A novel method for predicting the geochemical composition of tailings with laboratory field and hyperspectral airborne data using a regression and classification-based approach-Reference-Cited by-同舟云学术

A novel method for predicting the geochemical composition of tailings with laboratory field and hyperspectral airborne data using a regression and classification-based approach

Published:2022-08-01 Issue:1 Volume:55 Page:453-470
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Ogen Yaron¹^ORCID,Denk Michael¹^ORCID,Glaesser Cornelia¹^ORCID,Eichstaedt Holger²

Affiliation:

1. Institute of Geosciences and Geography, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany

2. Dimap-Spectral GmbH, Freiberg, Germany

Funder

Client II program of the German Federal Ministry of Education and Research

Publisher

Informa UK Limited

Subject

General Medicine

Link

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2022.2104173

Reference90 articles.

1. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

2. Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image

3. A predictive model for the water retention curve: application to tailings from hard-rock mines

4. Hyperspectral remote sensing for mapping and detection of Egyptian kaolin quality

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