激光与光电子学进展, 2021, 58 (2): 0228002, 网络出版: 2021-01-11 复制并引用该论文  本文已被引 1 次  被引通知

图 & 表

图 1. 研究区所在位置。(a)黄山市在安徽省的方位;(b)WorldView3真彩色示意图,方框为黄山风景区所在位置

Fig. 1. Location of the research area. (a) Huangshan City, Anhui Province; (b) true color schematic of WorldView3, the box indicates the location of Huangshan Mountain

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图 2. 单木树种遥感影像样本集的构建步骤。(a)研究区遥感影像;(b)树种分布图;(c)树冠圈定图;(d)树冠类别标注图;(e)单木树种遥感影像图;(f)单木树种遥感影像样本集

Fig. 2. Construction steps of sample set of remote sensing imagery of individual tree species. (a) Remote sensing imagery of research area; (b) distribution diagram of tree species; (c) delineation diagram of tree crown; (d) labeling diagram of tree crown category; (e) remote sensing imagery of individual tree species; (f) sample set of remote sensing imagery of individual tree species

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图 3. 单木树种遥感影像样本集类别标注结果

Fig. 3. Classification labeling result of sample set of remote sensing imagery of individual tree species

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图 4. CNN模型收敛时的训练精度、验证精度与网络层数柱形图

Fig. 4. Histogram of training accuracy, validation accuracy, and network layers when CNN model converges

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图 5. 黄山风景区树种分类图

Fig. 5. Classification diagram of tree species of Huangshan Mountain

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表 1数据增强前后的样本集划分结果

Table1. Results of sample set division before and after data augmentation

Tree species Training sample set Validation sample set Test sample set Before After Before After Ph.h 66 396 23 138 23 E.a 266 1596 89 534 89 C.l 83 498 28 168 28 Pi.h 1199 7194 401 2406 401 D.a 369 2214 124 744 124 Total 1983 11898 665 3990 665

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表 2LeNet5_relu模型参数

Table2. LeNet5_relu model parameter

Layer Output size Parameter Input 32×32×8 - Convolutional C1 28×28×6 Kernel 5×5, filter 6, stride 1, ReLU Pooling S1 14×14×6 Average_pooling 2×2, stride 2 Convolutional C2 10×10×16 Kernel 5×5, filter 16, stride 1, ReLU Pooling S2 5×5×16 Average_pooling 2×2, stride 2 Convolutional C3 1×1×120 Kernel 5×5, filter 120, stride 1, ReLU Fully-connected F1 84 Node 84, FC, ReLU Classification 5 Node 5, FC, Softmax

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表 3AlexNet_mini模型参数

Table3. AlexNet_mini model parameter

Layer Output size Parameter Input 32×32×8 - Convolutional C1 32×32×12 Kernel 7×7, filter 12, stride 1, ReLU Pooling S1 15×15×12 Average_pooling 3×3, stride 2 Convolutional C2 15×15×36 Kernel 5×5, filter 36, stride 1, ReLU Pooling S2 7×7×36 Average_pooling 3×3, stride 2 Convolutional C3 7×7×54 Kernel 3×3, filter 54, stride 1, ReLU Convolutional C4 7×7×54 Kernel 3×3, filter 54, stride 1, ReLU Convolutional C5 3×3×36 Kernel 3×3, filter 36, stride 1, ReLU Pooling S3 3×3×36 Average_pooling 3×3, stride 2 Fully-connected F1 320 Node 320, FC, ReLU Fully-connected F2 100 Node 100, FC, ReLU Classification 5 Node 5, FC, Softmax

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表 4GoogLeNet_mini56模型参数

Table4. GoogLeNet_mini56 model parameter

Layer Output size Parameter Input 32×32×8 -- Convolutional C1 32×32×12 Kernel 7×7, filter 12, stride 1 Inception V1 block (1a) 32×32×32 -- Inception V1 block (1b) 32×32×60 -- Pooling S1 15×15×60 Max_pooling 3×3, stride 2 Inception V1 block (2a) 15×15×64 -- Inception V1 block (2b) 15×15×64 -- Inception V1 block (2c) 15×15×64 -- Inception V1 block (2d) 15×15×66 -- Inception V1 block (2e) 15×15×104 -- Pooling S2 7×7×104 Max_pooling 3×3, stride 2 Inception V1 block (3a) 7×7×104 -- Inception V1 block (3b) 7×7×128 -- Pooling S3 1×1×128 Average_pooling 7×7, stride 1 Classification 5 Node 5, FC, Softmax

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表 5Inception V1模块参数

Table5. Inception V1 block parameter

Layer Inception V1 block Ⅰ Ⅱ Ⅲ Ⅳ Convolutionalkernel 1×1 Bottleneckkernel 1×1 Convolutionalkernel 3×3 Bottleneckkernel 1×1 Convolutionalkernel 5×5 Pooling3×3 Bottleneckkernel 1×1 1a Filter 8 Filter 12 Filter 16 Filter 2 Filter 4 Stride 1 Filter 4 1b Filter 16 Filter 16 Filter 24 Filter 4 Filter 12 Stride 1 Filter 8 2a Filter 24 Filter 12 Filter 26 Filter 2 Filter 6 Stride 1 Filter 8 2b Filter 20 Filter 14 Filter 28 Filter 3 Filter 8 Stride 1 Filter 8 2c Filter 16 Filter 16 Filter 32 Filter 3 Filter 8 Stride 1 Filter 8 2d Filter 14 Filter 18 Filter 36 Filter 4 Filter 8 Stride 1 Filter 8 2e Filter 32 Filter 20 Filter 40 Filter 4 Filter 16 Stride 1 Filter 16 3a Filter 32 Filter 20 Filter 40 Filter 4 Filter 16 Stride 1 Filter 16 3b Filter 48 Filter 24 Filter 48 Filter 6 Filter 16 Stride 1 Filter 16

