气候变化领域集成服务门户(CCPortal): Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection

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DOI	10.1016/j.rse.2020.112105
	Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection
	Zhang F.; Yang X.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	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 accuracy. Selecting an appropriate feature domain with the best landscape separability is therefore crucial in meeting the requirement of computational efficiency and model interpretability. Variable selection is widely used in pattern recognition to enhance model parsimony. This study focused on the variable selection process and proposed a series of methods to select the optimal feature domain to improve land cover classification in a complex urbanized coastal area. Two decision tree models (CART-Classification and Regression Tree and CIT-Conditional Inference Tree) and five variable importance measures (GINI, PVIM-Permutated Variable Importance Measure, MD- Minimum Depth, IPM-Intervention of Prediction Measure, and CPVIM-Conditional Permutation Variable Importance Measure) based on random forests were considered. Variable importance measures were applied to a set of spectral, spatial and temporal features derived from medium-resolution satellite images. Backward elimination methods were used to select the optimal feature subset. It is found that compared to the traditional band-only model, the variable selection process can significantly improve the model parsimony and computational efficiency. The CPVIM based on CIT decision tree model was more reliable in selecting relevant features regardless their correlations, but CART tended to generate higher classification accuracy. Therefore, the combination of the CART model and the ranking from the CPVIM variable measure is recommended to achieve higher classification accuracy and better data interpretability. The novelty of our work is with the insight into the merits of integrating variable selection in the land cover classification process over complex environments. © 2020 Elsevier Inc.
英文关键词	Complex environments; Land cover classification; Random forests; Variable selection
语种	英语
scopus关键词	Coastal zones; Computational efficiency; Decision trees; Efficiency; Feature extraction; Image enhancement; Learning to rank; Random forests; Classification accuracy; Classification and regression tree; Conditional inference; Decision tree modeling; Decision tree models; Land cover classification; Landscape heterogeneities; Medium-resolution satellite images; Classification (of information); accuracy assessment; classification; coastal zone; computer simulation; data set; heterogeneity; land cover; parsimony analysis; remote sensing; satellite data; urbanization
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179115
作者单位	HNU-ASU Joint International Tourism College, Hainan University, Haikou, Hainan 570228, China; Department of Geography, Florida State University, Tallahassee, Florida, 32306, United States
推荐引用方式 GB/T 7714	Zhang F.,Yang X.. Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection[J],2020,251.
APA	Zhang F.,&Yang X..(2020).Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection.Remote Sensing of Environment,251.
MLA	Zhang F.,et al."Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection".Remote Sensing of Environment 251(2020).