WO2016192552A1 - Built environment landscape characteristic recognition method based on network picture - Google Patents

Built environment landscape characteristic recognition method based on network picture Download PDF

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WO2016192552A1
WO2016192552A1 PCT/CN2016/083258 CN2016083258W WO2016192552A1 WO 2016192552 A1 WO2016192552 A1 WO 2016192552A1 CN 2016083258 W CN2016083258 W CN 2016083258W WO 2016192552 A1 WO2016192552 A1 WO 2016192552A1
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shooting
environment
point
visual
target environment
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PCT/CN2016/083258
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French (fr)
Chinese (zh)
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赵渺希
顾沁
边宇
贾锐澜
吴江月
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华南理工大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]

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  • the invention relates to a method for identifying a landscape feature of a built environment, in particular to a method for identifying a landscape feature of a built environment based on a network picture, and belongs to the field of urban landscape research in the field of urban planning.
  • Identifying the landscape characteristics of the urban environment is the premise and foundation of urban and rural planning and design, and the people's shooting behavior provides a lot of material for identifying the landscape features of the abbreviated environment. Photographic images for tourism visual research have always been the focus of scholars.
  • Liu Jing uses the film's pictures to study the spatial imagery of many cities.
  • Zhao Yuxi uses Google Images for principal factor analysis and portrays Guangdong 21 The landscape of a city.
  • network pictures for the identification of built environment landscapes in China.
  • the purpose of the present invention is to solve the above drawbacks of the prior art, and to provide a built-in environment landscape feature recognition method based on a network picture, which can perform network visual image analysis on a city built environment landscape, and is reproducible and operable. Sexuality, and the use of network pictures of the photographer's visual analysis and reduction analysis of shooting behavior, timeliness and objectivity.
  • a method for identifying a landscape feature of a built environment based on a network image comprising the following steps:
  • the shooting point, the shooting center point and the shooting line are imported into the analysis platform of ArcGIS, and the visual sensitive area of the landscape network picture is analyzed by ArcGIS software and the sensitivity analysis is performed;
  • step S1 the photographic image library of the target environment and the similar built environment is obtained by using the Baidu image search engine, and specifically includes:
  • Baidu pictures to separately query a number of similar built environment names, and download all the image search results in order, delete the pictures in the image search results that are not related to the similar built environment, and select the first 200 pictures in each picture search result.
  • a total of 1200 images are used as a network photo gallery of similar built environments.
  • step S2 the recording of the shooting standing point, the shooting visual center point, and the shooting line of sight in the photographic shooting behavior is recorded by AutoCAD, and specifically includes:
  • step S3 for the target environment, the shooting standing point, the shooting visual center point, and the shooting line of sight are imported into the analysis platform of ArcGIS, and the landscape is analyzed by using ArcGIS software.
  • the network maps the visually sensitive area and analyzes the sensitivity, including:
  • step S34 the calculation of the shooting stand sensitivity, the shooting visual center point sensitivity, and the shooting line sensitivity are specifically as follows:
  • n is the total number of grid points and b i is the grid point value
  • M i is the standard deviation
  • n is the total number of grid points
  • b i is the grid point value.
  • the sensitivity analysis formula for a certain point is:
  • k i is the sensitivity of a point
  • M i is the standard deviation of the line of sight density
  • b i is the value of the grid point.
  • step S4 the target network and the similar built-in environment network photography picture library are used to record the attributes of the network photography picture elements, including:
  • Each network picture is sequentially numbered, and the visual element is used as an optional element attribute of each numbered picture. If an element appears in the picture, the record is 1; otherwise, it is 0;
  • the target environment picture record is 1 and the similar built environment picture record is 2, and the result is saved as a target environment network photography picture element attribute table and a similar built environment network photography picture element attribute table.
  • step S4 the independent sample T test of the SPSS software is used to filter the visual features of the target environment, including:
  • the SPSS software is used to perform independent sample T-test analysis on the target environment and similar built environment visual elements, and the formula of the independent sample T test is:
  • t k is the T test statistic, with The value of the photographic picture element attribute numbered k in the target environment and similar built environment, respectively, n 1, k and n 2, k are the number of photographic picture samples numbered k in the target environment and similar built environment, S 1, k And S 2,k are the sample variances of the photographic image element attribute values numbered k in the target environment and the similar built environment, respectively;
  • T test statistic t k is the saliency index of each element. If the t k value of an element is greater than 0.1, the element is discarded. If the t k value of an element is not greater than 0.1, the element is retained as the target environment visual feature. The elements and corresponding records of their t k values will result in a target environment visual feature element table;
  • the present invention analyzes the visual characteristics of urban built environmental landscape by analyzing the behavioral process of people in the network media by means of geographic analysis, statistical analysis, correlation analysis and other quantitative analysis methods. Hotspots, statistical comparison of characteristic visual elements in different urban environments, assisting the design and planning of urban landscapes.
  • the invention compensates for the lack of subjectivity and hysteresis in the evaluation and design of the urban built environment landscape by reducing the process of photographing the network photography.
  • the present invention uses a network visual image analysis method for urban built environment landscape, which has reproducibility and operability, and utilizes the visual analysis of the photographer of the network picture and the reduction analysis of the shooting behavior, which is time-sensitive and objective. Sex.
  • FIG. 1 is a flowchart of a method for identifying a landscape feature of a built environment based on a network picture according to Embodiment 1 of the present invention.
  • FIG. 2 is a schematic diagram of a field reduction mode according to Embodiment 1 of the present invention.
  • Fig. 3 is a diagram showing the distribution of density points of the entire shooting shooting point according to the second embodiment of the present invention.
  • Embodiment 4 is a distribution diagram of an integral visual center point according to Embodiment 2 of the present invention.
  • Fig. 5 is a view showing the line-of-sight density distribution of the entire type of shooting according to the second embodiment of the present invention.
  • Fig. 6 is a diagram showing the distribution of the sensitivity of the shooting standing point according to the second embodiment of the present invention.
  • the method for identifying a landscape feature of a built-in environment based on a network picture of the embodiment includes the following steps:
  • the built environment refers to the urban environment that has been built in the city.
  • the target environment refers to the built environment that needs to identify the landscape features.
  • the similar built environment refers to other built environments with the same category, similarity and comparability with the target environment; Similar to the significant test of the built environment and the target environment, the elements with more significant differences in the similar built environment in the target environment can be obtained, which is the characteristic element of the target environment.
  • Open ArcToolbox use the Spatial Analyze tool to perform point density analysis on the shooting standing point and shooting focus, perform line density analysis on the shooting line of sight, and obtain the target environment shooting standing point density value, visual center point density value and shooting.
  • Line of sight density value ;
  • n is the total number of grid points and b i is the grid point value
  • M i is the standard deviation
  • n is the total number of grid points
  • b i is the grid point value.
  • the sensitivity analysis formula for a certain point is:
  • k i is the sensitivity of a point
  • M i is the standard deviation of the line of sight density
  • b i is the value of the grid point.
  • the ArcGIS software is divided into three levels of visual sensitivity for shooting stand sensitivity, shooting visual center point sensitivity, and shooting line of sight sensitivity from high to low.
  • Each network picture is numbered sequentially, and the visual element is used as an optional feature attribute of each numbered picture. If an element appears in the picture, the record is 1; otherwise, it is 0;
  • t k is the T test statistic, with The value of the photographic picture element attribute numbered k in the target environment and similar built environment, respectively, n 1, k and n 2, k are the number of photographic picture samples numbered k in the target environment and similar built environment, S 1, k And S 2,k are the sample variances of the photographic image element attribute values numbered k in the target environment and the similar built environment, respectively;
  • the T test statistic t k is used as the saliency indicator of each element. If an element t k value is greater than 0.1, the element is discarded. If an element t k value is not greater than 0.1, the element is reserved as the target environment.
  • the visual characteristic element correspondingly records the t k value of an element, and the target environment visual characteristic element table is obtained;
  • the visual characteristics of the target environment landscape elements are graded, so as to screen out the visual characteristics of the target environment;
  • the attribute element attribute table is established, and the line is defined as the feature element.
  • the column is defined as the shooting standing point, the visual center point, the shooting line of sight area, the identification of the importance of the element, and the optimization of the landscape pattern of the target environment. .
  • Yuyinshan Fangjing District in Panyu District, Guangzhou is selected to provide a comprehensive evaluation method for scenic visual information based on network pictures. Yuyinshan Fangjing District is located in the north street of the southeast corner of Nancun Town, Panyu District, Guangzhou City, Guangdongzhou. Lingnan Garden.
  • Open ArcToolbox use the Spatial Analyze tool to perform point density analysis on the shooting stand and shooting focus, perform line density analysis on the line of sight, and obtain the standing point density value, visual center point density value and shooting of Yu Yin Shan room respectively.
  • n is the total number of grid points
  • b i is the grid point value
  • the average point density is 31.99
  • M i is the standard deviation
  • n is the total number of grid points
  • b i is the grid point value.
  • M i is 103.58;
  • the sensitivity analysis formula for a certain point is:
  • k i is the sensitivity of a point
  • M i is the standard deviation of the line of sight density
  • b i is the value of the grid point.
  • Each network picture is numbered sequentially, and the visual element is used as an optional feature attribute of each numbered picture. If an element appears in the picture, the record is 1; otherwise, it is 0, as shown in Table 1 below;
  • t k is the T test statistic, which is the saliency indicator of each element.
  • n 1,k and n 2,k are the number of photographic picture samples numbered k in Yu Yin Shan Fang and the city park respectively
  • S 1,k and S 2, k are the sample variances of the photographic image element attribute values numbered k in the Yu Yin Shan Fang and the city park respectively; the independent sample T test results are shown in Table 2 below;
  • the elemental significance index t k can be obtained from the independent sample T test result table. If an element t k value is greater than 0.2, the element is discarded. If an element t k value is not greater than 0.2, the element is reserved as Yu Yinshan. The visual characteristics of the room and the corresponding t k value will be recorded, and the visual characteristic elements of the Yu Yin Shan Fang will be obtained.
  • the visual characteristic degree is classified on the characteristic elements of Yu Yin Shan Fang.
