A novel dynamic classifier selection algorithm using spatial-spectral information for hyperspectral classification

doi:10.6046/gtzyyg.2017.02.03

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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

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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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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.02.03 OR https://www.gtzyyg.com/EN/Y2017/V29/I2/15

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