aashishrai3799/Remote-Sensing-Image-Generation
Generate RS Images using Generative Adversarial Networks (GAN)
This project helps remote sensing image analysts, researchers, or anyone working with aerial and satellite imagery to generate new synthetic remote sensing images. It takes a random noise input and produces realistic 128x128 RGB remote sensing images, which can be used to augment datasets when real-world labeled samples are scarce. This is useful for improving the training of image classification models for scene analysis.
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Use this if you need to expand your dataset of remote sensing images to train more robust classification models, especially when obtaining real-world labeled data is challenging or expensive.
Not ideal if you require extremely high-resolution images or have strict requirements for the absolute authenticity and pixel-perfect accuracy of the generated images for sensitive applications.
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Jupyter Notebook
License
MIT
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Last pushed
Jun 30, 2019
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