A Comprehensive Study on Deep Learning Techniques used for SAR and Optical Image Registration

In this paper reviews recent advancements in deep learning-based remote sensing image processing, focusing on image registration, classification, and segmentation. Remote sensing faces challenges due to geometric, radiometric, and spectral differences between various imaging modalities like Synthetic Aperture Radar (SAR) and optical images. Traditional methods struggle with these discrepancies, especially in multimodal datasets. Recent deep learning techniques, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), have significantly improved accuracy in image alignment, feature extraction, and data fusion. This review synthesizes key studies that address these challenges and highlights future research opportunities to enhance remote sensing applications through deep learning models.