taochx/MSNet
multispectral semantic segmentation network for remote sensing images
This project helps remote sensing analysts automatically interpret satellite and aerial images by precisely outlining different features like water, vegetation, and buildings. It takes multispectral remote sensing images (including invisible light bands like near-infrared) and outputs a segmented image where each pixel is classified into a specific category. Earth observation specialists, urban planners, and environmental scientists would find this useful for detailed land cover mapping.
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Use this if you need highly accurate, automated classification of features within multispectral remote sensing imagery, leveraging both visible and invisible light spectrum data.
Not ideal if your primary need is general object detection or classification in standard RGB photographic images, or if you only work with visible light bands.
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Python
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Last pushed
Sep 28, 2022
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