Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties

Abstract Satellite-based remote sensing has a key role in the monitoring earth features, but due to flaws like cloud penetration capability and selective duration for remote sensing in traditional remote sensing methods, now the attention has shifted towards the use of alternative methods such as mi...

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Autor principal:	Deepak Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f73929e3e5bb43089c2f857f6c40dd5d
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spelling	oai:doaj.org-article:f73929e3e5bb43089c2f857f6c40dd5d2021-12-02T13:18:08ZUrban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties10.1038/s41598-021-85121-92045-2322https://doaj.org/article/f73929e3e5bb43089c2f857f6c40dd5d2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85121-9https://doaj.org/toc/2045-2322Abstract Satellite-based remote sensing has a key role in the monitoring earth features, but due to flaws like cloud penetration capability and selective duration for remote sensing in traditional remote sensing methods, now the attention has shifted towards the use of alternative methods such as microwave or radar sensing technology. Microwave remote sensing utilizes synthetic aperture radar (SAR) technology for remote sensing and it can operate in all weather conditions. Previous researchers have reported about effects of SAR pre-processing for urban objects detection and mapping. Preparing high accuracy urban maps are critical to disaster planning and response efforts, thus result from this study can help to users on the required pre-processing steps and its effects. Owing to the induced errors (such as calibration, geometric, speckle noise) in the radar images, these images are affected by several distortions, therefore these distortions need to be processed before any applications, as it causes issues in image interpretation and these can destroy valuable information about shapes, size, pattern and tone of various desired objects. The present work aims to utilize the sentinel-1 SAR datasets for urban studies (i.e. urban object detection through simulation of filter properties). The work uses C-band SAR datasets acquired from Sentinel-1A/B sensor, and the Google Earth datasets to validate the recognized objects. It was observed that the Refined-Lee filter performed well to provide detailed information about the various urban objects. It was established that the attempted approach cannot be generalised as one suitable method for sensing or identifying accurate urban objects from the C-band SAR images. Hence some more datasets in different polarisation combinations are required to be attempted.Deepak 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 Deepak Kumar Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties
description	Abstract Satellite-based remote sensing has a key role in the monitoring earth features, but due to flaws like cloud penetration capability and selective duration for remote sensing in traditional remote sensing methods, now the attention has shifted towards the use of alternative methods such as microwave or radar sensing technology. Microwave remote sensing utilizes synthetic aperture radar (SAR) technology for remote sensing and it can operate in all weather conditions. Previous researchers have reported about effects of SAR pre-processing for urban objects detection and mapping. Preparing high accuracy urban maps are critical to disaster planning and response efforts, thus result from this study can help to users on the required pre-processing steps and its effects. Owing to the induced errors (such as calibration, geometric, speckle noise) in the radar images, these images are affected by several distortions, therefore these distortions need to be processed before any applications, as it causes issues in image interpretation and these can destroy valuable information about shapes, size, pattern and tone of various desired objects. The present work aims to utilize the sentinel-1 SAR datasets for urban studies (i.e. urban object detection through simulation of filter properties). The work uses C-band SAR datasets acquired from Sentinel-1A/B sensor, and the Google Earth datasets to validate the recognized objects. It was observed that the Refined-Lee filter performed well to provide detailed information about the various urban objects. It was established that the attempted approach cannot be generalised as one suitable method for sensing or identifying accurate urban objects from the C-band SAR images. Hence some more datasets in different polarisation combinations are required to be attempted.
format	article
author	Deepak Kumar
author_facet	Deepak Kumar
author_sort	Deepak Kumar
title	Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties
title_short	Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties
title_full	Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties
title_fullStr	Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties
title_full_unstemmed	Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties
title_sort	urban objects detection from c-band synthetic aperture radar (sar) satellite images through simulating filter properties
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f73929e3e5bb43089c2f857f6c40dd5d
work_keys_str_mv	AT deepakkumar urbanobjectsdetectionfromcbandsyntheticapertureradarsarsatelliteimagesthroughsimulatingfilterproperties
_version_	1718393307537604608

Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties

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