  • p i of an element if p i ⁇ [0, 0.005) is a level 1 visual feature, if p i ⁇ [0.005,0.01) is a level 2 visual feature, if p i ⁇ [0.01,0.1] is a level 3 visual feature, as shown in Table 3 below;
  • Visual element p value Visual grading plant 0.000 1 railing 0.000 1 window 0.000 1 pool 0.001 1 roof 0.005 2 door 0.000 1 pavilion 0.000 1 mural 0.000 1 rockery 0.000 1 sculpture 0.000 1 Furniture 0.000 1 Calligraphy 0.006 2 bridge 0.001 1 Corridor 0.000 1
  • a feature element attribute table is established, and the line is defined as a feature element.
  • the column is defined as a shooting stand point, a visual center point, a shooting line of sight area, and the importance of the element is determined to guide the target environment, that is, Yu Yin Shan Fang Landscape pattern optimization;
  • the appearance of lanterns in the room of Yu Yinshan is relatively high. Suggestions are given on the hanging position of the lanterns in the festival day. For example, by identifying the line of sight, suggestions are given for the potting of the garden; for example, by shooting the standing point The identification of the area optimizes the design of the promenade and the grandstand in the original garden, and rationally guides the flow of people.
  • the present invention is based on the objective urban built-up area geographic data and network open data, and analyzes the urban built environment by analyzing the behavioral process of the people in the network media by means of geographic analysis, statistical analysis, correlation analysis and other quantitative analysis methods.
  • Visual hotspots of the landscape statistical comparison of characteristic visual elements in different urban environments, assisting the design and planning of urban landscapes.

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Abstract

Disclosed is a built environment landscape characteristic recognition method based on a network picture. The method comprises: acquiring photographic picture libraries of a target environment and a similar built environment by using a Baidu picture search engine; recording a photographing standing point, a photographing vision central point and a photographing sight line of a photographing behavior in the target environment, and importing the records into an analysis platform of ArcGIS, analyzing a visual sensitive region of the target environment in the network picture by using ArcGIS software, and analyzing the sensitivity; by using the network photographic picture libraries of the target environment and the similar built environment, recording element attributes of the network photographic pictures of the two network photographic picture libraries, and selecting a visual feature element of the target environment by using an independent-sample T test; and recognizing and analyzing a landscape characteristic of the target built environment. In this way, visual image analysis is performed on the landscape of a city built environment, and replicability and operability are provided, photographer visual analysis of the network picture and restoration analysis of the photographing behavior are utilized, and timeliness and objectivity are provided.

Description

一种基于网络图片的建成环境景观特征识别方法A Method for Identifying Landscape Characteristics of Built Environment Based on Network Picture 技术领域Technical field
本发明涉及一种建成环境景观特征识别方法，尤其是一种基于网络图片的建成环境景观特征识别方法，属于城市规划领域中的城市景观研究领域。The invention relates to a method for identifying a landscape feature of a built environment, in particular to a method for identifying a landscape feature of a built environment based on a network picture, and belongs to the field of urban landscape research in the field of urban planning.
背景技术Background technique
辨识城市环境的景观特征是城乡规划与设计的前提和基础，而民众的拍摄行为为识别简称环境的景观特征提供了大量素材。摄影图片进行旅游地视觉研究一直是学者关注的热点。Identifying the landscape characteristics of the urban environment is the premise and foundation of urban and rural planning and design, and the people's shooting behavior provides a lot of material for identifying the landscape features of the abbreviated environment. Photographic images for tourism visual research have always been the focus of scholars.
现阶段，有不少学者通过媒介表达研究城市或者街区的视觉意向，如刘敬凭借电影的画面来研究多个城市的空间意象，如赵渺希利用谷歌图片进行主因子分析，刻画了广东21个城市的景观风貌。但目前国内尚无利用网络图片进行建成环境景观辨识的相关研究。At this stage, many scholars use media to express the visual intention of cities or neighborhoods. For example, Liu Jing uses the film's pictures to study the spatial imagery of many cities. For example, Zhao Yuxi uses Google Images for principal factor analysis and portrays Guangdong 21 The landscape of a city. However, at present, there is no research on the use of network pictures for the identification of built environment landscapes in China.
杨俊宴、史宜在申请号为201310097613.7的发明专利申请中公开了一种城市天际轮廓线立面正射影像图的快速获取和测量方法，但是并没有对图像的内容和拍摄行为信息进行解读。Yang Junyan, Shi Yi, in the invention patent application No. 201310097613.7, discloses a method for quickly acquiring and measuring an orthophoto image of a city skyline contour, but does not interpret the content and shooting behavior information of the image.
发明内容Summary of the invention
本发明的目的是为了解决上述现有技术的缺陷，提供了一种基于网络图片的建成环境景观特征识别方法，该方法可以对城市建成环境景观进行网络视觉形象分析，具有可复制性和可操作性，并利用网络图片的拍摄者视觉分析和拍摄行为的还原分析，具有时效性和客观性。The purpose of the present invention is to solve the above drawbacks of the prior art, and to provide a built-in environment landscape feature recognition method based on a network picture, which can perform network visual image analysis on a city built environment landscape, and is reproducible and operable. Sexuality, and the use of network pictures of the photographer's visual analysis and reduction analysis of shooting behavior, timeliness and objectivity.
本发明的目的可以通过采取如下技术方案达到：The object of the present invention can be achieved by adopting the following technical solutions:
一种基于网络图片的建成环境景观特征识别方法，包括以下步骤：A method for identifying a landscape feature of a built environment based on a network image, comprising the following steps:
S1、利用百度图片搜索引擎获取目标环境和类似建成环境的摄影图片库；S1, using a Baidu image search engine to obtain a photographic image library of a target environment and a similar built environment;
S2、针对目标环境，使用AutoCAD记录摄影拍摄行为中的拍摄站立点、 拍摄视觉中心点和拍摄视线进行记录；S2. For the target environment, use AutoCAD to record the shooting standing point in the photographic shooting behavior, Record the visual center point and shoot the line of sight for recording;
S3、针对目标环境，将拍摄站立点、拍摄视觉中心点和拍摄视线导入ArcGIS的分析平台，利用ArcGIS软件分析景观地网络图片视觉敏感区并对敏感度分析；S3. For the target environment, the shooting point, the shooting center point and the shooting line are imported into the analysis platform of ArcGIS, and the visual sensitive area of the landscape network picture is analyzed by ArcGIS software and the sensitivity analysis is performed;
S4、利用目标环境和类似建成环境网络摄影图片库，记录两者网络摄影图片要素属性，利用SPSS软件的独立样本T检验筛选出目标环境视觉特色要素；S4. Using the target environment and a network image library similar to the built environment, record the attribute characteristics of the two network photography pictures, and use the independent sample T test of SPSS software to screen out the visual characteristics of the target environment;
S5、针对目标环境和类似建成环境，综合以上视觉信息分布情况，建立特征要素属性表，将行定义为特征要素，将列定义为拍摄站立点、视觉中心点、拍摄视线区域，进行要素重要性的判别，指导目标环境的景观格局优化。S5. For the target environment and similar built environment, integrate the above visual information distribution, establish a feature element attribute table, define the line as a feature element, define the column as a shooting stand point, a visual center point, and shoot a line of sight to perform element importance. Discrimination, guiding the optimization of the landscape pattern of the target environment.
作为一种实施方案，步骤S1中，所述利用百度图片搜索引擎获取目标环境和类似建成环境的摄影图片库，具体包括：As an implementation, in step S1, the photographic image library of the target environment and the similar built environment is obtained by using the Baidu image search engine, and specifically includes:
S11、利用百度图片查询目标环境名称，并按顺序下载所有图片搜索结果，将图片搜索结果中与目标环境无关的图片删除，选取图片搜索结果中前1200张图片，作为目标环境网络摄影图片库；S11, using Baidu images to query the target environment name, and downloading all the image search results in order, deleting the images in the image search result that are not related to the target environment, and selecting the first 1200 images in the image search result as the target environment network photography image library;
S12、利用百度图片分别查询若干类似建成环境名称，并分别按顺序下载所有图片搜索结果，将图片搜索结果中与各类似建成环境无关的图片删除，分别选取各个图片搜索结果中前200张图片，共1200张图片作为类似建成环境网络摄影图片库。S12. Using Baidu pictures to separately query a number of similar built environment names, and download all the image search results in order, delete the pictures in the image search results that are not related to the similar built environment, and select the first 200 pictures in each picture search result. A total of 1200 images are used as a network photo gallery of similar built environments.
作为一种实施方案，步骤S2中，所述使用AutoCAD记录摄影拍摄行为中的拍摄站立点、拍摄视觉中心点和拍摄视线进行记录，具体包括：As an embodiment, in step S2, the recording of the shooting standing point, the shooting visual center point, and the shooting line of sight in the photographic shooting behavior is recorded by AutoCAD, and specifically includes:
S21、将目标环境网络摄影图片库按照拍摄的地理位置分类；S21. classify the target environment network photography picture library according to the geographical location of the shooting;
S22、在目标环境现场观察，对照目标环境网络摄影图片库中的每一张图片，以目标环境平面图作为工作底图，将每一张图片的对角线交点拍摄位置作为视觉中心点，通过现场还原的方式，在CAD平面图中分别将拍摄站立点、视觉中心点和拍摄视线画在CAD工作底图上。S22. Observing in the target environment, comparing each picture in the target environment network photography picture library, taking the target environment plan as the working base map, taking the diagonal intersection position of each picture as the visual center point, passing the scene In the way of restoration, the photographing standing point, the visual center point and the shooting line of sight are drawn on the CAD working base map in the CAD plan.
