Zernike moments and SVM for shape classification in very high resolution satellite images
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 11, Issue 1 (31 Jan. 2014)9 p.
Publisher
Publication Date
2014-01-31
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
In this paper, a Zernike moments-based descriptor is used as a measure of shape information for the detection of buildings from very high spatial resolution satellite images.
The proposed approach comprises three steps.
First, the image is segmented into homogeneous objects based on the spectral and spatial information.
Mean-Shift segmentation method is used for this end.
Second, a Zernike feature vector is computed for each segment.
Finally, a support vector machines-based classification using the feature vectors as inputs is performed.
Experimental results and comparison with ENVI (Environment for Visualizing Images) commercial package confirm the effectiveness of the proposed approach.
American Psychological Association (APA)
Mahi, Habib& Serief, Chahira. 2014. Zernike moments and SVM for shape classification in very high resolution satellite images. The International Arab Journal of Information Technology،Vol. 11, no. 1.
https://search.emarefa.net/detail/BIM-334159
Modern Language Association (MLA)
Mahi, Habib& Serief, Chahira. Zernike moments and SVM for shape classification in very high resolution satellite images. The International Arab Journal of Information Technology Vol. 11, no. 1 (Jan. 2014).
https://search.emarefa.net/detail/BIM-334159
American Medical Association (AMA)
Mahi, Habib& Serief, Chahira. Zernike moments and SVM for shape classification in very high resolution satellite images. The International Arab Journal of Information Technology. 2014. Vol. 11, no. 1.
https://search.emarefa.net/detail/BIM-334159
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-334159