林地山区滑坡遥感的最优融合方法及应用
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石育青,梁继,李云星,孟赛颖,石倩
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Optimum Fusion Method and Application of Landslide Remote Sensing in Mountainous Woodland Areas
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Yuqing Shi,Ji Liang,Yunxing Li,Saiying Meng,Qian Shi
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表3 融合影像质量评价结果
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Table 3 Evaluation results of fusion image quality
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数据 | 波段 | 均值 | 标准差 | 信息熵 | 平均梯度 | 相关系数 | 扭曲程度 | 峰值信噪比 |
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Sentinel-2A | B2 | 258.65 | 178.67 | 1.27 | 56.43 | 1.00 | —— | —— | B3 | 442.20 | 241.09 | 1.74 | 78.05 | 1.00 | —— | —— | B4 | 417.30 | 306.18 | 1.70 | 82.10 | 1.00 | —— | —— | B8 | 2 109.70 | 796.63 | 3.60 | 312.91 | 1.00 | —— | —— | PCA-VH | B2 | 258.72 | 172.50 | 1.31 | 54.92 | 0.78 | 84.49 | 55.05 | B3 | 442.24 | 229.41 | 1.66 | 71.37 | 0.59 | 154.57 | 49.80 | B4 | 417.50 | 296.06 | 1.84 | 82.22 | 0.81 | 136.59 | 50.88 | B8 | 2 109.70 | 805.04 | 2.94 | 168.39 | -0.04 | 841.45 | 35.07 | PCA-VV | B2 | 258.70 | 175.87 | 1.31 | 55.11 | 0.79 | 82.75 | 55.08 | B3 | 442.22 | 235.24 | 1.65 | 71.88 | 0.61 | 151.38 | 49.83 | B4 | 417.48 | 302.14 | 1.83 | 82.52 | 0.81 | 133.78 | 50.92 | B8 | 2 109.70 | 800.39 | 2.81 | 170.31 | -0.04 | 824.03 | 35.11 | PCA-VH-DB | B2 | 258.78 | 171.05 | 1.41 | 55.65 | 0.78 | 94.56 | 54.98 | B3 | 442.25 | 226.99 | 1.86 | 73.88 | 0.58 | 173.09 | 49.72 | B4 | 417.85 | 292.44 | 2.02 | 83.38 | 0.80 | 152.64 | 50.83 | B8 | 2 109.70 | 807.14 | 3.67 | 218.32 | -0.06 | 942.35 | 35.00 | PCA-VV-DB | B2 | 259.06 | 177.96 | 1.44 | 55.63 | 0.79 | 91.70 | 55.10 | B3 | 442.79 | 238.98 | 1.89 | 74.03 | 0.61 | 167.83 | 49.84 | B4 | 418.33 | 305.11 | 2.03 | 83.55 | 0.82 | 147.98 | 50.95 | B8 | 2 110.90 | 793.18 | 3.65 | 218.37 | -0.05 | 915.43 | 35.10 | GS-VH | B2 | 260.23 | 173.36 | 1.46 | 57.31 | 0.24 | 154.47 | 49.62 | B3 | 443.41 | 233.78 | 1.70 | 69.44 | 0.08 | 231.64 | 46.19 | B4 | 421.05 | 295.50 | 2.09 | 89.45 | 0.31 | 254.13 | 45.36 | B8 | 2 105.70 | 815.55 | 3.35 | 223.49 | 0.15 | 771.01 | 35.91 | GS-VV | B2 | 260.18 | 176.60 | 1.46 | 57.99 | 0.27 | 151.07 | 49.72 | B3 | 443.39 | 237.75 | 1.70 | 70.35 | 0.12 | 225.70 | 46.30 | B4 | 420.90 | 302.31 | 2.07 | 90.49 | 0.34 | 248.17 | 45.46 | B8 | 2 105.60 | 801.37 | 3.29 | 223.99 | 0.16 | 750.83 | 36.02 | GS-VH-DB | B2 | 262.53 | 169.04 | 1.57 | 58.26 | 0.23 | 171.17 | 49.64 | B3 | 444.20 | 231.73 | 1.96 | 75.00 | 0.06 | 260.16 | 46.12 | B4 | 426.77 | 283.36 | 2.19 | 90.01 | 0.29 | 280.61 | 45.42 | B8 | 2 108.80 | 809.65 | 3.68 | 243.63 | 0.14 | 853.84 | 35.87 | GS-VV-DB | B2 | 263.79 | 173.60 | 1.60 | 58.41 | 0.30 | 164.52 | 49.93 | B3 | 446.30 | 236.19 | 1.98 | 74.70 | 0.15 | 248.79 | 46.44 | B4 | 428.69 | 294.43 | 2.23 | 90.65 | 0.37 | 269.48 | 45.73 | B8 | 2 106.70 | 782.13 | 3.64 | 244.60 | 0.15 | 823.00 | 36.08 | NNDiffuse-VH | B2 | 264.35 | 276.06 | 1.25 | 62.32 | 0.64 | 13.38 | 49.78 | B3 | 449.61 | 327.00 | 1.74 | 85.52 | 0.70 | 17.28 | 49.01 | B4 | 425.99 | 408.72 | 1.70 | 93.17 | 0.73 | 18.66 | 47.45 | B8 | 2 160.50 | 1599.60 | 3.59 | 391.22 | 0.47 | 86.78 | 33.34 | NNDiffuse-VV | B2 | 281.77 | 439.48 | 1.30 | 98.22 | 0.41 | 30.61 | 44.28 | B3 | 475.45 | 602.57 | 1.79 | 138.42 | 0.38 | 42.92 | 41.39 | B4 | 458.34 | 801.01 | 1.75 | 159.91 | 0.40 | 50.75 | 39.01 | B8 | 2 284.40 | 2646.40 | 3.64 | 641.98 | 0.26 | 209.96 | 28.16 | NNDiffuse-VH-DB | B2 | 258.15 | 175.94 | 1.25 | 52.04 | 1.00 | 4.10 | 79.32 | B3 | 441.70 | 237.80 | 1.73 | 72.59 | 1.00 | 5.47 | 77.48 | B4 | 416.80 | 302.81 | 1.69 | 76.90 | 1.00 | 5.58 | 76.71 | B8 | 2 109.20 | 782.83 | 3.58 | 293.23 | 1.00 | 21.03 | 66.54 | NNDiffuse-VV-DB | B2 | 258.15 | 175.86 | 1.25 | 51.90 | 1.00 | 4.22 | 79.08 | B3 | 441.71 | 237.76 | 1.73 | 72.46 | 1.00 | 5.56 | 77.38 | B4 | 416.80 | 302.74 | 1.69 | 76.76 | 1.00 | 5.68 | 76.58 | B8 | 2 109.20 | 782.83 | 3.58 | 292.96 | 1.00 | 21.15 | 66.56 |
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