生物多样性 ›› 2023, Vol. 31 ›› Issue (3): 22411. DOI: 10.17520/biods.2022411
黄雨菲1, 路春燕1,2,*(), 贾明明3, 王自立1, 苏越1, 苏艳琳1
收稿日期:
2022-07-19
接受日期:
2022-11-07
出版日期:
2023-03-20
发布日期:
2023-02-22
通讯作者:
路春燕
作者简介:
* E-mail: luchunyan@fafu.edu.cn基金资助:
Yufei Huang1, Chunyan Lu1,2,*(), Mingming Jia3, Zili Wang1, Yue Su1, Yanlin Su1
Received:
2022-07-19
Accepted:
2022-11-07
Online:
2023-03-20
Published:
2023-02-22
Contact:
Chunyan Lu
摘要:
明确滨海湿地植物物种类型及其分布状况是实现滨海湿地精细化生物多样性监测的基础, 对于滨海湿地的保护管理与生态可持续发展均具有重要意义。本研究以无人机可见光遥感影像为基础数据源, 在定量分析最优分割尺度与最优分类特征组合的基础上, 应用面向对象-U-net深度学习方法对闽江河口湿地植物物种类型进行分类, 并与K最近邻、决策树、随机森林和贝叶斯分类方法进行精度对比分析, 以期为滨海湿地植物物种遥感精细分类与生物多样性保护管理提供方法借鉴与科学参考。研究结果表明, 利用面向对象-U-net深度学习方法提取不同滨海湿地植物物种类型的分类精度可达95.67%, 总体精度较其他分类方法提高6.67%-13.67%, Kappa系数提高0.12-0.31, 且分类整体性好。此外, 实现植物物种光谱特征、形状特征、纹理特征与高度特征的最优特征选择对于有效提高湿地植物物种信息分类精度具有重要作用, 应用最优分割尺度实现影像分割可提高整体分类效率。
黄雨菲, 路春燕, 贾明明, 王自立, 苏越, 苏艳琳 (2023) 基于无人机影像与面向对象-深度学习的滨海湿地植物物种分类. 生物多样性, 31, 22411. DOI: 10.17520/biods.2022411.
Yufei Huang, Chunyan Lu, Mingming Jia, Zili Wang, Yue Su, Yanlin Su (2023) Plant species classification of coastal wetlands based on UAV images and object- oriented deep learning. Biodiversity Science, 31, 22411. DOI: 10.17520/biods.2022411.
类型 Types | 特征指标 Feature factors | 公式/解释 Formula/Explanation | 筛选为最优特征组合 Be screened into the optimal feature combination | 参考文献 References | |
---|---|---|---|---|---|
光谱特征 Spectral features | Mean_R | 红波段光谱亮度均值 Mean of red spectral brightness | 蔡林菲等, | ||
Mean_G | 绿波段光谱亮度均值 Mean of green spectral brightness | √ | 蔡林菲等, | ||
Mean_B | 蓝波段光谱亮度均值 Mean of blue spectral brightness | √ | 蔡林菲等, | ||
SD_R | 红波段光谱亮度标准差 Standard deviation of red spectral brightness | √ | 路春燕等, | ||
SD_G | 绿波段光谱亮度标准差 Standard deviation of green spectral brightness | √ | 路春燕等, | ||
SD_B | 蓝波段光谱亮度标准差 Standard deviation of blue spectral brightness | √ | 路春燕等, | ||
GBDI | 绿蓝差异指数 Green-blue difference index, GBDI = (G - B)/(R + G + B) | √ | 周涛等, | ||
ExG | 过绿指数 Excess green index, ExG = (2G - R - B)/(G + R + B) | √ | 井然等, | ||
NGBDI | 归一化绿蓝差异指数 Normalized green-blue difference index, NGBDI = (G - B)/(G + B) | √ | 汪小钦等, | ||
NGRDI | 归一化绿红差异指数 Normalized green-red difference index, NGRDI = (G - R)/(G + R) | √ | Hunt et al, | ||
VDVI | 可见光波段差异植被指数 Visible-band difference vegetation index, VDVI = (2G - B - R)/(2G + B + R) | 汪小钦等, | |||
ExG-ExR | 过绿减过红指数 Excess green minus excess red index, ExG-ExR = (3G - 2.4R - B)/(R + G + B) | √ | 丁雷龙等, | ||
DEVI | 差异增强植被指数 Difference enhanced vegetation index, DEVI = G/3G + R/3G + B/3G | 周涛等, | |||
纹理特征 Texture features | GLCM_Mean | 均值 Mean, | √ | 蔡林菲等, | |
