DFM: Difference Feature Modeling with Text-Guided Gated Contrastive Loss for Remote Sensing Image Change Captioning
arXiv:2606.27410v1 Announce Type: cross Abstract: The primary goal of Remote Sensing Image Change Captioning (RSICC) is to automatically generate descriptions of changes between remote sensing images captured at different time points. Existing models still rely on a single autoregressive generation paradigm, which tends to prioritize learning easily generated vocabulary over capturing discriminative differences between images. To address this, we reframe the training paradigm and propose a novel Difference Feature Modeling (DFM) framework. Specifically, we introduce a Text-guided Gated Contras
The proliferation of remote sensing data and advancements in AI, particularly large language models, are enabling more sophisticated analysis and description of Earth observation imagery.
Improved capabilities in remote sensing image change captioning hold significant implications for defense, environmental monitoring, and urban planning by automating the identification and description of critical changes.
This research introduces a new AI framework that enhances the ability to automate the detection and precise description of changes in remote sensing imagery, moving beyond existing autoregressive models.
- · Defense and intelligence agencies
- · Environmental monitoring services
- · Urban planning departments
- · Geospatial intelligence companies
- · Manual image analysts
- · Less sophisticated remote sensing platforms
Automated change detection in satellite imagery becomes more accurate and descriptive, reducing human effort.
Faster and more granular understanding of geopolitical and environmental shifts through constant, automated monitoring.
Enhanced AI-driven geopolitical forecasting and resource management from real-time, comprehensive Earth observation insights.
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Read at arXiv cs.LG