hyperspectral-regression

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

https://github.com/felixriese/hyperspectral-regression

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

README.rst

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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:: bibtex

    @incollection{riese2020supervised,
        author = {Riese, Felix~M. and Keller, Sina},
        title ={{Supervised, Semi-Supervised, and Unsupervised Learning for
                Hyperspectral Regression}},
        booktitle = {{Hyperspectral Image Analysis: Advances in Machine
                     Learning and Signal Processing}},
        editor = {Prasad, Saurabh and Chanussot, Jocelyn},
        year = {2020},
        publisher = {Springer International Publishing},
        address = {Cham},
        chapter = {7},
        pages = {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.

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3450676.svg
    :target: https://doi.org/10.5281/zenodo.3450676
    :alt: DOI

.. code:: bibtex

    @misc{riese2019hyperspectral,
        author = {Riese, Felix~M. and Keller, Sina},
        title = {{Hyperspectral Regression: Code Examples}},
        year = {2019},
        DOI = {10.5281/zenodo.3450676},
        publisher = {Zenodo},
        howpublished = {\href{https://doi.org/10.5281/zenodo.3450676}{doi.org/10.5281/zenodo.3450676}}
    }

Owner

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite both the article from preferred-citation and the software itself."
authors:
  - family-names: Riese
    given-names: Felix M.
    orcid: https://orcid.org/0000-0003-0596-9585
  - family-names: Keller
    given-names: Sina
    orcid: https://orcid.org/0000-0002-7710-5316
title: "Hyperspectral Regression: Code Examples"
version: 1.0.0
doi: "10.5281/zenodo.3450676"
date-released: 2019-09-19
repository-code: https://github.com/felixriese/hyperspectral-regression
license: BSD-3-Clause
preferred-citation:
  authors:
    - family-names: Riese
      given-names: Felix M.
    - family-names: Keller
      given-names: Sina
  title: "Supervised, Semi-Supervised, and Unsupervised Learning for Hyperspectral Regression"
  type: book
  year: 2020
  doi: "10.1007/978-3-030-38617-7_7"
  publisher:
    - name: "Springer International Publishing"
    - Publications city: "Cham"
  collection-title: "Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing"
  start: 187
  end: 232

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