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
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.
苏红军, 刘浩. 一种利用空间和光谱信息的高光谱遥感多分类器动态集成算法[J]. 国土资源遥感, 2017, 29(2): 15-21.
SU Hongjun, LIU Hao. A novel dynamic classifier selection algorithm using spatial-spectral information for hyperspectral classification. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 15-21.
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2017, Vol. 29 
