浙江农业学报 ›› 2021, Vol. 33 ›› Issue (11): 2116-2127.DOI: 10.3969/j.issn.1004-1524.2021.11.14
收稿日期:
2020-12-15
出版日期:
2021-11-25
发布日期:
2021-11-26
通讯作者:
张王菲
作者简介:
*张王菲,E-mail: mekmzwf@163.com基金资助:
LI Shitaoa(), ZHANG Wangfeib,*(), ZHAO Lixianb, WANG Xiyuanb
Received:
2020-12-15
Online:
2021-11-25
Published:
2021-11-26
Contact:
ZHANG Wangfei
摘要:
作物物候期识别是农情遥感监测的重要内容,及时准确识别作物物候期,对有效评估作物生长趋势、提高农情信息化管理水平有重要意义。提出了基于时间序列全极化合成孔径雷达(polarimetric synthetic aperture radar, PolSAR)数据结合决策树模型的油菜物候期识别方法。首先,采用3种极化分解方法提取PolSAR极化参数,并分析各极化参数对油菜物候期的动态响应规律;其次,基于各极化分解方法提取的参数建立决策树模型,并对油菜物候期进行分类识别;最后,采用基于混淆矩阵的方法对油菜物候期识别结果进行精度评价。采用5期Radarsat-2 PolSAR数据和地面物候观测数据进行实验验证。结果表明:提取的PolSAR参数中对物候期变化较为敏感的参数有H/A/alpha分解中的散射角(Alpha)、特征值(L2、L3)、伪熵(P2)、目标方位角(Beta1)参数,Freeman-Durden分解中的地面散射(Ground)和奇次散射(Odd)参数,Yamaguchi分解中的奇次散射(Odd_Y)和螺旋体散射(Helix)参数;决策树模型对油菜物候期识别结果较为准确,识别结果中组合3种极化分解方法提取参数建立的原始决策树模型分类总体精度最高,达94%。总体上,PolSAR极化分解参数对油菜物候期变化比较敏感,决策树模型能有效识别油菜物候期。
中图分类号:
李诗涛, 张王菲, 赵丽仙, 王熙媛. 基于时序PolSAR影像与决策树模型的油菜物候期识别[J]. 浙江农业学报, 2021, 33(11): 2116-2127.
LI Shitao, ZHANG Wangfei, ZHAO Lixian, WANG Xiyuan. Phenological period identification of oilseed rape based on time-series PolSAR image and decision tree model[J]. Acta Agriculturae Zhejiangensis, 2021, 33(11): 2116-2127.
生长发育时期 Growth period | 播后时间 Days after sowing/d | 物候阶段 Phenological phase |
---|---|---|
苗期Growth period | 1-15 | S1 |
蕾薹期Grey moss period | 16-39 | S2 |
花期Flowering period | 40-63 | S3 |
角果成熟期Ripening stage of pod | 64-87 | S4 |
成熟衰落期Maturity and decay period | 88-110 | S5 |
表1 油菜物候期的划分与生长时间
Table 1 Phenological stage division and growth time of rape
生长发育时期 Growth period | 播后时间 Days after sowing/d | 物候阶段 Phenological phase |
---|---|---|
苗期Growth period | 1-15 | S1 |
蕾薹期Grey moss period | 16-39 | S2 |
花期Flowering period | 40-63 | S3 |
角果成熟期Ripening stage of pod | 64-87 | S4 |
成熟衰落期Maturity and decay period | 88-110 | S5 |
分解方法 Decomposition methods | 分解参数 Decomposition parameters | 描述 Description |
---|---|---|
H/A/alpha分解 H/A/alpha decomposition | Alpha、Anisotropy、Entropy、 Lambda (Span) | 基于H/A/alpha的参数:目标散射角、反熵、散射熵、散射特征值(功率) Parameters based on H/A/alpha: target scattering angle, inverse entropy, scattering entropy, scattering eigenvalue (power) |
Alpha1、Alpha2、Alpha3;Beta1、 Beta2、Beta3;Delta1、Delta2、 Delta3;Gamma1、Gamma2、 Gamma3 | 基于特征向量的参数:3种散射机制对应的散射角,目标方位角,相位角差 Parameters based on eigenvectors: scattering angles corresponding to the three scattering mechanisms, target azimuth, phase angle difference | |
L1、L2、L3;P1、P2、P3;Pedestal; RVI | 基于特征值的参数:3种散射机制对应的特征值,3种散射机制对应的熵,极化特征的基准高度,雷达植被指数 Parameters based on eigenvalues: eigenvalues corresponding to the three scattering mechanisms, entropy corresponding to the three scattering mechanisms, reference height of the polarization characteristics, radar vegetation index (RVI) | |
Freeman-Durden 二、三分量分解 Freeman-Durden two-and three-component decomposition | Ground、Canopy、Odd、Dbl、Vol | 地面散射、冠层散射分量、奇次散射、偶次散射与体散射分量 Ground scattering, canopy scattering component, odd scattering, even scattering and volume scattering component |
Yamaguchi四分量分解 Yamaguchi four- component decomposition | Odd_Y、Dbl_Y、Vol_Y、Helix | 奇次散射、偶次散射、体散射与螺旋体散射分量 Odd scattering, even scattering, volume scattering and helix scattering components |
表2 不同极化分解方法提取的参数
Table 2 Parameters extracted by different polarization decomposition methods
分解方法 Decomposition methods | 分解参数 Decomposition parameters | 描述 Description |
---|---|---|
H/A/alpha分解 H/A/alpha decomposition | Alpha、Anisotropy、Entropy、 Lambda (Span) | 基于H/A/alpha的参数:目标散射角、反熵、散射熵、散射特征值(功率) Parameters based on H/A/alpha: target scattering angle, inverse entropy, scattering entropy, scattering eigenvalue (power) |
