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Proceedings Paper

Ensemble polarimetric SAR image classification based on contextual sparse representation
Author(s): Lamei Zhang; Xiao Wang; Bin Zou; Zhijun Qiao
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Paper Abstract

Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.

Paper Details

Date Published: 4 May 2016
PDF: 8 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 985709 (4 May 2016); doi: 10.1117/12.2229093
Show Author Affiliations
Lamei Zhang, Harbin Institute of Technology (China)
Xiao Wang, Harbin Institute of Technology (China)
Bin Zou, Harbin Institute of Technology (China)
Zhijun Qiao, Univ. of Texas-Rio Grande Valley (United States)


Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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