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Domain Adaptation for Semantic Segmentation of Aerial Images with Adversarial Attack

Title
Domain Adaptation for Semantic Segmentation of Aerial Images with Adversarial Attack
Translated Title
적대적 공격을 이용한 항공 이미지 분할에 대한 도메인 적응
Authors
Younghwan Na
DGIST Authors
Younghwan Na; Jihwan Choi; Sunghoon Im
Advisor(s)
최지환
Co-Advisor(s)
Sunghoon Im
Issue Date
2021
Available Date
2022-07-07
Degree Date
2021/02
Type
Thesis
Keywords
Adversarial network, building extraction, semantic segmentation, domain adaptation, 이미지 분할, 도메인 적응, 적대적 신경망, 건물 추출
Abstract
Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).
Table Of Contents
Ⅰ. Introduction 1 1.1. Semantic segmentation of aerial images 1 1.2. Challenging issue in CNN-based segmentation models 2 1.3. Overview of proposed scheme 2 Ⅱ. Basic architecture for semantic segmentation 4 2.1. Semantic segmentation systems 4 2.2. Inria aerial image labeling dataset 6 2.3. Training setup 6 2.4. Test results and comparison with other architectures 7 2.5. Test results with other datasets 8 2.6. Related work for domain adaptation 11 Ⅲ. Domain adaptive transfer attack (DATA) 11 3.1. Overview of the proposed model 12 3.2. Objective function for the generator 14 3.3. Objective function for discriminator 15 3.4. Training the adversarial attack model & discriminator 16 Ⅳ. DATA-based adversarial training and results 19 4.1. Adversary training setup 19 4.2. Comparison with Other Methods 21 4.3. Extended experiments in various environments 24 Ⅴ. Conclusion 26 References 28
URI
http://dgist.dcollection.net/common/orgView/200000361817
http://hdl.handle.net/20.500.11750/16647
DOI
10.22677/thesis.200000361817
Degree
Master
Department
Information and Communication Engineering
University
DGIST
Related Researcher
  • Author Im, Sunghoon Computer Vision Lab.
  • Research Interests Computer Vision; Deep Learning; Robot Vision
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Collection:
Department of Electrical Engineering and Computer ScienceThesesMaster


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