• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2023, Vol. 45 ›› Issue (09): 1593-1601.

• 图形与图像 • 上一篇    下一篇

孪生注意力门控融合的遥感图像变化检测编解码网络

陈海永1,吕承杰1,杜春2,陈鹏1   

  1. (1.河北工业大学人工智能与数据科学学院,天津 300401;2.国防科技大学电子科学学院,湖南 长沙 410073)
  • 收稿日期:2022-03-30 修回日期:2022-07-15 接受日期:2023-09-25 出版日期:2023-09-25 发布日期:2023-09-12
  • 基金资助:
    国家重点研发计划(2022YFB3303800);国家自然科学基金(U21A20482,62073117);河北省自然科学基金(F2019202305)

A Siamese attention-gated fusion encoding-decoding network for remote sensing image change detection

CHEN Hai-yong1,L Cheng-jie1,DU Chun2,CHEN Peng1   

  1. (1.School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401;
    2.College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China)
  • Received:2022-03-30 Revised:2022-07-15 Accepted:2023-09-25 Online:2023-09-25 Published:2023-09-12

摘要: 针对深度卷积神经网络中特征图分辨率降低,进而导致遥感图像小变化区域检测性能差以及难以有效区分外界干扰而产生伪变化等问题,提出了一种孪生注意力门控融合的遥感图像变化检测编解码网络。在编码部分引入了三重注意力网络模块,为进一步解决变化检测图中产生伪变化的问题,提出了注意力门控融合模块,从多个层次选择性地融合特征,在解码部分直接引入深度监督策略,增强了变化检测网络的特征提取能力。通过实验对本文所提网络的有效性进行了验证。

关键词: 图像处理, 高分辨率遥感图像, 变化检测, 注意力机制, 门控融合, 深度监督

Abstract: To address the problems of reduced feature map resolution in deep convolutional neural networks, which leads to poor performance in detecting small changes in remote sensing images and difficulty in effectively distinguishing external interference to produce false changes, a Siamese attention-gated fusion encoding-decoding network for remote sensing image change detection is proposed. A triple attention network module is introduced in the encoding part to further solve the problem of false changes in the change detection image. An attention-gated fusion module is proposed to selectively fuse features from multiple levels. A deep supervision strategy is directly introduced in the decoding part to enhance the feature extraction capability of the change detection network. The effectiveness of the proposed network is verified through experiments.

Key words: image processing, high-resolution remote sensing image, change detection, attention mechanism, gated fusion, deep supervision