Dimensionality reduction of imaging spectroscopy data

Solutions to deal with the high dimensionality of hyperspectral data
HYPERedu
HYPERedu

Unit Description

This unit introduces dimensionality reduction methods related to hyperspectral data or imaging spectroscopy. Following an overview of feature engineering methods that use selected parametric and nonparametric regression methods, feature selection or extraction using filter, wrapper, and embedded methods are discussed. Lastly, alternative methods are presented.

This unit was originally published in March 2020. The revised version is accessible since February 2023. It is recommended to study the unit “Principles of imaging spectroscopy” before starting this unit.

How to cite this slide collection: K. Berger, C. Atzberger, M. Danner, M. Wocher, T. Kuester, S. Foerster (2020). Dimensionality reduction of imaging spectroscopy data – Solutions to deal with the high dimensionality of hyperspectral data. HYPERedu, EnMAP education initiative, German Centre for Geosciences GFZ; originally published March 2020, revised February 2023.

Please help us to further improve the hyperspectral resources and send us your feedback to hyperedu@eo-college.org

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The SAR-EDU material is published under a Creative Commons Licence. This work is licenced under a Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

YOU ARE FREE TO:
Share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and built upon the material for any purpose, even commercially.

UNDER THE FOLLOWING TERMS:
Attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike – If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. The licensor cannot revoke these freedoms as long as you follow the license terms!

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The SAR-EDU material is published under a Creative Commons Licence. This work is licenced under a Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

YOU ARE FREE TO:
Share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and built upon the material for any purpose, even commercially.

UNDER THE FOLLOWING TERMS:
Attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike – If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. The licensor cannot revoke these freedoms as long as you follow the license terms!

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