egshkim/ConditionalBBDM-for-VHR-SAR-to-Optical
The official implementation of Conditional Brownian Bridge Diffusion Model for VHR SAR to Optical Image Translation
This project helps remote sensing analysts and geospatial experts transform radar imagery (SAR) into more visually interpretable optical images. It takes very high-resolution SAR images as input and generates corresponding optical images, making it easier to analyze geographical features and land changes. This is useful for professionals in environmental monitoring, urban planning, or disaster response who rely on satellite data.
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Use this if you need to convert Synthetic Aperture Radar (SAR) satellite images into natural-looking optical images for clearer visual analysis or integration with other optical data sources.
Not ideal if you are working with other types of image-to-image translation tasks not involving SAR or require real-time processing of satellite data.
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Language
Python
License
MIT
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Last pushed
Jun 25, 2025
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