广东工业大学学报2025，Vol.42Issue(3)：36-43,8.DOI:10.12052/gdutxb.240045
半监督遥感图像建筑物变化检测算法
Semisupervised Remote Sensing Image Building Change Detection Algorithm
摘要
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
遥感图像/变化检测/一致性正则化/半监督学习Key words
remote sensing images/change detection/consistency regularization/semi-supervised learning分类
计算机与自动化引用本文复制引用
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):36-43,8.基金项目
国家自然科学基金资助项目(61772143) （61772143）
广东省重点领域研发计划项目(2021B0101220006) （2021B0101220006）
