ZY-LIi/IEEE_TGRS_DEMAE
Pre-train a Masked Autoencoder with the idea of Diffusion Models for Hyperspectral Image Classification.
This project helps remote sensing analysts classify objects and materials in hyperspectral images more accurately, even when they have very few labeled examples for training. It takes raw hyperspectral image data and, after a pre-training step, outputs a classification of the image's contents. This is useful for scientists or engineers working with satellite imagery, environmental monitoring, or geological surveys.
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Use this if you need to classify objects within hyperspectral images and are challenged by a limited number of labeled training samples.
Not ideal if you are working with standard RGB images or already have abundant labeled data for your hyperspectral classification task.
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Python
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Sep 02, 2024
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