Ensemble polarimetric SAR image classification based on contextual sparse representation

Lamei Zhang; Xiao Wang; Bin Zou; Zhijun Qiao

doi:10.1117/12.2229093

4 May 2016 Ensemble polarimetric SAR image classification based on contextual sparse representation

Lamei Zhang, Xiao Wang, Bin Zou, Zhijun Qiao

Proceedings Volume 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics; 985709 (2016) https://doi.org/10.1117/12.2229093
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Lamei Zhang, Xiao Wang, Bin Zou, and Zhijun Qiao "Ensemble polarimetric SAR image classification based on contextual sparse representation", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 985709 (4 May 2016); https://doi.org/10.1117/12.2229093

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