Assessing the contribution of super-resolution in satellite derived bathymetry in the Antarctic

IF 2.6 3区 地球科学 Q1 MARINE & FRESHWATER BIOLOGY Estuarine Coastal and Shelf Science Pub Date : 2024-10-29 DOI:10.1016/j.ecss.2024.109007
Emre Gülher , İlhan Pala , Ugur Alganci
{"title":"Assessing the contribution of super-resolution in satellite derived bathymetry in the Antarctic","authors":"Emre Gülher ,&nbsp;İlhan Pala ,&nbsp;Ugur Alganci","doi":"10.1016/j.ecss.2024.109007","DOIUrl":null,"url":null,"abstract":"<div><div>The difficulty of defining the depth of near-shore seas (bathymetry) arises from the limits imposed by traditional ship-based approaches during data collection. Although LiDAR sensors with green lasers have been used to solve some of these problems, they come at a high cost in terms of their footprint and are prone to inaccuracies in turbid water. As shorelines undergo changes due to erosion, wetland loss, hurricane effects, sea-level rise, urban development, and population growth, consistent and accurate bathymetric data become crucial. These data play a significant role in comprehending and managing sensitive interfaces between land and water. Satellite-derived Bathymetry (SDB), which has been described by maritime and remote sensing researchers for over 50 years, emerges as a gap-filler, encompassing bathymetry extraction approaches using active (altimetry) and passive (optics) satellite sensors. In the past decade, advancements in sensor capabilities, computational power, and recognition by the International Hydrographic Organization (IHO) have propelled SDB to unprecedented popularity. This study explores the contribution of super-resolution in SDB for the first time in the shallow water zone of Horseshoe Island, Antarctica. Random forest and extreme gradient boosting machine learning-based regressors were used on Landsat-8 OLI images, which were atmospherically corrected by the ACOLITE algorithm and spatially enhanced twofold via the generative adversarial network for single image super-resolution (SRGAN). The bathymetry predictions with these two machine learning algorithms on SR images were benchmarked against previous studies in the same region and showed admissible results concerning the IHO standards. Furthermore, the results indicate that the bathymetric inversion performance of the spatially enhanced image via SRGAN is superior to the original multispectral image and pan-sharpened image in terms of the metrics observed, namely, root mean square error (RMSE), mean average error (MAE), and coefficient of determination (R<sup>2</sup>). Comparison between the original and SR image bathymetry inversion for the 0–15 m depth range indicate improvements of up to 0.13 m for RMSE, up to 0.30 m for MAE, and up to 11% for R<sup>2</sup>. These results promise possible effective usage of super-resolution in SDB with satellite images such as Sentinel −2, which do not include a panchromatic band.</div></div>","PeriodicalId":50497,"journal":{"name":"Estuarine Coastal and Shelf Science","volume":"310 ","pages":"Article 109007"},"PeriodicalIF":2.6000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Estuarine Coastal and Shelf Science","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0272771424003950","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MARINE & FRESHWATER BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The difficulty of defining the depth of near-shore seas (bathymetry) arises from the limits imposed by traditional ship-based approaches during data collection. Although LiDAR sensors with green lasers have been used to solve some of these problems, they come at a high cost in terms of their footprint and are prone to inaccuracies in turbid water. As shorelines undergo changes due to erosion, wetland loss, hurricane effects, sea-level rise, urban development, and population growth, consistent and