Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify...

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Бібліографічні деталі
Автори:	Żaneta Kaszta, Ruben Van De Kerchove, Abel Ramoelo, Moses Azong Cho, Sabelo Madonsela, Renaud Mathieu, Eléonore Wolff
Формат:	Artigo
Мова:	Inglês
Опубліковано:	MDPI AG 2016-09-01
Серія:	Remote Sensing
Предмети:	land cover classifiers random forest (RF) support vector machines (SVM) classification and regression trees (CART) maximum likelihood (ML) k-nearest neighbor (k-NN)
Онлайн доступ:	http://www.mdpi.com/2072-4292/8/9/763
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Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

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