BUILDING A HYPERSPECTRAL LIBRARY AND ITS INCORPORATION INTO SPARSE UNMIXING FOR MINERAL IDENTIFICATION

被引：0

作者：

Thanh Bui ^{[1
,4
]}

Orberger, Beate ^{[2
,3
]}

Blancher, Simon B. ^{[1
]}

Mohammad-Djafari, Ali ^{[4
]}

Pilliere, Henry ^{[5
]}

Salaun, Anne ^{[1
]}

Bourrat, Xavier ^{[6
]}

Maubec, Nicolas ^{[6
]}

Lefevre, Thomas ^{[5
]}

Rodriguez, Celine ^{[1
]}

Vaitkus, Antanas ^{[7
]}

Grazulis, Saulius ^{[7
]}

Duee, Cedric ^{[6
]}

Harang, Dominique ^{[5
]}

Wallmach, Thomas ^{[1
]}

El Mendili, Yassine ^{[8
]}

Chateigner, Daniel ^{[8
]}

Buxton, Mike ^{[9
]}

Le Guen, Monique ^{[10
]}

机构：

[1] Eramet Grp, Eramet Res, Trappes, France

[2] Univ Paris Sud Paris Saclay, GEOPS, Orsay, France

[3] Catura Geoprojects, Paris, France

[4] Univ Paris Saclay, Cent Supelec, CNRS, L2S, Paris, France

[5] ThermoFisher Sci TFS, Artenay, France

[6] Bur Rech Geol & Minieres, Orleans, France

[7] Vilnius Univ, Inst Biotechnol, Vilnius, Lithuania

[8] Normandie Univ, CNRS, CRISMAT, Caen, France

[9] Delft Univ Technol, Fac Civil Engn & Geosci, Delft, Netherlands

[10] Eramet Grp, Eramet Nickel Div, Trappes, France

来源：

IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年

关键词：

Hyperspectral library; sparse unmixing; shortwave infrared (SWIR);

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The objective of the SOLSA project (EU-H2020) is to develop an analytical expert system for on-line-on-mine-real-time mineralogical and geochemical analyses on sonic drill cores. As one aspect of the system, this paper presents the building of the hyperspectral library and its incorporation into sparse unmixing techniques for mineral identification. Twenty seven spectra representing 14 minerals have been collected for the library. Three sparse unmixing techniques have been investigated and evaluated using simulated data generated from our hyperspectral library, and real hyperspectral data acquired from a serpentinized harzburgite sample. Among the three techniques, the collaborative sparse unmixing by variable splitting and augmented Lagrangian (CLSUnSAL) method provided the best accurate results on the simulated data. In addition, the results of the CLSUnSAL method show high correlation with that of the QEMSCAN (R) analysis on the harzburgite hyperspectral data.

引用

页码：4261 / 4264

页数：4

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