Wetland Mapping integrating Sentinel-1 and Sentine-2 data: a Multilevel
Global-to-Local Feature Optimization Wetlands Hierarchical
Classification Approach
Abstract
In this article, Sentinel-1 and Sentine-2 data were integrated to
mapping wetlands in Yellow River Delta. Given that different wetlands
may owning different optimal feature subsets, we propose a multilevel
global-to-local feature optimization wetlands hierarchical
classification approach. This method constructs classification trees
according to the classification difficulty of wetlands and implements
wetland classification in three layers. During the process of
hierarchical classification, we utilized the effectiveness of random
forest algorithm in feature evaluation to select the most suitable
feature subset for each classification node of the classification tree
for training classifiers and implementing classification. Specifically,
this paper uses Stacking method to construct classifiers. Seven sets of
comparison experiments were designed to verify the effects of the
proposed multilevel global-to-local feature optimization wetlands
hierarchical classification approach. Using the proposed method, the
overall accuracy and kappa statistic are improved to 90.06% and 0.8768
compared with other classification approach.