{"title":"CTST: CNN and Transformer-Based Spatio-Temporally Synchronized Network for Remote Sensing Change Detection","authors":"Shuo Wang;Wenbin Wu;Zhiqing Zheng;Jinjiang Li","doi":"10.1109/JSTARS.2024.3455261","DOIUrl":null,"url":null,"abstract":"Remote sensing change detection has achieved amazing results in recent years, especially the application of convolutional neural networks (CNN) and Transformer networks, which have revolutionized the field. However, the complex ground cover changes and the differences in lighting conditions caused by different times still pose challenges to the detection accuracy. In order to further extract spatial feature information and suppress irrelevant influences, we innovatively propose an edge-enhanced and time-synchronized remote sensing change detection network, called CNN and transformer-based spatio-temporally synchronized network (CTST). CTST designs a unique feature-integrated coding model with CNN and Transformer architectures, which enhances the model's understanding of the global dependencies and the extraction effect of the local features through the dynamic weight allocation method. We designed the edge salient feature enhancement module, which uses a dual operator fusion structure to combine the edge semantic information with the depth feature information, greatly enhancing the model's ability to recognize the edges of important terrain and features in remote sensing images. In addition, the spatio-temporally synchronized module is used to fuse the difference and superposition relationships between bitemporal features, and an innovative correlation mapping weighting algorithm is proposed to evaluate the similarity and difference of the fused features. Finally, the feature decoding complementary module is proposed to combine and complement features at different scales to further refine the already fused bichronological remote sensing features. The network results are optimized by the deep supervision (DS) strategy, which ensures the model's high efficiency and accuracy. CTST outperforms mainstream and state-of-the-art methods on all three datasets, with an F1 of 92.08% and an IoU of 85.33 on the LEVIR-CD dataset, an F1 of 93.25% and an IoU of 87.36% on the WHU-CD dataset, and an F1 of 93.25% and an IoU of 87.36% on the GZ-CD dataset. CD dataset the F1 is 85.95% and IoU is 75.37%, Param is 31.87 M, and FLOPS is 29.58 G.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10667658","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10667658/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Remote sensing change detection has achieved amazing results in recent years, especially the application of convolutional neural networks (CNN) and Transformer networks, which have revolutionized the field. However, the complex ground cover changes and the differences in lighting conditions caused by different times still pose challenges to the detection accuracy. In order to further extract spatial feature information and suppress irrelevant influences, we innovatively propose an edge-enhanced and time-synchronized remote sensing change detection network, called CNN and transformer-based spatio-temporally synchronized network (CTST). CTST designs a unique feature-integrated coding model with CNN and Transformer architectures, which enhances the model's understanding of the global dependencies and the extraction effect of the local features through the dynamic weight allocation method. We designed the edge salient feature enhancement module, which uses a dual operator fusion structure to combine the edge semantic information with the depth feature information, greatly enhancing the model's ability to recognize the edges of important terrain and features in remote sensing images. In addition, the spatio-temporally synchronized module is used to fuse the difference and superposition relationships between bitemporal features, and an innovative correlation mapping weighting algorithm is proposed to evaluate the similarity and difference of the fused features. Finally, the feature decoding complementary module is proposed to combine and complement features at different scales to further refine the already fused bichronological remote sensing features. The network results are optimized by the deep supervision (DS) strategy, which ensures the model's high efficiency and accuracy. CTST outperforms mainstream and state-of-the-art methods on all three datasets, with an F1 of 92.08% and an IoU of 85.33 on the LEVIR-CD dataset, an F1 of 93.25% and an IoU of 87.36% on the WHU-CD dataset, and an F1 of 93.25% and an IoU of 87.36% on the GZ-CD dataset. CD dataset the F1 is 85.95% and IoU is 75.37%, Param is 31.87 M, and FLOPS is 29.58 G.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.