https://github.com/rushikeshghuge-19/sar_colorization

This project aims to enhance Synthetic Aperture Radar (SAR) imagery by developing a deep learning-based system that colorizes grayscale SAR images and extracts meaningful features. The solution integrates advanced feature extraction and natural language processing (NLP) for prompt-based user interactions, similar to ChatGPT.
https://github.com/rushikeshghuge-19/sar_colorization

cnn gan ml nlp nn python

Last synced: 26 days ago
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This project aims to enhance Synthetic Aperture Radar (SAR) imagery by developing a deep learning-based system that colorizes grayscale SAR images and extracts meaningful features. The solution integrates advanced feature extraction and natural language processing (NLP) for prompt-based user interactions, similar to ChatGPT.

• Host: GitHub
• URL: https://github.com/rushikeshghuge-19/sar_colorization
• Owner: RushikeshGhuge-19
• License: mit
• Created: 2025-01-09T16:07:40.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-09T16:20:34.000Z (over 1 year ago)
• Last Synced: 2025-07-01T09:06:26.304Z (12 months ago)
• Topics: cnn, gan, ml, nlp, nn, python
• Language: Python
• Homepage:
• Size: 4.48 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
SAR Image Colorization Project

Project Overview

This project aims to enhance Synthetic Aperture Radar (SAR) imagery by developing a deep learning-based system that colorizes grayscale SAR images and extracts meaningful features. The solution integrates advanced feature extraction and natural language processing (NLP) for prompt-based user interactions, similar to ChatGPT.

Features Implemented

- SAR Image Colorization: Utilized a Conditional Generative Adversarial Network (cGAN) to generate colorized versions of SAR images.

- Feature Extraction: Leveraged convolutional neural networks (CNNs) to identify and extract key features from SAR images.

- Prompt Analysis: Implemented basic NLP capabilities to analyze user prompts for targeted image analysis.

- Data Subsetting: Created subsets of large datasets for efficient model training and testing.

- Performance Metrics: Integrated evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to measure the quality of colorized images.

Technologies Used

- Python

- PyTorch: For building and training the deep learning models.

- Torchvision: For pre-trained models and image transformations.

- Matplotlib: For visualizing and saving generated images.

- NumPy: For array manipulations and numerical operations.

- scikit-image: For calculating PSNR and SSIM metrics.

- Git: For version control and project management.

How to Run

1. Clone the repository:

   git clone https://github.com/RushikeshGhuge-19/SAR_colorization.git

2. Navigate to the project directory:

   cd SAR_colorization

3. Install required dependencies:

   pip install -r requirements.txt

4. Run the main.py script to start the training and image generation:

   python main.py

Future Enhancements

- Fine-tune the prompt analysis using advanced NLP models.

- Implement additional data augmentation techniques to improve model generalization.

- Optimize model training with hyperparameter tuning.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

Special thanks to the open-source community for providing the libraries and tools that made this project possible.