accurate bathymetric data become crucial. These data play a significant role in comprehending and managing sensitive interfaces between land and water. Satellite-derived Bathymetry (SDB), which has been described by maritime and remote sensing researchers for over 50 years, emerges as a gap-filler, encompassing bathymetry extraction approaches using active (altimetry) and passive (optics) satellite sensors. In the past decade, advancements in sensor capabilities, computational power, and recognition by the International Hydrographic Organization (IHO) have propelled SDB to unprecedented popularity. This study explores the contribution of super-resolution in SDB for the first time in the shallow water zone of Horseshoe Island, Antarctica. Random forest and extreme gradient boosting machine learning-based regressors were used on Landsat-8 OLI images, which were atmospherically corrected by the ACOLITE algorithm and spatially enhanced twofold via the generative adversarial network for single image super-resolution (SRGAN). The bathymetry predictions with these two machine learning algorithms on SR images were benchmarked against previous studies in the same region and showed admissible results concerning the IHO standards. Furthermore, the results indicate that the bathymetric inversion performance of the spatially enhanced image via SRGAN is superior to the original multispectral image and pan-sharpened image in terms of the metrics observed, namely, root mean square error (RMSE), mean average error (MAE), and coefficient of determination (R2). Comparison between the original and SR image bathymetry inversion for the 0–15 m depth range indicate improvements of up to 0.13 m for RMSE, up to 0.30 m for MAE, and up to 11% for R2. These results promise possible effective usage of super-resolution in SDB with satellite images such as Sentinel −2, which do not include a panchromatic band.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
评估超分辨率对南极卫星测深的贡献
确定近岸海域深度(水深测量)的难度来自于数据收集过程中传统船基方法的限制。虽然使用绿色激光的激光雷达传感器解决了其中一些问题,但其占地面积大,成本高,在浑浊的海水中容易出现误差。随着海岸线因侵蚀、湿地丧失、飓风影响、海平面上升、城市发展和人口增长而发生变化,一致而准确的测深数据变得至关重要。这些数据在理解和管理敏感的水陆界面方面发挥着重要作用。卫星水深测量(SDB)已被海洋和遥感研究人员描述了 50 多年,它是一种填补空白的方法,包括使用主动(测高)和被动(光学)卫星传感器的水深测量提取方法。在过去的十年中,传感器能力、计算能力的进步以及国际水文学组织(IHO)的认可推动了 SDB 的空前普及。本研究首次在南极马蹄岛浅水区探索了超分辨率在 SDB 中的贡献。研究在 Landsat-8 OLI 图像上使用了基于随机森林和极端梯度提升机器学习的回归器,这些图像通过 ACOLITE 算法进行了大气校正,并通过用于单幅图像超分辨率的生成对抗网络(SRGAN)进行了两倍空间增强。利用这两种机器学习算法对 SR 图像进行的水深预测与之前在同一地区进行的研究进行了比对,结果显示符合 IHO 标准。此外,研究结果表明,从均方根误差(RMSE)、平均平均误差(MAE)和判定系数(R2)等观测指标来看,通过 SRGAN 空间增强图像的水深反演性能优于原始多光谱图像和平移锐化图像。对 0-15 米深度范围内的原始水深反演和 SR 图像水深反演进行比较后发现,均方根误差(RMSE)最多可改进 0.13 米,平均平均误差(MAE)最多可改进 0.30 米,判定系数(R2)最多可改进 11%。这些结果表明,在卫星图像(如 Sentinel -2 卫星图像,不包括全色波段)的 SDB 中可以有效地使用超分辨率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
5.60
自引率
7.10%
发文量
374
审稿时长
9 months
期刊介绍: Estuarine, Coastal and Shelf Science is an international multidisciplinary journal devoted to the analysis of saline water phenomena ranging from the outer edge of the continental shelf to the upper limits of the tidal zone. The journal provides a unique forum, unifying the multidisciplinary approaches to the study of the oceanography of estuaries, coastal zones, and continental shelf seas. It features original research papers, review papers and short communications treating such disciplines as zoology, botany, geology, sedimentology, physical oceanography.
期刊最新文献
Editorial Board Extreme heat and drought did not affect interspecific interactions between dune grasses Fluctuation asymmetry of Larimichthys polyactis otoliths from artificial and natural habitats: A study case in Haizhou Bay, China Temporal and spatial dynamics of the non-indigenous bryozoan, Amathia verticillata, and its associated invertebrate community Turbidity estimation from an acoustic backscatter signal in a tropical coral reef system
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1