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

Department of Geomatics Engineering

Istanbul Technical University

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages	6096-6099
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9554929
Publication status	Published - 2021
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Funding

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.

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

Keywords

Global Sensitivity Analysis
Leaf Area Index
PolSAR
Polynomial Chaos Expansion
Regression

Access to Document

10.1109/IGARSS47720.2021.9554929

Cite this

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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",

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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.

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

Abstract

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Keywords

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