Bea2010a

[Bea2010a]
Mean-shift and Hierarchical Clustering for Textured Polarimetric SAR Image Segmentation/Classification

Authors:Beaulieu Jean-Marie, Ridha Touzi

Conference:IEEE International Geoscience and Remote Sensing Symposium

 Honolulu, HI

 July 25-30, 2010, vol. IGARSS 2010, pp. 2519-2522

ISBN:978-1-4244-9565-8

URL:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5653919

DOI:10.1109/IGARSS.2010.5653919

Abstract:   Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed. The approach is applied on a 9-look polarimetric SAR image. Textured and non-textured image regions are considered. The K and Wishart distributions are used respectively. The unsupervised classification results can be very useful for image analysis and further supervised classification. The obtained region groups constitute an important simplification of the image.

Mean-shift and Hierarchical Clustering for Textured Polarimetric SAR Image Segmentation/Classification,
Beaulieu Jean-Marie, Ridha Touzi,
IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, July 25-30, 2010, pp. 2519-2522.
[Bibtex]

@Conference{Bea2010a,
author = {Beaulieu, Jean-Marie and Touzi, Ridha},
editor = {},
title = {Mean-shift and Hierarchical Clustering for Textured Polarimetric {SAR} Image Segmentation/Classification},
booktitle = {IEEE International Geoscience and Remote Sensing Symposium},
volume = {IGARSS 2010},
publisher = {},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5653919},
isbn = {978-1-4244-9565-8},
doi = {10.1109/IGARSS.2010.5653919},
address = {Honolulu, HI},
pages = {2519-2522},
year = {2010},
month = {July 25-30,},
abstract = {Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed. The approach is applied on a 9-look polarimetric SAR image. Textured and non-textured image regions are considered. The K and Wishart distributions are used respectively. The unsupervised classification results can be very useful for image analysis and further supervised classification. The obtained region groups constitute an important simplification of the image.},
mypdf = {11},
keywords = {9-look polarimetric SAR image; hierarchical clustering; hierarchical grouping; image analysis; image classification; image segmentation; image texture; K distribution; mean-shift step; nontextured image region; pattern clustering; radar imaging; radar polarimetry; segment mean value; statistical distributions; synthetic aperture radar; textured polarimetric SAR image; unsupervised classification; Wishart distribution},
openpdf = {},
openid = {}
}

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Published in: Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International
Date of Conference: 25-30 July 2010
ISSN : 2153-6996
E-ISBN :        978-1-4244-9564-1
Print ISBN:     978-1-4244-9565-8
INSPEC Accession Number: 11686160
Conference Location : Honolulu, HI
Publisher: IEEE