Recent Publications

Yue, J., Fang, L., Rahmani, H. and Ghamisi, P., 2021. Self-Supervised Learning With Adaptive Distillation for Hyperspectral Image Classification.

Schurr, B., Ratschbacher, L., Sippl, C., Gloaguen, R., Yuan, X., Mechie, J., 2014. Seismotectonics of the Pamir.

De La Rosa, R., Khodadadzadeh, M., Tusa, L., Kirsch, M., Gisbert, G., Tornos, F., Tolosana-Delgado, R. and Gloaguena, R., 2021. Mineral quantification at deposit scale using drill-core hyperspectral data: A case study in the Iberian Pyrite Belt.

Seidel, P., Lorenz, S., Heinig, T., Zimmermann, R., Booysen, R., Beyer, J., Heitmann, J. and Gloaguen, R., 2019. Fast 2D Laser-Induced Fluorescence Spectroscopy Mapping of Rare Earth Elements in Rock Samples.

Fuchs, M., Gloaguen, R., Merchel, S., Pohl, E., Sulaymonova, V., Andermann, C. and Rugel, G., 2015. Denudation rates across the Pamir based on 10Be concentrations in fluvial sediments: dominance of topographic over climatic factors.

Jackisch, R., Lorenz, S., Zimmermann, R., Möckel, R. and Gloaguen, R., 2018. Drone-Borne Hyperspectral Monitoring of Acid Mine Drainage: An Example from the Sokolov Lignite District.

Sharma, S., Köhler, T., Beyer, J., Fuchs, M., Gloaguen, R. and Heitmann, J., 2019. Extending the temperature sensing range using Eu3+ luminescence up to 865 K in a single crystal of EuPO4.

Jackisch, R., Heincke, B., Zimmermann, R., Sørensen, E., Pirttijärvi, M., Kirsch, M., Salmirinne, H., Lode, S., Kuronen, U. and Gloaguen, R., 2022. Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland.

Rasti, B., Ghamisi, P. and Gloaguen, R., 2021. OptFus: Optical Sensor Fusion for the Classification of Multisource Data: Application to Mineralogical Mapping.

Schirrmeister, L., Meyer, H., Andreev, A., Wetterich, S., Kienast, F., Bobrov, A., Fuchs, M., Sierralta, M., Herzschuh, U., 2016. Late Quaternary paleoenvironmental records from the Chatanika River valley near Fairbanks (Alaska).

Attarchi, S. and Gloaguen, R., 2014. Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest.

Sharma, S., Beyer, J., Gloaguen, R. and Heitmann, J., 2020. Comparing the optical properties and thermal stability of green (TbPO4), yellow (DyPO4), and red (PrPO4) emitting single crystal samples.