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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (2) : 15-21     DOI: 10.6046/gtzyyg.2017.02.03
Contents |
A novel dynamic classifier selection algorithm using spatial-spectral information for hyperspectral classification
SU Hongjun1, LIU Hao2
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China;
2. State Key Laboratory of InformationEngineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Abstract  To further improve the classification accuracy of hyperspectral remotely sensed imagery, this paper proposes a novel dynamic classifier selection algorithm, in which spatial and spectral information is used. The class labels of unlabeled pixels are predicted based on the percentage of their classified neighbors. The experiment is conducted between the proposed DCS-SSI algorithm and five dynamic classifier selection algorithms, and the results show that the proposed DCS-SSI algorithm can improve the robustness of classification performance for hyperspectral image analysis, which would be useful for high level classification of hyperspectral remote sensing images.
Keywords fully polarimetric synthetic aperture Radar(PolSAR)      scattering parameters      terrain classification      K-Wishart distribution      prior probability     
Issue Date: 03 May 2017
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XU Bin. A novel dynamic classifier selection algorithm using spatial-spectral information for hyperspectral classification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 15-21.
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