DocumentCode
2449561
Title
A Novel Unsupervised Iteration Classification Method For Fully Polarimetric SAR Image
Author
Lei, Yang ; Jun, Zhao Yong ; Gang, Wang Zhi
Author_Institution
Inst. of Inf. Eng., Inf. Eng. Univ., Zhengzhou
fYear
2006
fDate
26-29 Oct. 2006
Firstpage
1
Lastpage
5
Abstract
Based on the theory of eigen-decomposition of fully polarimetric synthetic aperture radar (SAR) and scatter criterion clustering method, an unsupervised iteration classification method is proposed. It is simple, flexible and with high precision. Several schemes of the iteration classification method are given in this paper. The characteristic of each scheme is carefully analyzed according to the properties of eigen-decomposition and scatter criterion, and each scheme holds its own merit. Experiments are done on SIR-C/X-SAR data near Tien Mountains and perfect classification results are obtained. More importantly, this method is robust and has high adaptability.
Keywords
eigenvalues and eigenfunctions; image classification; iterative methods; pattern clustering; radar imaging; synthetic aperture radar; SIR-C/X-SAR data; Tien Mountains; eigendecomposition; fully polarimetric SAR image; scatter criterion clustering method; synthetic aperture radar; unsupervised iteration classification method; Clustering methods; Entropy; Image analysis; Information analysis; Interferometry; Layout; Matrix decomposition; Radar scattering; Robustness; Synthetic aperture radar; Unsupervised classification; eigen-decomposition; fully polarimetic SAR; scatter criterion;
fLanguage
English
Publisher
ieee
Conference_Titel
Antennas, Propagation & EM Theory, 2006. ISAPE '06. 7th International Symposium on
Conference_Location
Guilin
Print_ISBN
1-4244-0162-3
Electronic_ISBN
1-4244-0163-1
Type
conf
DOI
10.1109/ISAPE.2006.353512
Filename
4168173
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