Paper Info
Title
Urban land cover mapping with TerraSAR-X using an edge-aware region-growing and merging algorithm
Abstract
TerraSAR X data has been analyzed for its suitability of urban land cover mapping using our recently developed object based image analysis tool KTH-SEG, which is based on an edge aware region growing and merging algorithm and a support vector machine classifier. Classification results over the Shanghai International Airport area using 8 classes, Water, Grass, Roads, Buildings, Crops, Forest, Bare Crops and Green Houses have proven with an overall accuracy just shy of 84% that this is very well the case. It has further been investigated which segment sizes and image configuration yield the best results.
Year
DOI
Venue
2014
10.1109/IGARSS.2014.6947577
Geoscience and Remote Sensing Symposium
Keywords
Field
DocType
land cover,support vector machines,synthetic aperture radar,vegetation mapping,KTH-SEG,Shanghai International Airport area,TerraSAR-X data,bare crop class,building class,edge-aware region-growing,forest class,grass class,green house class,image configuration yield,merging algorithm,object based image analysis tool,road class,segment size,support vector machine classifier,urban land cover mapping,water class,Image Classification,Land Cover Mapping,OBIA,SAR,Urban
Computer science,Support vector machine classifier,Remote sensing,International airport,Algorithm,Region growing,Merge (version control),Land cover
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
4
2
Name
Order
Citations
PageRank
Alexander Jacob130.83
Yifang Ban219422.83