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  • 赵丹培 ( 副教授 )

    的个人主页 http://shi.buaa.edu.cn/zhaodanpei/zh_CN/index.htm

  •   副教授   博士生导师   硕士生导师
  • 主要任职:航天信息工程系书记
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UGCNet: An Unsupervised Semantic Segmentation Network Embedded With Geometry Consistency for Remote-Sensing Images
点击次数:
影响因子:5.343
DOI码:10.1109/LGRS.2021.3129776
所属单位:Beihang Univ, Image Proc Ctr, Sch Astronaut
发表刊物:IEEE Geoscience and Remote Sensing Letters
关键字:Image segmentationSemanticsTrainingAdaptation modelsRemote sensingGeometryDecodingGenerative-adversarial learninggeometry consistency (GC)remote-sensing images (RSIs)semantic segmentationunsupervised
摘要:In remote-sensing image (RSI) semantic segmentation, the dependence on large-scale and pixel-level annotated data has been a critical factor restricting its development. In this letter, we propose an unsupervised semantic segmentation network embedded with geometry consistency (UGCNet) for RSIs, which imports the adversarial-generative learning strategy into a semantic segmentation network. The proposed UGCNet can be trained on a source-domain dataset and achieve accurate segmentation results on a different target-domain dataset. Furthermore, for refining the remote-sensing target geometric representation such as densely distributed buildings, we propose a geometry-consistency (GC) constraint that can be embedded in both image-domain adaptation process and semantic segmentation network. Therefore, our model could achieve cross-domain semantic segmentation with target geometric property preservation. The experimental results on Massachusetts and Inria buildings datasets prove that the proposed unsupervised UGCNet could achieve a very comparable segmentation accuracy with the fully supervised model, which validates the effectiveness of the proposed method.
论文类型:期刊论文
一级学科:控制科学与工程
卷号:19
ISSN号:10.1109/LGRS.2021.3129776
是否译文:否
发表时间:2022-01-21
收录刊物:SCI
发布期刊链接:https://ieeexplore.ieee.org/document/9623453/
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