DocumentCode
1994905
Title
Image classification with spectral and texture features based on SVM
Author
Chen, Fen ; Zhang, Zhiru ; Yan, Dongmei
Author_Institution
Coll. of Autom., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear
2010
fDate
18-20 June 2010
Firstpage
1
Lastpage
4
Abstract
In this paper, a three-step classification method is proposed for remote sensing images with the spectral and texture features based on the Support Vector Machine (SVM) classifier. The image is first segmented into regions with the spectral features. Then, texture features are extracted from each region by the undecimated wavelet transform. Third, the SVM is used to classify the image with these extracted texture features. A postprocessing method is also proposed to handle the small regions and anomalistic regions.
Keywords
geophysical image processing; geophysical techniques; image classification; image segmentation; support vector machines; image classification; image segmentation; remote sensing images; spectral feature; support vector machine classifier; texture feature; wavelet transform; Feature extraction; Image segmentation; Pixel; Remote sensing; Support vector machines; Wavelet transforms; Image classification; Image segmentation; Texture;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoinformatics, 2010 18th International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-7301-4
Type
conf
DOI
10.1109/GEOINFORMATICS.2010.5567663
Filename
5567663
Link To Document