样本数量不平衡下的建筑群模式识别方法研究

通过综合空间关系、几何、语义等多种特征对建筑群模式进行智能识别,在地图多尺度表达和数字化制图等领域有着重要的意义。将图卷积神经网络用于建筑群模式智能识别能够克服传统方法依赖人工经验设置参数、制定的规则过于严格等缺点。但是该方法往往存在样本比例失衡的问题,容易导致样本数量较少的类别无法正确识别。本文首先以建筑物质心作为样本进行聚类获得建筑群组。其次,将同一群组内的建筑物质心作为样本进行Delaunay三角剖分来构建建筑群的图结构,其中图节点特征选取能够描述建筑群几何特征的面积、大小、方向等相关指标。再次,通过图结构过采样的方式对样本数量较少的建筑群图结构进行增强,然后将样本数量较少的建筑群图结构增强前后的数据分别输入图卷积神经网络模型进行训练,并结合ROC曲线等多个评价指标对模型的性能进行了评测。实验结果表明,对样本数量较少的建筑群图结构增强之后,模型对于样本数量较少的建筑群识别准确率有了明显的提高。

Pattern Recognition of Regular Buildings with Unbalanced Sample Size

It is of profound significance in the fields of multi-scale map expression and digital mapping to recognize the architectural pattern intelligently by integrating spatial relations, geometry, semantics, and other features. Applying graph convolution neural network to intelligent recognition of building pattern can overcome the shortcomings of traditional methods, such as relying on artificial experience to set parameters and making rules too strict. However, this method often has the problem of sample proportion imbalance, which easily leads to the category with a small number of samples can not be properly identified. In this paper, the building centriod is used as the sample to cluster to obtain the building groups. Secondly, the building centriod in the same group is used as the node to construct the graph structure of the building group of Delaunay triangulation. The feature selection of the graph node can describe the area, size, direction, and other related indicators of the geometric characteristics of the building group. Thirdly, the building groups graph structure with small number of samples is enhanced by oversampling the graph structure. Then the data before and after the enhancement of the architectural group graph structure with small number of samples are input into the graph convolution neural network model for training, and the performance of the model is evaluated with several evaluation indexes such as ROC curve. The experimental results show that the recognition accuracy of the model for buildings with lesser number of samples is significantly improved after the structure of buildings with small number of samples is enhanced.