@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},
slide = {https://BeaulieuJM.ca/slide/slideBea2010a.pdf},
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}
}