Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier

Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a...

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書誌詳細

主要な著者:	Tianxiang Zhang, Jinya Su, Zhiyong Xu, Yulin Luo, Jiangyun Li
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2021-01-01
シリーズ:	Applied Sciences
主題:	Sentinel-2 satellite random forest bayesian optimization hyperparameter tuning urban management land cover classification
オンライン･アクセス:	https://www.mdpi.com/2076-3417/11/2/543
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