An Extensive Multisensor Hyperspectral Benchmark Datasets of Intimate Mixtures of Mineral Powders-Reference-Cited by-同舟云学术

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An Extensive Multisensor Hyperspectral Benchmark Datasets of Intimate Mixtures of Mineral Powders

Published:2023-07-16 Issue: Volume: Page:
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Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Koirala Bikram¹,Rasti Behnood²,Bnoulkacem Zakaria¹,De Lima Ribeiro Andréa²,Madriz Yuleika²,Herrmann Erik²,Gestels Arthur³,De Kerf Thomas⁴,Janssens Koen³,Steenackers Gunther⁴,Gloaguen Richard²,Scheunders Paul¹

Affiliation:

1. University of Antwerp (CDE),Imec-Visionlab,Belgium

2. Helmholtz-Zentrum Dresden-Rossendorf (HZDR),Germany

3. University of Antwerp,Antwerp X-Ray Imaging and Spectroscopy (AXIS),Belgium

4. University of Antwerp,InViLab Research Group, UAntwerp Industrial Vision Lab,Belgium

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10281467.pdf?arnumber=10281467

Reference12 articles.

1. Bidirectional reflectance spectroscopy: 1. Theory

2. HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network

3. Robust Supervised Method for Nonlinear Spectral Unmixing Accounting for Endmember Variability

4. The Opposition Effect of the Moon: Coherent BackscatterandShadow Hiding

5. A Review of Nonlinear Hyperspectral Unmixing Methods

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