meteahishali/SRL-SOA
Hyperspectral Band Selection using Self-Representation Learning with Sparse 1D-Operational Autoencoder (SRL-SOA)
This project helps remote sensing analysts and scientists efficiently process hyperspectral images. It takes raw hyperspectral data, which contains hundreds of spectral bands, and intelligently selects a smaller, more informative subset of bands. The output is a refined dataset that allows for more accurate and faster classification and analysis of the terrain or objects captured in the image.
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Use this if you are working with hyperspectral imagery and need to reduce data dimensionality while retaining critical information for classification tasks.
Not ideal if your primary goal is general image processing or if you are not working with hyperspectral data where spectral band selection is a key challenge.
Stars
25
Forks
9
Language
Python
License
MIT
Category
Last pushed
Apr 05, 2025
Commits (30d)
0
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