Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data
In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , , , |
Format: | Text |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2018
|
Subjects: | |
Online Access: | https://doi.org/10.3390/rs10081190 |
id |
ftmdpi:oai:mdpi.com:/2072-4292/10/8/1190/ |
---|---|
record_format |
openpolar |
spelling |
ftmdpi:oai:mdpi.com:/2072-4292/10/8/1190/ 2023-08-20T04:09:45+02:00 Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data Denise Dettmering Alan Wynne Felix L. Müller Marcello Passaro Florian Seitz agris 2018-07-28 application/pdf https://doi.org/10.3390/rs10081190 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs10081190 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 10; Issue 8; Pages: 1190 leads satellite altimetry CryoSat-2 classification peakiness polar ocean Text 2018 ftmdpi https://doi.org/10.3390/rs10081190 2023-07-31T21:39:01Z In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans. Text Sea ice MDPI Open Access Publishing Remote Sensing 10 8 1190 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
leads satellite altimetry CryoSat-2 classification peakiness polar ocean |
spellingShingle |
leads satellite altimetry CryoSat-2 classification peakiness polar ocean Denise Dettmering Alan Wynne Felix L. Müller Marcello Passaro Florian Seitz Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data |
topic_facet |
leads satellite altimetry CryoSat-2 classification peakiness polar ocean |
description |
In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans. |
format |
Text |
author |
Denise Dettmering Alan Wynne Felix L. Müller Marcello Passaro Florian Seitz |
author_facet |
Denise Dettmering Alan Wynne Felix L. Müller Marcello Passaro Florian Seitz |
author_sort |
Denise Dettmering |
title |
Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data |
title_short |
Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data |
title_full |
Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data |
title_fullStr |
Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data |
title_full_unstemmed |
Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data |
title_sort |
lead detection in polar oceans—a comparison of different classification methods for cryosat-2 sar data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs10081190 |
op_coverage |
agris |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Remote Sensing; Volume 10; Issue 8; Pages: 1190 |
op_relation |
https://dx.doi.org/10.3390/rs10081190 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs10081190 |
container_title |
Remote Sensing |
container_volume |
10 |
container_issue |
8 |
container_start_page |
1190 |
_version_ |
1774723406856454144 |