GLCM_SD | 标准差 Standard deviation, | √ | 蔡林菲等, | ||
GLCM_Homogeneity | 协同性 Homogeneity, | 路春燕等, | |||
GLCM_Contrast | 对比度 Contrast, | 路春燕等, | |||
GLCM_Dissimilarity | 相异性 Dissimilarity, | √ | 路春燕等, | ||
GLCM_Entropy | 信息熵 Entropy, | √ | 路春燕等, | ||
GLCM_Secondary moment | 二阶矩 Secondary moment, | 路春燕等, | |||
GLCM_Correlation | 相关性 Correlation, | √ | 路春燕等, | ||
形状特征 Shape features | Shape index | 形状指数, 反映整个斑块形状特点的指数 Shape index, an index reflecting the shape characteristics of the entire patch | √ | 耿仁方等, | |
Compactness | 紧凑性, 反映斑块在空间分布上的紧密程度 Compactness, reflecting the tightness of patch distribution in space | √ | 耿仁方等, | ||
Density | 密度, 反映单位面积上的斑块数量 Density, reflecting the number of patches per unit area | √ | 耿仁方等, | ||
Area | 面积, 反映斑块所占空间的大小 Area, reflecting the size of the space occupied by the patch | √ | 耿仁方等, | ||
Rectangularity fit | 矩形度, 反映斑块对其外接矩形的充满程度 Rectangularity fit, reflecting the degree of patch filling its surrounding rectangle | √ | 耿仁方等, | ||
特征类型 Feature types | 特征指标 Feature factors | 公式/解释 Formula/Explanation | 筛选为最优特征组合 Be screened into the optimal feature combination | 参考文献 References | |
高度特征 Height features | Max_Height | 高度最大值 Maximum height | 徐逸等, | ||
Min_Height | 高度最小值 Minimum height | 徐逸等, | |||
Mean_Height | 高度均值 Average height | 徐逸等, | |||
SD_Height | 高度标准差 Standard deviation of height | 徐逸等, |
表1 基于无人机可见光影像的特征空间信息
Table 1 Information of feature space based on the visible-light images of unmanned aerial vehicle
类型 Types | 特征指标 Feature factors | 公式/解释 Formula/Explanation | 筛选为最优特征组合 Be screened into the optimal feature combination | 参考文献 References | |
---|---|---|---|---|---|
光谱特征 Spectral features | Mean_R | 红波段光谱亮度均值 Mean of red spectral brightness | 蔡林菲等, | ||
Mean_G | 绿波段光谱亮度均值 Mean of green spectral brightness | √ | 蔡林菲等, | ||
Mean_B | 蓝波段光谱亮度均值 Mean of blue spectral brightness | √ | 蔡林菲等, | ||
SD_R | 红波段光谱亮度标准差 Standard deviation of red spectral brightness | √ | 路春燕等, | ||
SD_G | 绿波段光谱亮度标准差 Standard deviation of green spectral brightness | √ | 路春燕等, | ||
SD_B | 蓝波段光谱亮度标准差 Standard deviation of blue spectral brightness | √ | 路春燕等, | ||
GBDI | 绿蓝差异指数 Green-blue difference index, GBDI = (G - B)/(R + G + B) | √ | 周涛等, | ||
ExG | 过绿指数 Excess green index, ExG = (2G - R - B)/(G + R + B) | √ | 井然等, | ||
NGBDI | 归一化绿蓝差异指数 Normalized green-blue difference index, NGBDI = (G - B)/(G + B) | √ | 汪小钦等, | ||
NGRDI | 归一化绿红差异指数 Normalized green-red difference index, NGRDI = (G - R)/(G + R) | √ | Hunt et al, | ||
VDVI | 可见光波段差异植被指数 Visible-band difference vegetation index, VDVI = (2G - B - R)/(2G + B + R) | 汪小钦等, | |||
