北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (3): 451-460.doi: 10.13700/j.bh.1001-5965.2020.0455

• 论文 • 上一篇    下一篇

基于混合网络的异源遥感图像变化检测

周圆1, 李祥瑞2, 杨晶1   

  1. 1. 天津大学 电气自动化与信息工程学院, 天津 300072;
    2. 天津大学 国际工程师工程学院, 天津 300072
  • 收稿日期:2020-08-24 发布日期:2021-04-08
  • 通讯作者: 周圆 E-mail:zhouyuan@tju.edu.cn
  • 作者简介:周圆,女,博士,教授,博士生导师。主要研究方向:机器学习、计算机视觉、3D图像/视频编码与传输、无线传感器网络、网络视频通信等。

Heterogeneous remote sensing image change detection based on hybrid network

ZHOU Yuan1, LI Xiangrui2, YANG Jing1   

  1. 1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;
    2. Tianjin International Engineering Institute, Tianjin University, Tianjin 300072, China
  • Received:2020-08-24 Published:2021-04-08

摘要: 为了更加准确地进行异源遥感图像的变化检测任务,提出了一种基于混合网络的异源遥感图像变化检测算法。利用伪孪生网络提取异源遥感图像块间空间维度的变化特征,利用早期融合网络提取异源遥感图像块间光谱维度的变化特征,将2支网络提取的特征进行融合,并将融合后的变化特征输入到sigmoid层进行二分类检测。同时,在伪孪生网络中加入对比损失函数,通过优化对比损失函数,使得在特征空间中,未变化图像对的空间特征差异更小,变化图像对的空间特征差异更大,以提升网络的区分能力和收敛速度。

关键词: 变化检测, 异源, 伪孪生网络, 早期融合网络, 对比损失函数

Abstract: In order to more quickly and accurately perform the change detection task of heterogeneous remote sensing images, this paper presents a heterogeneous remote sensing image change detection algorithm based on a hybrid network. The algorithm uses a pseudo-siamese network to extract change features between the heterogeneous image blocks in spatial dimension, and uses an early fusion network to extract change features between the heterogeneous image blocks in spectral dimension. The features extracted from the two networks are fused and the fused features are input to the sigmoid layer for binary classification to determine whether the feature has changed. In addition, the contrast loss function is added to the pseudo-siamese network, so that in the features space, the spatial features of the unchanged image pair are closer, and the spatial features of the changed image pair are farther away, which is conducive to improving the network's distinction ability and convergence speed.

Key words: change detection, heterogeneous, pseudo-siamese network, early fusion network, contrast loss function

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