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:
- Data
- Features
- Supervised Learning
- Active Learning
- Model Selection and Evaluation
- Generative Adversarial Networks
- Install Python 3, e.g. with Anaconda
Install the required packages
conda install --file requirements.txt
Start jupyter
jupyter notebook
- Open the notebook folder in this repository in the Jupyter browser and select the desired notebook.
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.
@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.