Unpaired Cross-Domain Calibration of DMSP to VIIRS Nighttime Light Data Based on CUT Network
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908893662150656 |
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| author | Tong, Zhan Zhou, ChenXu Tang, Fei Tu, Yiming Qin, Tianyu Fang, Kaihao |
| author_facet | Tong, Zhan Zhou, ChenXu Tang, Fei Tu, Yiming Qin, Tianyu Fang, Kaihao |
| contents | Defense Meteorological Satellite Program (DMSP-OLS) and Suomi National Polar-orbiting Partnership (SNPP-VIIRS) nighttime light (NTL) data are vital for monitoring urbanization, yet sensor incompatibilities hinder long-term analysis. This study proposes a cross-sensor calibration method using Contrastive Unpaired Translation (CUT) network to transform DMSP data into VIIRS-like format, correcting DMSP defects. The method employs multilayer patch-wise contrastive learning to maximize mutual information between corresponding patches, preserving content consistency while learning cross-domain similarity. Utilizing 2012-2013 overlapping data for training, the network processes 1992-2013 DMSP imagery to generate enhanced VIIRS-style raster data. Validation results demonstrate that generated VIIRS-like data exhibits high consistency with actual VIIRS observations (R-squared greater than 0.87) and socioeconomic indicators. This approach effectively resolves cross-sensor data fusion issues and calibrates DMSP defects, providing reliable attempt for extended NTL time-series. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16385 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unpaired Cross-Domain Calibration of DMSP to VIIRS Nighttime Light Data Based on CUT Network Tong, Zhan Zhou, ChenXu Tang, Fei Tu, Yiming Qin, Tianyu Fang, Kaihao Computer Vision and Pattern Recognition I.4.3; I.5.4; J.2 Defense Meteorological Satellite Program (DMSP-OLS) and Suomi National Polar-orbiting Partnership (SNPP-VIIRS) nighttime light (NTL) data are vital for monitoring urbanization, yet sensor incompatibilities hinder long-term analysis. This study proposes a cross-sensor calibration method using Contrastive Unpaired Translation (CUT) network to transform DMSP data into VIIRS-like format, correcting DMSP defects. The method employs multilayer patch-wise contrastive learning to maximize mutual information between corresponding patches, preserving content consistency while learning cross-domain similarity. Utilizing 2012-2013 overlapping data for training, the network processes 1992-2013 DMSP imagery to generate enhanced VIIRS-style raster data. Validation results demonstrate that generated VIIRS-like data exhibits high consistency with actual VIIRS observations (R-squared greater than 0.87) and socioeconomic indicators. This approach effectively resolves cross-sensor data fusion issues and calibrates DMSP defects, providing reliable attempt for extended NTL time-series. |
| title | Unpaired Cross-Domain Calibration of DMSP to VIIRS Nighttime Light Data Based on CUT Network |
| topic | Computer Vision and Pattern Recognition I.4.3; I.5.4; J.2 |
| url | https://arxiv.org/abs/2603.16385 |