Laboratory for the use of algorithms in HyperSpectral imagery.
This repository contains the following submodules:
In order to download the submodules in the cloning process, use the following instruction:
git clone --recurse-submodules [email protected]:SolidusAbi/HySpecLab.git
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- version 1.10 or above
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- version 0.11 or above
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- version 0.12.2
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- version 0.23
- Recommended to install scikit-learn-intelex package
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- version 0.8.1
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- version 0.7
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- version 0.22
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- For Endmember Extraction Algorithms.
- version 0.15
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Others:
- Matplotlib, tensorboard, tqdm...
If you have included a new library in your environment, please update the environment using the following command:
conda env export --from-history | grep -v "^prefix: " > environments/Linux.yml
TODO
- Setup library.
- Fix HyperSpectralUnderSampler.
- Use VCA as endmember extraction algorithm.
- Extensive testing of the VCA with the original code.
- UnDIP documentation.
- NFINDR implementation based on pysptools. Básicamente para cargarme la dependencia a PySpTools.
- Experimenting with UnDIP with different data transformation.
- Endmember extraction by SVDD
- Proposed on 10.1109/IGARSS.2007.4423675
- SparseWeightedFeatureSelector
- Move the KL implementation to Sparse library
- Feature selection experiment
- Plastic dataset, algorithms and sparse representation test
- Subspace SVDD
- Quizás es la solución al mal performance que da el SVDD como eea pero... puedes ser demasiado complejo.
- Paper