Figure Data for ViSCoNet-Based High-Resolution Brightness Temperature Reconstruction in Distributed Synthetic Aperture Interferometric Radiometers

This dataset contains the figure data associated with the manuscript entitled “Deep Learning-Based High Resolution Image Reconstruction for Distributed Synthetic Aperture Radiometers With Sparse Sampling.” The data were generated to support the evaluation of ViSCoNet, a physics-constrained deep learning framework for high-resolution brightness temperature reconstruction in distributed synthetic aperture interferometric radiometer systems under sparse, nonuniform, and dynamically varying visibility sampling conditions.The dataset includes reconstructed brightness temperature images, visibility-domain sampling patterns, antenna array configurations, and performance metrics used to generate the figures and tables in the manuscript.

The data cover comparisons among conventional and learning-based reconstruction methods, including FFT, G-matrix, SAIR-CNN, SR-SEN, and the proposed ViSCoNet method. The included results support analyses of reconstruction accuracy, spatial resolution enhancement, robustness under amplitude-phase perturbations and missing-baseline conditions, array-configuration dependence, point-source and extended-source response, and effective radiometric sensitivity.The brightness temperature scenes used in the experiments were generated from simulated distributed SAIR observations.