GAN-based Synthetic VIIRS-like Image Generation over India

Monthly nighttime lights (NTL) can clearly depict an area's prevailing intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992 onwards). In the current study, an attempt has been made to generate synthetic monthly VIIRS-like products of 1992-2012, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992-2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done using low and high thresholds. Improved DMSP and corrected VIIRS images from April 2012 - December 2013 are used in a conditional generative adversarial network (cGAN) along with Land Use Land Cover, as auxiliary input, to generate VIIRS-like imagery from 1992-2012. The modelled imagery was aggregated annually and showed an R2 of 0.94 with the results of other annual-scale VIIRS-like imagery products of India, R2 of 0.85 w.r.t GDP and R2 of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an R2 of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an R2 of 0.56 and 0.53 respectively. Qualitative analysis for also performed on both national and state scales. Visual analysis over 1992-2013 confirms a gradual increase in the brightness of the lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, a synthetically generated monthly VIIRS-like product is delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.

月度夜间灯光（Nighttime Lights, NTL）可清晰刻画区域年内主要的社会经济动态。科罗拉多矿业学院（Colorado School of Mines）的地球观测小组提供了两类月度夜间灯光产品：分别来自国防气象卫星计划（Defense Meteorological Satellite Program, DMSP）卫星搭载的业务线扫描系统（Operational Linescan System, OLS）传感器（1992年4月起），以及可见红外成像辐射计套件（Visible and Infrared Imaging Suite, VIIRS）卫星搭载的日夜间波段（Day Night Band, DNB）传感器（2012年4月起）。本研究尝试基于深度学习图像翻译网络，生成1992-2012年的类VIIRS月度合成产品。首先对216张1992-2013年的DMSP月度影像进行缺陷校正，以消除几何误差、背景噪声与辐射误差；同时通过高低阈值法对VIIRS月度影像进行校正，去除背景噪声与瞬时灯光。以2012年4月至2013年12月的经改进DMSP影像与校正后的VIIRS影像为基础，结合土地利用与土地覆盖（Land Use Land Cover）作为辅助输入，通过条件生成对抗网络（conditional generative adversarial network, cGAN）生成1992-2012年的类VIIRS影像。将生成的影像按年聚合后，与印度其他年度类VIIRS影像产品对比，决定系数（R-squared, R²）达0.94；相较于国内生产总值（Gross Domestic Product, GDP）的决定系数为0.85，相较于人口数据的决定系数为0.69。针对印度区域2012年与2013年的真实VIIRS影像与生成的类VIIRS产品进行回归分析，结果显示二者拟合效果良好，对应R²分别为0.64与0.67；但空间密度分析表明，在极高辐射亮度值区间，模型存在对亮度值的低估现象，对应R²分别为0.56与0.53。研究还在国家与省级尺度开展了定性分析。1992-2013年的视觉分析结果证实，灯光亮度呈逐步上升趋势，表明cGAN生成的影像能够准确还原夜间灯光的实际分布模式。最后，本研究将合成生成的月度类VIIRS产品开放给科研社区，该数据集可用于时序社会经济动态变化的相关研究。