作为一种实施方案，步骤S3中，针对目标环境，将拍摄站立点、拍摄视觉中心点和拍摄视线导入ArcGIS的分析平台，利用ArcGIS软件分析景观 地网络图片视觉敏感区并对敏感度分析，具体包括：As an implementation, in step S3, for the target environment, the shooting standing point, the shooting visual center point, and the shooting line of sight are imported into the analysis platform of ArcGIS, and the landscape is analyzed by using ArcGIS software. The network maps the visually sensitive area and analyzes the sensitivity, including:
S31、将拍摄站立点、视觉中心点和拍摄视线的CAD数据分别导入ArcGIS软件中进行GIS空间落位与坐标纠偏；S31. Importing the CAD data of the shooting point, the visual center point and the shooting line into the ArcGIS software for GIS spatial positioning and coordinate correction;
S32、打开ArcGIS软件中的ArcToolbox，利用Spatial Analyze工具对拍摄站立点和视觉中心点进行点密度分析操作，对拍摄视线进行线密度分析操作，分别得到目标环境拍摄站立点密度值、视觉中心点密度值和拍摄视线密度值；S32. Open the ArcToolbox in the ArcGIS software, use the Spatial Analyze tool to perform a point density analysis operation on the shooting standing point and the visual center point, perform a line density analysis operation on the shooting line of sight, and obtain the standing point density value and the visual center point density respectively in the target environment. Value and shooting line of sight density value;
S33、利用INT工具使目标环境拍摄站立点密度值、视觉中心点密度值和拍摄视线密度值变为整形数据，得到整型拍摄站立点密度分布、整型视觉中心点密度分布和整型拍摄视线线密度分布；S33. Using the INT tool, the target environment shooting standing point density value, the visual center point density value, and the shooting line density value are changed into shaping data, and the integer shooting standing point density distribution, the integer visual center point density distribution, and the integer shooting line of sight are obtained. Linear density distribution;
S34、利用ArcGIS软件计算拍摄站立点敏感度、拍摄视觉中心点敏感度和拍摄视线敏感度；S34. Using ArcGIS software to calculate the sensitivity of the shooting point, the sensitivity of the shooting center point, and the sensitivity of the shooting line of sight;
S35、利用ArcGIS软件对拍摄站立点敏感度、拍摄视觉中心点敏感度和拍摄视线敏感度由高到低分为三个级别的视觉敏感度。S35. Using ArcGIS software, the sensitivity of shooting standing point, sensitivity of shooting visual center point and sensitivity of shooting line of sight are divided into three levels of visual sensitivity.
作为一种实施方案，步骤S34中，所述拍摄站立点敏感度、拍摄视觉中心点敏感度和拍摄视线敏感度的计算，具体为：As an implementation, in step S34, the calculation of the shooting stand sensitivity, the shooting visual center point sensitivity, and the shooting line sensitivity are specifically as follows:
1)利用处理过的整型拍摄站立点密度值，ArcGIS属性表中新建字段，计算点密度平均值，点密度平均值
Figure PCTCN2016083258-appb-000001
的计算公式为：1) Use the processed integer to take the standing point density value, create a new field in the ArcGIS property sheet, calculate the point density average, and point density average
Figure PCTCN2016083258-appb-000001
The calculation formula is:
Figure PCTCN2016083258-appb-000002
Figure PCTCN2016083258-appb-000002
其中，n为栅格点的总数，bi为栅格点数值；Where n is the total number of grid points and b i is the grid point value;
2)将点密度值的计算结果导出到Excel，在Excel中计算标准差，标准差的计算公式为：2) Export the calculation result of the point density value to Excel, calculate the standard deviation in Excel, and calculate the standard deviation as:
Figure PCTCN2016083258-appb-000003
Figure PCTCN2016083258-appb-000003
其中，Mi为标准差，n为栅格点的总数，bi为栅格点数值，
Figure PCTCN2016083258-appb-000004
为点密度平均值； Where M i is the standard deviation, n is the total number of grid points, and b i is the grid point value.
Figure PCTCN2016083258-appb-000004
Average point density;
3)利用密度分析与栅格图层叠加分析技术，进行拍摄站立点敏感性分析，对于某一点的敏感度分析计算公式为：3) Using the density analysis and raster layer overlay analysis techniques to perform the sensitivity analysis of the shooting stand point. The sensitivity analysis formula for a certain point is:
Figure PCTCN2016083258-appb-000005
Figure PCTCN2016083258-appb-000005
其中，ki为某点的敏感度，Mi为拍摄视线密度标准差，bi为栅格点数值，
Figure PCTCN2016083258-appb-000006
为拍摄视线密度平均值；Where k i is the sensitivity of a point, M i is the standard deviation of the line of sight density, and b i is the value of the grid point.
Figure PCTCN2016083258-appb-000006
For taking the average line of sight density;
4)新建矢量格式数据，新建字段，将点平均密度值赋予到属性表当中；4) Create new vector format data, create a new field, and assign the point average density value to the attribute table;
5)利用根据字段值将矢量数据转换为栅格数据工具，将上述矢量数据分别转为栅格数据，得到代表点平均密度值栅格图层；5) using the vector data according to the field value to convert the raster data into a raster data tool, respectively converting the above vector data into raster data, to obtain a representative point average density value raster layer;
6)根据敏感度计算公式，利用ArcGIS中Map Algebra工具进行地图叠加计算，得到拍摄站立点敏感度分布图；6) According to the sensitivity calculation formula, use Map Algebra tool in ArcGIS to carry out map superposition calculation, and obtain the distribution map of shooting standing point sensitivity;
7)重复上述步骤1)至步骤6)的方法，得到拍摄视觉中心点敏感度分布图和拍摄视线敏感度分布图。7) Repeat the above steps 1) to 6) to obtain a photographing visual center point sensitivity distribution map and a photographing line of sight sensitivity distribution map.
作为一种实施方案，步骤S4中，所述利用目标环境和类似建成环境网络摄影图片库，记录两者网络摄影图片要素属性，具体包括：As an implementation, in step S4, the target network and the similar built-in environment network photography picture library are used to record the attributes of the network photography picture elements, including:
S41、根据目标环境网络摄影图片库和类似建成环境网络摄影图片库，将所有图片中的视觉要素记录在Excel表格中；S41. Record visual elements in all the pictures in an Excel table according to the target environment network photography picture library and a similar built environment network photography picture library;
S42、对每张网络图片依次编号，将视觉要素作为每张编号图片的备选要素属性，若图片中出现某要素则记录为1，否则为0；S42. Each network picture is sequentially numbered, and the visual element is used as an optional element attribute of each numbered picture. If an element appears in the picture, the record is 1; otherwise, it is 0;
S43、添加图片归属属性，目标环境图片记录为1和类似建成环境图片记录为2，结果保存为目标环境网络摄影图片要素属性表和类似建成环境网络摄影图片要素属性表。S43. Adding a picture attribution attribute, the target environment picture record is 1 and the similar built environment picture record is 2, and the result is saved as a target environment network photography picture element attribute table and a similar built environment network photography picture element attribute table.
作为一种实施方案，步骤S4中，所述用SPSS软件的独立样本T检验筛选出目标环境视觉特色要素，具体包括：As an implementation, in step S4, the independent sample T test of the SPSS software is used to filter the visual features of the target environment, including:
S44、根据目标环境网络摄影图片要素属性表和类似建成环境网络摄影图片要素属性表，利用SPSS软件对目标环境和类似建成环境视觉要素进行独立样本T检验分析，独立样本T检验的公式为： S44. According to the target environment network photography picture element attribute table and the similar built environment network photography picture element attribute table, the SPSS software is used to perform independent sample T-test analysis on the target environment and similar built environment visual elements, and the formula of the independent sample T test is:
Figure PCTCN2016083258-appb-000007
Figure PCTCN2016083258-appb-000007
其中，tk为T检验统计量，
Figure PCTCN2016083258-appb-000008
和
Figure PCTCN2016083258-appb-000009
分别为目标环境和类似建成环境中编号为k的摄影图片要素属性值，n1，k和n2，k分别为目标环境和类似建成环境中编号为k的摄影图片样本数量，S1，k和S2，k分别为目标环境和类似建成环境中编号为k的摄影图片要素属性值的样本方差；Where t k is the T test statistic,
Figure PCTCN2016083258-appb-000008
with
Figure PCTCN2016083258-appb-000009
The value of the photographic picture element attribute numbered k in the target environment and similar built environment, respectively, n 1, k and n 2, k are the number of photographic picture samples numbered k in the target environment and similar built environment, S 1, k And S 2,k are the sample variances of the photographic image element attribute values numbered k in the target environment and the similar built environment, respectively;
S45、T检验统计量tk即为各要素显著性指标，若某元素tk值大于0.1，则舍去该元素，若某元素tk值不大于0.1，则保留该元素作目标环境视觉特色要素并对应记录其tk值，将得到目标环境视觉特色要素表；S45, T test statistic t k is the saliency index of each element. If the t k value of an element is greater than 0.1, the element is discarded. If the t k value of an element is not greater than 0.1, the element is retained as the target environment visual feature. The elements and corresponding records of their t k values will result in a target environment visual feature element table;
S46、利用目标环境视觉特色要素表，对目标环境景观特色元素进行视觉特色度分级，从而筛选出目标环境视觉特色要素。S46. Using the target environment visual characteristic element table to classify the visual characteristics of the target environment landscape elements, thereby screening out the visual characteristics of the target environment.
本发明相对于现有技术具有如下的有益效果：The present invention has the following beneficial effects as compared with the prior art:
1、本发明以客观的城市建成区地理数据和网络开放数据为基础，借助地理分析、统计分析、相关性分析等计量分析方法，通过分析网络媒介中民众拍摄行为过程分析城市建成环境景观的视觉热点，统计对比不同城市环境中特征视觉元素，辅助城市景观的设计与规划。1. Based on the objective urban built-up area geographic data and network open data, the present invention analyzes the visual characteristics of urban built environmental landscape by analyzing the behavioral process of people in the network media by means of geographic analysis, statistical analysis, correlation analysis and other quantitative analysis methods. Hotspots, statistical comparison of characteristic visual elements in different urban environments, assisting the design and planning of urban landscapes.
2、本发明通过还原网络摄影图片拍摄过程，弥补了城市建成环境景观特征评估和设计中的主观性及滞后性的不足。2. The invention compensates for the lack of subjectivity and hysteresis in the evaluation and design of the urban built environment landscape by reducing the process of photographing the network photography.
