CS550 - Machine Learning : SAR Image Colorization

Code for Paper "SAR Image Colorization"

SAR (Synthetic Aperture Radar) provides terrain structural information. These information are in the form of greyscale images which has a lot of noise.

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This project implements a deep learning model to colorize grayscale SAR images, enhancing their interpretability for applications like remote sensing and environmental monitoring. By leveraging paired SAR and optical data, the model transforms monochromatic SAR imagery into intuitive color representations, improving the usability of SAR data.

Deep learning model for SAR-to-Optical image translation using Pix2Pix (U-Net + PatchGAN). Pre-trained on Sentinel-1/2, fine-tuned on QXSLAB_SAROPT.

The project uses Generative Adversarial Networks (GANs) to convert grayscale Synthetic Aperture Radar (SAR) images into visually meaningful RGB images, improving interpretability for analysis, surveillance, and disaster management.

Developing a deep learning-based model to enhance grayscale Synthetic Aperture Radar (SAR) images by adding realistic color, improving visualization and interpretation for various applications.

Synthetic Aperture Radar (SAR) image converter that transforms SAR images into RGB format using a Generative Adversarial Network (GAN). The model employs the pix2pix framework with a U-Net architecture featuring skip connections with suitable generator and discriminator.

This repository implements a paired image-to-image translation pipeline for colorizing Synthetic Aperture Radar (SAR) images into RGB using a Pix2Pix-style architecture. It uses a U-Net Generator + PatchGAN Discriminator (PyTorch) with SAR preprocessing (dB scaling) using a paired dataset loader with random flips and rotations.

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Deep learning project for colorizing Synthetic Aperture Radar (SAR) images using PyTorch and GANs. Transforms grayscale SAR images into realistic RGB representations using encoder-decoder architecture.

Ai based Synthetic Aperture Radar (SAR) images colorization

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Developed Coloursar, a deep learning-based SAR image colorization program using U-Net CNN architecture on EuroSAT dataset.

Innovative SAR Image Colorization using a diffusion-based encoder-decoder in PyTorch. Converts grayscale SAR images to realistic RGB, enhancing interpretability. Supports training on paired datasets, single/multi-image prediction, and automated grayscale input generation—bridging SAR analysis with deep learning.

**AI-Powered SAR Colorization for Disaster Prediction** This project enhances disaster risk prediction by converting grayscale SAR images to color using AI models like GANs and Transformers. Integrating Sentinel-1 SAR and Sentinel-2 Optical data, it improves flood, landslide, and wildfire forecasts, with Gradio and Flask.

This project applies diffusion models for SAR image colorization in PyTorch, turning grayscale radar data into realistic RGB images. Features include a custom encoder-decoder pipeline, dataset preparation tools, training with MSE loss, and flexible single/multi-image prediction support.

SAR IMAGE COLORIZATION

SAR Image Colorization

SAR image colorization software

SAR image colorization using Deep learning

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