Applicability of Hyperspectral Fluorescence Imaging to Mineral Sorting

Transcription

1 Institute of Industrial Information Technology Applicability of Hyperspectral Fluorescence Imaging to Mineral Sorting Optical Characterization of Materials, March 19, 2015 Sebastian Bauer, M.Sc. (Head: Prof. Puente), Faculty of Electrical Engineering and Information Technology KIT University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association

2 Motivation minerals are difficult to discriminate by means of color images different techniques available: laser-induced breakdown spectroscopy (LIBS) near-infrared hyperspectral imaging (NIR-HSI) X-ray fluorescence (XRF) X-ray transmission (XRT) all those methods have their own advantages and disadvantages in terms of accuracy, speed, cost, handling etc. are there other discrimination techniques with less disadvantages? 2 30 March 2015

3 Agenda 1. Motivation 2. Light-emitting phenomena 3. Experimental setup 4. Hyperspectral fluorescence images and their characteristics 5. Classification and its results 6. Conclusion and outlook 3 30 March 2015

4 Agenda 1. Motivation 2. Light-emitting phenomena 3. Experimental setup 4. Hyperspectral fluorescence images and their characteristics 5. Classification and its results 6. Conclusion and outlook 4 30 March 2015

5 Other physical light-emitting phenomena intensity idea: low light phenomena? X-rays ultraviolet visible infrared microwaves wavelength 5 30 March 2015

6 Photoluminescence incident photon excites electron electron emits photon when falling back to ground level emitted photon carries less energy than incident photon shorter wavelength fluorescence: short-lived kind of photoluminescence (<10-7 s) excited states (singlet) internal loss spin conversion triplet state absorption ground state (singlet) fluorescence phosphorescence 6 30 March 2015

7 Fluorescence characteristics factors enabling/enhancing fluorescence: presence of enough photoluminescent centers with activators, e.g., certain kinds of ions quenching: factors eliminating fluorescence collisional quenching: collisional encounters between the fluorophore and quencher static quenching: fluorophore and quencher form a complex that does not fluoresce Relatively small proportions of the sample determine the fluorescence Fluorescence color depends on the origin of the sample 7 30 March 2015

8 Agenda 1. Motivation 2. Light-emitting phenomena 3. Experimental setup 4. Hyperspectral fluorescence images and their characteristics 5. Classification and its results 6. Conclusion and outlook 8 30 March 2015

9 Experimental setup camera filter light source: 300 W Xenon lamp (~ nm) optical filter: acousto-optic tunable filter (AOTF) camera: electron-multiplying charge-coupled device (EMCCD) mirror sample light source with monochromator 9 30 March 2015

10 Image acquisition excitation: nm in steps of 20 nm (full width at half max.: 15 nm) acquisition: nm, full width at half max. and increment 4 nm each exposure time: s, depending on excitation wavelength excitation acquisition wavelength/nm March 2015

11 Samples class number type 1 magnesite, white 2 2 magnesite, yellow 2 3 magnesite, cauliflower 2 4 magnesite, dark 2 5 magnesite, middlings 2 6 talc, partly with pyrite 7 7 dolomite 2 8 granite 2 9 chlorite 3 10 quartz 2 number of samples March 2015

12 Agenda 1. Motivation 2. Light-emitting phenomena 3. Experimental setup 4. Hyperspectral fluorescence images and their characteristics 5. Classification and its results 6. Conclusion and outlook March 2015

13 Image example: excitation 320 nm image at 526 nm original image at 526 nm filtered image at 526 nm March 2015

14 Image example: excitation 320 nm, acquisition 526 nm granite granite talc talc talc talc/pyr talc/pyr talc/pyr talc/pyr yellow magnesite white magnesite chlorite chlorite chlorite March 2015

15 average pixel value/counts Magnesite sample mean spectra (360 nm exc.) origin 1 origin 2 origin 3 class 1 white class 1 white class 2 yellow class 2 yellow class 3 cauliflower class 3 cauliflower class 4 dark class 4 dark class 5 middlings class 5 middlings wavelength/nm March 2015

16 relative mean value Magnesite sample mean spectra normalized origin 1 origin 2 origin 3 class 1 white class 1 white class 2 yellow class 2 yellow class 3 cauliflower class 3 cauliflower class 4 dark class 4 dark class 5 middlings class 5 middlings wavelength/nm March 2015

17 average pixel value/counts Talc sample mean spectra (360 nm exc.) sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample wavelength/nm March 2015

18 relative mean value Talc sample mean spectra normalized sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample wavelength/nm March 2015

19 average pixel value/counts Chlorite sample mean spectra (360 nm exc.) sample 1 sample 2 sample wavelength/nm March 2015

20 Spatial variability magnesite 280 nm exc. 300 nm exc nm exc nm exc. 360 nm exc March 2015

21 Spatial variability dolomite 280 nm exc. 300 nm exc nm exc nm exc. 360 nm exc March 2015

22 Agenda 1. Motivation 2. Light-emitting phenomena 3. Experimental setup 4. Hyperspectral fluorescence images and their characteristics 5. Classification and its results 6. Conclusion and outlook March 2015

23 band 2 Classification used classifiers linear discriminant analysis (LDA) k-nearest neighbors algorithm (KNN) spectral angle mapper (SAM): : test spectrum : reference spectrum band March 2015

24 Classificator training one sample of each class: training data other samples: test data LDA training: all pixels of the training sample used as training pixels KNN training: N pixels of each training sample used as training pixels for fair comparison (N: number of pixels of smallest training sample) SAM training: mean spectra of the training samples used as class reference spectra March 2015

25 Classification results excitation LDA KNN SAM 260 nm nm nm nm nm nm low classification results due to no preprocessing similar spectra class composition March 2015

26 Agenda 1. Motivation 2. Light-emitting phenomena 3. Experimental setup 4. Hyperspectral fluorescence images and their characteristics 5. Classification and its results 6. Conclusion and outlook March 2015

27 Summary of hyperspectral mineral fluorescence images properties hyperspectral fluorescence images can be a useful tool for discriminating spectra spectra vary between different mineral samples of the same class origin dependence intensity information allows for, e.g., normalization hyperspectral investigations provide valuable information about spatial properties of minerals not all minerals fluoresce? Depends on illumination power (!) if hyperspectral data are needed, their acquisition (VIS-range) can be more cost efficient than the one of hyperspectral NIR images March 2015

28 Outlook determination of feasible excitation wavelengths (problemdependent) implementation using more powerful light sources investigate intra-sample variability investigate different mineral species improve classification March 2015

29 Thank you for your attention March 2015

30 average pixel value/counts All samples mean spectra (360 nm exc.) 280 nm exc. 360 nm exc magnesite 1 magnesite 2 magnesite 3 magnesite 4 magnesite 5 talc dolomite granite chlorite quartz wavelength/nm March 2015

31 intensity (a.u.) intensity (a.u.) Dolomite excitation dependence 300 nm exc. 320 nm exc. origin 1 origin 2 origin 3 sample 1 sample nm exc. 360 nm exc March 2015

32 pixel value/counts Yellow magnesite comparison between samples sample 1 sample 2 mean sample 1 mean sample wavelength/nm March 2015

33 relative pixel value Yellow magnesite normalized spectra sample 1 sample 2 mean sample 1 mean sample wavelength/nm March 2015