Color Night-Light Remote Sensing Image Fusion With Two-Branch Convolutional Neural Network

Abstract

Night-light remote sensing imagery (NLRSI) effectively reflects urban economic and human activities and has important value in the field of remote sensing. However, the applications are limited by their low spatial resolution. In recent years, multisource remote sensing image fusion has become an important method for enhancing the spatial and spectral resolution of single-image data. To address the low-resolution limitation of NLRSI, this study proposes a multisource remote sensing image fusion framework based on the two-branch convolutional neural network (TbCNN), which fuses Landsat-8 and NPP/VIIRS data to generate high-resolution color night-light remote sensing imagery (CNLRSI). First, TbCNN features a deep two-branch structure for multiscale feature extraction, yielding richer spatial texture features. The framework also integrates a multilevel feature fusion module and a residual learning mechanism, further improving the fusion performance of CNLRSI. Quantitative evaluations demonstrate TbCNNs superiority over other methods, achieving optimal values in objective evaluation metrics. Second, in the built-up area extraction experiment, CNLRSI better identifies the true morphology of urban built-up areas compared with NPP/VIIRS data, reducing the overestimation of central urban areas and the underestimation of suburban areas in NPP/VIIRS data. Finally, the enhanced classification capability of CNLRSI is quantitatively validated through confusion matrix analysis, achieving a higher Kappa coefficient (0.814 versus 0.774) than NPP/VIIRS in urban pixel recognition.