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
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;
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2010.5649890