用AI把旧卫星夜光数据转成新格式，让长期城市监测更准确
Unpaired Cross-Domain Calibration of DMSP to VIIRS Nighttime Light Data Based on CUT Network
- 用对比无配对翻译网络，学习不同卫星间夜光图像的对应关系
- 生成数据与真实VIIRS数据相关性超0.87，能反映真实社会经济状况
- 适合做城市化长期研究、需要跨年份夜光数据的学者使用
国防气象卫星计划（DMSP-OLS）与太阳/国家极轨合作卫星（SNPP-VIIRS）的夜间灯光数据对监测城市化至关重要，但传感器差异阻碍了长期分析。本研究提出一种基于对比无配对翻译（CUT）网络的跨传感器校准方法，将DMSP数据转换为类似VIIRS的格式，修正其固有缺陷。该方法采用多层局部块对比学习，最大化对应块间的互信息，在保持内容一致性的同时学习跨域相似性。利用2012–2013年重叠时段数据训练模型，处理1992–2013年全时段的DMSP影像，生成增强型VIIRS风格栅格数据。验证结果显示，生成数据与实际VIIRS观测结果高度一致（决定系数R² > 0.87），并与社会经济指标具有良好相关性。该方法有效解决了跨传感器数据融合难题，校正了DMSP缺陷，为扩展夜间灯光时间序列提供了可靠方案。
原文摘要 · Abstract (English)
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.
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