IEICE TRANSACTIONS on Communications
Unsupervised Land Cover Classification Using H/
/TP Space Applied to POLSAR Image Analysis
Summary :
This paper takes full advantage of polarimetric scattering parameters and total power to classify polarimetric SAR image data. The parameters employed here are total power, polarimetric entropy, and averaged alpha angle (alphabar). Since these parameters are independent each other and represent all the scattering characteristics, they seem to be one of the best combinations to classify Polarimetric Synthetic Aperture Radar (POLSAR) images. Using unsupervised classification scheme with iterative Maximum Likelihood classifier, it is possible to decompose multi-look averaged coherency matrix with complex Wishart distribution effectively. The classification results are shown using Pi-SAR image data set comparing with other representative methods.
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- IEICE TRANSACTIONS on Communications Vol.E87-B No.6 pp.1639-1647
- Publication Date
- 2004/06/01
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- Type of Manuscript
- PAPER
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- Sensing
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Koji KIMURA, Yoshio YAMAGUCHI, Hiroyoshi YAMADA, "Unsupervised Land Cover Classification Using H/
Abstract: This paper takes full advantage of polarimetric scattering parameters and total power to classify polarimetric SAR image data. The parameters employed here are total power, polarimetric entropy, and averaged alpha angle (alphabar). Since these parameters are independent each other and represent all the scattering characteristics, they seem to be one of the best combinations to classify Polarimetric Synthetic Aperture Radar (POLSAR) images. Using unsupervised classification scheme with iterative Maximum Likelihood classifier, it is possible to decompose multi-look averaged coherency matrix with complex Wishart distribution effectively. The classification results are shown using Pi-SAR image data set comparing with other representative methods.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e87-b_6_1639/_p
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@ARTICLE{e87-b_6_1639,
author={Koji KIMURA, Yoshio YAMAGUCHI, Hiroyoshi YAMADA, },
journal={IEICE TRANSACTIONS on Communications},
title={Unsupervised Land Cover Classification Using H/
year={2004},
volume={E87-B},
number={6},
pages={1639-1647},
abstract={This paper takes full advantage of polarimetric scattering parameters and total power to classify polarimetric SAR image data. The parameters employed here are total power, polarimetric entropy, and averaged alpha angle (alphabar). Since these parameters are independent each other and represent all the scattering characteristics, they seem to be one of the best combinations to classify Polarimetric Synthetic Aperture Radar (POLSAR) images. Using unsupervised classification scheme with iterative Maximum Likelihood classifier, it is possible to decompose multi-look averaged coherency matrix with complex Wishart distribution effectively. The classification results are shown using Pi-SAR image data set comparing with other representative methods.},
keywords={},
doi={},
ISSN={},
month={June},}
Copy
TY - JOUR
TI - Unsupervised Land Cover Classification Using H/
T2 - IEICE TRANSACTIONS on Communications
SP - 1639
EP - 1647
AU - Koji KIMURA
AU - Yoshio YAMAGUCHI
AU - Hiroyoshi YAMADA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E87-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2004
AB - This paper takes full advantage of polarimetric scattering parameters and total power to classify polarimetric SAR image data. The parameters employed here are total power, polarimetric entropy, and averaged alpha angle (alphabar). Since these parameters are independent each other and represent all the scattering characteristics, they seem to be one of the best combinations to classify Polarimetric Synthetic Aperture Radar (POLSAR) images. Using unsupervised classification scheme with iterative Maximum Likelihood classifier, it is possible to decompose multi-look averaged coherency matrix with complex Wishart distribution effectively. The classification results are shown using Pi-SAR image data set comparing with other representative methods.
ER -
English
