Dimensionality reduction of imaging spectroscopy data

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

Unit Description

This unit provides an introduction to dimensionality reduction methods related to hyperspectral data or imaging spectroscopy. First, feature engineering methods using selected parametric and nonparametric regression methods are presented. Second, feature selection or extraction, using filter, wrapper and embedded methods will be discussed. Finally, some alternative methods are presented.

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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).

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Download Resource

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!

Download Resource

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!