FMT: Foundation Model-based Transformer for Remote Sensing Change Detection

FMT: Foundation Model-based Transformer for Remote Sensing Change Detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article FMT: Foundation Model-based Transformer for Remote Sensing Change Detection xianran zhang, Zhengpeng Li, Jiansheng Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7112910/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 20 You are reading this latest preprint version Abstract Change detection is a popular topic in the field of remote sensing, aiming to detect significant changes between bi-temporal images. With the technological advancements, advanced satellites capture more complex geographical information, making change detection more challenging. Existing models often use convolutional networks and Transformers to learn changes between bi-temporal images, but they often fail to fully utilize the knowledge and scalability of the foundation model, neglecting the importance of filtering invariant background information, which leads to unfiltered tokens interfering with model performance. In this work, we demonstrate the advantages of the foundation model and the necessity of token filtering. We propose a Foundation Model-based Transformer for Remote Sensing Change Detection (FMT). We introduce a collaborative feature extraction module that utilises a modified ResNet18 and a frozen foundation model. We also propose a multi-scale cross-axis attention fusion module that combines general features extracted by the foundation model with ResNet18 backbone network features. Additionally, we design an anchor token filtering module that uses algorithms such as TVConv, k-means, and top-k to filter change region anchor tokens based on a fuzzy prediction map and background information. Subsequently, the relationships between tokens are learned through a self-attention mechanism, and finally, a dual cross-attention module is used to interact with original and enhanced features, generating a prediction map with a convolutional decoder. The FMT was evaluated on the WHU‑CD, LEVIR‑CD, and DSIFN datasets, demonstrating superior performance compared to existing models. Remote Sensing Change Detection Foundation Model Self-Attention Transformer Networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Nov, 2025 Reviews received at journal 21 Nov, 2025 Reviews received at journal 19 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviews received at journal 12 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 15 Jul, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 13 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7112910","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545676905,"identity":"148d1414-629d-4d65-809a-e64ca227124e","order_by":0,"name":"xianran zhang","email":"","orcid":"","institution":"University of Science and Technology Liaoning","correspondingAuthor":false,"prefix":"","firstName":"xianran","middleName":"","lastName":"zhang","suffix":""},{"id":545676906,"identity":"c6857f12-c701-498f-ae3e-4bae2b6ef9f9","order_by":1,"name":"Zhengpeng Li","email":"","orcid":"","institution":"University of Science and Technology 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With the technological advancements, advanced satellites capture more complex geographical information, making change detection more challenging. Existing models often use convolutional networks and Transformers to learn changes between bi-temporal images, but they often fail to fully utilize the knowledge and scalability of the foundation model, neglecting the importance of filtering invariant background information, which leads to unfiltered tokens interfering with model performance. In this work, we demonstrate the advantages of the foundation model and the necessity of token filtering. We propose a Foundation Model-based Transformer for Remote Sensing Change Detection (FMT). We introduce a collaborative feature extraction module that utilises a modified ResNet18 and a frozen foundation model. We also propose a multi-scale cross-axis attention fusion module that combines general features extracted by the foundation model with ResNet18 backbone network features. 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