激光与光电子学进展, 2021, 58 (8): 0810010, 网络出版: 2021-04-12 复制并引用该论文  本文已被引 5 次  被引通知

图 & 表

图 1. R-HybridSN和M-HybridSN模块的对比。(a) R-HybridSN第一层的多尺度卷积层;(b) R-HybridSN的非恒等残差连接;(c) M-HybridSN的多特征融合模块

Fig. 1. Comparison of R-HybridSN and M-HybridSN modules. (a) Multi-scale convolutional layer of the first layer of R-HybridSN; (b) non-identical residual connection of the R-HybridSN; (c) multi-feature fusion module of the M-HybridSN

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图 2. M-HybridSN的结构

Fig. 2. Structure of the M-HybridSN

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图 3. 数据集IP的分类结果

Fig. 3. Classification results of the data set IP

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图 4. 数据集SA的分类结果

Fig. 4. Classification results of the data set SA

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图 5. 数据集PU的分类结果

Fig. 5. Classification results of the data set PU

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图 6. 非恒等残差连接不同条件下的对比实验结果

Fig. 6. Comparative experiment results under different conditions of the non-identical residual connection

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表 1数据集IP的分布情况

Table1. Distribution situation of the data set IP

No. Category Labeled sample Training Validation Testing 1 alfalfa 46 2 3 41 2 corn-notill 1428 71 72 1285 3 corn-mintill 830 42 41 747 4 corn 237 12 12 213 5 grass-pasture 483 24 24 435 6 grass-trees 730 36 37 657 7 grass-pasture-mowed 28 2 1 25 8 hay-windrowed 478 24 24 430 9 oats 20 1 1 18 10 soybean-notill 972 48 49 875 11 soybean-mintill 2455 123 122 2210 12 soybean-clean 593 30 29 534 13 wheat 205 10 10 185 14 woods 1265 63 63 1139 15 buildings-grass-trees-drives 386 19 20 347 16 stone-steel-towers 93 5 4 84 Total 10249 512 512 9225

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表 2数据集SA的分布情况

Table2. Distribution situation of the data set SA

No. Category Labeled sample Training Validation Testing 1 brocoli_green_weeds_1 2009 20 20 1969 2 brocoli_green_weeds_2 3726 37 37 3652 3 fallow 1976 20 20 1936 4 fallow_rough_plow 1394 14 14 1366 5 fallow_smooth 2678 27 27 2624 6 stubble 3959 39 40 3880 7 celery 3579 36 36 3507 8 grapes_untrained 11271 113 112 11046 9 soil_vinyard_develop 6203 62 62 6079 10 corn_senesced_green_weeds 3278 33 33 3212 11 lettuce_romaine_4wk 1068 11 10 1047 12 lettuce_romaine_5wk 1927 19 20 1888 13 lettuce_romaine_6wk 916 9 9 898 14 lettuce_romaine_7wk 1070 11 10 1049 15 vinyard_untrained 7268 72 73 7123 16 vinyard_vertical_trellis 1807 18 18 1771 Total 54129 541 541 53047

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表 3数据集PU的分布情况

Table3. Distribution situation of the data set PU

No. Category Labeled sample Training Validation Testing 1 asphalt 6631 66 66 6499 2 meadows 18649 186 186 18277 3 gravel 2099 21 21 2057 4 trees 3064 30 31 3003 5 painted metal sheets 1345 14 13 1318 6 bare Soil 5029 50 50 4929 7 bitumen 1330 14 13 1303 8 self-blocking bricks 3682 37 37 3608 9 shadows 947 9 10 928 Total 42776 427 427 41922

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表 4不同模型的参数量和输入数据规模

Table4. Parameter number and input data scale of different models

Model Res-2D-CNN Res-3D-CNN HybridSN R-HybridSN M-HybridSN Parameter number 1065360 231184 5122176 719112 659296 Input data scale 5×5×200 9×9×200 25×25×30 15×15×16 15×15×16