3、本发明使用了一种针对城市建成环境景观的网络视觉形象分析方法，具有可复制性和可操作性，并利用网络图片的拍摄者视觉分析和拍摄行为的还原分析，具有时效性和客观性。3. The present invention uses a network visual image analysis method for urban built environment landscape, which has reproducibility and operability, and utilizes the visual analysis of the photographer of the network picture and the reduction analysis of the shooting behavior, which is time-sensitive and objective. Sex.
附图说明DRAWINGS
图1为本发明实施例1的基于网络图片的建成环境景观特征识别方法的流程图。FIG. 1 is a flowchart of a method for identifying a landscape feature of a built environment based on a network picture according to Embodiment 1 of the present invention.
图2为本发明实施例1的现场还原方式示意图。2 is a schematic diagram of a field reduction mode according to Embodiment 1 of the present invention.
图3为本发明实施例2的整型拍摄站立点密度分布图。Fig. 3 is a diagram showing the distribution of density points of the entire shooting shooting point according to the second embodiment of the present invention.
图4为本发明实施例2的整型视觉中心点分布图。 4 is a distribution diagram of an integral visual center point according to Embodiment 2 of the present invention.
图5为本发明实施例2的整型拍摄视线密度分布图。Fig. 5 is a view showing the line-of-sight density distribution of the entire type of shooting according to the second embodiment of the present invention.
图6为本发明实施例2的拍摄站立点敏感度分布图。Fig. 6 is a diagram showing the distribution of the sensitivity of the shooting standing point according to the second embodiment of the present invention.
具体实施方式detailed description
下面结合实施例及附图对本发明作进一步详细的描述，但本发明的实施方式不限于此。The present invention will be further described in detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.
实施例1：Example 1:
如图1所示，本实施例的基于网络图片的建成环境景观特征识别方法，包括以下步骤：As shown in FIG. 1 , the method for identifying a landscape feature of a built-in environment based on a network picture of the embodiment includes the following steps:
1)目标环境及类似建成环境网络摄影图片库的获取1) Acquisition of the target environment and similar built-in environment network photography photo library
建成环境是指城市中已经建设完成的城市环境，目标环境是指需要进行景观特征识别的建成环境，类似建成环境是指与目标环境同类别、具有相似性和可比较性的其他建成环境；利用类似建成环境与目标环境的显著性检验，可得到目标环境中较类似建成环境差异差异较为显著的元素，即为目标环境的特色元素。The built environment refers to the urban environment that has been built in the city. The target environment refers to the built environment that needs to identify the landscape features. The similar built environment refers to other built environments with the same category, similarity and comparability with the target environment; Similar to the significant test of the built environment and the target environment, the elements with more significant differences in the similar built environment in the target environment can be obtained, which is the characteristic element of the target environment.
1.1)利用百度图片查询目标环境名称，并按顺序下载所有图片搜索结果，将图片搜索结果中与目标环境无关的图片删除，选取图片搜索结果中前1200张图片，作为目标环境网络摄影图片库；1.1) Use Baidu images to query the target environment name, and download all image search results in order, delete the images in the image search results that are not related to the target environment, and select the first 1200 images in the image search results as the target environment network photography image library;
1.1)利用百度图片分别查询若干类似建成环境名称，并分别按顺序下载所有图片搜索结果，将图片搜索结果中与各类似建成环境无关的图片删除，分别选取各个图片搜索结果中前200张图片，共1200张图片作为类似建成环境网络摄影图片库；1.1) Use Baidu pictures to query several similar built environment names, and download all the image search results in order, delete the pictures in the image search results that are not related to the similar built environment, and select the first 200 pictures in each image search result. A total of 1200 pictures as a network photo library of similar built environment;
2)旅游地视觉敏感区域识别2) Identification of visually sensitive areas in tourist destinations
2.1)摄影图片拍摄行为的目标环境现场还原2.1) The target environment of the photographic picture shooting behavior is restored on site
2.1.1)将目标环境网络摄影图片库按照拍摄的地理位置分类；2.1.1) classify the target environment network photography photo library according to the geographical location of the shooting;
2.1.2)目标环境网络摄影图片的拍摄信息记录2.1.2) Shooting information record of the target environment network photography picture
在目标环境现场观察，对照目标环境网络摄影图片库中的每一张图片，以目标环境平面图作为工作底图，将每一张图片的对角线交点拍摄位置作为视觉中心点，通过如图2所示的现场还原方式，在CAD平面图中分别 将拍摄点、视觉中心点和拍摄路线(即拍摄点与视觉中心点之间的连线)画在CAD工作底图上；In the target environment observation, compare each picture in the target environment network photography picture library with the target environment plan as the working base map, and take the diagonal intersection position of each picture as the visual center point, as shown in Figure 2. The on-site reduction method shown in the CAD plan Draw the shooting point, the visual center point, and the shooting route (that is, the line connecting the shooting point and the visual center point) on the CAD work base map;
2.2)视觉敏感区域的识别2.2) Identification of visually sensitive areas
2.2.1)拍摄行为还原数据的处理2.2.1) Processing of the restoration behavior of the shooting behavior
2.2.1.1)将拍摄站立点、拍摄焦点和拍摄路线的CAD数据分别导入ArcGIS软件中进行GIS空间落位与坐标纠偏；2.2.1.1) Import the CAD data of the shooting point, shooting focus and shooting route into ArcGIS software for GIS spatial positioning and coordinate correction;
2.2.1.2)打开ArcToolbox，利用Spatial Analyze工具对拍摄站立点和拍摄焦点进行点密度分析操作，对拍摄视线进行线密度分析操作，分别得到目标环境拍摄站立点密度值、视觉中心点密度值和拍摄视线密度值；2.2.1.2) Open ArcToolbox, use the Spatial Analyze tool to perform point density analysis on the shooting standing point and shooting focus, perform line density analysis on the shooting line of sight, and obtain the target environment shooting standing point density value, visual center point density value and shooting. Line of sight density value;
2.2.1.3)利用INT工具使其变为整形数据，得到整型拍摄站立点密度分布、整型视觉中心点密度分布和整型拍摄视线线密度分布；2.2.1.3) Using the INT tool to make it into the shaping data, the density distribution of the standing point density, the density distribution of the integer visual center point, and the line-of-sight density distribution of the integer type are obtained.
2.2.2)视觉敏感度的计算2.2.2) Calculation of visual sensitivity
2.2.2.1)利用处理过的整型拍摄站立点密度值，ArcGIS属性表中新建字段，计算点密度平均值，点密度平均值
Figure PCTCN2016083258-appb-000010
的计算公式为：2.2.2.1) Use the processed integer to take the standing point density value, create a new field in the ArcGIS property sheet, calculate the point density average, point density average
Figure PCTCN2016083258-appb-000010
The calculation formula is:
Figure PCTCN2016083258-appb-000011
Figure PCTCN2016083258-appb-000011
其中，n为栅格点的总数，bi为栅格点数值；Where n is the total number of grid points and b i is the grid point value;
2.2.2.2)将点密度值的计算结果导出到Excel，在Excel中计算标准差，标准差的计算公式为：2.2.2.2) Export the calculation result of the point density value to Excel, calculate the standard deviation in Excel, and calculate the standard deviation as:
Figure PCTCN2016083258-appb-000012
Figure PCTCN2016083258-appb-000012
其中，Mi为标准差，n为栅格点的总数，bi为栅格点数值，
Figure PCTCN2016083258-appb-000013
为点密度平均值；Where M i is the standard deviation, n is the total number of grid points, and b i is the grid point value.
Figure PCTCN2016083258-appb-000013
Average point density;
2.2.2.3)利用密度分析与栅格图层叠加分析技术，进行拍摄站立点敏感性分析，对于某一点的敏感度分析计算公式为：2.2.2.3) Using the density analysis and raster layer overlay analysis techniques, the sensitivity analysis of the standing point is taken. The sensitivity analysis formula for a certain point is:
Figure PCTCN2016083258-appb-000014
Figure PCTCN2016083258-appb-000014
其中，ki为某点的敏感度，Mi为拍摄视线密度标准差，bi为栅格点数值，
Figure PCTCN2016083258-appb-000015
为拍摄视线密度平均值；Where k i is the sensitivity of a point, M i is the standard deviation of the line of sight density, and b i is the value of the grid point.
Figure PCTCN2016083258-appb-000015
For taking the average line of sight density;
2.2.2.4)新建矢量格式数据，新建字段，将点平均密度值赋予到属性表当中；2.2.2.4) Create new vector format data, create a new field, and assign the point average density value to the attribute table;
2.2.2.5)利用根据字段值将矢量数据转换为栅格数据工具，将上述矢量数据分别转为栅格数据，得到代表点平均密度值栅格图层；2.2.2.5) Using the vector data to be converted into a raster data tool according to the field value, the above vector data is respectively converted into raster data to obtain a representative point average density value raster layer;
2.2.2.6)根据敏感度计算公式，利用ArcGIS中Map Algebra工具进行地图叠加计算，得到拍摄站立点敏感度分布图；2.2.2.6) According to the sensitivity calculation formula, use Map Algebra tool in ArcGIS to perform map superposition calculation, and obtain the distribution map of shooting standing point sensitivity;
2.2.2.7)拍摄视觉中心点敏感度分布图和拍摄视线敏感度分布图数据制作方法与上述一致，重复上述步骤可以得到拍摄视觉中心点敏感度分布图和拍摄视线敏感度分布图；2.2.2.7) Shooting visual center point sensitivity distribution map and shooting line of sight sensitivity distribution map data production method is consistent with the above, repeating the above steps can obtain a photographing visual center point sensitivity distribution map and shooting line of sight sensitivity distribution map;
2.2.3)视觉敏感度分级2.2.3) Visual sensitivity rating
利用ArcGIS软件对拍摄站立点敏感度、拍摄视觉中心点敏感度和拍摄视线敏感度由高到低分为三个级别的视觉敏感度。The ArcGIS software is divided into three levels of visual sensitivity for shooting stand sensitivity, shooting visual center point sensitivity, and shooting line of sight sensitivity from high to low.
