Title:Automated Colorization of Synthetic Aperture Radar Images Using Deep Learning Model


Authors:Chetna Sontaki, Dhruv Sharma, Divyanshu Aaliwal, Shruti Lashkari, Nidhi Nigam


Published in: Volume 2 Issue 2 July-December 2025, Page No. 40-45


DOI: https://doi.org/10.63844/IJAITR.v2.i1.2025.22-29
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Keywords: Convolution Neural Network, Noise Reduction, Aperture Radar Images


Abstract:
This research explores the application of deep learning models to colorize grayscale Synthetic Aperture Radar images, improving their interpretability for remote sensing tasks. SAR is widely used in fields such as geological studies and environmental monitoring due to its ability to capture images in all weather conditions. However, the grayscale nature of SAR images limits their usability for visual analysis. The study focuses on developing deep learning architectures, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and autoencoders, to predict and apply appropriate color schemes to SAR data. Key challenges such as noise reduction, model design, and data limitations are addressed through careful data pre-processing and model training. The colorized images aim to enhance feature distinction, enabling more accurate interpretation in applications like geological mapping and environmental monitoring. Validation methods include both quantitative metrics like Mean Squared Error (MSE) and qualitative evaluations by remote sensing experts. The anticipated outcome is a more efficient and visually interpretable SAR image analysis process, allowing for better insights into natural phenomena and improved decision-making in remote sensing applications. This research contributes to advancing the state of SAR image analysis by introducing automated deep learning-driven colorization techniques.


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