Alpha1、Alpha2、Alpha3;Beta1、 Beta2、Beta3;Delta1、Delta2、 Delta3;Gamma1、Gamma2、 Gamma3 | 基于特征向量的参数:3种散射机制对应的散射角,目标方位角,相位角差 Parameters based on eigenvectors: scattering angles corresponding to the three scattering mechanisms, target azimuth, phase angle difference | |
L1、L2、L3;P1、P2、P3;Pedestal; RVI | 基于特征值的参数:3种散射机制对应的特征值,3种散射机制对应的熵,极化特征的基准高度,雷达植被指数 Parameters based on eigenvalues: eigenvalues corresponding to the three scattering mechanisms, entropy corresponding to the three scattering mechanisms, reference height of the polarization characteristics, radar vegetation index (RVI) | |
Freeman-Durden 二、三分量分解 Freeman-Durden two-and three-component decomposition | Ground、Canopy、Odd、Dbl、Vol | 地面散射、冠层散射分量、奇次散射、偶次散射与体散射分量 Ground scattering, canopy scattering component, odd scattering, even scattering and volume scattering component |
Yamaguchi四分量分解 Yamaguchi four- component decomposition | Odd_Y、Dbl_Y、Vol_Y、Helix | 奇次散射、偶次散射、体散射与螺旋体散射分量 Odd scattering, even scattering, volume scattering and helix scattering components |
图4 三种分解方法提取的参数组合后的原始决策树示例
Fig.4 Example identification result of original decision tree model using the combination of parameters extracted by three decomposition methods
物候期 Phenological phase | S1 | S2 | S3 | S4 | S5 | 总计 Total |
---|---|---|---|---|---|---|
S1 | 65 | 7 | 0 | 0 | 0 | 72 |
S2 | 4 | 91 | 0 | 0 | 0 | 95 |
S3 | 0 | 0 | 90 | 0 | 5 | 95 |
S4 | 0 | 0 | 1 | 90 | 4 | 95 |
S5 | 0 | 0 | 8 | 2 | 85 | 95 |
总计Total | 69 | 98 | 99 | 92 | 94 |
表3 三种分解方法组合参数后的原始决策树识别结果
Table 3 Accuracy evaluation of original decision tree after combining parameters of the three decomposition methods
物候期 Phenological phase | S1 | S2 | S3 | S4 | S5 | 总计 Total |
---|---|---|---|---|---|---|
S1 | 65 | 7 | 0 | 0 | 0 | 72 |
S2 | 4 | 91 | 0 | 0 | 0 | 95 |
S3 | 0 | 0 | 90 | 0 | 5 | 95 |
S4 | 0 | 0 | 1 | 90 | 4 | 95 |
S5 | 0 | 0 | 8 | 2 | 85 | 95 |
总计Total | 69 | 98 | 99 | 92 | 94 |
精度Accuracy | S1 | S2 | S3 | S4 | S5 |
---|---|---|---|---|---|
制图精度 | 90.28 | 95.79 | 94.74 | 94.74 | 89.47 |
Mapping accuracy | |||||
用户精度 | 94.20 | 92.86 | 90.90 | 97.83 | 90.42 |
User accuracy |
表4 三种分解方法组合参数后的原始决策树精度
Table 4 Accuracy evaluation of original decision tree after combining parameters of the three decomposition methods %
精度Accuracy | S1 | S2 | S3 | S4 | S5 |
---|---|---|---|---|---|
制图精度 | 90.28 | 95.79 | 94.74 | 94.74 | 89.47 |
Mapping accuracy | |||||
用户精度 | 94.20 | 92.86 | 90.90 | 97.83 | 90.42 |
User accuracy |
决策树模型 Decision tree model | 极化分解方法Polarization decomposition method | ||||
---|---|---|---|---|---|
Freeman-Durden分解 Freeman decomposition | Yamaguchi分解 Yamaguchi decomposition | H/A/alpha分解 H/A/alpha decomposition | 3种分解组合 Three kinds of decomposition synthesis | ||
原始决策树Primitive decision tree | 89.45 | 89.34 | 93.40 | 94.00 | |
minleaf决策树Minleaf decision tree | 82.96 | 84.87 | 88.63 | 89.54 | |
剪枝决策树Pruning decision tree | 86.13 | 87.83 | 92.21 | 91.61 |
表5 决策树分类模型总体精度
Table 5 Overall precision of decision tree classification model %
决策树模型 Decision tree model | 极化分解方法Polarization decomposition method | ||||
---|---|---|---|---|---|
Freeman-Durden分解 Freeman decomposition | Yamaguchi分解 Yamaguchi decomposition | H/A/alpha分解 H/A/alpha decomposition | 3种分解组合 Three kinds of decomposition synthesis | ||
原始决策树Primitive decision tree | 89.45 | 89.34 | 93.40 | 94.00 | |
minleaf决策树Minleaf decision tree | 82.96 | 84.87 | 88.63 | 89.54 | |
剪枝决策树Pruning decision tree | 86.13 | 87.83 | 92.21 | 91.61 |
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