激光与光电子学进展, 2020, 57 (10): 101001, 网络出版: 2020-05-08 复制并引用该论文  本文已被引 4 次  被引通知

图 & 表

图 1. Res-U-Net模型

Fig. 1. Res-U-Net model

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图 2. 本文算法的流程图

Fig. 2. Flow chart of proposed method

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图 3. 不同分割尺度的全局平均得分

Fig. 3. Global average score for each segmentation scale

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图 4. 研究区植被影像与面向对象分割结果

Fig. 4. Vegetation image and object-oriented segmentation results in the study area

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图 5. 标签制作。(a)标签图;(b)样本和对应标签的旋转图

Fig. 5. Label making. (a) Label map; (b) rotation of the sample and corresponding label

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图 6. n_tree与OOB误差的关系

Fig. 6. Relationship between n_tree and OOB error

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图 7. 各特征变量的平均下降正确率

Fig. 7. Average drop accuracy rate of each characteristic variable

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图 8. 训练过程中的损失率与精度变化。(a)损失率;(b)精度

Fig. 8. Loss and accuracy change during training. (a) Loss; (b) accuracy

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图 9. 不同方法的分类结果。 (a)真实地物图;(b)原始RGB数据与VDVI、ExG-ExR数据融合的FCN法;(c)基于原始RGB数据FCN法; (d)基于33个特征变量RF法

Fig. 9. Classification results by different methods. (a) Groundtruth map; (b) FCN method with fused data including original RGB data, VDVI data and ExG-ExR data; (c) FCN method with original RGB data; (d) RF method with 33 feature variables

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图 10. 面向对象分割对FCN分类结果修正。 (a)面向对象修正前 (b)面向对象修正后

Fig. 10. Correction of FCN classification results by object-oriented segmentation. (a) Before object-oriented amendments; (b) after object-oriented amendments

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表 1对无人机影像进行分类的精度评价

Table1. Accuracy evaluation of classification of UAV images

Vegetation FCN method withfeature variables FCN method withoutfeature variable RF method with33 feature variables Producer accuracy User accuracy Producer accuracy User accuracy Producer accuracy User accuracy Bamboo 0.996 0.994 0.986 0.987 0.707 0.992 Camphor tree 0.993 0.997 0.991 0.980 0.990 0.999 Fern 0.961 0.943 0.919 0.839 0.911 0.916 Fir 0.966 0.950 0.896 0.945 0.983 0.705 Masson pine 0.920 0.944 0.792 0.791 0.725 0.885 Overall accuracy 0.978 0.954 0.890 Kappa 0.970 0.937 0.850

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表 2面向对象分割对FCN分类结果修正精度评价

Table2. Correction of FCN classification results by object-oriented segmentation

Vegetation Bamboo Camphortree Fern Fir Massonpine Produceraccuracy 1.000 0.997 0.989 0.977 0864 Useraccuracy 0.996 0.999 0.965 0.972 0.964 Overallaccuracy 0.987 Kappa 0.983

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戴鹏钦, 丁丽霞, 刘丽娟, 董落凡, 黄依婷. 基于FCN的无人机可见光影像树种分类[J]. 激光与光电子学进展, 2020, 57(10): 101001. Pengqin Dai, Lixia Ding, Lijuan Liu, Luofan Dong, Yiting Huang. Tree Species Identification Based on FCN Using the Visible Images Obtained from an Unmanned Aerial Vehicle[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101001.