Semisupervised Remote Sensing Image Building Change Detection Algorithm

Chai Wenguang; Luo Chongxi

doi:10.12052/gdutxb.240045

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Semisupervised Remote Sensing Image Building Change Detection Algorithm

更新时间：2026-05-27

- Semisupervised Remote Sensing Image Building Change Detection Algorithm
- Vol. 42, Issue 3, Pages: 36-43(2025)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240045
  CLC： TP391.4;TP751.1
- Published：2025
- 稿件说明：
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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

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