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标题：META-LEARNING FOR WETLAND CLASSIFICATION USING A COMBINATION OF SENTINEL-1 AND SENTINEL-2 IMAGERY
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作者：H. Jafarzadeh ; M. Mahdianpari ; E. Gill 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2022
卷号：V-3-2022
页码：47-52
DOI：10.5194/isprs-annals-V-3-2022-47-2022
语种：English
出版社：Copernicus Publications
摘要：In wetland mapping, a lot of uncertainty is related to the task of selecting an appropriate classification approach. Although the individual models are available and well-established in the literature for the classification task, the combination approaches have become popular recently. Hence, selecting an appropriate method is challenging, whether an individual approach or combination. In this work, a meta-learning study is performed to prove that combining the result of individual machine learning models could be better than using the best single model. This study investigates the applicability of the meta-learning method for wetland classification. We will first explore the importance of extracted features for each model. Then, the essential features are fed to the model with the well-tuned hyper-parameters. Finally, the voting classifier as a meta-learning approach is adopted to improve the classification result. The classification map of the study area reached the highest accuracy (Overall Accuracy = 93.9% and Kappa = 0.92) when the proposed ensemble classifier was employed. The results show the superiority of a combination of methods over simple model selection approaches. The results of this study can provide new insights for researchers to find new combination strategies to improve the classification results.
关键词：Meta-learning; Wetland; Classification; Sentinel; Ensemble classifier