基于Fisher准则和TrAdaboost的高光谱相似样本分类算法
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刘万军, 李天慧, 曲海成
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Hyperspectral similar sample classification algorithm based on Fisher criterion and TrAdaboost
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Wanjun LIU, Tianhui LI, Haicheng QU
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表6 样本2上算法间的分类精度对比
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Tab.6 Accuracy compare of each algorithm in sample 2
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| 算法 | 统计值 | 15% | 10% | 5% | 3% | 2% | 1% | | SVM | OA/% | 77.00 | 76.00 | 73.00 | 71.50 | 70.00 | 70.50 | | Kappa | 0.76 | 0.76 | 0.73 | 0.71 | 0.70 | 0.70 | | TrAdaboost | OA/% | 79.50 | 77.50 | 75.00 | 72.50 | 73.50 | 72.50 | | Kappa | 0.77 | 0.77 | 0.75 | 0.73 | 0.73 | 0.72 | | H_TrAdaboost | OA/% | 80.50 | 79.00 | 76.50 | 74.50 | 73.50 | 73.00 | | Kappa | 0.81 | 0.79 | 0.76 | 0.74 | 0.73 | 0.73 |
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