Paper Info
Title
Research on Urban Spatial Information Extraction and Land Use Analysis
Abstract
According to the characteristics of urban spatial information, this paper presents a multi-scale urban spatial information extraction mode from high-resolution remote sensing images based on feature units. Scale-transforming technique is emphasized in this mode. By rough-classification using region partition method based on GMRF-SVM, we can get relatively great target areas and extract water body first. And then, using block parcel unit extraction method based on histogram threshold segmentation and linear parcel unit extraction method based on edge detection, we obtain urban construction area, road area and vegetation area respectively. Based on the extraction mode of feature units, a set of methods are built up to extract urban spatial information from high-resolution remote sensing images. At last, this paper uses QuickBird image, and chooses a typical urban area as the test data. The image is classified as water body, road, green space and construction area, and we use landscape models to further analyze spatial information of urban land use.
Year
DOI
Venue
2008
10.1109/IGARSS.2008.4779130
IGARSS
Keywords
Field
DocType
rough classification,remote sensing,land use analysis,gmrf-svm,quickbird image,land use planning,urban area,high-resolution remote sensing,image classification,region partition method,edge detection,geophysical signal processing,information extraction,feature unit,histogram threshold segmentation,block parcel unit extraction,scale transforming technique,urban spatial information extraction,image segmentation,image resolution,data mining,high resolution,feature extraction,information analysis,construction industry,green space,land use,spatial information,histograms,spatial resolution
Spatial analysis,Computer vision,Computer science,Edge detection,Remote sensing,Feature extraction,Image segmentation,Information extraction,Artificial intelligence,Contextual image classification,Urban area,Land-use planning
Conference
Volume
ISBN
Citations 
2
978-1-4244-2808-3
0
PageRank 
References 
Authors
0.34
6
4
Name
Order
Citations
PageRank
Wen Liu100.34
Qiuhai Zhong221.11
Jian-Cheng Luo39920.75
Zhanfeng Shen46812.60