Shape classifying method of map building based on skeleton line

doi:10.3785/j.issn.1008-973X.2024.12.007

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

Issue (12): 2479-2488 DOI: 10.3785/j.issn.1008-973X.2024.12.007

Shape classifying method of map building based on skeleton line

Xiaomin LU1,2,3(

),Ben MA1,2,3,Haowen YAN1,2,3,Pengbo LI1,2,3

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China

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Abstract

The template-based matching method for building morphology recognition is prone to interference from local features. To improve the accuracy of building morphology recognition and classification, on the basis of the advantages of traditional template-based matching method, a building shape classification model was constructed by introducing least squares template, skeleton lines and feature vector. The impact of small depressions and protrusions was avoided in the method by constructing least squares templates and extracting skeleton lines. Based on that, the feature vectors of the building and the templates’ skeleton lines were calculated, the cosine similarity was introduced to calculate their similarity, and the template with the highest similarity was treated as the classification result for building shapes. Data of 4540 buildings were used for classification validation in the experiment, to verify the feasibility and effectiveness of the proposed method. Results show that the method can resolve the interference problem of local features on the overall shape description of the building, resulting in high classification accuracy. The method lays foundation for further building matching and generalization evaluation operations.

Key words： shape classification template matching skeleton line feature vector cosine similarity

Received: 07 January 2024 Published: 25 November 2024

CLC:

P 28

Fund: 国家自然科学基金地区项目（42161066）；国家自然科学基金重点项目（41930101）；国家自然科学基金青年项目（41801395）.

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	Xiaomin LU
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Cite this article:

Xiaomin LU,Ben MA,Haowen YAN,Pengbo LI. Shape classifying method of map building based on skeleton line. Journal of ZheJiang University (Engineering Science), 2024, 58(12): 2479-2488.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.12.007 OR https://www.zjujournals.com/eng/Y2024/V58/I12/2479

基于骨架线的地图建筑物形状分类方法

基于模板匹配的建筑物形态识别方法容易受到局部特征干扰. 为了提高建筑物形态识别和分类的精度，在继承传统模板匹配方法优势的基础上，引入最小二乘模板、骨架线及特征向量构建建筑物形状分类模型. 通过构建建筑物最小二乘模板并提取其骨架线的方式克服建筑物细小凹陷与凸出对整体形态识别的影响；在此基础上，计算骨架线特征向量并利用其余弦相似度实现建筑物与模板的匹配，相似度最高的模板即被确定为建筑物形状分类结果. 为了验证模型的适用性和有效性，实验采用4540个建筑物数据进行分类验证. 结果表明，该模型分类精度较高，能有效解决局部特征对建筑物整体形状描述的干扰问题，为进一步的建筑物匹配及其综合评价操作打下基础.

关键词： 形状分类, 模板匹配, 骨架线, 特征向量, 余弦相似度

Fig.1 Characteristic points of building polygons

Fig.2 Relationship between actual threshold and predicted threshold in Douglas-Peucker algorithm

Fig.3 Building polygons and its least squares template

Fig.4 Building and its skeleton line

Fig.5 Eleven typical building shape templates and their corresponding samples

Fig.6 Feature vectors of templates

Fig.7 Example for feature vector generation

Fig.8 Preliminary screening of building templates based on feature vectors

Fig.9 Examples of building samples to be classified

Fig.10 Cosine similarity between buildings and templates

Tab.1 Statistical results of building template matching experiment

Fig.11 Building of type ‘十’ and its Delaunay triangles

Fig.12 Building of type ‘E’ misclassified and templates of ‘E’ and ‘F’

Fig.13 Comparison of matching results between selected buildings and templates based on two methods

Fig.14 Comparisons in classification of irregular buildings like “E” type

Fig.15 Comparison of experiment results between proposed method and method based on corner function

Fig.16 Classification process of irregular building shapes based on corner function

Tab.2 Comparison of building boundary simplification results


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