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Siamese NestedUNet Networks for Change Detection of High Resolution Satellite Image

Kaiyu LiZhe LiSheng Fang

Shandong University of Science and Technology

摘要:Change detection is an important task in remote sensing(RS) image analysis.With the development of deep learning and the increase of RS data,there are more and more change detection methods based on supervised learning.In this paper,we improve the semantic segmentation network UNet++ and propose a fully convolutional siamese network(Siam-NestedUNet) for change detection.We combine three types of siamese structures with UNet++ respectively to explore the impact of siamese structures on the change detection task under the condition of a backbone network with strong feature extraction capabilities.In addition,for the characteristics of multiple outputs in Siam-NestedUNet,we design a set of experiments to explore the importance level of the output at different semantic levels.According to the experimental results,our method improves greatly on a number of indicators,including precision,recall,F1-Score and overall accuracy,and has better performance than other SOTA change detection methods.Our implementation will be released at https://github.com/likyoo/Siam-NestedUNet.
会议名称:

2020 International Conference on Control, Robotics and Intelligent System (CCRIS 2020)

会议时间:

2020-10-27

会议地点:

中国福建厦门

  • 专辑:

    工程科技Ⅱ辑; 信息科技

  • 专题:

    工业通用技术及设备; 自动化技术; 自动化技术

  • DOI:

    10.26914/c.cnkihy.2020.045871

  • 分类号:

    TP751;TP18

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