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.
@article{arxiv.2603.16385,
title = {Unpaired Cross-Domain Calibration of DMSP to VIIRS Nighttime Light Data Based on CUT Network},
author = {Zhan Tong and ChenXu Zhou and Fei Tang and Yiming Tu and Tianyu Qin and Kaihao Fang},
journal= {arXiv preprint arXiv:2603.16385},
year = {2026}
}
Comments
16 pages, 10 figures, 8 tables. Submitted to Remote Sensing of Environment. Code and data available at: https://github.com/[your-repo-link]