ExG-ExR | 过绿减过红指数 Excess green minus excess red index, ExG-ExR = (3G - 2.4R - B)/(R + G + B) | √ | 丁雷龙等, | ||
DEVI | 差异增强植被指数 Difference enhanced vegetation index, DEVI = G/3G + R/3G + B/3G | 周涛等, | |||
纹理特征 Texture features | GLCM_Mean | 均值 Mean, | √ | 蔡林菲等, | |
GLCM_SD | 标准差 Standard deviation, | √ | 蔡林菲等, | ||
GLCM_Homogeneity | 协同性 Homogeneity, | 路春燕等, | |||
GLCM_Contrast | 对比度 Contrast, | 路春燕等, | |||
GLCM_Dissimilarity | 相异性 Dissimilarity, | √ | 路春燕等, | ||
GLCM_Entropy | 信息熵 Entropy, | √ | 路春燕等, | ||
GLCM_Secondary moment | 二阶矩 Secondary moment, | 路春燕等, | |||
GLCM_Correlation | 相关性 Correlation, | √ | 路春燕等, | ||
形状特征 Shape features | Shape index | 形状指数, 反映整个斑块形状特点的指数 Shape index, an index reflecting the shape characteristics of the entire patch | √ | 耿仁方等, | |
Compactness | 紧凑性, 反映斑块在空间分布上的紧密程度 Compactness, reflecting the tightness of patch distribution in space | √ | 耿仁方等, | ||
Density | 密度, 反映单位面积上的斑块数量 Density, reflecting the number of patches per unit area | √ | 耿仁方等, | ||
Area | 面积, 反映斑块所占空间的大小 Area, reflecting the size of the space occupied by the patch | √ | 耿仁方等, | ||
Rectangularity fit | 矩形度, 反映斑块对其外接矩形的充满程度 Rectangularity fit, reflecting the degree of patch filling its surrounding rectangle | √ | 耿仁方等, | ||
特征类型 Feature types | 特征指标 Feature factors | 公式/解释 Formula/Explanation | 筛选为最优特征组合 Be screened into the optimal feature combination | 参考文献 References | |
高度特征 Height features | Max_Height | 高度最大值 Maximum height | 徐逸等, | ||
Min_Height | 高度最小值 Minimum height | 徐逸等, | |||
Mean_Height | 高度均值 Average height | 徐逸等, | |||
SD_Height | 高度标准差 Standard deviation of height | 徐逸等, |
分割尺度 Segmentation scale | 分割效果 Segmentation effect | 分割尺度 Segmentation scale | 分割效果 Segmentation effect |
---|---|---|---|
33 | 49 | ||
35 | 51 | ||
37 | 54 | ||
39 | 56 | ||
43 | 58 | ||
46 | 62 |
表2 最优精细至最优粗略分割尺度分割效果对比
Table 2 Performance comparison of optimal fine to rough segmentation scales
分割尺度 Segmentation scale | 分割效果 Segmentation effect | 分割尺度 Segmentation scale | 分割效果 Segmentation effect |
---|---|---|---|
33 | 49 | ||
35 | 51 | ||
37 | 54 | ||
39 | 56 | ||
43 | 58 | ||
46 | 62 |
湿地植被类型 Wetland vegetation type | K最近邻 K-nearest neighbor | 决策树 Decision tree | 随机森林 Random forest | 贝叶斯 Bayes | U-net深度学习 U-net deep learning | |||||
---|---|---|---|---|---|---|---|---|---|---|
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 (%) | 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 (%) | |
短叶茳芏 Cyperus malaccensis | 60.00 | 92.31 | 80.95 | 100.00 | 90.48 | 100.00 | 71.43 | 100.00 | 75.00 | 93.75 |
