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遥感技术与应用  2020, Vol. 35 Issue (3): 576-586    DOI: 10.11873/j.issn.1004-0323.2020.3.0576
LUCC专栏     
整合无人机和面向对象的农村居住环境信息提取
郝睿1(),李兆富1(),张舒昱1,潘剑君1,姜小三1,张文敏2,宋金超2
1.南京农业大学 资源与环境科学学院,江苏 南京 210095
2.哥本哈根大学 地球科学和自然资源管理系,丹麦 哥本哈根 1350
Integrating UAV and Object-based Image Analysis for Rural Residential Environment Information Extraction
Rui Hao1(),Zhaofu Li1(),Shuyu Zhang1,Jianjun Pan1,Xiaosan Jiang1,Wenmin Zhang2,Jinchao Song2
1.College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
2.Department of Geosciences and Natural Resource Management,University of Copenhagen, Copenhagen 1350, Denmark
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摘要:

无人机遥感和面向对象图像分析技术在环境监测中得到越来越多的发展。然而在科学文献领域,使用无人机和面向对象制图农村居住环境的文献仍然很少。因此本研究构造一个整合框架用于提取农村居住环境中各类地物信息。首先利用尺度参数评估(ESP, Estimation of Scale Parameter)工具和专家判断来确定最优分割尺度参数;然后分别采用专家规则集和监督分类算法提取农村居住环境中各类地物;最后采用基于面的精度评价方法对分类性能进行评估。结果表明,利用ESP工具和专家判断确定最优分割尺度是可行的。总体精度为75.19%,说明基于规则的提取方法对研究区各类地物的提取效果不佳。但在农村居住环境中利用模板匹配结合阈值规则对太阳能热水器提取精度达92%。分析训练样本和特征对随机森林(RF,Random Forest)、支持向量机(SVM, Support Vector Machines)和K最近邻 (KNN, K-Nearest Neighbor) 分类器分类结果的影响,说明RF分类器对农村居住环境分类效果最好,总体分类精度高达91.34%。研究结果表明:该框架在农村居住环境地物提取方面是一种有价值的工具。

关键词: 无人机面向对象农村居住环境整合框架太阳能热水器    
Abstract:

The remote sensing of Unmanned Aerial Vehicle (UAV) and Object-Based Image Analysis(OBIA) technologies have advanced increasingly for environmental monitoring in recent years. However, references to the uses of UAV and OBIA for mapping rural residential environment are still scarce in the field of scientific literature. In this study, an integration framework was developed to extract various ground objects in rural residential environment. First, Estimation of Scale Parameter (ESP) tool and expert judgement were used to identify the optimal Segmentation Scale Parameter (SSP). Then, the expert rule-sets and supervised classification algorithms were applied to extract information of ground objects in rural residential environment, respectively. Finally, the performance accuracy was evaluated by using an area-based method. The results indicated that using ESP tool and expert judgment to determine the optimal SSP is feasible. Furthermore, the overall accuracy (OA) is 75.19%, indicating that the rule-based extraction method is not good at extracting all kinds of ground objects in study area. However, Solar Water Heaters (SWHs) were successfully extracted in rural residential environment by using template matching combined with threshold rules and the extraction accuracy can achieve 92%. Moreover, the influences of training samples and features were analyzed on the classification results of the Random Forest (RF), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) classifiers, showing that the RF classifier has the best classification result, with its value of OA reaching 91.34%. The results indicated that the integrate framework is a valuable tool in the extraction of ground objects from rural residential environment.

Key words: UAV    OBIA    Rural residential environment    Integration framework    Solar water heater
收稿日期: 2019-03-12 出版日期: 2020-07-10
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目“太湖地区湖库水源地流域湿地景观格局多样性的水环境过程与功能响应机制”(41571171);江苏省信息农业重点实验室开放课题基金(KLIAKF1801)
通讯作者: 李兆富     E-mail: 2016103078@njau.edu.cn;lizhaofu@njau.edu.cn
作者简介: 郝睿(1994—),男,山东淄博人,硕士研究生,主要从事无人机遥感研究。E?mail: 2016103078@njau.edu.cn
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引用本文:

郝睿,李兆富,张舒昱,潘剑君,姜小三,张文敏,宋金超. 整合无人机和面向对象的农村居住环境信息提取[J]. 遥感技术与应用, 2020, 35(3): 576-586.

