供了一种自动报告居住区发生的不同事件的方法。这显著促进了公共安全和交通管理应用。在卷积神经网络方法得到广泛应用后,如果提供一个健壮的数据集,语义分割算法的精度很容易超过80%。尽管取得了成功,但部署了一个预先培训的细分模型来调查一个未包含在培训集中的新城市,这大大降低了准确性。这是由于模型训练的源数据集与新城市图像的新目标域之间的域转换。本文针对这一问题,考虑了域自适应在航空图像语义分割中的挑战。我们设计了一种使用生成对抗网络(gans)减少域移动影响的算法。在实验中,我们在国际摄影测量与遥感学会(ISPRS)的语义分割数据集上测试了所提出的方法,发现从波茨坦域(被认为是源域)到瓦辛根域(被认为是目标域),我们的方法的总体精度从35%提高到52%。此外,该方法还可以有效地恢复由于传感器变化而导致的反向类。特别是,由于传感器的变化范围从14%提高到61%,从而提高了反向类的平均分割精度。

URL

https://arxiv.org/abs/1905.03198

PDF

https://arxiv.org/pdf/1905.03198.pdf


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