半监督遥感图像建筑物变化检测算法

柴文光; 罗崇熙

doi:10.12052/gdutxb.240045

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

半监督遥感图像建筑物变化检测算法

计算机科学与技术 | 更新时间：2026-05-27

- 半监督遥感图像建筑物变化检测算法
- Semisupervised Remote Sensing Image Building Change Detection Algorithm
- 广东工业大学学报 2025年42卷第3期页码：36-43
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  柴文光(1969–)，男，副教授，博士，主要研究方向为人工智能与应用技术、计算机系统结构及高性能计算，E-mail：chaiwg@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61772143);广东省重点领域研发计划项目(2021B0101220006)
- DOI：10.12052/gdutxb.240045
  中图分类号： TP391.4;TP751.1
- 纸质出版：2025
- 稿件说明：
移动端阅览
柴文光, 罗崇熙. 半监督遥感图像建筑物变化检测算法[J]. 广东工业大学学报, 2025, 42(3): 36–43.

Chai Wenguang, Luo Chongxi. Semisupervised Remote Sensing Image Building Change Detection Algorithm[J]. 2025, 42(3): 36-43.
柴文光, 罗崇熙. 半监督遥感图像建筑物变化检测算法[J]. 广东工业大学学报, 2025, 42(3): 36–43. DOI： 10.12052/gdutxb.240045

Chai Wenguang, Luo Chongxi. Semisupervised Remote Sensing Image Building Change Detection Algorithm[J]. 2025, 42(3): 36-43. DOI： 10.12052/gdutxb.240045.

摘要

建筑物的变化检测在遥感图像处理和模式识别领域中具有重要意义，但在深度学习算法的应用中，数据标注一直是一个显著的挑战，特别是在变化检测的场景下。因此，针对基于深度学习的变化检测算法中数据标注的难题，本文提出了一种半监督学习方法。该方法采用了融合双时相特征的孪生网络来进行特征提取，并构建了一个教师−学生网络框架以实施模型的半监督训练。为了进一步提升半监督变化检测的准确度，本文在深度特征上引入了随机扰动，以此来实现一致性正则化。此外，在图像深度特征的层面上，本文还提出通过捕获双时相图像特征的差异来形成决策边界以区分双时相图变化的方法。该方法在Levir-CD和WHU_Building两个公开数据集上分别实现了83.04%和85.57%的交并比(Intersection over Union

IoU)。实验结果表明，本文提出的方法在使用少量标注数据的前提下，能够达到与全监督训练相近的性能。

Abstract

Change detection in buildings holds significant importance in the fields of remote sensing image processing and pattern recognition. However

data annotation has always been a prominent challenge in the application of deep learning algorithms

especially in change detection scenarios. To address the data annotation challenges in deep learning-based change detection algorithms

this paper proposes an innovative semi-supervised learning method. This method employs a Siamese network that fuses bi-temporal features for feature extraction and constructs a teacher-student network framework for semi-supervised model training. To further enhance the accuracy of semi-supervised change detection

this paper introduces random perturbations in deep features to achieve consistency regularization. Additionally

on the level of image deep features

the paper proposes a method for forming decision boundaries by capturing differences in bi-temporal image features to distinguish changes in bi-temporal images. This method achieved Intersection over Union (IoU) scores of 83.04% and 85.57% on the Levir-CD and WHU Building datasets

respectively. Experimental results show that the proposed method can achieve performance levels close to fully supervised training with a limited amount of labeled data.

关键词

Keywords

references

ZHANG C, YUE P, TAPETE D, et al . A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images [J ] . ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 166: 183-200.

RODRIGO C D, BERTRAND L S, ALEXANDREl B. Fully convolutional siamese networks for change detection[C]//2018 25th IEEE International Conference on Image Processing (ICIP). Athens: IEEE, 2018: 4063-4067.

CHEN P, ZHANG B, HONG D, et al . FCCDN: Feature constraint network for VHR image change detection [J ] . ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 187: 101-119.

ZHANG F X, HUANG J, LI H. Light-weight bilateral input D-WNet for building change detection in aerial imagery [J]. Laser Optoelectronics Progress, 2024, 61(8): 3.

TARVAINEN A, VALPOLA H. Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results [J]. Advances InNeural Information Processing Systems, 2017, 30: 1195-1204.

SOHN K, BERTHELOT D, CARLINI N, et al . Fixmatch: simplifying semi-supervised learning with consistency and confidence [J ] . Advances in Neural Information Processing Systems, 2020, 33: 596-608.

YANG L, QI L, FENG L, et al. Revisiting weak-to-strong consistency in semi-supervised semantic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 7236-7246.

XIE E, WANG W, YU Z, et al . SegFormer: simple and efficient design for semantic segmentation with transformers [J ] . Advances in Neural Information Processing Systems, 2021, 34: 12077-12090.

LIN T Y, DOLLR P, GIRSHICK. et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 2117-2125.

MITTAL S, TATARCHENKO M, BROX T. Semi-supervised semantic segmentation with high-and low-level consistency [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(4): 1369-1379.

PENG D, LORENZO B, ZHANG Y J, et al . SemiCDNet: a semisupervised convolutional neural network for change detection in high resolution remote-sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(7): 5891-5906.

FANG S, LI K, SHAO J. et al. SNUNet-CD: a densely connected Siamese network for change detection of VHR images [J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.

CODEGONI A, LOMBARDI G, FERRARI A. TINYCD: a (not so) deep learning model for change detection [J]. Neural Computing and Applications, 2023, 35(11): 8471-8486.

BANDARA W G C, PATEL V M. A transformer-based siamese network for change detection[C]//IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. Malaysia: IEEE, 2022: 207-210.

KOLESNIKOV A, BEYER L, ZHAI X, et al. Big transfer (bit): general visual representation learning[C]//Computer Vision-ECCV. Glasgow: ECCV, 2020: 491-507.

SUN C, WU J, CHEN H, et al . SemiSANet: a semi-supervised high-resolution remote sensing image change detection model using Siamese networks with graph attention [J ] . Remote Sensing, 2022, 14(12): 2801.

SUN C, CHEN H, DU C, et al . SemiBuildingChange: a semi-supervised high-resolution remote sensing image building change detection method with a pseudo bitemporal data generator [J ] . IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-19.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

结合GAN损失与预训练模型的半监督SPECT重建方法

具守恒量波动方程的循环双子网物理信息神经网络

多尺度对比嵌入增强的自适应入侵检测模型

基于高效时序建模的RGB-T跟踪

面向灾后救援的多无人机通感算一体化资源分配优化方法