Proportion Inference Using Deep Neural Networks. Applications to X-Ray Diffraction and Hyperspectral Imaging
- Resource Type
- Conference
- Authors
- Simonnet, Titouan; Fall, Mame Diarra; Galerne, Bruno; Claret, Francis; Grangeon, Sylvain
- Source
- 2023 31st European Signal Processing Conference (EUSIPCO) European Signal Processing Conference (EUSIPCO), 2023 31st. :1310-1314 Sep, 2023
- Subject
- Signal Processing and Analysis
Training
Earth
Deep learning
X-ray scattering
Powders
Europe
Artificial neural networks
Proportion inference
Hyperspectral Unmixing
X-Ray Diffraction
Neural Networks
Dirichlet distribution
- Language
- ISSN
- 2076-1465
Deep learning is considered as a disruptive method in the field of mineralogy and hyperspectral imaging. Many techniques exist to gain mineralogical information. Amongst them powder X-Ray diffraction (XRD) is very popular and powerful, while hyperspectral imaging is used in many applications such as Earth observation. A key issue for both XRD and hyperspectral imaging is not only to identify the endmembers constituting a mixture but also quantify the abundance of each endmember. In this study, we propose completely novel neural network (NN) training losses specifically designed for proportion inference. Extensive experiments illustrate that the proposed approach allows validated NN architectures to be trained to infer accurately on proportions.