felixriese / hyperspectral-regression

Code examples for the book chapter "Supervised, Semi-Supervised and Unsupervised Learning for Hyperspectral Regression".

Home Page:https://doi.org/10.5281/zenodo.3450676

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Hyperspectral Regression: Code Examples

This repository consists of additional material and exemplary implementations for our book chapter.

The code in this repository is provided via notebooks. The notebooks are structured as follows:

  1. Data
  2. Features
  3. Supervised Learning
  4. Active Learning
  5. Model Selection and Evaluation
  6. Generative Adversarial Networks

Description

License

3-Clause BSD license

Authors

Felix M. Riese, Sina Keller

Citation

see Citation

Paper

Riese and Keller (2020)

Requirements

Python 3 with these packages

How to use this repository?

  1. Install Python 3, e.g. with Anaconda
  2. Install the required packages

    conda install --file requirements.txt

  3. Start jupyter

    jupyter notebook

  4. Open the notebook folder in this repository in the Jupyter browser and select the desired notebook.

Citation

The bibtex file including both references is available in bibliography.bib.

Paper:

Felix M. Riese and Sina Keller, "Supervised, Semi-Supervised, and Unsupervised Learning for Hyperspectral Regression", in Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing, Saurabh Prasad and Jocelyn Chanussot, Eds. Cham: Springer International Publishing, 2020, ch. 7, pp. 187–232, doi:10.1007/978-3-030-38617-7_7.

Code:

Felix M. Riese and Sina Keller, "Hyperspectral Regression: Code Examples", Zenodo, doi:10.5281/zenodo.3450676, 2019.

DOI

About

Code examples for the book chapter "Supervised, Semi-Supervised and Unsupervised Learning for Hyperspectral Regression".

https://doi.org/10.5281/zenodo.3450676

License:BSD 3-Clause "New" or "Revised" License


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