DotWang/DCN-T
[TIP 2023] The official repo for the paper "DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification".
This project helps remote sensing specialists and geoscientists accurately identify different materials or land cover types from satellite or aerial hyperspectral imagery. It takes raw hyperspectral image data as input and outputs classified images, where each pixel is labeled with its corresponding material or land cover. Researchers in environmental monitoring, urban planning, or resource management can use this to analyze complex scenes.
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Use this if you need to classify objects or land cover in hyperspectral images with high precision, especially for complex scenes where traditional methods struggle.
Not ideal if you are working with standard RGB images or do not have access to hyperspectral datasets.
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Language
Python
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Last pushed
May 13, 2025
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