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Title:
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CONTEXTUAL CLASSIFICATION OF POLARIMETRIC SAR IMAGES |
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Author(s):
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Assia Kourgli, Amirouche Benchallal, Mounira Ouarzeddine, Youcef Oukil |
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ISBN:
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978-972-8939-22-9 |
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Editors:
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Yingcai Xiao, Tomaz Amon and Piet Kommers |
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Year:
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2010 |
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Edition:
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Single |
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Keywords:
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Polarimetric SAR images, context, texture, fuzzy clustering |
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Type:
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Full Paper |
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First Page:
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109 |
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Last Page:
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116 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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A variety of polarimetric classification algorithms have been proposed in the literature for segmentation and/or classification of polarimetric SAR (Synthetic Aperture Radar) images into classes reflecting canonical scattering processes and/or some statistical properties. However, classification based on polarimetric data alone does not provide sufficient sensitivity for the separation of some classes such as forests. The use of other kinds of characteristics such as spatial information provides better sensitivity for class separation. In this paper, we wish to address this issue, testing and comparing some polarimetric SAR classification approaches incorporating contextual information. This analysis will allow us to evaluate the importance of spatial/textural information considering by the use of fuzzy clustering. The test area used is the Oberpfaffenhofen in Munich and the SAR images are acquired in the P band |
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