Urban land cover mapping with TerraSAR-X using an edge-aware region-growing and merging algorithm

Author

Jacob, Alexander ; Yifang Ban

Author_Institution

Div. of Geoinf., KTH - R. Inst. of Technol., Stockholm, Sweden

fYear

2014

fDate

13-18 July 2014

Firstpage

4836

Lastpage

4839

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.

Keywords

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; Accuracy; Agriculture; Buildings; Image edge detection; Image resolution; Image segmentation; Roads; Image Classification; Land Cover Mapping; OBIA; SAR; Urban;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International

Conference_Location

Quebec City, QC

Type

conf

DOI

10.1109/IGARSS.2014.6947577

Filename

6947577