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Unpaired Cross-Domain Calibration of DMSP to VIIRS Nighttime Light Data Based on CUT Network

Zhan Tong, ChenXu Zhou, Fei Tang, Yiming Tu, Tianyu Qin, Kaihao Fang

Abstract

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.

Unpaired Cross-Domain Calibration of DMSP to VIIRS Nighttime Light Data Based on CUT Network

Abstract

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.
Paper Structure (29 sections, 6 equations, 10 figures, 8 tables)

This paper contains 29 sections, 6 equations, 10 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Study Area and Data
  3. Study Object
  4. Data Sources
  5. DMSP-OLS Data
  6. VIIRS DNB Data
  7. Auxiliary Data
  8. Data Preprocessing
  9. Resolution Alignment via Upsampling
  10. Patch Extraction and Spatial Filtering
  11. Radiometric Distribution Analysis and Outlier Filtering
  12. Data Formatting for Network Training
  13. Training/Validation/Test Split with Spatial Independence
  14. Methodology
  15. Problem Formulation
  16. ...and 14 more sections

Figures (10)

  • Figure 1: NTL visualization and cumulative analysis of longitude and latitude
  • Figure 2: NTL data from DMSP-OLS and NPP-VIIRS
  • Figure 3: Schematic of the preprocessing workflow. The pipeline consists of resolution alignment, patch extraction with spatial filtering, radiometric distribution analysis, and format conversion for network training.
  • Figure 4: Radiometric distribution analysis. Top: histograms with log1p transformation to visualize long-tail behavior. Bottom: cumulative distribution functions. Vertical dashed lines indicate the 99.9th percentile thresholds used for outlier clipping.
  • Figure 5: Overall network pipeline. The generator $G$ translates DMSP to VIIRS-like imagery, while the discriminator distinguishes real from fake samples. Multilayer patchwise contrastive loss operates on intermediate features to ensure spatial consistency without requiring cycle reconstruction.
  • ...and 5 more figures