A novel method for predicting the geochemical composition of tailings with laboratory field and hyperspectral airborne data using a regression and classification-based approach
The increasing demand for precise and dependable models has led to the development of both sensors and statistical algorithms. However, numerous studies have demonstrated that model performance is highly dependent on a range of environmental factors, such as spatio-temporal fluctuations of moisture,...
Saved in:
| Main Authors: | Yaron Ogen, Michael Denk, Cornelia Glaesser, Holger Eichstaedt |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2022.2104173 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hyperspectral Investigation of an Abandoned Waste Mining Site: The Case of Sidi Bou Azzouz (Morocco)
by: Daniela Guglietta, et al.
Published: (2025-05-01) -
[retracted] Assessment of geochemistry and reprocessing capacity of old copper tailings in Province of Benguet, Philippines
by: Alexandria Tanciongco, et al.
Published: (2025-01-01) -
Drone-Based VNIR–SWIR Hyperspectral Imaging for Environmental Monitoring of a Uranium Legacy Mine Site
by: Victor Tolentino, et al.
Published: (2025-04-01) -
Investigating Abiotic Sources of Spectral Variability From Multitemporal Hyperspectral Airborne Acquisitions Over the French Guyana Canopy
by: Colin Prieur, et al.
Published: (2024-01-01) -
Estimating and mapping tailings properties of the largest iron cluster in China for resource potential and reuse: A new perspective from interpretable CNN model and proposed spectral index based on hyperspectral satellite imagery
by: Haimei Lei, et al.
Published: (2025-05-01)