3)目标环境景观视觉特色要素构成分析3) Analysis of the composition of the visual characteristics of the target environment landscape
3.1)目标环境和类似建成环境图片视觉要素的确定3.1) Determination of the visual elements of the target environment and similar built environment pictures
根据目标环境网络摄影图片库和类似建成环境网络摄影图片库，将所有图片中出现的植物、栏杆、窗户、水池、屋顶等作为视觉要素并记录在Excel表格中；According to the target environment network photography photo library and similar built-in environment network photography photo library, all the plants, railings, windows, pools, roofs, etc. appearing in the pictures are recorded as visual elements in the Excel table;
3.2)目标环境和类似建成环境网络摄影图片要素属性的判别3.2) Identification of the attributes of network photography pictures in the target environment and similar built environment
3.2.1)对每张网络图片依次编号，将视觉要素作为每张编号图片的备选要素属性，若图片中出现某要素则记录为1，否则为0；3.2.1) Each network picture is numbered sequentially, and the visual element is used as an optional feature attribute of each numbered picture. If an element appears in the picture, the record is 1; otherwise, it is 0;
3.2.2)添加图片归属属性，目标环境图片记录为1和类似建成环境图片记录为2，结果保存为目标环境网络摄影图片要素属性表和类似建成环境网络摄影图片要素属性表；3.2.2) Add the image attribution attribute, the target environment picture record is 1 and the similar built environment picture record is 2, and the result is saved as the target environment network photography picture element attribute table and the similar built environment network photography picture element attribute table;
3.3)目标环境景观视觉特色要素的显著性判别3.3) Significant discrimination of visual characteristics of target environment landscape
3.3.1)根据目标环境网络摄影图片要素属性表和类似建成环境网络摄影图片要素属性表，利用SPSS软件对目标环境和类似建成环境视觉要素进 行独立样本T检验分析，独立样本T检验的公式为：3.3.1) According to the target environment network photography picture element attribute table and similar built-in environment network photography picture element attribute table, use SPSS software to enter the target environment and visual elements of similar built environment The independent sample T test analysis, the formula of the independent sample T test is:
Figure PCTCN2016083258-appb-000016
Figure PCTCN2016083258-appb-000016
其中，tk为T检验统计量，
Figure PCTCN2016083258-appb-000017
和
Figure PCTCN2016083258-appb-000018
分别为目标环境和类似建成环境中编号为k的摄影图片要素属性值，n1，k和n2，k分别为目标环境和类似建成环境中编号为k的摄影图片样本数量，S1，k和S2，k分别为目标环境和类似建成环境中编号为k的摄影图片要素属性值的样本方差；Where t k is the T test statistic,
Figure PCTCN2016083258-appb-000017
with
Figure PCTCN2016083258-appb-000018
The value of the photographic picture element attribute numbered k in the target environment and similar built environment, respectively, n 1, k and n 2, k are the number of photographic picture samples numbered k in the target environment and similar built environment, S 1, k And S 2,k are the sample variances of the photographic image element attribute values numbered k in the target environment and the similar built environment, respectively;
3.3.2)T检验统计量tk即作为各要素显著性指标，若某元素tk值大于0.1，则舍去该元素，若某元素tk值不大于0.1，则保留该元素作目标环境视觉特色要素并对应记录其某元素tk值，将得到目标环境视觉特色要素表；3.3.2) The T test statistic t k is used as the saliency indicator of each element. If an element t k value is greater than 0.1, the element is discarded. If an element t k value is not greater than 0.1, the element is reserved as the target environment. The visual characteristic element correspondingly records the t k value of an element, and the target environment visual characteristic element table is obtained;
3.4)目标环境景观视觉特色要素的视觉特色分级3.4) Visual characteristics of the visual characteristics of the target environment landscape
利用目标环境视觉特色要素表，对目标环境景观特色元素进行视觉特色度分级，从而筛选出目标环境视觉特色要素；Using the target environment visual feature element table, the visual characteristics of the target environment landscape elements are graded, so as to screen out the visual characteristics of the target environment;
4)城市建成区域景观特征的综合评价4) Comprehensive evaluation of landscape features of urban built-up areas
综合以上视觉信息分布情况，建立特征要素属性表，将行定义为特征要素，将列定义为拍摄站立点、视觉中心点、拍摄视线区域，进行要素重要性的判别，指导目标环境的景观格局优化。Based on the distribution of visual information above, the attribute element attribute table is established, and the line is defined as the feature element. The column is defined as the shooting standing point, the visual center point, the shooting line of sight area, the identification of the importance of the element, and the optimization of the landscape pattern of the target environment. .
实施例2：Example 2:
本实施例是一个应用实例，选取广州番禺区余荫山房景区，提供了一种基于网络图片的景区视觉信息综合评价方法，余荫山房景区位于广东省广州市番禺区南村镇东南角北大街，是典型的岭南园林。This embodiment is an application example. The Yuyinshan Fangjing District in Panyu District, Guangzhou is selected to provide a comprehensive evaluation method for scenic visual information based on network pictures. Yuyinshan Fangjing District is located in the north street of the southeast corner of Nancun Town, Panyu District, Guangzhou City, Guangdong Province. Lingnan Garden.
1)余荫山房网络摄影图片库的获取1) Acquisition of the network photography photo gallery of Yu Yin Shan Fang
1.1)利用百度图片查询关键词“余荫山房”，并按顺序下载所有图片搜索结果，将图片搜索结果中与余荫山房无关的图片删除，选取图片搜索结果中前1200张图片，作为余荫山房网络摄影图片库；1.1) Use Baidu pictures to search for the keyword "Yu Yin Shan Fang", and download all the image search results in order, delete the pictures that are not related to Yu Yin Shan Fang in the image search results, and select the first 1200 pictures in the image search results as the network photography of Yu Yin Shan Fang Photo gallery
1.2)利用百度图片分别查询广州类似建成环境关键词，本例选取“越秀公园”、“天河公园”、“荔湾湖公园”、“大夫山森林公园”、“东山湖公园”、“人民公园”，并分别按顺序下载所有图片搜索结果。将图片搜索结果中 与公园无关的图片删除，分别选取各公园图片搜索结果中前200张图片，共1200张图片作为全市公园网络摄影图片库；1.2) Use Baidu pictures to query the similar built environment keywords in Guangzhou. In this case, select “Yuexiu Park”, “Tianhe Park”, “Liwan Lake Park”, “Dafu Mountain Forest Park”, “Dongshan Lake Park” and “People's Park”. And download all image search results in order. In the image search results Delete the pictures unrelated to the park, select the first 200 pictures in the search results of each park, and a total of 1200 pictures as the city park network photography picture library;
2)余荫山房视觉敏感区域识别2) Identification of visually sensitive areas in Yu Yin Shan Fang
2.1)摄影图片拍摄行为的余荫山房现场还原2.1) Photographing the shooting behavior of Yu Yin Shan Fang on-site restoration
2.1.1)将余荫山房网络摄影图片库按照拍摄的地理位置分类2.1.1) Classification of the network photo gallery of Yu Yin Shan Fang according to the geographical location of the shooting
2.1.2)采用3名规划专业人员在余荫山房现场观察，对照余荫山房网络摄影图片库中的每一张图片，以描画的余荫山房景区CAD地图作为工作底图，利用AutoCAD软件，建立拍摄站立点、视觉中心点、拍摄视线三个图层；在拍摄站立点图层上用point命令标注每一张图片的拍摄站立点在视觉中心点图层上用point命令标注每一张图片的对角线交点拍摄位置作为拍摄焦点，在视觉中心点图层上用line命令连接拍摄站立点和拍摄焦点作为拍摄视线；2.1.2) Three planning professionals were observed at the scene of Yu Yin Shan Fang. According to each picture in the network photo gallery of Yu Yin Shan Fang, the CAD map of the Yu Yin Shan Fang Scenic Area was used as the working base map, and the AutoCAD software was used to establish the standing position. Point, visual center point, and three lines of sight; use the point command on the photographing standing point layer to mark the shooting point of each picture. Use the point command to mark the diagonal of each picture on the visual center point layer. The line intersection shooting position is taken as the shooting focus, and the line point command is used to connect the shooting point and the shooting focus on the visual center point layer as the shooting line of sight;
2.2)视觉敏感区域的识别2.2) Identification of visually sensitive areas
2.3.1)拍摄行为还原数据的处理2.3.1) Processing of the restoration behavior of the shooting behavior
2.3.1.1)将拍摄站立点、拍摄焦点和拍摄视线的CAD数据分别导入ArcGIS软件中进行GIS空间落位与坐标纠偏；2.3.1.1) Import the CAD data of the shooting point, shooting focus and shooting line into the ArcGIS software for GIS spatial positioning and coordinate correction;
2.3.1.2)打开ArcToolbox，利用Spatial Analyze工具对拍摄站立点和拍摄焦点进行点密度分析操作，对拍摄视线进行线密度分析操作，分别得到余荫山房拍摄站立点密度值、视觉中心点密度值和拍摄视线密度值；2.3.1.2) Open ArcToolbox, use the Spatial Analyze tool to perform point density analysis on the shooting stand and shooting focus, perform line density analysis on the line of sight, and obtain the standing point density value, visual center point density value and shooting of Yu Yin Shan room respectively. Line of sight density value;
2.3.1.3)利用INT工具使其变为整形数据，得到整型余荫山房整型拍摄站立点点密度分布、整型视觉中心点密度分布和整型拍摄视线密度分布，分别如图3、图4和图5所示；2.3.1.3) Using the INT tool to make it into the shaping data, the distribution of the standing point density of the whole type of Yuyinshanfang, the density distribution of the integral visual center point and the line-of-sight density distribution of the whole type are obtained, as shown in Fig. 3 and Fig. 4, respectively. Figure 5;
2.3.2)视觉敏感度的计算2.3.2) Calculation of visual sensitivity
2.3.2.1)利用处理过的整型拍摄站立点点密度值，ArcGIS属性表中新建字段，计算点密度平均值，点密度平均值的计算公式为：2.3.2.1) Using the processed integer to take the standing point density value, the new field in the ArcGIS attribute table, calculate the average point density, and calculate the average point density:
Figure PCTCN2016083258-appb-000019
Figure PCTCN2016083258-appb-000019
其中，n为栅格点的总数，bi为栅格点数值，得到点密度平均值为31.99； Where n is the total number of grid points, b i is the grid point value, and the average point density is 31.99;
2.3.2.2)将点密度值的计算结果导出到Excel，在Excel中计算标准差，标准差的计算公式为：2.3.2.2) Export the calculation result of the point density value to Excel, calculate the standard deviation in Excel, and calculate the standard deviation as:
Figure PCTCN2016083258-appb-000020
Figure PCTCN2016083258-appb-000020
其中，Mi为标准差，n为栅格点的总数，bi为栅格点数值，
Figure PCTCN2016083258-appb-000021
为点密度平均值，得到标准差Mi为103.58；Where M i is the standard deviation, n is the total number of grid points, and b i is the grid point value.
