Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests

CORC > 厦门大学 > 信息技术－已发表论文

	Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests
	Guan, Haiyan ; Li, Jonathan ; Chapman, Michael ; Deng, Fei ; Ji, Zheng ; Yang, Xu ; Li J(李军)
刊名	http://dx.doi.org/10.1080/01431161.2013.788261
	2013
关键词	LAND-COVER CLASSIFICATION REMOTE-SENSING DATA AERIAL IMAGERY AIRBORNE LIDAR LANDSCAPE FEATURES AREAS
英文摘要	Natural Sciences and Engineering Research Council of Canada (NSERC); National Natural Science Foundation of China (NSFC) [41071257, 41001305]; Using high-spatial-resolution multispectral imagery alone is insufficient for achieving highly accurate and reliable thematic mapping of urban areas. Integration of lidar-derived elevation information into image classification can considerably improve classification results. Additionally, traditional pixel-based classifiers have some limitations in regard to certain landscape and data types. In this study, we take advantage of current advances in object-based image analysis and machine learning algorithms to reduce manual image interpretation and automate feature selection in a classification process. A sequence of image segmentation, feature selection, and object classification is developed and tested by the data sets in two study areas (Mannheim, Germany and Niagara Falls, Canada). First, to improve the quality of segmentation, a range image of lidar data is incorporated in an image segmentation process. Among features derived from lidar data and aerial imagery, the random forest, a robust ensemble classifier, is then used to identify the best features using iterative feature elimination. On the condition that the number of samples is at least two or three times the number of features, a segmentation scale factor has no particular effect on the selected features or classification accuracies. The results of the two study areas demonstrate that the presented object-based classification method, compared with the pixel-based classification, improves by 0.02 and 0.05 in kappa statistics, and by 3.9% and 4.5% in overall accuracy, respectively.
语种	英语
出版者	TAYLOR & FRANCIS LTD
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92556]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Guan, Haiyan,Li, Jonathan,Chapman, Michael,et al. Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests[J]. http://dx.doi.org/10.1080/01431161.2013.788261,2013.
APA	Guan, Haiyan.,Li, Jonathan.,Chapman, Michael.,Deng, Fei.,Ji, Zheng.,...&李军.(2013).Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests.http://dx.doi.org/10.1080/01431161.2013.788261.
MLA	Guan, Haiyan,et al."Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests".http://dx.doi.org/10.1080/01431161.2013.788261 (2013).