基于边缘引导和动态可变形Transformer的遥感图像变化检测

雷涛; 翟钰杰; 许叶彤; 王营博; 公茂果

doi:10.12263/DZXB.20230583

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基于边缘引导和动态可变形Transformer的遥感图像变化检测

学术论文 | 更新时间：2026-04-10

- 基于边缘引导和动态可变形Transformer的遥感图像变化检测
- Edge Guided and Dynamically Deformable Transformer Network for Remote Sensing Images Change Detection
- 电子学报 2024年52卷第1期页码：107-117
- 作者机构：
  
  1.陕西省人工智能联合实验室（陕西科技大学），陕西西安 710021
  2.陕西科技大学电子信息与人工智能学院，陕西西安 710021
  3.西安电子科技大学协同智能系统教育部重点实验室，陕西西安 710071
- 作者简介：
  
  [ "雷涛男，1981年11月出生，陕西大荔人.2011年在西北工业大学获得博士学位，现为陕西科技大学教授，博士生导师.主要从事图像处理、模式识别和计算机视觉等方面的研究工作. E-mail: leitao@sust.edu.cn" ]
  [ "翟钰杰男，1999年11月出生，山西运城人.现为陕西科技大学电子信息与人工智能学院硕士研究生.主要研究方向为计算机视觉. E-mail: 211612114@sust.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62271296;62201334);陕西省杰出青年科学基金(2021JC-47);陕西省重点研发计划(2022GY-436;2021ZDLGY08-07);陕西省自然科学基础研究计划(2022JQ-634;2022JQ-018);陕西省创新能力支撑计划(2020SS-03)
- DOI：10.12263/DZXB.20230583
  中图分类号： TP391
- 收稿：2023-06-26，
  
  修回：2024-01-16，
  
  纸质出版：2024-01-25
- 稿件说明：
移动端阅览
雷涛,翟钰杰,许叶彤,等.基于边缘引导和动态可变形Transformer的遥感图像变化检测[J].电子学报,2024,52(01):107-117.

LEI Tao, ZHAI Yu-jie, XU Ye-tong, et al.Edge Guided and Dynamically Deformable Transformer Network for Remote Sensing Images Change Detection[J].Acta Electronica Sinica, 2024, 52(01): 107-117.
雷涛,翟钰杰,许叶彤,等.基于边缘引导和动态可变形Transformer的遥感图像变化检测[J].电子学报,2024,52(01):107-117. DOI：10.12263/DZXB.20230583

LEI Tao, ZHAI Yu-jie, XU Ye-tong, et al.Edge Guided and Dynamically Deformable Transformer Network for Remote Sensing Images Change Detection[J].Acta Electronica Sinica, 2024, 52(01): 107-117. DOI：10.12263/DZXB.20230583

摘要

卷积神经网络（Convolutional Neural Network，CNN）和Transformer的混合架构能够有效建模图像的局部与全局特征，已成为遥感图像变化检测任务的主流网络.然而这类网络仍面临着一些挑战.CNN分支中的卷积和池化运算通常会抑制遥感图像中的高频信息，降低目标边界的精度；此外，Transformer分支对图像像素进行等同长程依赖关系建模，忽略了变化目标的形状及语义关联信息，导致网络对变化目标特征的表达不足.为解决上述问题，提出了基于边缘引导和动态可变形Transformer的遥感图像变化检测网络.在CNN分支中设计了边缘信息引导模块，利用高频信息增强目标区域的边缘信息，从而改善变化目标的轮廓精度.同时设计了一种新颖的动态可变形Transformer，能够自适应地匹配形状不同的变化目标，选择与变化相关的特征建模长程依赖关系，以提高网络的特征表达能力.实验结果表明，提出的方法在三个公开数据集LEVIR-CD、CDD和DSIFN-CD上显著提高了检测精度，在变化目标的边界精度和内部完整性方面都明显优于当前的主流网络.

Abstract

The hybrid architecture of convolutional neural network (CNN) and Transformer can effectively model local and global features of images

and has emerged as the predominant choice for remote sensing images change detection tasks. Nevertheless

these networks still confront challenges. The convolution and pooling operations employed by the CNN branch typically suppress the high-frequency information of remote sensing images

resulting in decreased precision of object boundaries in change detection results. Additionally

the Transformer branch equivalently models long-range dependencies for all pixels in remote sensing images

thereby disregarding shape information and semantic associations of objects

which limits the network’s feature representation ability on changed objects in remote sensing images. To address these challenges

a remote sensing images change detection network is proposed based on edge guidance and dynamic deformable Transformer. In the CNN branch

an edge information guidance module (EIG) is designed to enhance the edge information of changed objects by leveraging the high-frequency details of images. This enhancement improves the edge accuracy of the changed objects. Simultaneously

an innovative dynamically deformable Transformer (DDaT) is designed to adaptively match changed objects with different shapes

selecting features relevant to changes to model long-range dependency relationships and enhance the network’s feature expression capability. Experimental results show that the proposed method significantly improves the detection accuracy on three public datasets: LEVIR-CD

CDD and DSIFN-CD

and is significantly better than the current mainstream networks in terms of edge accuracy and internal integrity of changed objects.

关键词

Keywords

references

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