孙忠平等:Fine classification of construction land using high-resolution remote sensing images: a case study in planning restricted zone of nuclear power plant
被阅读 67 次
2017-12-11
Fine classification of construction land using high-resolution remote sensing images: a case study in planning restricted zone of nuclear power plant 
作者:Sun, ZP (Sun, Zhong-ping)[ 1,2 ] ; Liu, SH (Liu, Suhong)[ 1 ] ; Cao, F (Cao, Fei)[ 2 ] ; Shi, YL (Shi, Yuanli)[ 2 ] ; Wang, CZ (Wang, Chang-zuo)[ 2 ]  
ARABIAN JOURNAL OF GEOSCIENCES 
卷: 10 
期: 22 
文献号: 495 
DOI: 10.1007/s12517-017-3248-x 
出版年: NOV 18 2017 
 
摘要
Detailed construction land information plays a significant role in monitoring planning restricted zone of nuclear power plant and ecological environment protection. This study focuses on developing fine classifying method of construction land in planning restricted zone of nuclear power plant using high spatial resolution GF(GaoFen)-1 remote sensing images. The object-oriented classification method is used in this study; the important process of which is image segmentation and classification. Multi-scale segmentation method, rule-based decision tree, and the nearest neighbor classifier are used in classifying construction land classes, i.e., road, industrial, and residential. An optimal segmentation scale is crucial to image segmentation in object-oriented classification. Instead of laborious trial-and-error experiments for optimal image segmentation, the change rates of the local variance in the homogeneous region are calculated to get the optimal segmentation scales. Multi-level classification strategy is used in the following classification. Rule-based decision tree is used to classify road and water, vegetation and non-vegetation, and industrial and residential. And the nearest neighbor classifier is used to classify cropland and forest within the vegetation land use type. The accuracy assessment result shows that the overall accuracy is 89.67% and Kappa coefficient is 0.85 for object-oriented classification, which is much higher than pixel-based maximum likelihood classifier (overall accuracy is 79.17% and Kappa coefficient is 0.74) and support vector machine classifier (overall accuracy is 74.16% and Kappa coefficient is 0.68).
 
通讯作者地址: Sun, ZP (通讯作者)
 Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
通讯作者地址: Sun, ZP (通讯作者)
 Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China. 
地址:  
 [ 1 ] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
 [ 2 ] Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China