Mineral identification using indoor hyperspectral Bardia...

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Bardia Yousefi, Saeed Sojasi, Clemente Ibarra-Castanedo, Georges Beaudoin , François Huot, Xavier P. V. Maldague Martin Chamberland, and Erik Lalonde Mineral identification using indoor hyperspectral imaging Département de génie électrique et de génie informatique TELOPS, Inc., 100-2600 St-Jean-Baptiste Ave, Quebec, Qc, G2E 6J5, Canada Canada Research Chair in Multipolar Infrared Vision – Vision Infrarouge Multipolaire: MiViM

Transcript of Mineral identification using indoor hyperspectral Bardia...

Page 1: Mineral identification using indoor hyperspectral Bardia ...vision.gel.ulaval.ca/~bardia/files/DSS-SPIEApril2016.pdf · François Huot, Xavier P. V. Maldague Martin Chamberland, and

Bardia Yousefi, Saeed Sojasi, Clemente

Ibarra-Castanedo, Georges Beaudoin ,

François Huot, Xavier P. V. Maldague

Martin Chamberland, and Erik Lalonde

Mineral identification using indoor hyperspectral

imaging

Département de génie électrique et de génie informatique

TELOPS, Inc., 100-2600 St-Jean-Baptiste Ave, Quebec, Qc, G2E 6J5, Canada

Canada Research Chair in Multipolar Infrared Vision – Vision Infrarouge Multipolaire: MiViM

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Outline

• Introduction

• Hyperspectral Imaging

• Problem Statement

• What we are doing

• Experimental Results

• Measurement

• Method

• HYMID

• conclusion

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HYPERSPECTRAL IMAGING

Hyperspectral imaging, like other spectral imaging,

collects and processes information from across the

electromagnetic spectrum. The goal of hyperspectral

imaging is to obtain the spectrum for each pixel in

the image of a scene, with the purpose of finding objects,

identifying materials, or detecting processes.

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photonics.com

Spatial

Spectral

Applications

• vegetation

• urban area

• geology

• target detection

• medical

• etc

northropgrumman.com View of the Hyperspectral Imager for Coastal Oceans (HICO)

nasa.gov

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WHAT WE ARE DOING

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Hyperspectral in Satellite

Hyperspectral in Laboratory

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INTRODUCTION(Problem Statement)

Mineral identification is challenging in the field of geology

and mineralogy.

It relates to geological research and is usually conducted by

geologists (mineralogy experts).

It is extensively investigated by hyperspectral remote sensing

(and airborne) sensing and has been the subject of many

research studies.

Manual identification of mineral samples by a mineralogy

expert which is a time consuming process and provides high

level of disparity due to fatigue or inadequate methods for this

specific application.

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INTRODUCTION

The main objective of this research lies under the

automatic mineral identification which is considered

as hyperspectral unmixing.

Making automated system for mineral

identification

Hyperspectral unmixing in the hyperspectral

infrared image.

It use(d/s) for long time and most of the HSI

researches are basically for remote sensing(and

airborne) research. (e.g. Only in 2015 the geological

based hyperspectral infrared imagery (contributions) papers

were more 100> )

What

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Photograph showing measurements being made with the PIMA

II field spectrometer. Picture from: Kruse, F. A. (1996).

Identification and mapping of minerals in drill core using hyperspectral

image analysis of infrared reflectance spectra. International journal of

remote sensing, 17(9), 1623-1632.