Figure PCTCN2016083258-appb-000021
For the point density average, the standard deviation M i is 103.58;
2.2.2.3)利用密度分析与栅格图层叠加分析技术，进行拍摄站立点敏感性分析，对于某一点的敏感度分析计算公式为：2.2.2.3) Using the density analysis and raster layer overlay analysis techniques, the sensitivity analysis of the standing point is taken. The sensitivity analysis formula for a certain point is:
Figure PCTCN2016083258-appb-000022
Figure PCTCN2016083258-appb-000022
其中，ki为某点的敏感度，Mi为拍摄视线密度标准差，bi为栅格点数值，
Figure PCTCN2016083258-appb-000023
为拍摄视线密度平均值；Where k i is the sensitivity of a point, M i is the standard deviation of the line of sight density, and b i is the value of the grid point.
Figure PCTCN2016083258-appb-000023
For taking the average line of sight density;
2.2.2.4)新建矢量格式数据，新建字段，将点平均密度值赋予到属性表当中；2.2.2.4) Create new vector format data, create a new field, and assign the point average density value to the attribute table;
2.2.2.5)利用根据字段值将矢量数据转换为栅格数据工具，将上述矢量数据分别转为栅格数据，得到代表点平均密度值栅格图层；2.2.2.5) Using the vector data to be converted into a raster data tool according to the field value, the above vector data is respectively converted into raster data to obtain a representative point average density value raster layer;
2.2.2.6)根据敏感度计算公式，利用ArcGIS中Map Algebra工具进行地图叠加计算，得到拍摄站立点敏感度分布图，如图6所示；2.2.2.6) According to the sensitivity calculation formula, use the Map Algebra tool in ArcGIS to perform map overlay calculation, and obtain the map of the sensitivity of the shooting standing point, as shown in Figure 6;
2.2.2.7)拍摄视觉中心点敏感度分布图和拍摄视线敏感度分布图数据制作方法与上述一致，重复上述步骤可以得到拍摄视觉中心点敏感度分布图和拍摄视线敏感度分布图；2.2.2.7) Shooting visual center point sensitivity distribution map and shooting line of sight sensitivity distribution map data production method is consistent with the above, repeating the above steps can obtain a photographing visual center point sensitivity distribution map and shooting line of sight sensitivity distribution map;
2.2.3)视觉敏感度分级2.2.3) Visual sensitivity rating
利用ArcGIS软件对拍摄站立点敏感度、拍摄视觉中心点敏感度及视线敏感度进行分级：若ki∈[0，5)则为1级视觉敏感度，若ki∈[5，9)则为2级视觉敏感度，ki∈[9，13]则为3级视觉敏感度。Use ArcGIS software to grade the sensitivity of the shooting point, the sensitivity of the shooting center point and the line of sight sensitivity: if k i ∈[0,5) is the level 1 visual sensitivity, if k i ∈[5,9) For level 2 visual sensitivity, k i ∈[9,13] is a level 3 visual sensitivity.
3)余荫山房视觉特色要素构成分析3) Analysis of the elements of visual characteristics of Yuyinshanfang
3.1)余荫山房和全市公园的图片的视觉要素的确定 3.1) Determination of the visual elements of the pictures of Yu Yin Shan Fang and the city park
根据全市公园网络摄影图片库和余荫山房网络摄影图片库，将所有图片中出现的植物、栏杆、窗户、水池、屋顶、门、亭子、壁画、人、假山、雕塑、家具、字画、桥、连廊、路牌、瀑布、塔、灯笼、鱼、楼梯、牌坊作为视觉要素并记录在Excel表格中；According to the city park network photography photo library and the Yu Yin Shan Fang network photography photo library, all the plants, railings, windows, pools, roofs, doors, pavilions, murals, people, rockeries, sculptures, furniture, calligraphy, bridges, and buildings appear in the pictures. Gallery, street signs, waterfalls, towers, lanterns, fish, stairs, arches are used as visual elements and recorded in an Excel spreadsheet;
3.2)判别余荫山房和全市公园网络摄影图片要素属性3.2) Judging the attributes of the network photography picture elements of Yu Yin Shan Fang and the citywide park
3.2.1)对每张网络图片依次编号，将视觉要素作为每张编号图片的备选要素属性，若图片中出现某要素则记录为1，否则为0，如下表1所示；3.2.1) Each network picture is numbered sequentially, and the visual element is used as an optional feature attribute of each numbered picture. If an element appears in the picture, the record is 1; otherwise, it is 0, as shown in Table 1 below;
Figure PCTCN2016083258-appb-000024
Figure PCTCN2016083258-appb-000024
Figure PCTCN2016083258-appb-000025
Figure PCTCN2016083258-appb-000025
表1 网络图片要素属性表Table 1 Network Picture Feature Attribute Table
3.2.2)添加图片归属属性，余荫山房图片记录为1，全市公园图片记录为2，将结果保存为余荫山房和全市公园网络摄影图片要素属性表；3.2.2) Add the attribution attribute of the picture, the picture record of Yu Yin Shan Fang is 1, the picture record of the city park is 2, and the result is saved as the attribute list of the network photography picture elements of Yu Yin Shan Fang and the city park;
3.3)余荫山房视觉特色要素的显著性判别3.3) Significant discrimination of visual characteristics of Yuyinshanfang
3.3.1)根据余荫山房和全市公园网络摄影图片要素属性表，利用SPSS软件对余荫山房和全市公园网络摄影图片要素进行独立样本T检验分析，独立样本T检验的公式为：3.3.1) According to the attribute table of the network photos of Yuyinshan and the city park, the SPSS software is used to analyze the elements of the network of the Yuyinshan and the city's parks. The formula of the independent sample T test is:
Figure PCTCN2016083258-appb-000026
Figure PCTCN2016083258-appb-000026
其中，tk为T检验统计量，即为各要素显著性指标，
Figure PCTCN2016083258-appb-000027
和
Figure PCTCN2016083258-appb-000028
分别为余荫山房和全市公园中编号为k的摄影图片要素属性值，n1，k和n2，k分别为余荫山房和全市公园中编号为k的摄影图片样本数量，S1，k和S2，k分别为分别为余荫山房和全市公园中编号为k的摄影图片要素属性值的样本方差；独立样本T检验结果，如下表2所示；Where t k is the T test statistic, which is the saliency indicator of each element.
Figure PCTCN2016083258-appb-000027
with
Figure PCTCN2016083258-appb-000028
The attribute values of the photographic picture elements numbered k in Yu Yin Shan Fang and the city park, respectively, n 1,k and n 2,k are the number of photographic picture samples numbered k in Yu Yin Shan Fang and the city park respectively, S 1,k and S 2, k are the sample variances of the photographic image element attribute values numbered k in the Yu Yin Shan Fang and the city park respectively; the independent sample T test results are shown in Table 2 below;
Figure PCTCN2016083258-appb-000029
Figure PCTCN2016083258-appb-000029
Figure PCTCN2016083258-appb-000030
Figure PCTCN2016083258-appb-000030
表2 独立样本T检验结果表Table 2 Independent sample T test results table
3.3.2)由独立样本T检验结果表可以得到各要素显著性指标tk，若某元素tk值大于0.2则舍去该元素，若某元素tk值不大于0.2则保留该元素作余荫山房视觉特色要素并对应记录其tk值，将得到余荫山房视觉特色要素表。3.3.2) The elemental significance index t k can be obtained from the independent sample T test result table. If an element t k value is greater than 0.2, the element is discarded. If an element t k value is not greater than 0.2, the element is reserved as Yu Yinshan. The visual characteristics of the room and the corresponding t k value will be recorded, and the visual characteristic elements of the Yu Yin Shan Fang will be obtained.
3.4)余荫山房视觉特色要素的视觉特色分级3.4) Visual characteristics of visual characteristics of Yuyinshanfang
利用余荫山房视觉特色要素表，对余荫山房是觉特色元素进行视觉特色度分级，对于某元素的显著性指标pi，若pi∈[0，0.005)则为1级视觉特色，若pi∈[0.005，0.01)则为2级视觉特色，若pi∈[0.01，0.1]则为3级视觉特色，如下表3所示；Using the visual characteristic element table of Yu Yin Shan Fang, the visual characteristic degree is classified on the characteristic elements of Yu Yin Shan Fang. For the significant index p i of an element, if p i ∈[0, 0.005) is a level 1 visual feature, if p i ∈[0.005,0.01) is a level 2 visual feature, if p i ∈[0.01,0.1] is a level 3 visual feature, as shown in Table 3 below;
视觉要素Visual element p值p value 视觉分级Visual grading
植物plant 0.0000.000 11
栏杆railing 0.0000.000 11
窗window 0.0000.000 11
水池pool 0.0010.001 11
屋顶roof 0.0050.005 22
门door 0.0000.000 11
亭子pavilion 0.0000.000 11
壁画mural 0.0000.000 11
假山rockery 0.0000.000 11
雕塑sculpture 0.0000.000 11
家具Furniture 0.0000.000 11
字画Calligraphy 0.0060.006 22
桥bridge 0.0010.001 11
连廊Corridor 0.0000.000 11
瀑布waterfall 0.0100.010 33
灯笼lantern 0.0000.000 11
鱼fish 0.0000.000 11
楼梯stairs 0.0230.023 33
牌坊Archway 0.1050.105 33
表3 视觉特色要素的视觉特色分级表Table 3 Visual characteristics of visual characteristics
4)旅游地视觉信息综合评价4) Comprehensive evaluation of visual information of tourist destinations
综合以上视觉信息分布情况，建立特征要素属性表，将行定义为特征要素，将列定义为拍摄站立点、视觉中心点、拍摄视线区域，进行要素重要性的判别，指导目标环境，即余荫山房的景观格局优化；Based on the distribution of visual information above, a feature element attribute table is established, and the line is defined as a feature element. The column is defined as a shooting stand point, a visual center point, a shooting line of sight area, and the importance of the element is determined to guide the target environment, that is, Yu Yin Shan Fang Landscape pattern optimization;
余荫山房中灯笼的出现频次较高，在节庆日课对灯笼的悬挂位置给出建议；又如，通过对视线区域的识别，对园林的盆栽摆放给出建议；再如，通过拍摄站立点区域的识别，优化原来园林中长廊和看台的设计，合理引导人流组织。The appearance of lanterns in the room of Yu Yinshan is relatively high. Suggestions are given on the hanging position of the lanterns in the festival day. For example, by identifying the line of sight, suggestions are given for the potting of the garden; for example, by shooting the standing point The identification of the area optimizes the design of the promenade and the grandstand in the original garden, and rationally guides the flow of people.
