This project reproduces the model DMTS-Net from the paper Blind Unmixing Using Dispersion Model-Based Autoencoder to Address Spectral Variability. It validates the model using the Jasper Ridge hyperspectral dataset. Our goal is to make spectral analysis easier and more efficient.
To use DMTS-HS-Unmixing:
- Download the software from the Releases page.
- Follow the setup instructions below.
- Start analyzing hyperspectral data with ease.
- Operating System: Windows 10 or newer, macOS 10.14 or newer
- RAM: At least 8 GB
- Disk Space: 500 MB free space
- Python: Version 3.7 or newer
- Dependencies: You will need to install PyTorch and any additional libraries specified in the requirements file.
- Visit the Releases Page: Go to the Releases page to download.
- Download the Latest Version: Click on the latest version link to download the installer.
- Run the Installer: Locate the downloaded file and double-click to start the installation. Follow any prompts to complete the installation.
- Open DMTS-HS-Unmixing: Find the application icon on your desktop or in your applications folder.
- Load Your Data: Click on Load Data and select your hyperspectral images. Ensure your images are in a supported format.
- Configure Settings: Adjust any settings as needed, such as analysis parameters. Refer to the user guide for detailed explanations.
- Start Analysis: Click the Run Analysis button to begin unmixing. Results will display on the screen once processing is complete.
- User-Friendly Interface: Designed for ease of use with clear options.
- Powerful Analysis: Uses advanced models to deliver accurate spectral unmixing.
- Customizable Settings: Tailor analysis parameters according to your needs.
- Comprehensive User Guide: Detailed instructions included within the application.
For more detailed instructions and insights, please refer to the User Guide.
If you encounter any issues or have questions, please reach out via the GitHub Issues section on the repository.
Join our community! Share your experiences and findings using DMTS-HS-Unmixing. Collaborate with other users through the Issues section to improve the application and exchange valuable insights.
We welcome contributions! If you want to help improve DMTS-HS-Unmixing, please read the contribution guidelines available in the repository. Your support can make a difference in enhancing our spectral analysis tool.
DMTS-HS-Unmixing is released under the MIT License.
Remember to visit the Releases page to download the latest version and enjoy easier spectral analysis.
With these instructions, you can easily download, install, and start using DMTS-HS-Unmixing for your hyperspectral imaging needs. Enjoy your journey in spectral analysis!