摘要
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4 540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.
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.
作者
禄小敏
马犇
闫浩文
李蓬勃
LU Xiaomin;MA Ben;YAN Haowen;LI Pengbo(Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China;Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第12期2479-2488,共10页
Journal of Zhejiang University：Engineering Science
基金
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)。
关键词
形状分类
模板匹配
骨架线
特征向量
余弦相似度
shape classification
template matching
skeleton line
feature vector
cosine similarity
