华东师范大学学报（自然科学版）Issue(4)：49-60,12.DOI:10.3969/j.issn.1000-5641.2025.04.005
C-T Net:融合CNN和Transformer的遥感图像变化检测模型
C-T Net:Remote sensing image change detection model integrating CNN and Transformer
摘要
Abstract
Due to factors such as differences in acquisition time,angle,and sensor characteristics,dual temporal remote sensing images often manifest various pseudo-changes.Moreover,certain changes may have an uninteresting nature and typically correlate with adjacent objects.However,the utilization of a fully convolutional neural network(FCN)may lead to the loss of long-range information.To address this issue,this study proposes a network that integrates convolutional neural networks(CNN)and Transformer(C-T Net),which has an overall network architecture consisting of a deep feature extraction section and a detection head section.The network backbone combines CNN and Swin Transformer.Additionally,two novel fusion modules,C-to-T and T-to-C,are designed to amalgamate local features and global features.The detection head section utilizes Transformer encoding and decoding to derive refined feature maps for discerning change regions.Comparative experiments with multiple change detection models validate the efficacy of C-T Net.On the LEVIR-CD and WHU-CD datasets,the proposed method achieves the highest F1_1(90.63%,86.24%)and pIoU_1(82.87%,75.81%).Results across both datasets affirm that our proposed algorithm outperforms existing methodologies from both visual and data-centric perspectives.关键词
多时相/变化检测/卷积神经网络/转换器/特征融合Key words
multi-temporal/change detection/convolutional neural networks(CNN)/transformer/feature fusion分类
信息技术与安全科学引用本文复制引用
武一,贠世林..C-T Net:融合CNN和Transformer的遥感图像变化检测模型[J].华东师范大学学报（自然科学版）,2025,(4):49-60,12.基金项目
国家自然科学基金(51977059) （51977059）
河北省自然科学基金(E2020202042) （E2020202042）
