HQ-SFM: A Quantum-Photon Inspired Multi-Modal Fusion Framework for Noise-Robust Satellite Imaging
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| Natura: | Recurso digital |
| Lingua: | inglese |
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2025
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| _version_ | 1866902039080992768 |
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| author | Al Dahlake, Rana |
| author_facet | Al Dahlake, Rana |
| contents | <p>This preprint presents HQ-SFM, a quantum-photon inspired multi-modal fusion framework designed to enhance robustness in satellite imaging under realistic spaceborne degradation conditions. The proposed architecture integrates heterogeneous sensing modalities, including RGB, hyperspectral, thermal infrared, and SAR data, through modality-specific encoders, cross-modal attention, and noise-aware gating mechanisms.</p> <p> </p> <p>Unlike conventional fusion approaches that assume stationary noise and stable modality availability, HQ-SFM explicitly models modality reliability and non-stationary degradation, enabling adaptive reweighting under noise, partial modality loss, and temporal distribution shift. The quantum-photon component is introduced as an abstract, plug-in noise-awareness interface inspired by photon-level uncertainty modeling, allowing future integration with hardware-based quantum or photon-counting sensors.</p> <p> </p> <p>Experimental evaluations demonstrate consistent improvements in image quality and robustness compared to early-fusion and transformer-based baselines across multiple degradation scenarios. This work targets applications in Earth observation and planetary imaging, particularly for resource-constrained platforms such as CubeSats.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18001618 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HQ-SFM: A Quantum-Photon Inspired Multi-Modal Fusion Framework for Noise-Robust Satellite Imaging Al Dahlake, Rana Multimodal Image Fusion Remote Sensing Satellite Imaging Noise-Robust Learning Cross-Modal Attention Hyperspectral Imaging Synthetic Aperture Radar (SAR) Quantum-Inspired Models <p>This preprint presents HQ-SFM, a quantum-photon inspired multi-modal fusion framework designed to enhance robustness in satellite imaging under realistic spaceborne degradation conditions. The proposed architecture integrates heterogeneous sensing modalities, including RGB, hyperspectral, thermal infrared, and SAR data, through modality-specific encoders, cross-modal attention, and noise-aware gating mechanisms.</p> <p> </p> <p>Unlike conventional fusion approaches that assume stationary noise and stable modality availability, HQ-SFM explicitly models modality reliability and non-stationary degradation, enabling adaptive reweighting under noise, partial modality loss, and temporal distribution shift. The quantum-photon component is introduced as an abstract, plug-in noise-awareness interface inspired by photon-level uncertainty modeling, allowing future integration with hardware-based quantum or photon-counting sensors.</p> <p> </p> <p>Experimental evaluations demonstrate consistent improvements in image quality and robustness compared to early-fusion and transformer-based baselines across multiple degradation scenarios. This work targets applications in Earth observation and planetary imaging, particularly for resource-constrained platforms such as CubeSats.</p> |
| title | HQ-SFM: A Quantum-Photon Inspired Multi-Modal Fusion Framework for Noise-Robust Satellite Imaging |
| topic | Multimodal Image Fusion Remote Sensing Satellite Imaging Noise-Robust Learning Cross-Modal Attention Hyperspectral Imaging Synthetic Aperture Radar (SAR) Quantum-Inspired Models |
| url | https://doi.org/10.5281/zenodo.18001618 |