https://github.com/sharathk4/sar-imagery-colorization

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.
https://github.com/sharathk4/sar-imagery-colorization

colorization convolutional-neural-networks deep-learning environmental-monitoring image-processing machine-learning python remote-sensing sar-image-colorization tensorflow

Last synced: 6 months ago
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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.

• Host: GitHub
• URL: https://github.com/sharathk4/sar-imagery-colorization
• Owner: SharathK4
• Created: 2024-09-08T10:30:25.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-09-09T18:46:35.000Z (9 months ago)
• Last Synced: 2024-11-21T23:58:54.412Z (6 months ago)
• Topics: colorization, convolutional-neural-networks, deep-learning, environmental-monitoring, image-processing, machine-learning, python, remote-sensing, sar-image-colorization, tensorflow
• Homepage:
• Size: 430 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SAR-Imagery-Colorization

# SAR Image Colorization for Comprehensive Insight using Deep Learning Model (h)

## Project Overview

Synthetic Aperture Radar (SAR) imagery is known for its rich structural and textural information, making it a critical tool in applications ranging from geological studies to environmental monitoring and space-borne remote sensing. However, SAR images are typically presented in grayscale, which limits their intuitive visual interpretability. This project seeks to address that limitation by leveraging Deep Learning (DL) techniques to colorize grayscale SAR images, enhancing their usability and interpretability for remote sensing analysts and other users.

Our approach involves the design and training of a novel deep learning model capable of generating color representations for SAR images by learning from paired SAR and optical image data. The goal is to minimize a custom loss function that accurately captures the differences between the predicted and true colorized images. By doing so, we aim to improve the distinguishability of surface features, making the SAR images more visually informative and easier to analyze.

## Key Features

- **Innovative DL Model**: A deep learning architecture specifically designed to predict and apply color to SAR images using paired SAR and optical data.

- **Improved Interpretability**: This project focuses on enhancing the visual representation of SAR imagery, making surface features more distinguishable and easier to interpret for applications like geological mapping and environmental monitoring.

- **Custom Loss Function**: A tailored loss function is developed to minimize the gap between predicted and actual color images, ensuring high accuracy in colorization.

- **Comprehensive Solution**: The model is trained to effectively and reliably colorize SAR images, transforming grayscale data into more intuitive, full-color images that can be easily understood by remote sensing analysts.

## Challenges

The project faces several challenges, including:

- **Data Preprocessing**: SAR images are inherently noisy, requiring sophisticated preprocessing techniques to prepare them for use in deep learning models.

- **Model Design**: The model must be carefully constructed to handle the unique characteristics of SAR data while successfully predicting the color channels.

- **Evaluation Methodologies**: Rigorous evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR) will be used to ensure the quality of the output colorized images.

## Project Impact

The expected outcome of this project is a robust DL-based SAR image colorization software that can significantly improve the usability of SAR data. By providing intuitive and informative visual representations, this tool will help remote sensing analysts and researchers gain deeper insights into various natural and human-made features. The colorization of SAR imagery opens new possibilities in the interpretation of complex data, facilitating better decision-making in critical areas such as geological studies, urban planning, and environmental conservation.

## Motivation for the Project

While various existing DL models have been proposed for SAR image processing, their performance in the specific task of colorization has often been unsatisfactory. This project aims to fill that gap by developing a solution that effectively balances accuracy, processing time, and visual appeal. The ultimate goal is to make SAR imagery more accessible and usable by adding the color dimension, which has been traditionally absent but offers critical advantages in analysis and interpretation.

## Usage

The software is targeted toward **Remote Sensing Image Analysts** and other professionals who require enhanced SAR data for comprehensive environmental monitoring, geological studies, and other applications. With intuitive colorized outputs, this tool enables faster, more accurate interpretation of SAR images, improving the effectiveness of these analyses.

## Conclusion

The SAR Image Colorization project offers a transformative approach to the visualization of SAR data. By integrating deep learning with SAR and optical data, the project aims to provide intuitive, colorized representations that can vastly improve the understanding of the underlying data. This cutting-edge solution has the potential to redefine how SAR images are utilized across various domains.

## Dataset

Link: https://drive.google.com/drive/folders/1W3iMpkehng7ADXmhz9pPvmgFQBayq22t

## Original VS Predicted

![P383-modified](https://github.com/user-attachments/assets/22f3d855-91e2-46ac-8c47-d7e311637d82)

![predicted_color_image](https://github.com/user-attachments/assets/621d2cf0-1069-47bd-8a9f-fdd0b4babf73)

## How It Works

Our model uses a deep learning architecture to colorize grayscale SAR images by learning from paired SAR and optical data. The model is built using convolutional and upsampling layers to predict AB color channels in the LAB color space, which are then combined with the grayscale L channel to form an RGB image. For a more detailed explanation, refer to the [[model architecture document](https://github.com/SharathK4/SAR-Imagery-Colorization/blob/main/SIH2024_SOLANA_Presentation.pptx)]().