Regression-based unmixing of urban land cover

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Tutorial Description

This tutorial focuses on regression-based unmixing of urban land cover from simulated EnMAP imagery. The slide collection provides the theoretical foundation for the tutorial, including general introductions to urban land cover mapping and regression-based unmixing using synthetically mixed training data. The practical provides hands-on training for working with the EnMAP-Box, including a basic introduction to its functionalities and spectral library handling, as well as the regression-based unmixing processing chain.

This tutorial focuses on regression-based unmixing of urban land cover from simulated EnMAP imagery. Hands-on training for working with the EnMAP-Box, including a basic introduction to its functionalities and spectral library handling, is part of this tutorial, as well as hands-on training on the regression-based unmixing processing chain. The associated slide collection “Imaging spectroscopy for urban mapping” provides the theoretical foundation for this tutorial, including a general overview of urban land cover mapping, as well as regression-based unmixing using synthetically mixed training data.

This tutorial was originally published in September 2020. The revised version is accessible since February 2023. Complementary to this tutorial, the unit “Imaging spectroscopy Imaging spectroscopy for urban mapping” provides an introduction to the capabilities of imaging spectroscopy (IS) for urban mapping.

How to cite this tutorial: A. Okujeni, S. van der Linden (2021). Regression-based unmixing of urban land cover – Introductory slides to the EnMAP-Box tutorial. HYPERedu, EnMAP education initiative, Humboldt-Universität zu Berlin; originally published March 2019, 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!

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!