{"title":"IceEB: An ensemble-based method to map river ice type from radar images","authors":"Plante Lévesque Valérie, Chokmani Karem, Gauthier Yves, Bernier Monique","doi":"10.1016/j.jag.2024.104317","DOIUrl":null,"url":null,"abstract":"<div><div>This paper introduces IceEB, i.e., an innovative ensemble-based method that is designed to automate mapping of river ice types using radar imagery. Its goal is the merger of outcomes from three classifiers (IceMAP-R, RIACT, and IceBC) through ensemble-estimation, resulting in a highly performant and fully automated river ice-type map, which is applicable under all meteorological conditions. The first step of our research is the development of a <em>meta</em>-classifier and a confidence estimation index, then we validate our method using ground-truth datasets and finally compare the performance between IceEB and the original classifiers. The anticipated outcome was a map exhibiting superior results compared to individual classifiers. Validation and comparison of IceEB employed six RADARSAT-2 HH-HV C-band images that were selected from historical datasets of Quebec and Alberta rivers (Canada). IceEB integrates RADARSAT-2 satellite imagery, a digital elevation model, and a river mask, undergoing preprocessing tasks before activating the three initial classifiers. The <em>meta</em>-classifier then performs ensemble-based classification, yielding a legend comprised of water, sheet ice and rubble ice. This approach facilitates broad participation in validation data collection, differentiation between ice covers and ice jams, and minimization of assumptions regarding ice formation. We conclude that IceEB successfully combines existing radar remote sensing ice- classification models to create accurate river ice-type maps. IceEB’s ensemble-based approach outperforms individual classifiers, achieving overall accuracy >91 % for each class. Shortcomings of the original classifiers are effectively offset through parallel use, resulting in marked improvements in automation and generalizability across diverse Canadian meteorological conditions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104317"},"PeriodicalIF":7.6000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843224006757","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
Abstract
This paper introduces IceEB, i.e., an innovative ensemble-based method that is designed to automate mapping of river ice types using radar imagery. Its goal is the merger of outcomes from three classifiers (IceMAP-R, RIACT, and IceBC) through ensemble-estimation, resulting in a highly performant and fully automated river ice-type map, which is applicable under all meteorological conditions. The first step of our research is the development of a meta-classifier and a confidence estimation index, then we validate our method using ground-truth datasets and finally compare the performance between IceEB and the original classifiers. The anticipated outcome was a map exhibiting superior results compared to individual classifiers. Validation and comparison of IceEB employed six RADARSAT-2 HH-HV C-band images that were selected from historical datasets of Quebec and Alberta rivers (Canada). IceEB integrates RADARSAT-2 satellite imagery, a digital elevation model, and a river mask, undergoing preprocessing tasks before activating the three initial classifiers. The meta-classifier then performs ensemble-based classification, yielding a legend comprised of water, sheet ice and rubble ice. This approach facilitates broad participation in validation data collection, differentiation between ice covers and ice jams, and minimization of assumptions regarding ice formation. We conclude that IceEB successfully combines existing radar remote sensing ice- classification models to create accurate river ice-type maps. IceEB’s ensemble-based approach outperforms individual classifiers, achieving overall accuracy >91 % for each class. Shortcomings of the original classifiers are effectively offset through parallel use, resulting in marked improvements in automation and generalizability across diverse Canadian meteorological conditions.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.