三棱藨草 Scirpus mariqueter | 58.06 | 58.06 | 80.00 | 66.67 | 86.67 | 61.90 | 96.67 | 72.50 | 90.32 | 82.35 |
厚藤 Ipomoea pescaprae | 75.00 | 100.00 | 75.00 | 54.55 | 81.25 | 65.00 | 100.00 | 72.73 | 87.50 | 100.00 |
芦苇 Phragmites australis | 93.30 | 84.78 | 87.08 | 92.39 | 83.73 | 96.15 | 88.04 | 97.87 | 98.56 | 97.17 |
秋茄 Kandelia candel | 37.50 | 64.29 | 79.17 | 67.86 | 95.83 | 62.16 | 95.83 | 65.71 | 100.00 | 100.00 |
总体精度 Overall accuracy (%) | 82.00 | 84.67 | 85.33 | 89.00 | 95.67 | |||||
Kappa系数 Kappa coefficient | 0.60 | 0.70 | 0.73 | 0.79 | 0.91 |
表3 不同分类方法分类精度比较
Table 3 Comparison of classification accuracy of different methods
湿地植被类型 Wetland vegetation type | K最近邻 K-nearest neighbor | 决策树 Decision tree | 随机森林 Random forest | 贝叶斯 Bayes | U-net深度学习 U-net deep learning | |||||
---|---|---|---|---|---|---|---|---|---|---|
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 (%) | 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 (%) | |
短叶茳芏 Cyperus malaccensis | 60.00 | 92.31 | 80.95 | 100.00 | 90.48 | 100.00 | 71.43 | 100.00 | 75.00 | 93.75 |
三棱藨草 Scirpus mariqueter | 58.06 | 58.06 | 80.00 | 66.67 | 86.67 | 61.90 | 96.67 | 72.50 | 90.32 | 82.35 |
厚藤 Ipomoea pescaprae | 75.00 | 100.00 | 75.00 | 54.55 | 81.25 | 65.00 | 100.00 | 72.73 | 87.50 | 100.00 |
芦苇 Phragmites australis | 93.30 | 84.78 | 87.08 | 92.39 | 83.73 | 96.15 | 88.04 | 97.87 | 98.56 | 97.17 |
秋茄 Kandelia candel | 37.50 | 64.29 | 79.17 | 67.86 | 95.83 | 62.16 | 95.83 | 65.71 | 100.00 | 100.00 |
总体精度 Overall accuracy (%) | 82.00 | 84.67 | 85.33 | 89.00 | 95.67 | |||||
Kappa系数 Kappa coefficient | 0.60 | 0.70 | 0.73 | 0.79 | 0.91 |
图6 面向对象-U-net深度学习(a)与U-net深度学习(b)方法分类结果局部细节对比。群落交界处各植物种类混生, 边界不清晰, 特征相似, 基于像素分类方法的结果“椒盐”现象明显, 易出现错分问题, 而面向对象分类方法可有效避免该问题, 能更为准确地区分不同植物种类。
Fig. 6 Local detail comparison of classification results between object-oriented U-net deep learning (a) and U-net deep learning (b). At the community boundary, plant species are often mixed with unclear outline and similar image characteristics. The results based on pixel classification method show obvious “pepper and salt” phenomenon, which is prone to misclassification. In contrast, object-oriented method could effectively avoid this problem and distinguish different plant species more accurately.
图7 面向对象-U-net深度学习(a)与U-net深度学习(b)分类方法的模型训练与验证损失值及精度对比
Fig. 7 Loss value and accuracy comparison of model training and validation between object-oriented U-net deep learning (a) and U-net deep learning (b)
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