气候变化领域集成服务门户(CCPortal): SO–CNN based urban functional zone fine division with VHR remote sensing image

CCPortal

DOI	10.1016/j.rse.2019.111458
	SO–CNN based urban functional zone fine division with VHR remote sensing image
	Zhou W.; Ming D.; Lv X.; Zhou K.; Bao H.; Hong Z.
发表日期	2020
ISSN	00344257
卷号	236
英文摘要	Functional zone reflects city's spatial structures, and as a carrier of social and economic activities, it is of critical significance to urban management, resource allocation and planning. However, most researches on functional zone division are based on a large spatial scale such as blocks or other scales larger than it. Aiming at a subtle fine functional result, the concept of Super Object (SO) was especially explained, also a Super Object - Convolutional Neural Network (SO–CNN) based urban functional zone fine division method with very high resolution (VHR) remote sensing image was proposed. The original image was firstly segmented into different SOs which correspond to the basic functional zone units in geography. A random point generation algorithm was used to generate the voting points for functional zone category identification, and then a trained CNN model was employed to assign functional attributes to those voting points. Then a statistical method was involved to count the frequency of the classified voting points of different functional attributes in each basic functional zone units. By voting process, the functional attribute with the highest frequency was assigned to the basic functional zone unit, which corrected the misclassification results of CNN to some extent. This paper also explored the scale effect of the SO on the final functional zone classification result from two aspects, spatial scale of SO and the sampling window size of CNN model. Because of the natural differences between functional zone division and land cover classification, region based overall accuracy assessment method was used to evaluate functional zone division result. Compared with other methods, SO–CNN method can generate higher accuracy and subtle result, based on which larger spatial scale results can be available by scaling-up, so SO–CNN method plays a great significant role on small scale functional space structure research. © 2019 Elsevier Inc.
英文关键词	Basic functional zone unit; Category identification of functional zone unit; CNN; Super object
语种	英语
scopus关键词	Neural networks; Space optics; Convolutional neural network; Functional attribute; Functional zones; Generation algorithm; Land cover classification; Remote sensing images; Resource allocation and planning; Super object; Remote sensing; artificial neural network; assessment method; land cover; remote sensing; satellite imagery; urban area
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/179568
作者单位	School of Information Engineering China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing, Haidian 100083, China; Polytechnic Center for Natural Resources Big-data, MNR of China, Beijing, 100036, China
推荐引用方式 GB/T 7714	Zhou W.,Ming D.,Lv X.,等. SO–CNN based urban functional zone fine division with VHR remote sensing image[J],2020,236.
APA	Zhou W.,Ming D.,Lv X.,Zhou K.,Bao H.,&Hong Z..(2020).SO–CNN based urban functional zone fine division with VHR remote sensing image.Remote Sensing of Environment,236.
MLA	Zhou W.,et al."SO–CNN based urban functional zone fine division with VHR remote sensing image".Remote Sensing of Environment 236(2020).