Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection

Land cover mapping in complex environments can be challenging due to their landscape heterogeneity. With the increasing availability of various open-access remotely sensed datasets, more images acquired by different sensors and on different dates tend to be used to improve land cover classification...

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Veröffentlicht in:	Remote sensing of environment Jg. 251; S. 112105
Hauptverfasser:	Zhang, Fang, Yang, Xiaojun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Inc 15.12.2020 Elsevier BV
Schlagworte:	Accuracy Classification Coastal zone Coasts Complex environments Computational efficiency Computer applications Computing time data collection decision support systems Decision trees Domains environment Feature selection Heterogeneity Image acquisition Land cover Land cover classification Landscape landscapes Pattern recognition Permutations prediction Random forests Regression analysis Remote sensing Remote sensors Satellite imagery satellites Temporal variations urbanization Variable selection Complex environments Land cover classification Random forests Variable selection
ISSN:	0034-4257, 1879-0704
Online-Zugang:	Volltext
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