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Mineral identification

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Data-mining

Measurement

Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

•Data retrieval

Data-mining• Data retrieval

• Creating a system for automatic

mineral identification:

•Feature extraction /Selection

•Classification- Pattern

recognition task & machine

learning

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Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

Measurement

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Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

New mineral grains have been prepared for the new experiment

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Hyperspectral imaging

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HyperCam

HeatingElement

MineralSamples

InfraGold

HeatingElement

Imaging are done at Telops Inc. 02 April 2015

Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

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List of all samples

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Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

1. ILM

2. ILM – NEW

3. OL

4. OL – NEW

5. CHR

6. CHR – NEW

7. PYR

8. QTZ 1&2

9. QTZ-NEW

10. QTZ- FELD

11. MIX 1

12. MIX 2

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50

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250

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IR View

IR ViewIR View

IR View

297.5

298

298.5

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299.5

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300.5

301

301.5

302

IR View

297.5

298

298.5

299

299.5

300

300.5

301

301.5

302

Olivine - OL

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50100150200250

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10.77 mm

FileName = '20150402_211927875_OL_ON.radiance.sc';

OL1

OL2

OL3

OFF (before) OFF (after)ON

Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

ASTER

OUR Test

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Chromite - NEW- CHR

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CHR1

CHR2CHR3

Binocular

Heating source OFFHeating source ON

Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

ASTER

OUR Test

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ASTER - Chromite

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Current Band

Possible Signature

Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

Picture adopted from ASTER

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ASTER – Ilmenite & Chromite

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Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

Current Band

Possible Signature

Current Band

Possible Signature

There is no signature for Ilmenite and Chromite in LWIR

Pictures are adopted from ASTER

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Ilmenite – ILM- NEW

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Binocular

Heating source OFFHeating source ON

Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

OUR Test

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Pyrope - PYR

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PYR1

PYR2PYR3

Binocular

Heating source OFFHeating source ON

Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

ASTER

OUR Test

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QUARTZ –NEW - QTZ

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QTZ1

QTZ2QTZ3

Binocular

Heating source OFFHeating source ON

Measurement•Mineral preparation

•Conducting the experiments

•Producing the clean data

•Finding the reference datasets

ASTER

OUR Test

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HYMIDHyperspectral Mineral Identifier (HYMID): a tool for applying spectral analysis

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Reference (EEA)

ASTER/JPL

Approx. EE

METHODOLOGY

Matched

Filter

Spectral Angle

Mapper

Abundance

Map

MAM map

Material abundance map (MAM) generation

Spectral Comparison

Input HSI

Normalize

cross

correlation

NormXCorr

SAM

MF

SID

Supervised

ELM

SVM

Unsupervised

Kernelled HSV K-

mean

Spectral Info.

Divergence

Matched

Filter

Spectral Angle

Mapper

Normalize

cross

correlation

NormXCorr

SAM

MF

SID

Spectral Info.

Divergence

First Derivative

(FD)

Matched

Filter

Spectral Angle

Mapper

Normalize

cross

correlation

NormXCorr

SAM

MF

SID

Spectral Info.

Divergence

Signature band

(SFF)

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RESULTS

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Simple looking at the problem, applying a simple threshold in the certain wavelength.

Feng, J., Rivard, B., Rogge, D., & Sánchez-Azofeifa, A. (2013). The longwave infrared (3–14μm) spectral properties of rock encrusting lichens based on laboratory spectra and

airborne SEBASS imagery. Remote Sensing of Environment, 131, 173-181.

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RESULTS

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RESULTS

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RESULTS Minimum Noise Fraction (MNF)

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RESULTS

Fast Fourier Transform (FFT)

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CLUSTERING/CLASSIFICATION

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K-MEANS

ELM

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CONCLUSION

The problem of mineral identification has been discussed.

Systems for automated identification of the minerals have been introduced using the hyperspectral infrared imaging system. Clustering and classification of the mineral has been analyzed.

Hyperspectral infrared in the wavelength (7.7-11.8 µm) provides distinctive signatures of Quartz, Pyrope ,and Olivine.

On the contrary, the hyperspectral profiles for Ilmenite and Chromite are difficult to identify in this spectral band.

Using ELM is not a good option as its training is hard for overcoming this issue unsupervised approaches are recommended.

Future Work:

• Further investigate clustering techniques to improve performance,

• Investigate the application of spectral transformation techniques such as the S-transform

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THANK YOU

FRQ-NTFoundation

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GROUND TRUTH

Orthogonal subspace projection (OSP) & Adaptive matched subspace detector (AMSD) algorithm