Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms

Abstract Given the key role wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical...

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Autores principales:	Sarina Adeli, Bahram Salehi, Masoud Mahdianpari, Lindi J. Quackenbush, Bruce Chapman
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	NISAR machine learning random forest SVM classification wetland mapping Astronomy QB1-991 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/5b9df8647fad4a7dbf69ab567c1d105f
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spelling	oai:doaj.org-article:5b9df8647fad4a7dbf69ab567c1d105f2021-11-23T21:03:09ZMoving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms2333-508410.1029/2021EA001742https://doaj.org/article/5b9df8647fad4a7dbf69ab567c1d105f2021-11-01T00:00:00Zhttps://doi.org/10.1029/2021EA001742https://doaj.org/toc/2333-5084Abstract Given the key role wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data coupled with advanced machine learning techniques have provided an unprecedented opportunity for mapping complex wetlands’ ecosystems. A recent partnership between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) resulted in the design of the NASA‐ISRO SAR (NISAR) mission. In this study, the capability of L‐band simulated NISAR data for wetland mapping in Yucatan Lake, Louisiana, is investigated using two object‐based machine learning approaches: Support vector machine (SVM) and random forest (RF). L‐band Unmanned Aerial Vehicle SAR (UAVSAR) data are exploited as a proxy for NISAR data. Specifically, we evaluated the synergistic use of different polarimetric features for efficient delineation of wetland types, extracting 84 polarimetric features from more than 10 polarimetric decompositions. High spatial resolution National Agriculture Imagery Program imagery is applied for image segmentation using the mean‐shift algorithm. Overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrate the great possibility of L‐band prototype NISAR data for wetland mapping and monitoring. In addition, variable importance analysis using the Gini index for RF classifier suggests that H/A/ALPHA, Freeman‐Durden, and Aghababaee features have the highest contribution to the overall accuracy.Sarina AdeliBahram SalehiMasoud MahdianpariLindi J. QuackenbushBruce ChapmanAmerican Geophysical Union (AGU)articleNISARmachine learningrandom forestSVMclassificationwetland mappingAstronomyQB1-991GeologyQE1-996.5ENEarth and Space Science, Vol 8, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	NISAR machine learning random forest SVM classification wetland mapping Astronomy QB1-991 Geology QE1-996.5
spellingShingle	NISAR machine learning random forest SVM classification wetland mapping Astronomy QB1-991 Geology QE1-996.5 Sarina Adeli Bahram Salehi Masoud Mahdianpari Lindi J. Quackenbush Bruce Chapman Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms
description	Abstract Given the key role wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data coupled with advanced machine learning techniques have provided an unprecedented opportunity for mapping complex wetlands’ ecosystems. A recent partnership between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) resulted in the design of the NASA‐ISRO SAR (NISAR) mission. In this study, the capability of L‐band simulated NISAR data for wetland mapping in Yucatan Lake, Louisiana, is investigated using two object‐based machine learning approaches: Support vector machine (SVM) and random forest (RF). L‐band Unmanned Aerial Vehicle SAR (UAVSAR) data are exploited as a proxy for NISAR data. Specifically, we evaluated the synergistic use of different polarimetric features for efficient delineation of wetland types, extracting 84 polarimetric features from more than 10 polarimetric decompositions. High spatial resolution National Agriculture Imagery Program imagery is applied for image segmentation using the mean‐shift algorithm. Overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrate the great possibility of L‐band prototype NISAR data for wetland mapping and monitoring. In addition, variable importance analysis using the Gini index for RF classifier suggests that H/A/ALPHA, Freeman‐Durden, and Aghababaee features have the highest contribution to the overall accuracy.
format	article
author	Sarina Adeli Bahram Salehi Masoud Mahdianpari Lindi J. Quackenbush Bruce Chapman
author_facet	Sarina Adeli Bahram Salehi Masoud Mahdianpari Lindi J. Quackenbush Bruce Chapman
author_sort	Sarina Adeli
title	Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms
title_short	Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms
title_full	Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms
title_fullStr	Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms
title_full_unstemmed	Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms
title_sort	moving toward l‐band nasa‐isro sar mission (nisar) dense time series: multipolarization object‐based classification of wetlands using two machine learning algorithms
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/5b9df8647fad4a7dbf69ab567c1d105f
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Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms

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