UNMIXING TECHNIQUES Introduction Bui 1,4 , Beate Orberger 2,3 , - - PowerPoint PPT Presentation

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UNMIXING TECHNIQUES Introduction Bui 1,4 , Beate Orberger 2,3 , - - PowerPoint PPT Presentation

Feb 09, 2023 576 likes •708 views

MIN INERAL ID IDENTIFICATION USIN ING A NEW HYPERSPECTRAL LIB IBRARY AND SPARSE 1. 3. Results UNMIXING TECHNIQUES Introduction Bui 1,4 , Beate Orberger 2,3 , Simon B. Blancher 1 , Ali Mohammad- Thanh Bui Djafari 4 , Henry Pilliere 5 , Anne

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SLIDE 1

MIN INERAL ID IDENTIFICATION USIN ING A NEW HYPERSPECTRAL LIB IBRARY AND SPARSE UNMIXING TECHNIQUES

Thanh Bui Bui1,4, Beate Orberger2,3, Simon B. Blancher1, Ali Mohammad- Djafari4, Henry Pilliere5, Anne Salaun1, Xavier Bourrat6, Nicolas Maubec6, Thomas Lefevre5, Celine Rodriguez1, Antanas Vaitkus7, Saulius Grazulis7, Cedric Duée6, Dominique Harang5, Thomas Wallmach1, Yassine El Mendili8, Daniel Chateigner8, Mike Buxton9, Monique Le Guen10

1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions

Navigation

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1) Eramet Research, Eramet Group, Trappes, France; 2) GEOPS-Université Paris Sud-Paris Saclay, Orsay, France; 3) Catura Geoprojects, Paris, France; 4) L2S, CNRS, Centrale Supélec, Université Paris-Saclay, France; 5) ThermoFisher Scientific (TFS), Artenay, France; 6) BRGM, Orléans, France; 7) Vilnius University Institute of Biotechnology, Vilnius, Lithuania; 8) CRISMAT-CNRS, Normandie Université, Caen, France; 9) Delft University of Technology, Delft, The Netherlands; 10) Eramet Nickel Division, Eramet Group, Trappes, France

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SLIDE 2

Introduction

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al. Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions

SOLSA ID Analyse & Identification in field and industrial applications

Profilometer, RGB camera, VNIR/SWIR cameras, XRF Localisation of ROIs on drill cores XRD – XRF – Raman on ROIs Data processing SOLSA ID A, measurement SOLSA ID A, processing SOLSA ID B, measurement SOLSA ID B, processing depth Drill core (Drill core ID)

Contributions of this work:

  • Build a new hyperspectral (SWIR) library
  • Integrate the hyperspectral library into sparse

unmixing techniques for mineral identification

  • Evaluate the results

H2020 SOLSA (Sonic Online and Sample Analysis) project aims at constructing an analytical expert system for on-line-on- mine-real-time mineralogical and geochemical analyses on sonic drill cores.

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SLIDE 3

Methods – hyperspectral library (1/2)

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al.

  • Rock and mineral samples provided by BRGM,

ERAMET and the National Museum of Natural History, France

  • Spectra extraction: ENVI 5.4 and G-MEX by taking

into account the wavelength positions and the relative intensities of the absorption features. Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions

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SLIDE 4

Methods – sparse unmixing (2/2)

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al.

𝑍 = 𝐵𝑌

Y L x n A L x m X m x n min

𝑌

𝐵𝑌 − 𝑍 𝐺

2 + 𝜇 𝑌 2,1

subject to: X≥ 0, 1𝑼X = 1

Iordache et al., IEEE Trans, 2014

  • The observed image signatures can be

expressed in the form of linear combinations

  • f a number of pure spectral signatures

known in advance (spectral library).

  • Unmixing amounts to finding the optimal

subset of signatures in a spectral library that can best model each mixed pixel in the scene.

  • The sparse unmixing exploits the usual very

low number of endmembers (maximum of 4, Berman et al., CSIRO, 2017) present in real images, out of a spectral library. Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions

More details

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SLIDE 5

Results (1/2)

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al. 37 spectra representing 21 minerals have been collected: ankerite, calcite, dolomite, magnesite lizardite, nepouite, antigorite, chrysotite, saponite, montmorillonite, nontronite, kaolinite, pimelite, talc, sepiolite, alunite, asbolane, chromite, diaspore, enstatite, forsterite Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions bands Number of spectra

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SLIDE 6

Results (2/2)

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al. QEMSCAN results RGB image

1 cm

K FCLS SUnSAL CLSUnSAL SRE Time SRE time SRE time 2 14.24 0.022 14.94 0.254 16.74 0.228 3 6.41 0.019 7.45 0.259 11.95 0.230 4 5.25 0.022 7.07 0.499 7.16 0.453

Signal to reconstruction error (SRE) ratio Simulated data Data acquired from a serpentinized harzburgite sample Unmixing results Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions

More details

FCLS: Fully constrained least squares SUnSAL: Sparse unmixing by variable splitting and augmented Lagrangian CLSUnSAL: Collaborative sparse unmixing by variable splitting and augmented Lagrangian

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SLIDE 7

Conclusions

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al.

  • A new hyperspectral library is under construction.
  • Sparse unmixing, CLSUnSAL, method provides relatively accurate unmixing results.
  • Continue enlarging the hyperspectral library and evaluating the unmixing techiques
  • For more efficient solutions, classification techniques have been developing : Random forests, SVMs

and Deep learning (CNN)

2D CNN, Testing accuracy: 0.953 1D CNN, Testing accuracy: 0.926

Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions

More details

Indian pines dataset

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SLIDE 8

Sparse unmixing:

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al. min

𝑌

𝐵𝑌 − 𝑍 𝐺

2 + 𝜇 𝑌 2,1

subject to: X ≥ 0, 𝟐𝑼X = 1 CLSUnSAL (Collaborative sparse unmixing by variable splitting and augmented Lagrangian): subject to: X ≥ 0, 𝟐𝑼 X = 1 SUnSAL (Sparse unmixing by variable splitting and augmented Lagrangian): min

𝑌

𝐵𝑌 − 𝑍 𝐺

2

subject to: X ≥ 0, 𝟐𝑼 X = 1 FCLS (Fully contrained least squares): X(m x n) The optimization is based on the alternating direction method of multipliers (ADMM) min

𝑌

𝐵𝑌 − 𝑍 𝐺

2 + 𝜇 𝑌 1,1

Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions

Iordache et al., IEEE Trans, 2014

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SLIDE 9

Classification:

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al. Spectra Pre-processing + Feature Extraction Training using SVM Spectrum Pre-processing + Feature Extraction Features Labels Features Trained Classifier Label

Training phase Prediction phase

Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions

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SLIDE 10

Classification:

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al. Navigation

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1. Introduction

  • 3. Results
  • 2. Methods

4. Conclusions 2D conv. 2D conv. Fully connected layer Logistic classification

5x5xL 3x3x64 1x1x64 128 16 (64, 3x3, 1) (64, 3x3, 1)

2D CNN

Dropout Dropout

L

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SLIDE 11

Unmixing results

MIN INERAL IDE IDENTIFICATION USI SING A NEW HYPERSPECTRAL LIB LIBRARY AND SP SPARSE UNMIXING TE TECHNIQUES

Thanh Bu Bui, i, Be Beate Or Orberger, Sim Simon B.

  • B. Blan

Blancher, Moniq ique Le Le Gu Guen et t al al. RGB image

1 cm

Preprocessed image