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表 6ResNet_mini56模型参数

Table6. ResNet_mini56 model parameter

Layer Output size Parameter Residual blockfilter Ⅰ Ⅱ Ⅲ Bottleneckkernel 1×1 Convolutionalkernel 3×3 Bottleneckkernel 1×1 Input 32×32×8 -- -- Convolutional C1 16×16×12 Kernel 7×7, filter 12, stride 2 -- Residual block(1) Pooling S1 8×8×12 Max_pooling 3×3, stride 2 -- Bottleneck 8×8×12 × 3 12 -- -- Convolutional 8×8×12 -- 12 -- Bottleneck 8×8×48 -- -- 48 Residual block(2) Bottleneck 4×4×24 × 4 24 -- -- Convolutional 4×4×24 -- 24 -- Bottleneck 4×4×96 -- -- 96 Residual block(3) Bottleneck 2×2×48 ×8 48 -- -- Convolutional 2×2×48 -- 48 -- Bottleneck 2×2×192 -- -- 192 Residual block(4) Bottleneck 1×1×96 × 3 96 -- -- Convolutional 1×1×96 -- 96 -- Bottleneck 1×1×384 -- -- 384 Classification 384 Global_average_pooling -- 5 Node 5, FC,Softmax

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表 7DenseNet_BC_mini56模型参数

Table7. DenseNet_BC_mini56 model parameter

Layer Output size Parameter Dense block filter Ⅰ Ⅱ Bottleneckkernel 1×1 Convolutionalkernel 1×1 Input 32×32×8 -- -- Convolutional C1 32×32×12 Kernel 3×3, filter 12, stride 2 -- Dense block(1) Bottleneck 32×32×42 ×5 24 -- Convolutional -- 6 Compression(1) 32×32×21 Kernel 1×1 -- 16×16×21 Average_pooling 2×2, stride 2 Dense block(2) Bottleneck 16×16×51 × 5 24 -- Convolutional -- 6 Compression(2) 16×16×36 Kernel 1×1 -- 8×8×36 Average_pooling 2×2, stride 2 Dense block(3) Bottleneck 8×8×66 ×5 24 -- Convolutional -- 6 Compression(3) 8×8×51 Kernel 1×1 -- 4×4×51 Average_pooling 2×2, stride 2 Dense block(4) Bottleneck 4×4×81 × 5 24 -- Convolutional -- 6 Compression(4) 4×4×66 Kernel 1×1 -- 2×2×66 Average_pooling 2×2, stride 2 Dense block(5) Bottleneck 2×2×99 ×5 24 -- Convolutional -- 6 Classification 99 Global_average_pooling -- 5 Node 5, FC, Softmax

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表 8CNN模型参数

Table8. CNN model parameter

Model name Total parameter Trainable parameter Non-trainable parameter Network layer LeNet5_relu 62331 62331 0 5 AlexNet_mini 213537 213537 0 8 GoogLeNet_mini56 97251 97227 24 56 ResNet_mini56 934025 924401 9624 56 DenseNet_BC_mini56 82979 78839 4140 56

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表 9CNN模型分类精度评价指标

Table9. Classification accuracy evaluation index of CNN model

Model name Evaluation index Tree species Ph.h E.a C.l Pi.h D.a LeNet5_relu Producer accuracy /% 86.96 67.42 75.00 98.00 87.90 User accuracy /% 90.91 76.92 87.50 93.57 90.08 Overall accuracy /% 90.68 Kappa coefficient 0.84 AlexNet_mini Producer accuracy /% 86.96 71.91 64.29 97.76 92.74 User accuracy /% 90.91 79.01 78.26 94.00 94.26 Overall accuracy /% 91.58 Kappa coefficient 0.85 GoogLeNet_mini56 Producer accuracy /% 95.65 74.16 75.00 97.76 97.58 User accuracy /% 100.00 84.62 95.45 94.92 93.08 Overall accuracy /% 93.53 Kappa coefficient 0.89 ResNet_mini56 Producer accuracy /% 95.65 76.40 78.57 97.51 92.74 User accuracy /% 100.00 80.95 100.00 94.90 92.00 Overall accuracy /% 92.93 Kappa coefficient 0.88 DenseNet_BC_mini56 Producer accuracy /% 95.65 75.28 85.71 98.25 95.97 User accuracy /% 100.00 87.01 100.00 94.71 94.44 Overall accuracy /% 94.14 Kappa coefficient 0.90

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欧阳光, 荆林海, 阎世杰, 李慧, 唐韵玮, 谭炳香. 基于卷积神经网络的高分遥感影像单木树种分类[J]. 激光与光电子学进展, 2021, 58(2): 0228002. Guang Ouyang, Linhai Jing, Shijie Yan, Hui Li, Yunwei Tang, Bingxiang Tan. Classification of Individual Tree Species in High-Resolution Remote Sensing Imagery Based on Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0228002.