Image Fusion and Quantum Machine Learning for Remote Sensing Applications

The integration of Synthetic Aperture Radar (SAR) and optical imagery through image fusion techniques is important in refining the accuracy and interpretability of images in remote sensing applications. The current array of available image fusion methods presents a complex landscape, each with its own set of advantages and limitations. This survey paper describes existing image fusion methods, with a primary focus on SAR and optical image classification. This will provide valuable insights for enhanced decision-making in remote sensing applications. In addition to examining image fusion methods, we also study various machine learning algorithms that can be used to classify the data. Although the emphasis is on SAR and optical image fusion, the study will also include a broader discussion of other applications, of image fusion methods across various domains.