An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica

The Sentinel-1A satellite was launched in April 2014 with a primary C-Band terrain observation with progressive scans synthetic aperture radar (TOPSAR) onboard and has collected plenty of high-quality images for global change studies. However, low magnitude signals around image margins (black margin...

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Published in:Remote Sensing
Main Authors: Xianwei Wang, David M. Holland
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2020
Subjects:
Online Access:https://doi.org/10.3390/rs12071175
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spelling ftmdpi:oai:mdpi.com:/2072-4292/12/7/1175/ 2023-08-20T04:02:06+02:00 An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica Xianwei Wang David M. Holland agris 2020-04-06 application/pdf https://doi.org/10.3390/rs12071175 EN eng Multidisciplinary Digital Publishing Institute Environmental Remote Sensing https://dx.doi.org/10.3390/rs12071175 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 7; Pages: 1175 Sentinel-1A black margin extra wide swath interferometric wide swath edge detector synthetic aperture radar Text 2020 ftmdpi https://doi.org/10.3390/rs12071175 2023-07-31T23:20:18Z The Sentinel-1A satellite was launched in April 2014 with a primary C-Band terrain observation with progressive scans synthetic aperture radar (TOPSAR) onboard and has collected plenty of high-quality images for global change studies. However, low magnitude signals around image margins (black margins) does not preserve the normal standard level, influencing the potential usage with these data. Through image analysis, we find that the signal from black margin (BM) is highly dominated by the closest effective signals and the signal in BM shows an increasing trend along the direction from image boundary to image center. An edge detector is developed based on the signal characteristics of BM. Furthermore, an automatic method to discriminate and eliminate BM is designed. Images from both extra wide (EW) and interferometric wide (IW) swath observation modes, covering the land, ocean, and coast of the Antarctic, are taken to verify the robustness of our method. Through comparison with BM edges extracted by human interpretation, our method has the maximum BM edge extraction error of 1.9 ± 3.2 pixels. When considering perimeter (or area) difference along radial direction of BM edge, our method has an averaging extraction accuracy of −0.35 ± 0.11 (or 0.14 ± 1.38) pixels, which suggests that our method is effective and can be potentially used to eliminate BM for multidisciplinary applications of Sentinel-1 data. Text Antarc* Antarctic Antarctica MDPI Open Access Publishing Antarctic The Antarctic The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Remote Sensing 12 7 1175
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic Sentinel-1A
black margin
extra wide swath
interferometric wide swath
edge detector
synthetic aperture radar
spellingShingle Sentinel-1A
black margin
extra wide swath
interferometric wide swath
edge detector
synthetic aperture radar
Xianwei Wang
David M. Holland
An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica
topic_facet Sentinel-1A
black margin
extra wide swath
interferometric wide swath
edge detector
synthetic aperture radar
description The Sentinel-1A satellite was launched in April 2014 with a primary C-Band terrain observation with progressive scans synthetic aperture radar (TOPSAR) onboard and has collected plenty of high-quality images for global change studies. However, low magnitude signals around image margins (black margins) does not preserve the normal standard level, influencing the potential usage with these data. Through image analysis, we find that the signal from black margin (BM) is highly dominated by the closest effective signals and the signal in BM shows an increasing trend along the direction from image boundary to image center. An edge detector is developed based on the signal characteristics of BM. Furthermore, an automatic method to discriminate and eliminate BM is designed. Images from both extra wide (EW) and interferometric wide (IW) swath observation modes, covering the land, ocean, and coast of the Antarctic, are taken to verify the robustness of our method. Through comparison with BM edges extracted by human interpretation, our method has the maximum BM edge extraction error of 1.9 ± 3.2 pixels. When considering perimeter (or area) difference along radial direction of BM edge, our method has an averaging extraction accuracy of −0.35 ± 0.11 (or 0.14 ± 1.38) pixels, which suggests that our method is effective and can be potentially used to eliminate BM for multidisciplinary applications of Sentinel-1 data.
format Text
author Xianwei Wang
David M. Holland
author_facet Xianwei Wang
David M. Holland
author_sort Xianwei Wang
title An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica
title_short An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica
title_full An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica
title_fullStr An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica
title_full_unstemmed An Automatic Method for Black Margin Elimination of Sentinel-1A Images over Antarctica
title_sort automatic method for black margin elimination of sentinel-1a images over antarctica
publisher Multidisciplinary Digital Publishing Institute
publishDate 2020
url https://doi.org/10.3390/rs12071175
op_coverage agris
long_lat ENVELOPE(73.317,73.317,-52.983,-52.983)
geographic Antarctic
The Antarctic
The Sentinel
geographic_facet Antarctic
The Antarctic
The Sentinel
genre Antarc*
Antarctic
Antarctica
genre_facet Antarc*
Antarctic
Antarctica
op_source Remote Sensing; Volume 12; Issue 7; Pages: 1175
op_relation Environmental Remote Sensing
https://dx.doi.org/10.3390/rs12071175
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs12071175
container_title Remote Sensing
container_volume 12
container_issue 7
container_start_page 1175
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