SAR Image Colorization for Comprehensive Insight using Deep Learning Model

Abstract

Synthetic Aperture Radar (SAR) imagery provides critical structural and textural information for remote sensing applications; however, the lack of natural color limits intuitive interpretation and visual analysis. This report presents a Deep Learning (DL) based framework for SAR image colorization aimed at enhancing the usability and interpretability of SAR data. The proposed approach leverages paired SAR and optical imagery to train a neural network capable of predicting realistic color representations from monochromatic SAR inputs. The model integrates advanced preprocessing techniques, feature extraction, and a customized loss function to minimize the gap between predicted and true color images. Key challenges addressed include speckle noise reduction, domain alignment between SAR and optical data, and the development of suitable evaluation metrics for qualitative and quantitative validation. Experimental results demonstrate the potential of the DL-based system to improve visual perception of surface features, thereby supporting improved analysis in applications such as land cover mapping, geological studies, and environmental monitoring. The outcome of this work contributes toward an effective SAR image colorization software solution, enabling remote sensing analysts to gain more comprehensive insights from SAR datasets.

Introduction

This text discusses the use of deep learning for SAR (Synthetic Aperture Radar) image colorization, aiming to convert grayscale SAR images into realistic color images similar to optical satellite imagery.

Introduction

Optical satellite images are widely used because they are easy to interpret, but they are affected by clouds, weather conditions, and limited visibility at night. SAR imagery, captured using microwave signals, can operate in all weather and lighting conditions, making it a reliable source of Earth observation data. However, SAR images are difficult to interpret due to their grayscale appearance and radar-specific textures. Deep learning-based colorization addresses this issue by translating SAR images into visually meaningful color images while preserving structural information.

Literature Survey

Previous research has explored various deep learning approaches for SAR colorization, including:

• Deep Neural Networks (DNNs) for pseudo-color image generation.
• CycleGANs for unpaired SAR-to-RGB translation.
• CNNs, U-Net, Pix2Pix GANs, and Attention GANs for improving image realism and preserving texture details.
• Transfer Learning models (ResNet + GAN) for faster training and better generalization.
• Multi-scale U-Net and Hybrid CNN-GAN architectures achieving high image quality with PSNR values around 28–29 dB and SSIM values up to 0.92.
  Overall, deep learning methods significantly outperform traditional regression-based approaches in SAR image colorization.

Methodology

1. Data Collection

• Paired SAR and optical images were obtained from datasets such as SEN12MS and SpaceNet-6.
• SAR images served as inputs, while optical images acted as ground truth references.

2. Data Preprocessing

• Images were aligned, normalized, resized, and converted into the Lab color space.
• SAR images provided the luminance (L) channel, while optical images supplied the color channels (a, b).
• Noise filtering and normalization improved data consistency.

3. Model Architecture

• A CNN-based encoder-decoder network was developed.
• The encoder extracted spatial and texture features from SAR images.
• The decoder reconstructed the corresponding color image.
• Skip connections, batch normalization, and ReLU activation functions helped preserve details and improve training.

4. Model Training and Evaluation

• The model was trained using:
  • L1 Loss for pixel-level accuracy,
  • SSIM Loss for structural similarity,
  • Perceptual Loss for realistic visual quality.
• Dataset split: 77% training and 23% testing.
• Performance was measured using:
  • PSNR (Peak Signal-to-Noise Ratio)
  • SSIM (Structural Similarity Index)

5. Prediction and Visualization

• The trained model colorized unseen SAR images.
• Results were compared with corresponding optical images.
• Visualization included the input SAR image, colorized output, and ground truth image.

6. Testing and Optimization

• Techniques such as hyperparameter tuning, data augmentation, dropout, early stopping, and speckle noise reduction were applied.
• These improvements reduced overfitting and enhanced performance across different terrains and imaging conditions.

Conclusion

The project titled “SAR Image Colorization for Comprehensive Insight using Deep Learning” showcases the potential of artificial intelligence in enhancing the interpretability of Synthetic Aperture Radar (SAR) imagery. By utilizing paired Sentinel-1 and Sentinel-2 datasets, the system employs U-Net and Pix2Pix GAN architectures to translate grayscale SAR images into realistic RGB representations. Through effective preprocessing, normalization, and model optimization, the framework achieves high accuracy validated by PSNR and SSIM metrics. The colorized outputs significantly improve human perception of terrain features, vegetation, and urban structures, enhancing the usability of SAR data for geological studies, environmental monitoring, agriculture, and disaster management. Furthermore, deployment through a Flask/FastAPI interface enables an accessible and efficient visualization platform. Overall, this work demonstrates how deep learning can transform traditional radar data into visually interpretable information, contributing to more insightful and efficient AI-driven Earth observation applications..

Copyright

Copyright © 2026 Dr. Nandkumar Kulkarni, Mr. Yash Tagunde, Mr. Shrishant Madke, Mr. Vedant Patil, Mr. Prem Urkude. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.