PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY

Mehmet Furkan Celik; Esra Erten

doi:10.1109/IGARSS47720.2021.9554929

PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY

Mehmet Furkan Celik
, Esra Erten

Harita Mühendisliği Bölümü

Istanbul Technical University

Araştırma çıktısı: Konferansa katkı › Yazı › Hakem

3 Atıf (Scopus)

Özet

Predicting biophysical parameters with high accuracy and fast speed based on remote sensing-based modeling is an attractive topic. In this context, the revisit time, coverage, and illumination condition in-dependency make the Polarimetric Synthetic Aperture Radar (PolSAR) data is an attractive tool. In this paper, one of the most studied biophysical parameters, Leaf Area Index (LAI), is chosen to assess Polynomial Chaos Expansion (PCE) regression, commonly used metamodeling due to its precise and rapid approximation performance. Experimental analysis based on AgriSAR 2009 campaign, including oat and canola, is given to validate the PCE in the regression. According to the accuracy analysis, the Pearson correlation of 88% and 95% for oat and canola, respectively, were achieved.

Orijinal dil	İngilizce
Sayfalar	6096-6099
Sayfa sayısı	4
DOI'lar	https://doi.org/10.1109/IGARSS47720.2021.9554929
Yayın durumu	Yayınlandı - 2021
Etkinlik	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Süre: 12 Tem 2021 → 16 Tem 2021

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???event.eventtypes.event.conference???	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Ülke/Bölge	Belgium
Şehir	Brussels
Periyot	12/07/21 → 16/07/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

Finansman

Radarsat-2 Data and Products MacDonald, Dettwiler and Associates Ltd. (MDA, 2009) All Rights Reserved. Radarsat is an official trademark of the Canadian Space Agency (CSA). All Radarsat-2 images have been provided by MDA and CSA in the framework of the ESA-funded AgriSAR2009 campaign. The ground data collection was conducted by the Indian Head Agriculture Research Facility (IHARF) and the University of Regina. The Polynomial Chaos Expansion computations are carried out in UQLab [10]. This study was funded by ITU Scientific Research Project with Protocol Number: MDK-42745.

Finansörler	Finansör numarası
Dettwiler and Associates Ltd.
ITU Scientific Research Project	MDK-42745
Indian Head Agriculture Research Facility
Muscular Dystrophy Association
University of Regina
Canadian Space Agency

Belgeye Erişim

10.1109/IGARSS47720.2021.9554929

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@conference{db8786e131984ba986b89de4344c233b,

title = "PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY",

abstract = "Predicting biophysical parameters with high accuracy and fast speed based on remote sensing-based modeling is an attractive topic. In this context, the revisit time, coverage, and illumination condition in-dependency make the Polarimetric Synthetic Aperture Radar (PolSAR) data is an attractive tool. In this paper, one of the most studied biophysical parameters, Leaf Area Index (LAI), is chosen to assess Polynomial Chaos Expansion (PCE) regression, commonly used metamodeling due to its precise and rapid approximation performance. Experimental analysis based on AgriSAR 2009 campaign, including oat and canola, is given to validate the PCE in the regression. According to the accuracy analysis, the Pearson correlation of 88\% and 95\% for oat and canola, respectively, were achieved.",

keywords = "Global Sensitivity Analysis, Leaf Area Index, PolSAR, Polynomial Chaos Expansion, Regression",

author = "Celik, \{Mehmet Furkan\} and Esra Erten",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9554929",

language = "English",

pages = "6096--6099",

}

TY - CONF

T1 - PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY

AU - Celik, Mehmet Furkan

AU - Erten, Esra

PY - 2021

Y1 - 2021

N2 - Predicting biophysical parameters with high accuracy and fast speed based on remote sensing-based modeling is an attractive topic. In this context, the revisit time, coverage, and illumination condition in-dependency make the Polarimetric Synthetic Aperture Radar (PolSAR) data is an attractive tool. In this paper, one of the most studied biophysical parameters, Leaf Area Index (LAI), is chosen to assess Polynomial Chaos Expansion (PCE) regression, commonly used metamodeling due to its precise and rapid approximation performance. Experimental analysis based on AgriSAR 2009 campaign, including oat and canola, is given to validate the PCE in the regression. According to the accuracy analysis, the Pearson correlation of 88% and 95% for oat and canola, respectively, were achieved.

AB - Predicting biophysical parameters with high accuracy and fast speed based on remote sensing-based modeling is an attractive topic. In this context, the revisit time, coverage, and illumination condition in-dependency make the Polarimetric Synthetic Aperture Radar (PolSAR) data is an attractive tool. In this paper, one of the most studied biophysical parameters, Leaf Area Index (LAI), is chosen to assess Polynomial Chaos Expansion (PCE) regression, commonly used metamodeling due to its precise and rapid approximation performance. Experimental analysis based on AgriSAR 2009 campaign, including oat and canola, is given to validate the PCE in the regression. According to the accuracy analysis, the Pearson correlation of 88% and 95% for oat and canola, respectively, were achieved.

KW - Global Sensitivity Analysis

KW - Leaf Area Index

KW - PolSAR

KW - Polynomial Chaos Expansion

KW - Regression

UR - https://www.scopus.com/pages/publications/85124886839

U2 - 10.1109/IGARSS47720.2021.9554929

DO - 10.1109/IGARSS47720.2021.9554929

M3 - Paper

AN - SCOPUS:85124886839

SP - 6096

EP - 6099

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

Y2 - 12 July 2021 through 16 July 2021

ER -

PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY

Özet

???event.eventtypes.event.conference???

Bibliyografik not

Finansman

Belgeye Erişim

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