基于高分二号遥感影像的树种分类方法
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李哲,张沁雨,彭道黎
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Classification Method of Tree Species based on GF-2 Remote Sensing Images
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Zhe Li,Qinyu Zhang,Daoli Peng
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表8 不同特征维度下SVM的精度比较
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Table 8 Accuracy comparison of SVM under different feature dimensions
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| C5.0 | | SVM-RFE | | FSO | | ALL |
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| 生产者精度/% | 用户精度/% | | 生产者精度/% | 用户精度/% | | 生产者精度/% | 用户精度/% | | 生产者精度/% | 用户精度/% | 灌木林地 | 92.79 | 90.35 | | 92.79 | 90.35 | | 92.79 | 90.35 | | 92.79 | 90.35 | 非林地 | 100.00 | 91.04 | | 100.00 | 91.04 | 100.00 | 91.04 | 100.00 | 91.04 | 山杏 | 93.75 | 81.08 | | 78.13 | 65.79 | 84.38 | 72.97 | 84.38 | 81.82 | 侧柏 | 86.79 | 68.66 | 81.13 | 74.14 | 92.45 | 70.00 | 92.45 | 70.00 | 榆树 | 76.67 | 69.70 | 63.33 | 61.29 | 73.33 | 73.33 | 73.33 | 75.86 | 刺槐 | 64.29 | 79.41 | 57.14 | 66.67 | 61.90 | 81.25 | 61.90 | 76.47 | 油松 | 85.32 | 96.88 | 85.32 | 91.18 | 87.16 | 96.94 | 84.40 | 94.85 | 元宝枫 | 85.42 | 95.35 | 70.83 | 89.47 | 77.08 | 86.05 | 85.42 | 85.42 | 杨树 | 81.08 | 96.77 | 67.57 | 71.43 | 81.08 | 88.24 | 78.38 | 90.63 | 华北落叶松 | 89.47 | 85.00 | 89.47 | 73.91 | 89.47 | 100.00 | 89.47 | 94.44 | 总体精度/% | 86.90 | | 81.92 | | 86.16 | | 86.16 | Kappa系数 | 0.85 | | 0.79 | | 0.84 | | 0.84 |
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