[1]陶贵鑫,周宏轩,王昭清,等. 道路绿视率不同识别方法比较研究——以徐州市为例[J].西北林学院学报,2024,39(2):156-165.[doi:10.3969/j.issn.1001-7461.2024.02.20]
　TAO Gui-xin,ZHOU Hong-xuan,WANG Zhao-qing,et al. A Comparative Study on Different Identification Methods for Road Green Wiew Index——A Case Study of Xuzhou City[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2024,39(2):156-165.[doi:10.3969/j.issn.1001-7461.2024.02.20]
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 道路绿视率不同识别方法比较研究——以徐州市为例()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第39卷
期数:
2024年第2期
页码:
156-165
栏目:
出版日期:
2024-03-31

文章信息/Info

Title:
 A Comparative Study on Different Identification Methods for Road Green Wiew Index——A Case Study of Xuzhou City
文章编号:
1001-7461(2024)02-0156-10
作者:
 陶贵鑫1; 周宏轩2; 王昭清2; 聂艳霞2; 周凤林2
 （1.同济大学 建筑与城市规划学院，上海 200092；2.中国矿业大学 建筑与设计学院，江苏 徐州 221116）
Author(s):
 TAO Gui-xin1; ZHOU Hong-xuan2; WANG Zhao-qing2; NIE Yan-xia2; ZHOU Feng-lin2
 (1.College of Architecture and Urban Planning,Tongji University,Shanghai 200092,China; 2.College of Architecture and Design,China University of Mining and Technology,Xuzhou 221116,Jiangsu,China)
关键词:
 ; 绿视率; 街景图像; RGB-VI; HSL-VI; SegNet
Keywords:
 ; green view index;  street image;  RGB-VI;  HSL-VI;  SegNet
分类号:
S731.8
DOI:
10.3969/j.issn.1001-7461.2024.02.20
文献标志码:
A
摘要:
 探究不同识别方法对绿视率的影响，对于提高绿化识别准确率以及绿视率标准的制定有重要意义。以徐州市3类特征用地为研究对象，运用3种不同识别方法对道路移动采样数据集进行绿视率提取，进而分析不同方法对道路绿视率识别值以及准确率的影响。结果表明，1）基于RGB、HSL颜色的方法对于绿视率值存在低估，机器学习SegNet方法在识别精度上优于基于颜色提取的RGB和HSL方法；2）不同用地分组和图像亮度与绿视率差异和精度差异相关性不显著，色偏系数与绿视率差异呈显著正相关。研究结果为政府绿视率标准制定和绿视率监测工作提供了方法论的实证参考及建议。
Abstract:
 It is of great significance to explore the influence of different identification methods on green view index (GVI) to improve the accuracy rate of GVI and the formulation of GVI standard.Taking three types of characteristic sections in Xuzhou City as the research objects,three different recognition methods were used to extract road GVI in local mobile datasets.The influence of different methods on the recognition value and accuracy of road GVI was analyzed.The results showed that 1) the method based on RGB and HSL color underestimated the value of green apparent ratio.The SegNet method based on machine learning was superior to the RGB and HSL methods based on color extraction in recognition accuracy.2) The group of different sections and image brightness had no significant correlation with the difference of GVI and the difference of accuracy,while the color bias coefficient had a significantly positive correlation with the difference of GVI.The results of this study provide methodological empirical reference and suggestion for the government GVI standard formulation and GVI monitoring.

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备注/Memo

备注/Memo:
 收稿日期：2023-03-20修回日期：2023-05-23
基金项目：国家自然科学基金项目(51908544)；教育部人文社会科学研究(19YJC760169)；江苏省研究生实践与创新项目（KYCX21_2436）；中国矿业大学研究生创新计划(2021WLJCRCZL171)。
第一作者：陶贵鑫，博士在读。研究方向：城市热环境、生态修复。E-mail:taoguixin@tongji.edu.cn
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