swalpa/G2C-Conv3D-HSI
PyTorch implementation of the paper - Revisiting Deep Hyperspectral Feature Extraction Networks via Gradient Centralized Convolution
When analyzing satellite or aerial hyperspectral images to identify different land-cover types, it can be challenging to distinguish between areas with similar textures. This project helps improve the accuracy of pixel-level classification by extracting more robust and distinct features from these images. It takes raw hyperspectral image data and associated ground truth labels to produce more accurate classifications of land-cover types. This is ideal for remote sensing scientists, geographers, and environmental researchers working with hyperspectral imagery for tasks like land-use mapping.
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Use this if you need to improve the accuracy of land-cover classification from hyperspectral images, especially when dealing with fine-grained textures or spectrally similar regions.
Not ideal if you are working with standard RGB images or do not require highly precise pixel-wise land-cover classification from hyperspectral data.
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Mar 15, 2022
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