HSG-AIML/MaskedSST
Code repository for Scheibenreif, L., Mommert, M., & Borth, D. (2023). Masked Vision Transformers for Hyperspectral Image Classification, In CVPRW EarthVision 2023
This project helps scientists and analysts accurately classify land cover using hyperspectral images from aerial or satellite sources. It takes raw hyperspectral image data as input and produces precise classifications of geographical features, such as vegetation types or urban areas. This is designed for researchers and professionals working with Earth observation data.
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Use this if you need to perform land-cover classification on hyperspectral aerial or satellite imagery with high accuracy.
Not ideal if you are working with standard RGB images or do not have access to hyperspectral datasets.
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Python
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Last pushed
Jul 10, 2024
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