Enhancing Remote Sensing Image Quality through Data Fusion and Synthetic Aperture Radar (SAR): A Comparative Analysis of CNN, Lightweight ConvNet, and VGG16 Models

Remote sensing technology benefits many parties, especially for carrying out land surveillance with comprehensive coverage without needing to move the equipment close to photograph the area. However, this technology needs to improve: the image quality depends on natural conditions, so objects such as fog, clouds, and smoke can interfere with the image results. This study uses data fusion techniques to enhance the quality of remote-sensing images affected by natural conditions. The method involves using Synthetic Aperture Radar (SAR) to combine adjacent satellite images from different viewpoints, thereby improving image coverage. Three image classification models were evaluated to process the fused data: Convolutional Neural Network (CNN), Lightweight ConvNet, and Visual Geometry Group 16 (VGG16). The results indicate that all three models achieve similar accuracy and execution speed, namely 0.925, with VGG16 demonstrating a slight superiority over the others, namely 0.90.