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: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › 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
Ana bilgisayar yayını başlığı	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	6096-6099
Sayfa sayısı	4
ISBN (Elektronik)	9781665403696
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 - Online, Virtual, Belgium Süre: 12 Tem 2021 → 16 Tem 2021

Yayın serisi

Adı	International Geoscience and Remote Sensing Symposium (IGARSS)
ISSN (Basılı)	2153-6996
ISSN (Elektronik)	2153-7003

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

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

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

Belgeye Erişim

10.1109/IGARSS47720.2021.9554929

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Celik, M. F., & Erten, E. (2021). PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 6096-6099). (International Geoscience and Remote Sensing Symposium (IGARSS) ). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS47720.2021.9554929

Celik, Mehmet Furkan ; Erten, Esra. / PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY. IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 6096-6099 (International Geoscience and Remote Sensing Symposium (IGARSS) ).

@inproceedings{e22a8ec6bc8f4322905b61fb932d9e67,

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",

series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6096--6099",

booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",

address = "United States",

}

Celik, MF & Erten, E 2021, PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY. in IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS) , Institute of Electrical and Electronics Engineers Inc., pp. 6096-6099, 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Online, Virtual, Belgium, 12/07/21. https://doi.org/10.1109/IGARSS47720.2021.9554929

PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY. / Celik, Mehmet Furkan; Erten, Esra.
IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 6096-6099 (International Geoscience and Remote Sensing Symposium (IGARSS) ).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

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 - Conference contribution

AN - SCOPUS:85124886839

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 6096

EP - 6099

BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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

Y2 - 12 July 2021 through 16 July 2021

ER -

Celik MF, Erten E. PRINCIPAL COMPONENT ANALYSIS BASED POLYNOMIAL CHAOS EXPANSION REGRESSION OF LEAF AREA INDEX FROM POLSAR IMAGERY. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 6096-6099. (International Geoscience and Remote Sensing Symposium (IGARSS) ). doi: 10.1109/IGARSS47720.2021.9554929

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

Özet

Yayın serisi

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Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

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