Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery

Oil spill (OS), as one of the main pollutions in the ocean, is a serious threat to the marine environment. Thus, timely and accurate OS detection (OSD) is necessary for ocean management. In this regard, remote sensing (RS) plays a key role due to multiple advantages over large and remote ocean envir...

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Autores principales:	Seyd Teymoor Seydi, Mahdi Hasanlou, Meisam Amani, Weimin Huang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Convolution neural network (CNN) deep learning Landsat-5 multiscale kernel convolution ocean oil spill detection remote sensing (RS) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/3907b0bbefc643cf8a7a8a855acf33f2
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id	oai:doaj.org-article:3907b0bbefc643cf8a7a8a855acf33f2
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spelling	oai:doaj.org-article:3907b0bbefc643cf8a7a8a855acf33f22021-11-18T00:00:19ZOil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery2151-153510.1109/JSTARS.2021.3123163https://doaj.org/article/3907b0bbefc643cf8a7a8a855acf33f22021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591296/https://doaj.org/toc/2151-1535Oil spill (OS), as one of the main pollutions in the ocean, is a serious threat to the marine environment. Thus, timely and accurate OS detection (OSD) is necessary for ocean management. In this regard, remote sensing (RS) plays a key role due to multiple advantages over large and remote ocean environments. In this study, a new OSD framework based on a deep learning algorithm was developed for optical RS imagery. The proposed method was based on a multiscale multidimensional residual kernel convolution neural network. The proposed method investigated the deep features by the two-dimensional multiscale residual blocks and, then, utilized them at one-dimensional multiscale residual blocks. In this study, Landsat-5 satellite imagery acquired over the Gulf of Mexico was applied to evaluate the performance of the proposed method. The overall accuracy of the proposed method was more than 95%, and the miss detection and false alarm rates were less than 5%, indicating its high potential for OSD. Moreover, it was observed that the proposed method had better performance compared to other OSD algorithms that were investigated in this study.Seyd Teymoor SeydiMahdi HasanlouMeisam AmaniWeimin HuangIEEEarticleConvolution neural network (CNN)deep learningLandsat-5multiscale kernel convolutionocean oil spill detectionremote sensing (RS)Ocean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 10941-10952 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Convolution neural network (CNN) deep learning Landsat-5 multiscale kernel convolution ocean oil spill detection remote sensing (RS) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Convolution neural network (CNN) deep learning Landsat-5 multiscale kernel convolution ocean oil spill detection remote sensing (RS) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Seyd Teymoor Seydi Mahdi Hasanlou Meisam Amani Weimin Huang Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
description	Oil spill (OS), as one of the main pollutions in the ocean, is a serious threat to the marine environment. Thus, timely and accurate OS detection (OSD) is necessary for ocean management. In this regard, remote sensing (RS) plays a key role due to multiple advantages over large and remote ocean environments. In this study, a new OSD framework based on a deep learning algorithm was developed for optical RS imagery. The proposed method was based on a multiscale multidimensional residual kernel convolution neural network. The proposed method investigated the deep features by the two-dimensional multiscale residual blocks and, then, utilized them at one-dimensional multiscale residual blocks. In this study, Landsat-5 satellite imagery acquired over the Gulf of Mexico was applied to evaluate the performance of the proposed method. The overall accuracy of the proposed method was more than 95%, and the miss detection and false alarm rates were less than 5%, indicating its high potential for OSD. Moreover, it was observed that the proposed method had better performance compared to other OSD algorithms that were investigated in this study.
format	article
author	Seyd Teymoor Seydi Mahdi Hasanlou Meisam Amani Weimin Huang
author_facet	Seyd Teymoor Seydi Mahdi Hasanlou Meisam Amani Weimin Huang
author_sort	Seyd Teymoor Seydi
title	Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
title_short	Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
title_full	Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
title_fullStr	Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
title_full_unstemmed	Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
title_sort	oil spill detection based on multiscale multidimensional residual cnn for optical remote sensing imagery
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/3907b0bbefc643cf8a7a8a855acf33f2
work_keys_str_mv	AT seydteymoorseydi oilspilldetectionbasedonmultiscalemultidimensionalresidualcnnforopticalremotesensingimagery AT mahdihasanlou oilspilldetectionbasedonmultiscalemultidimensionalresidualcnnforopticalremotesensingimagery AT meisamamani oilspilldetectionbasedonmultiscalemultidimensionalresidualcnnforopticalremotesensingimagery AT weiminhuang oilspilldetectionbasedonmultiscalemultidimensionalresidualcnnforopticalremotesensingimagery
_version_	1718425217862205440

Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery

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