DocumentCode :
3306489
Title :
Geometrical features for the classification of very high resolution multispectral remote-sensing images
Author :
Luo, Bin ; Chanussot, Jocelyn
Author_Institution :
GIPSA-Lab., Grenoble Inst. of Technol., Grenoble, France
fYear :
2010
fDate :
26-29 Sept. 2010
Firstpage :
1045
Lastpage :
1048
Abstract :
In order to extract geometrical features from a multispectral image and derive a classification, an approach based on the topographic map of the image is proposed. For each pixel, the most significant structure containing it is extracted. The classification of this pixel is based on its spectral information and the geometrical features of the corresponding structure (its area and perimeter). The results obtained on multispectral remote sensing images taken by two different sensors show the efficiency of the extracted geometrical features for separating some classes with very similar spectral attributes but of different semantic meanings.
Keywords :
computational geometry; feature extraction; image classification; image resolution; geometrical feature extraction; high resolution multispectral remote sensing image; image classification; pixel; spectral information; topographic map; Accuracy; Buildings; Feature extraction; Pixel; Remote sensing; Roads; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
ISSN :
1522-4880
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2010.5649890
Filename :
5649890
Link To Document :
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