综上所述，本发明以客观的城市建成区地理数据和网络开放数据为基础，借助地理分析、统计分析、相关性分析等计量分析方法，通过分析网络媒介中民众拍摄行为过程分析城市建成环境景观的视觉热点，统计对比不同城市环境中特征视觉元素，辅助城市景观的设计与规划。In summary, the present invention is based on the objective urban built-up area geographic data and network open data, and analyzes the urban built environment by analyzing the behavioral process of the people in the network media by means of geographic analysis, statistical analysis, correlation analysis and other quantitative analysis methods. Visual hotspots of the landscape, statistical comparison of characteristic visual elements in different urban environments, assisting the design and planning of urban landscapes.
以上所述，仅为本发明专利优选的实施例，但本发明专利的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明专利所公开的范围内，根据本发明专利的技术方案及其发明专利构思加以等同替换或改变，都属于本发明专利的保护范围。 The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto, and any person skilled in the art is within the scope disclosed by the present invention, according to the patent of the present invention. The technical solutions and their inventive patent concepts are equivalently replaced or changed, and are all within the scope of protection of the present invention.

Claims (7)

  1. 一种基于网络图片的建成环境景观特征识别方法，其特征在于：所述方法包括以下步骤：A method for identifying a landscape feature of a built environment based on a network picture, characterized in that the method comprises the following steps:
    S1、利用百度图片搜索引擎获取目标环境和类似建成环境的摄影图片库；S1, using a Baidu image search engine to obtain a photographic image library of a target environment and a similar built environment;
    S2、针对目标环境，使用AutoCAD记录摄影拍摄行为中的拍摄站立点、拍摄视觉中心点和拍摄视线进行记录；S2. For the target environment, use AutoCAD to record the shooting standing point in the shooting behavior, take a visual center point, and take a line of sight to record;
    S3、针对目标环境，将拍摄站立点、拍摄视觉中心点和拍摄视线导入ArcGIS的分析平台，利用ArcGIS软件分析景观地网络图片视觉敏感区并对敏感度分析；S3. For the target environment, the shooting point, the shooting center point and the shooting line are imported into the analysis platform of ArcGIS, and the visual sensitive area of the landscape network picture is analyzed by ArcGIS software and the sensitivity analysis is performed;
    S4、利用目标环境和类似建成环境网络摄影图片库，记录两者网络摄影图片要素属性，利用SPSS软件的独立样本T检验筛选出目标环境视觉特色要素；S4. Using the target environment and a network image library similar to the built environment, record the attribute characteristics of the two network photography pictures, and use the independent sample T test of SPSS software to screen out the visual characteristics of the target environment;
    S5、针对目标环境和类似建成环境，综合以上视觉信息分布情况，建立特征要素属性表，将行定义为特征要素，将列定义为拍摄站立点、视觉中心点、拍摄视线区域，进行要素重要性的判别，指导目标环境的景观格局优化。S5. For the target environment and similar built environment, integrate the above visual information distribution, establish a feature element attribute table, define the line as a feature element, define the column as a shooting stand point, a visual center point, and shoot a line of sight to perform element importance. Discrimination, guiding the optimization of the landscape pattern of the target environment.
  2. 根据权利要求1所述的一种基于网络图片的建成环境景观特征识别方法，其特征在于：步骤S1中，所述利用百度图片搜索引擎获取目标环境和类似建成环境的摄影图片库，具体包括：The method for identifying a landscape feature of a built-in environment based on a network image according to claim 1, wherein in step S1, the photographic image library of the target environment and a similar built environment is obtained by using a Baidu image search engine, and specifically includes:
    S11、利用百度图片查询目标环境名称，并按顺序下载所有图片搜索结果，将图片搜索结果中与目标环境无关的图片删除，选取图片搜索结果中前1200张图片，作为目标环境网络摄影图片库；S11, using Baidu images to query the target environment name, and downloading all the image search results in order, deleting the images in the image search result that are not related to the target environment, and selecting the first 1200 images in the image search result as the target environment network photography image library;
    S12、利用百度图片分别查询若干类似建成环境名称，并分别按顺序下载所有图片搜索结果，将图片搜索结果中与各类似建成环境无关的图片删除，分别选取各个图片搜索结果中前200张图片，共1200张图片作为类似建成环境网络摄影图片库。S12. Using Baidu pictures to separately query a number of similar built environment names, and download all the image search results in order, delete the pictures in the image search results that are not related to the similar built environment, and select the first 200 pictures in each picture search result. A total of 1200 images are used as a network photo gallery of similar built environments.
  3. 根据权利要求1所述的一种基于网络图片的建成环境景观特征识别 方法，其特征在于：步骤S2中，所述使用AutoCAD记录摄影拍摄行为中的拍摄站立点、拍摄视觉中心点和拍摄视线进行记录，具体包括：A network image based built environment landscape feature recognition according to claim The method is characterized in that: in step S2, the recording of the shooting standing point, the shooting visual center point, and the shooting line of sight in the photographic shooting behavior is recorded by using AutoCAD, and specifically includes:
    S21、将目标环境网络摄影图片库按照拍摄的地理位置分类；S21. classify the target environment network photography picture library according to the geographical location of the shooting;
    S22、在目标环境现场观察，对照目标环境网络摄影图片库中的每一张图片，以目标环境平面图作为工作底图，将每一张图片的对角线交点拍摄位置作为视觉中心点，通过现场还原的方式，在CAD平面图中分别将拍摄站立点、视觉中心点和拍摄视线画在CAD工作底图上。S22. Observing in the target environment, comparing each picture in the target environment network photography picture library, taking the target environment plan as the working base map, taking the diagonal intersection position of each picture as the visual center point, passing the scene In the way of restoration, the photographing standing point, the visual center point and the shooting line of sight are drawn on the CAD working base map in the CAD plan.
  4. 根据权利要求3所述的一种基于网络图片的建成环境景观特征识别方法，其特征在于：步骤S3中，针对目标环境，将拍摄站立点、拍摄视觉中心点和拍摄视线导入ArcGIS的分析平台，利用ArcGIS软件分析景观地网络图片视觉敏感区并对敏感度分析，具体包括：The method for identifying a landscape feature based on a network image according to claim 3, wherein in step S3, for the target environment, the shooting point, the shooting center point, and the shooting line are imported into the analysis platform of ArcGIS. Using ArcGIS software to analyze the visual sensitive area of the landscape network image and analyze the sensitivity, including:
    S31、将拍摄站立点、视觉中心点和拍摄视线的CAD数据分别导入ArcGIS软件中进行GIS空间落位与坐标纠偏；S31. Importing the CAD data of the shooting point, the visual center point and the shooting line into the ArcGIS software for GIS spatial positioning and coordinate correction;
    S32、打开ArcGIS软件中的ArcToolbox，利用Spatial Analyze工具对拍摄站立点和视觉中心点进行点密度分析操作，对拍摄视线进行线密度分析操作，分别得到目标环境拍摄站立点密度值、视觉中心点密度值和拍摄视线密度值；S32. Open the ArcToolbox in the ArcGIS software, use the Spatial Analyze tool to perform a point density analysis operation on the shooting standing point and the visual center point, perform a line density analysis operation on the shooting line of sight, and obtain the standing point density value and the visual center point density respectively in the target environment. Value and shooting line of sight density value;
    S33、利用INT工具使目标环境拍摄站立点密度值、视觉中心点密度值和拍摄视线密度值变为整形数据，得到整型拍摄站立点密度分布、整型视觉中心点密度分布和整型拍摄视线线密度分布；S33. Using the INT tool, the target environment shooting standing point density value, the visual center point density value, and the shooting line density value are changed into shaping data, and the integer shooting standing point density distribution, the integer visual center point density distribution, and the integer shooting line of sight are obtained. Linear density distribution;
    S34、利用ArcGIS软件计算拍摄站立点敏感度、拍摄视觉中心点敏感度和拍摄视线敏感度；S34. Using ArcGIS software to calculate the sensitivity of the shooting point, the sensitivity of the shooting center point, and the sensitivity of the shooting line of sight;
    S35、利用ArcGIS软件对拍摄站立点敏感度、拍摄视觉中心点敏感度和拍摄视线敏感度由高到低分为三个级别的视觉敏感度。S35. Using ArcGIS software, the sensitivity of shooting standing point, sensitivity of shooting visual center point and sensitivity of shooting line of sight are divided into three levels of visual sensitivity.