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表 5不同模型对数据集IP的分类结果

Table5. Classification results of the data set IP by different models unit: %

No. Res-2D-CNN Res-3D-CNN HybridSN R-HybridSN M-HybridSN 1 9.51 23.78 58.54 58.17 65.61 2 72.39 83.73 93.08 94.98 95.28 3 60.31 76.53 96.57 97.38 97.36 4 37.86 53.47 75.09 92.16 94.51 5 80.14 93.54 94.00 96.68 97.01 6 94.00 96.54 97.19 99.08 98.63 7 34.60 71.20 82.40 94.00 99.80 8 99.13 98.66 98.73 99.81 99.93 No. Res-2D-CNN Res-3D-CNN HybridSN R-HybridSN M-HybridSN 9 3.89 67.50 83.89 63.06 76.67 10 78.42 85.75 94.27 95.81 96.58 11 84.12 90.02 97.93 98.31 98.55 12 54.19 63.40 84.49 92.43 91.97 13 85.16 88.43 92.68 98.46 97.41 14 89.44 97.48 97.96 99.25 99.03 15 52.98 79.35 83.18 92.52 96.80 16 80.54 93.63 83.33 98.21 95.54 Kappa 74.0 ± 2.8 84.5 ± 2.4 93.4 ± 1.2 96.3 ± 0.6 96.7 ± 0.4 OA 77.28 ± 2.33 86.42 ± 2.13 94.26 ± 1.08 96.74 ± 0.52 97.09 ± 0.38 AA 63.54 ± 4.66 78.94 ± 3.22 88.33 ± 2.40 91.90 ± 2.58 93.79 ± 1.99

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表 6不同模型对数据集SA的分类结果

Table6. Classification results of the data set SA by different models unit: %

No. Res-2D-CNN Res-3D-CNN HybridSN R-HybridSN M-HybridSN 1 66.09 97.13 99.98 100.00 99.92 2 99.36 99.92 99.97 99.96 99.99 3 61.79 93.00 99.82 99.62 99.56 4 99.19 99.09 97.39 98.87 99.22 5 94.62 97.75 98.79 98.83 99.21 6 99.95 99.97 99.78 99.90 99.91 7 97.34 98.24 99.77 99.88 99.91 8 82.99 87.66 99.04 98.33 98.96 9 99.19 99.58 100.00 99.99 99.96 10 85.81 91.16 98.98 98.06 98.89 11 83.73 90.83 98.95 98.62 98.83 12 98.32 99.20 99.09 99.88 99.29 13 95.23 97.88 97.28 92.41 96.99 14 96.07 98.25 96.60 93.96 97.17 15 70.49 77.52 98.57 96.61 98.90 16 91.08 86.44 99.69 99.46 99.56 Kappa 86.1 ± 1.6 91.6 ± 0.8 99.1 ± 0.3 98.5 ± 0.3 99.2 ± 0.3 OA 87.54 ± 1.40 92.48 ± 0.69 99.20 ± 0.27 98.66 ± 0.31 99.30 ± 0.24 AA 88.83 ± 2.64 94.60 ± 0.50 98.98 ± 0.28 98.40 ± 0.43 99.14 ± 0.30

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表 7不同模型对数据集PA的分类结果

Table7. Classification results of the data set PA by different models unit: %

No. Res-2D-CNN Res-3D-CNN HybridSN R-HybridSN M-HybridSN 1 92.18 90.81 92.15 96.21 95.04 2 97.47 96.63 99.53 99.70 99.88 3 15.33 66.54 90.51 90.93 93.77 4 94.94 96.24 92.50 94.62 92.92 5 99.45 99.86 97.75 99.79 99.57 6 88.04 80.75 99.46 99.25 99.50 7 40.70 68.12 96.25 94.36 94.92 8 86.93 80.01 91.75 94.09 95.68 9 97.40 97.38 75.04 94.23 92.85 Kappa 85.0 ± 1.2 86.9 ± 1.9 94.8 ± 1.3 96.7 ± 0.6 96.8 ± 0.4 OA 88.72 ± 0.85 90.16 ± 1.40 96.07 ± 0.96 97.55 ± 0.48 97.60 ± 0.33 AA 79.16 ± 3.10 86.26 ± 2.10 92.77 ± 2.33 95.91 ± 0.96 96.01 ± 0.60

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冯凡, 王双亭, 张津, 王春阳. 基于多特征融合和混合卷积网络的高光谱图像分类[J]. 激光与光电子学进展, 2021, 58(8): 0810010. Fan Feng, Shuangting Wang, Jin Zhang, Chunyang Wang. Hyperspectral Images Classification Based on Multi-Feature Fusion and Hybrid Convolutional Neural Networks[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810010.