Multimodal Remote-Sensing Change Interpretation through Saliency-Guided Feature Correspondence and Geometry-Aware Scene Reconstruction across Variable Viewpoints, Resolutions, and Imaging Conditions in Earth Observation
DOI:
https://doi.org/10.71465/jgs63Keywords:
Earth Observation, Multimodal Data, Change Detection, Scene ReconstructionAbstract
The rapid proliferation of Earth observation satellites has generated vast repositories of multimodal remote-sensing data, offering unprecedented opportunities for large-scale environmental monitoring and urban development analysis. However, interpreting structural and semantic changes across disparate sensors, such as Synthetic Aperture Radar and optical imagery, remains profoundly challenging due to extreme variations in viewpoints, spatial resolutions, and temporal imaging conditions. This paper introduces a comprehensive framework designed to address these fundamental limitations through the integration of saliency-guided feature correspondence and geometry-aware scene reconstruction. By employing a novel feature alignment paradigm that prioritizes salient regions, the proposed methodology mitigates the catastrophic failures typically associated with cross-sensor matching under severe radiometric distortion. Concurrently, a geometry-aware reconstruction module normalizes variable viewing angles and resolutions, projecting disparate temporal inputs into a unified three-dimensional spatial representation. Through extensive theoretical analysis and rigorous experimental validation on complex heterogeneous datasets, the integrated system demonstrates substantial improvements in change interpretation accuracy, robustness against seasonal and illumination variations, and computational efficiency. The results indicate that coupling geometric constraints with salient semantic features effectively resolves the ambiguity inherent in uncalibrated, multimodal remote-sensing streams, establishing a new state-of-the-art methodology for robust Earth observation analytics.
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Copyright (c) 2026 Bethany A. Webb (Author)
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All articles published in the Journal of Geospace Science (JOGSS) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to share, copy, distribute, and adapt the work, provided proper attribution is given to the original author(s) and source.