Scientia Silvae Sinicae ›› 2020, Vol. 56 ›› Issue (3): 48-60.doi: 10.11707/j.1001-7488.20200306
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Ying Guo,Zengyuan Li,Erxue Chen*,Xu Zhang,Lei Zhao,Yan Chen,Yahui Wang
Received:
2019-07-12
Online:
2020-03-25
Published:
2020-04-08
Contact:
Erxue Chen
CLC Number:
Ying Guo,Zengyuan Li,Erxue Chen,Xu Zhang,Lei Zhao,Yan Chen,Yahui Wang. A Deep Learning Method for Forest Fine Classification Based on High Resolution Remote Sensing Images: Two-Branch FCN-8s[J]. Scientia Silvae Sinicae, 2020, 56(3): 48-60.
Table 1
Classification system of the study area"
一级 Class one | 二级 Class two | 三级 Class three |
林地Forest land | 有林地Woodland | 油松Pinus tabulaeformis |
华北落叶松Larix principis-rupprechtii | ||
红松Pinus koraiensis | ||
白桦Betula platyphylla | ||
山杨Populus davidiana | ||
蒙古栎Quercus mongolica | ||
非林地Non-forest land | 灌木林地Shrub land | / |
其他林地Other forest land | / | |
耕地Cultivated land | / | |
草地Grassland | / | |
建设用地Construction land | / | |
其他非林地Other non-forest land | / |
Table 2
Confusion matrix of classification result of two-branch FCN-8s"
油松 Pinus tabulaeformis | 华北落叶松 Larix principis-rupprechtii | 红松 Pinus koraiensis | 白桦 Betula platyphylla | 蒙古栎 Quercus mongolica | 山杨 Populus davidiana | 其他林地 Other forest land | 耕地 Cultivated land | 建设用地 Construction land | 灌木林地 Shrub land | 草地 Grassland | 其他非林地 Other non-forest land | 总计 Total | |
油松Pinus tabulaeformis | 70 | 2 | 0 | 4 | 0 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 80 |
华北落叶松 Larix principis-rupprechtii | 2 | 62 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 66 |
红松Pinus koraiensis | 0 | 0 | 15 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
白桦Betula platyphylla | 0 | 1 | 0 | 23 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 28 |
蒙古栎Quercus mongolica | 0 | 0 | 0 | 0 | 27 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 30 |
山杨Populus davidiana | 1 | 0 | 0 | 10 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 18 |
其他林地 Other forest land | 1 | 3 | 0 | 8 | 0 | 0 | 0 | 1 | 8 | 0 | 0 | 0 | 21 |
耕地Cultivated land | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 34 | 0 | 2 | 1 | 0 | 39 |
建设用地Construction land | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 40 | 0 | 0 | 1 | 40 |
灌木林地Shrub land | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 21 | 0 | 0 | 25 |
草地Grassland | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 11 |
其他非林地Other non-forest land | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 30 |
总计Total | 74 | 69 | 15 | 46 | 28 | 15 | 16 | 36 | 40 | 23 | 12 | 30 | 404 |
Table 3
Comparison of classification accuracy among two-branch FCN-8s, FCN-8s and SVM"
地类 Land cover type | 双支FCN-8s Two-branch FCN-8s | FCN-8s | SVM | |||||
生产者精度 Producer accuracy(%) | 用户精度 User accuracy(%) | 生产者精度 Producer accuracy(%) | 用户精度 User accuracy(%) | 生产者精度 Producer accuracy(%) | 用户精度 User accuracy(%) | |||
油松Pinus tabulaeformis | 94.59 | 87.50 | 90.54 | 82.72 | 79.73 | 81.94 | ||
华北落叶松Larix principis-rupprechtii | 89.86 | 93.94 | 94.20 | 87.84 | 88.41 | 70.93 | ||
红松Pinus koraiensis | 100.00 | 93.75 | 100.00 | 93.75 | 93.33 | 100.00 | ||
白桦Betula platyphylla | 50.00 | 82.14 | 26.09 | 80.00 | 56.52 | 68.42 | ||
蒙古栎Quercus mongolica | 96.43 | 90.00 | 100.00 | 82.35 | 96.43 | 79.41 | ||
山杨Populus davidiana | 40.00 | 33.33 | 40.00 | 26.09 | 66.67 | 43.48 | ||
其他林地Other forest land | 50.00 | 38.10 | 56.25 | 42.86 | 31.25 | 62.50 | ||
耕地Cultivated land | 94.44 | 87.18 | 97.22 | 87.50 | 97.22 | 56.45 | ||
建设用地Construction land | 100.00 | 100.00 | 100.00 | 100.00 | 97.50 | 97.50 | ||
灌木林地Shrub land | 91.30 | 80.77 | 86.96 | 95.24 | 21.74 | 100.00 | ||
草地Grassland | 91.67 | 100.00 | 75.00 | 100.00 | 50.00 | 100.00 | ||
其他非林地Other non-forest land | 100.00 | 90.91 | 93.33 | 93.33 | 53.33 | 100.00 | ||
总体精度Overall accuracy(%) | 85.89 | 82.67 | 75.00 | |||||
Kappa系数Kappa coefficient | 0.841 4 | 0.805 0 | 0.716 8 |
Table 4
Impact of NDVI features and finetune strategy on classification accuracy of two-branch FCN-8s model"
地类 Land cover type | 双支FCN-8s Two-branch FCN-8s | 双支FCN-8s不使用微调策略 Two-branch FCN-8s without finetune strategy | 双支FCN-8s不加入NDVI特征 Two-branch FCN-8s without NDVI | |||||
生产者精度 Producer accuracy(%) | 用户精度 User accuracy(%) | 生产者精度 Producer accuracy(%) | 用户精度 User accuracy(%) | 生产者精度 Producer accuracy(%) | 用户精度 User accuracy(%) | |||
油松Pinus tabulaeformis | 94.59 | 87.50 | 82.43 | 88.41 | 86.49 | 84.21 | ||
华北落叶松Larix principis-rupprechtii | 89.86 | 93.94 | 89.86 | 82.67 | 89.86 | 87.32 | ||
红松Pinus koraiensis | 100.00 | 93.75 | 100.00 | 100.00 | 100.00 | 100.00 | ||
白桦Betula platyphylla | 50.00 | 82.14 | 36.96 | 73.91 | 56.52 | 70.27 | ||
蒙古栎Quercus mongolica | 96.43 | 90.00 | 92.86 | 74.29 | 96.43 | 90.00 | ||
山杨Populus davidiana | 40.00 | 33.33 | 46.67 | 36.84 | 26.67 | 30.77 | ||
其他林地Other forest land | 50.00 | 38.10 | 31.25 | 25.00 | 43.75 | 38.89 | ||
耕地Cultivated land | 94.44 | 87.18 | 91.67 | 78.57 | 97.22 | 70.00 | ||
建设用地Construction land | 100.00 | 100.00 | 95.00 | 100.00 | 82.50 | 100.00 | ||
灌木林地Shrub land | 91.30 | 80.77 | 82.61 | 76.00 | 91.30 | 95.45 | ||
草地Grassland | 91.67 | 100.00 | 75.00 | 81.82 | 66.67 | 100.00 | ||
其他非林地Other non-forest land | 100.00 | 90.91 | 96.67 | 90.63 | 96.67 | 93.55 | ||
总体精度Overall accuracy(%) | 85.89 | 79.46 | 81.93 | |||||
Kappa系数Kappa coefficient | 0.841 4 | 0.769 3 | 0.796 3 |
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