  5. 根据权利要求4所述的一种基于网络图片的建成环境景观特征识别方法，其特征在于：步骤S34中，所述拍摄站立点敏感度、拍摄视觉中心点敏感度和拍摄视线敏感度的计算，具体为：The method for recognizing a built-in environment landscape feature based on a network picture according to claim 4, wherein in step S34, the photographing of the sensitivity of the standing point, the sensitivity of the shooting center point, and the sensitivity of the shooting line of sight are calculated. Specifically:
    1)利用处理过的整型拍摄站立点密度值，ArcGIS属性表中新建字段，计算点密度平均值，点密度平均值
    Figure PCTCN2016083258-appb-100001
    的计算公式为：     1) Use the processed integer to take the standing point density value, create a new field in the ArcGIS property sheet, calculate the point density average, and point density average
    Figure PCTCN2016083258-appb-100001
    The calculation formula is:
    Figure PCTCN2016083258-appb-100002
    Figure PCTCN2016083258-appb-100002
    其中，n为栅格点的总数，bi为栅格点数值；Where n is the total number of grid points and b i is the grid point value;
    2)将点密度值的计算结果导出到Excel，在Excel中计算标准差，标准差的计算公式为：2) Export the calculation result of the point density value to Excel, calculate the standard deviation in Excel, and calculate the standard deviation as:
    Figure PCTCN2016083258-appb-100003
    Figure PCTCN2016083258-appb-100003
    其中，Mi为标准差，n为栅格点的总数，bi为栅格点数值，
    Figure PCTCN2016083258-appb-100004
    为点密度平均值；    Where M i is the standard deviation, n is the total number of grid points, and b i is the grid point value.
    Figure PCTCN2016083258-appb-100004
    Average point density;
    3)利用密度分析与栅格图层叠加分析技术，进行拍摄站立点敏感性分析，对于某一点的敏感度分析计算公式为：3) Using the density analysis and raster layer overlay analysis techniques to perform the sensitivity analysis of the shooting stand point. The sensitivity analysis formula for a certain point is:
    Figure PCTCN2016083258-appb-100005
    Figure PCTCN2016083258-appb-100005
    其中，ki为某点的敏感度，Mi为拍摄视线密度标准差，bi为栅格点数值，
    Figure PCTCN2016083258-appb-100006
    为拍摄视线密度平均值；    Where k i is the sensitivity of a point, M i is the standard deviation of the line of sight density, and b i is the value of the grid point.
    Figure PCTCN2016083258-appb-100006
    For taking the average line of sight density;
    4)新建矢量格式数据，新建字段，将点平均密度值赋予到属性表当中；4) Create new vector format data, create a new field, and assign the point average density value to the attribute table;
    5)利用根据字段值将矢量数据转换为栅格数据工具，将上述矢量数据分别转为栅格数据，得到代表点平均密度值栅格图层；5) using the vector data according to the field value to convert the raster data into a raster data tool, respectively converting the above vector data into raster data, to obtain a representative point average density value raster layer;
    6)根据敏感度计算公式，利用ArcGIS中Map Algebra工具进行地图叠加计算，得到拍摄站立点敏感度分布图；6) According to the sensitivity calculation formula, use Map Algebra tool in ArcGIS to carry out map superposition calculation, and obtain the distribution map of shooting standing point sensitivity;
    7)重复上述步骤1)至步骤6)的方法，得到拍摄视觉中心点敏感度分布图和拍摄视线敏感度分布图。7) Repeat the above steps 1) to 6) to obtain a photographing visual center point sensitivity distribution map and a photographing line of sight sensitivity distribution map.
  6. 根据权利要求1所述的一种基于网络图片的建成环境景观特征识别方法，其特征在于：步骤S4中，所述利用目标环境和类似建成环境网络摄影图片库，记录两者网络摄影图片要素属性，具体包括：The method for identifying a landscape feature of a built-in environment based on a network image according to claim 1, wherein in step S4, the target image environment and a network image library similar to the built environment are used to record the attributes of the network photography image elements. Specifically, including:
    S41、根据目标环境网络摄影图片库和类似建成环境网络摄影图片库， 将所有图片中的视觉要素记录在Excel表格中；S41. According to the target environment, the network photography picture library and the similar built environment network photography picture library, Record visual elements from all images in an Excel spreadsheet;
    S42、对每张网络图片依次编号，将视觉要素作为每张编号图片的备选要素属性，若图片中出现某要素则记录为1，否则为0；S42. Each network picture is sequentially numbered, and the visual element is used as an optional element attribute of each numbered picture. If an element appears in the picture, the record is 1; otherwise, it is 0;
    S43、添加图片归属属性，目标环境图片记录为1和类似建成环境图片记录为2，结果保存为目标环境网络摄影图片要素属性表和类似建成环境网络摄影图片要素属性表。S43. Adding a picture attribution attribute, the target environment picture record is 1 and the similar built environment picture record is 2, and the result is saved as a target environment network photography picture element attribute table and a similar built environment network photography picture element attribute table.
  7. 根据权利要求6所述的一种基于网络图片的建成环境景观特征识别方法，其特征在于：步骤S4中，所述用SPSS软件的独立样本T检验筛选出目标环境视觉特色要素，具体包括：The method for identifying a landscape feature of a built-in environment based on a network image according to claim 6, wherein in step S4, the independent sample T test of the SPSS software is used to filter out visual features of the target environment, including:
    S44、根据目标环境网络摄影图片要素属性表和类似建成环境网络摄影图片要素属性表，利用SPSS软件对目标环境和类似建成环境视觉要素进行独立样本T检验分析，独立样本T检验的公式为：S44. According to the target environment network photography picture element attribute table and the similar built environment network photography picture element attribute table, the SPSS software is used to perform independent sample T-test analysis on the target environment and similar built environment visual elements, and the formula of the independent sample T test is:
    Figure PCTCN2016083258-appb-100007
    Figure PCTCN2016083258-appb-100007
    其中，tk为T检验统计量，
    Figure PCTCN2016083258-appb-100008
    和
    Figure PCTCN2016083258-appb-100009
    分别为目标环境和类似建成环境中编号为k的摄影图片要素属性值，n1，k和n2，k分别为目标环境和类似建成环境中编号为k的摄影图片样本数量，S1，k和S2，k分别为目标环境和类似建成环境中编号为k的摄影图片要素属性值的样本方差；    Where t k is the T test statistic,
    Figure PCTCN2016083258-appb-100008
    with
    Figure PCTCN2016083258-appb-100009
    The value of the photographic picture element attribute numbered k in the target environment and similar built environment, respectively, n 1, k and n 2, k are the number of photographic picture samples numbered k in the target environment and similar built environment, S 1, k And S 2,k are the sample variances of the photographic image element attribute values numbered k in the target environment and the similar built environment, respectively;
    S45、T检验统计量tk即为各要素显著性指标，若某元素tk值大于0.1，则舍去该元素，若某元素tk值不大于0.1，则保留该元素作目标环境视觉特色要素并对应记录其tk值，将得到目标环境视觉特色要素表；S45, T test statistic t k is the saliency index of each element. If the t k value of an element is greater than 0.1, the element is discarded. If the t k value of an element is not greater than 0.1, the element is retained as the target environment visual feature. The elements and corresponding records of their t k values will result in a target environment visual feature element table;
    S46、利用目标环境视觉特色要素表，对目标环境景观特色元素进行视觉特色度分级，从而筛选出目标环境视觉特色要素。 S46. Using the target environment visual characteristic element table to classify the visual characteristics of the target environment landscape elements, thereby screening out the visual characteristics of the target environment.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110175215A (en) * 2019-04-29 2019-08-27 河南大学 A method of dividing geographical intermediate zone
CN114626966A (en) * 2022-03-18 2022-06-14 杭州师范大学 Method for quantizing large-scale urban blue-green space cooling scale based on landscape pattern
WO2024021359A1 (en) * 2022-07-27 2024-02-01 东南大学 Built environment dominant color measurement method and system based on image eeg sensitivity data

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104933229B (en) * 2015-05-29 2018-04-13 华南理工大学 A kind of built environment Landscape Characteristics recognition methods based on network picture
CN108053458A (en) * 2017-12-08 2018-05-18 河海大学 A kind of watershed generalization figure based on GIS-Geographic Information System makes and methods of exhibiting
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CN113159122B (en) * 2021-03-16 2022-03-15 重庆市地理信息和遥感应用中心 City geomorphology analysis method based on social media image data

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101208698A (en) * 2005-07-01 2008-06-25 金文贞 System and method for designing landscape architecture based on real image
CN104077447A (en) * 2014-07-01 2014-10-01 东南大学 Urban three-dimensional space vector modeling method based on paper plane data
EP2797011A1 (en) * 2013-04-25 2014-10-29 Thomson Licensing Method of and apparatus for providing access to content in a database based on environment-derived contextual information
CN104933229A (en) * 2015-05-29 2015-09-23 华南理工大学 Built environment landscape characteristic recognition method based on internet picture

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5631970A (en) * 1993-05-21 1997-05-20 Hsu; Shin-Yi Process for identifying simple and complex objects from fused images and map data
DE202012013403U1 (en) * 2011-08-26 2016-10-27 Terra Bella Technologies Inc. Capture and process adaptive images with image analysis feedback
CN103196429B (en) * 2013-03-25 2015-03-04 东南大学 Method for quickly obtaining and measuring orthophotoquad of city skyline contour line facade
CN103823914A (en) * 2013-05-24 2014-05-28 东南大学 Touring line planning method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101208698A (en) * 2005-07-01 2008-06-25 金文贞 System and method for designing landscape architecture based on real image
EP2797011A1 (en) * 2013-04-25 2014-10-29 Thomson Licensing Method of and apparatus for providing access to content in a database based on environment-derived contextual information
CN104077447A (en) * 2014-07-01 2014-10-01 东南大学 Urban three-dimensional space vector modeling method based on paper plane data
CN104933229A (en) * 2015-05-29 2015-09-23 华南理工大学 Built environment landscape characteristic recognition method based on internet picture

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110175215A (en) * 2019-04-29 2019-08-27 河南大学 A method of dividing geographical intermediate zone
CN110175215B (en) * 2019-04-29 2022-11-11 河南大学 Method for dividing geographical transition zone
CN114626966A (en) * 2022-03-18 2022-06-14 杭州师范大学 Method for quantizing large-scale urban blue-green space cooling scale based on landscape pattern
WO2024021359A1 (en) * 2022-07-27 2024-02-01 东南大学 Built environment dominant color measurement method and system based on image eeg sensitivity data

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