Rui Hao,Zhaofu Li,Shuyu Zhang,Jianjun Pan,Xiaosan Jiang,Wenmin Zhang,Jinchao Song. Integrating UAV and Object-based Image Analysis for Rural Residential Environment Information Extraction. Remote Sensing Technology and Application, 2020, 35(3): 576-586.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.3.0576        http://www.rsta.ac.cn/CN/Y2020/V35/I3/576

图1  研究区位置及其遥感影像
图2  框架全部流程
指数公式参考
rR/(R+G+B)

Torres-Sanchez等[26]

Sellaro等[27]

Raymond等[28]

Torres-Sanchez等[26]

Meyer等[29]

Louhaichi等[30]

g

b

红绿比指数(RGRI, Red Green Ratio Index)

G/(R+G+B)

B/(R+G+B)

R/G

蓝绿比指数(GBRI, Green Blue Ratio Index)G/B

红蓝比指数(RBRI, Red Blue Ratio Index)

归一化绿红差异指数(NGRDI, Normalized Green Red Difference Index)

R/B

G-R/G+R

归一化蓝红差异指数(NBRDI, Normalized Blue Red Difference Index)B-R/B+R

归一化蓝绿差异指数(NBGDI, Normalized Blue Green Difference Index)

过绿指数(ExG, Excess Green)

过绿减过红指数(ExGR, Excess Green Minus Excess Red)

绿叶指数(GLI, Green Leaf Index)

B-G/B+G

2g-r-b

ExG-(1.4r-g)

2G-R-B/2G+R+B

表1  选择的特征指数
方案特征的数量训练样本数量对象数量训练集比/%
A231406 6852
B323406 6855
C324406 6856.5
D234406 6856.5
表 2  方案A、B、C和D的描述
图3  研究区ESP工具输出结果
尺度形状紧致度对象数量应用
1300.10.516 287太阳能热水器的提取
2800.20.54 008农村居住环境中其他地物的提取
3500.20.56 685监督分类
表 3  吴村多尺度分割参数
类别规则备注
太阳能热水器1Brightness>203, Solar_Buffer>0

除了在表 1中的

自定义特征,其余

皆为eCognition软

件内置特征,具体解

释可参照eCognition

用户指导[21]

道路2Brightness>203,Mean R>204
房屋2

HIS Transformation Hue≥0.79,

HIS Transformation Saturation≥0.19, ExGR≦-0.24,NGRDI>0.22,NBGDI>0.03,

EXG<-0.047, Area>8 000Pxl,

Length/width(only main line)<12

村庄绿化2Ratio B≤0.16,Max.dff.>1.2,GLI>0.01
村庄空地2

-0.047<ExG≤-0.01

Rel.border to unclassified>0

GLCM_Hom<0.23

硬化地面2

-0.156<ExGR<-0.14, 180<GLCM_Mean<223

Brightness>189,Brightness≥180

阴影2Ratio R≤0.13, Mean diff. neighbors R(0)<-41
表4  多层次分类规则
图4  基于规则方法分类结果
类别房屋街道太阳能热水器村庄空地

硬化

地面

村庄

绿化

阴影房屋道路太阳能热水器

村庄

空地

硬化

地面

村庄

绿化

阴影
房屋84 1676 8573 19203 7096 686032020310
道路7 523191 949255069 12600322001600
太阳能热水器001 858000000230000
村庄空地00067 1492 0980000016500
硬化地面4 0610050 8829 3120022013400
村庄绿化00000140 94926 74200000244
阴影 PA/%

0

87.90

0

96.55

0

35.02

0

56.90

0

11

8 117

90.50

78 036

74.48

0

86.49

0

91.67

0

92

0

44.83

0

14.29

3

85.71

25

86.21

UA/%80.1671.4010096.9714.584.0590.5884.2153.6610076.1919.0485.7189.29
OA/%75.1973
Kappa 系数0.69Kappa系数0.68
表5  基于规则分类混淆矩阵结果
图5  不同方案监督分类结果
图6  4个方案下3个分类器总体精度的比较结果
类别房屋道路太阳能热水器村庄空地硬化地面村庄绿化阴影UA/%
房屋118 3800190914002 55597
道路1 23193 554151020 5280081.02
太阳能热水器002 3090000100
村庄空地00037 82000089.54
硬化地面01 1561123 50325 0370084
村庄绿化0000040 7791 81395.74
阴影00448000

26 193

85.70

98.31
PA(%)98.9798.7871.9389.5454.95100
OA/%: 91.34 Kappa 系数: 0.89
表6  C方案随机森林分类器混淆矩阵结果
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