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Unsupervised classification of polarimetric SAR images integrating color features | IEEE Conference Publication | IEEE Xplore
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Unsupervised classification of polarimetric SAR images integrating color features


Abstract:

In conventional terrain classification for the polarimetric SAR (POLSAR) images, color features are rarely involved unless in one recent supervised work. Unlike that work...Show More

Abstract:

In conventional terrain classification for the polarimetric SAR (POLSAR) images, color features are rarely involved unless in one recent supervised work. Unlike that work, the color features are exploited for the unsupervised classification in this paper. Firstly, based on the polarimetric decomposition of the POLSAR data, the common color spaces, such as RGB, HSI, and CIELab are calculated. The color feature is quantitatively selected from these color spaces by introducing the color entropy. Then together with the spatial information, extended scattering power entropy and the copolarized ratio, the adaptive Mean-shift algorithm is used to segment the POLSAR image. Finally, the segments are merged according to the Wishart distance measurement. The experiments using AIRSAR L-band POLSAR data indicate that the proposed method has better discriminative ability for urban areas and for boundary preservation compared with existing works.
Date of Conference: 13-18 July 2014
Date Added to IEEE Xplore: 06 November 2014
Electronic ISBN:978-1-4799-5775-0

ISSN Information:

Conference Location: Quebec City, QC, Canada

References

References is not available for this document.