Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data

Abstract Oil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vaishali Chaudhary, Shashi Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fc02fcce355d411b8c6017fb9a56840e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fc02fcce355d411b8c6017fb9a56840e
record_format	dspace
spelling	oai:doaj.org-article:fc02fcce355d411b8c6017fb9a56840e2021-12-02T16:55:46ZDark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data10.1038/s41598-021-88301-92045-2322https://doaj.org/article/fc02fcce355d411b8c6017fb9a56840e2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88301-9https://doaj.org/toc/2045-2322Abstract Oil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.Vaishali ChaudharyShashi KumarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-24 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vaishali Chaudhary Shashi Kumar Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data
description	Abstract Oil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.
format	article
author	Vaishali Chaudhary Shashi Kumar
author_facet	Vaishali Chaudhary Shashi Kumar
author_sort	Vaishali Chaudhary
title	Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data
title_short	Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data
title_full	Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data
title_fullStr	Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data
title_full_unstemmed	Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data
title_sort	dark spot detection for characterization of marine surface slicks using uavsar quad-pol data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/fc02fcce355d411b8c6017fb9a56840e
work_keys_str_mv	AT vaishalichaudhary darkspotdetectionforcharacterizationofmarinesurfaceslicksusinguavsarquadpoldata AT shashikumar darkspotdetectionforcharacterizationofmarinesurfaceslicksusinguavsarquadpoldata
_version_	1718382836999782400

Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data

Ejemplares similares