Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm

A. M. Sánchez; E. Zafra; J. Piera

doi:10.1109/WHISPERS.2014.8077610

Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm

A. M. Sánchez
, E. Zafra
, J. Piera

Research output: Book chapter › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Particle modeling is usually exploited, along with measured data, to infer the water content. However, the particle properties must be accurately known. In this paper, a methodology to estimate the hyperspectral complex-refractive-index signatures of marine particles is presented. It is is based on the Mie-Lorentz and T-matrix characterizations to obtain the particle inherent optical properties and uses a genetic algorithm for search optimization. This methodology is tested by accurately estimating the hyperspectral complex refractive indexes on two different examples, including monodisperse and polydisperse particle size distributions of spherical and non-spherical particles.

Original language	English
Title of host publication	2014 6th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publication	Evolution in Remote Sensing, WHISPERS 2014
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467390125
DOIs	https://doi.org/10.1109/WHISPERS.2014.8077610
Publication status	Published - 28 Jun 2014
Externally published	Yes
Event	6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 - Lausanne, Switzerland Duration: 24 Jun 2014 → 27 Jun 2014

Publication series

Name	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume	2014-June
ISSN (Print)	2158-6276

Conference

Conference	6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
Country/Territory	Switzerland
City	Lausanne
Period	24/06/14 → 27/06/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

Keywords

Genetic algorithm
microplastics
Mie-Lorentz
particle modeling
phytoplankton
T-matrix

Access to Document

10.1109/WHISPERS.2014.8077610

Cite this

Sánchez, A. M., Zafra, E., & Piera, J. (2014). Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm. In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 Article 8077610 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2014-June). IEEE Computer Society. https://doi.org/10.1109/WHISPERS.2014.8077610

Sánchez, A. M. ; Zafra, E. ; Piera, J. / Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm. 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014. IEEE Computer Society, 2014. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing).

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title = "Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm",

abstract = "Particle modeling is usually exploited, along with measured data, to infer the water content. However, the particle properties must be accurately known. In this paper, a methodology to estimate the hyperspectral complex-refractive-index signatures of marine particles is presented. It is is based on the Mie-Lorentz and T-matrix characterizations to obtain the particle inherent optical properties and uses a genetic algorithm for search optimization. This methodology is tested by accurately estimating the hyperspectral complex refractive indexes on two different examples, including monodisperse and polydisperse particle size distributions of spherical and non-spherical particles.",

keywords = "Genetic algorithm, microplastics, Mie-Lorentz, particle modeling, phytoplankton, T-matrix",

author = "S{\'a}nchez, \{A. M.\} and E. Zafra and J. Piera",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 ; Conference date: 24-06-2014 Through 27-06-2014",

year = "2014",

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day = "28",

doi = "10.1109/WHISPERS.2014.8077610",

language = "English",

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Sánchez, AM, Zafra, E & Piera, J 2014, Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm. in 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014., 8077610, Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, vol. 2014-June, IEEE Computer Society, 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014, Lausanne, Switzerland, 24/06/14. https://doi.org/10.1109/WHISPERS.2014.8077610

Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm. / Sánchez, A. M.; Zafra, E.; Piera, J.
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014. IEEE Computer Society, 2014. 8077610 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 2014-June).

Research output: Book chapter › Conference contribution › peer-review

TY - GEN

T1 - Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm

AU - Sánchez, A. M.

AU - Zafra, E.

AU - Piera, J.

PY - 2014/6/28

Y1 - 2014/6/28

N2 - Particle modeling is usually exploited, along with measured data, to infer the water content. However, the particle properties must be accurately known. In this paper, a methodology to estimate the hyperspectral complex-refractive-index signatures of marine particles is presented. It is is based on the Mie-Lorentz and T-matrix characterizations to obtain the particle inherent optical properties and uses a genetic algorithm for search optimization. This methodology is tested by accurately estimating the hyperspectral complex refractive indexes on two different examples, including monodisperse and polydisperse particle size distributions of spherical and non-spherical particles.

AB - Particle modeling is usually exploited, along with measured data, to infer the water content. However, the particle properties must be accurately known. In this paper, a methodology to estimate the hyperspectral complex-refractive-index signatures of marine particles is presented. It is is based on the Mie-Lorentz and T-matrix characterizations to obtain the particle inherent optical properties and uses a genetic algorithm for search optimization. This methodology is tested by accurately estimating the hyperspectral complex refractive indexes on two different examples, including monodisperse and polydisperse particle size distributions of spherical and non-spherical particles.

KW - Genetic algorithm

KW - microplastics

KW - Mie-Lorentz

KW - particle modeling

KW - phytoplankton

KW - T-matrix

UR - https://www.scopus.com/pages/publications/85038600093

U2 - 10.1109/WHISPERS.2014.8077610

DO - 10.1109/WHISPERS.2014.8077610

M3 - Conference contribution

AN - SCOPUS:85038600093

T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

BT - 2014 6th Workshop on Hyperspectral Image and Signal Processing

PB - IEEE Computer Society

T2 - 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014

Y2 - 24 June 2014 through 27 June 2014

ER -

Sánchez AM, Zafra E, Piera J. Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm. In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014. IEEE Computer Society. 2014. 8077610. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing). doi: 10.1109/WHISPERS.2014.8077610

Hyperspectral characterization of marine particles based on MIE-LORENTZ and T-matrix codes and a genetic algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this