Sar Image Colorization Using Deep Learning

Synthetic Aperture Radar (SAR) imagery plays a crucial role in remote sensing applications due to its ability to capture highresolution images under all weather conditions and during both day and night. However, SAR images are inherently grayscale and often difficult to interpret by human observers because of speckle noise and lack of intuitive visual representation. To address this limitation, this project proposes a SAR Image Colorization using Deep Learning approach that enhances the visual quality and interpretability of SAR data by transforming grayscale radar images into realistic colorized outputs. The proposed system leverages advanced deep learning models, particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), to learn complex mappings between SAR images and corresponding optical images. The model is trained on paired datasets consisting of SAR and RGB satellite images, enabling it to understand spatial features, textures, and semantic information. Preprocessing techniques such as noise reduction and normalization are applied to improve data quality, while data augmentation is used to enhance model generalization. The network learns to assign appropriate color values to different regions based on learned patterns, producing visually meaningful and context-aware colorized images. Experimental results demonstrate that the proposed method significantly improves image interpretability and visual clarity compared to traditional colorization techniques. The model achieves high performance in terms of structural similarity and perceptual quality, making it useful for applications such as environmental monitoring, disaster management, and military surveillance. In conclusion, this work highlights the potential of deep learning in bridging the gap between SAR and optical imagery, providing an effective solution for enhanced visualization and analysis.