引入方向熵的建筑物群组模式识别方法

Building Group Pattern Recognition Based on Directional Entropy

建筑物群组具有明显的空间分布模式,其相关研究在地图制图、地图匹配及查询等领域均有诸多应用。已有的建筑物群组模式识别研究主要采用传统几何方法和机器学习方法,取得了较好的实验结果。但同时,传统几何方法存在规则定义复杂、识别出的模式较为单一等缺点;而机器学习则存在对样本数据要求高、分类特征选择困难等问题。方向熵是信息熵的一种,常用于评估空间中不同方向性随机变量的不确定性,可应用于描述空间现象方向的分布特征和规律。本研究引入方向熵进行典型建筑物群组的多模式识别。首先,使用人工视觉方法对兰州市的建筑物数据进行聚类,并构建最小生成树几何模型。其次,按照7:3的比例将建筑物群组划分为样本集1和样本集2。通过样本集1计算直线型、格网型和不规则型建筑物群组的分类阈值,并在样本集2上验证,结果显示,这3种建筑物群组的分类精度均达到了97%以上。最后,将所提方法应用于上海市的建筑物数据中,并获得了符合人类认知的结果。这也从另外一个角度说明方向熵可以应用于群组目标分布模式识别。

The recognition of building group pattern is crucial for building integration,and an efficient building group pattern recognition method can significantly enhance the quality of automatic map synthesis.Geometric and machine learning/deep learning approaches are the two main strategies that have been most frequently utilized in the field of building group pattern detection in recent years.However,there are limitations associated with geometric approaches,such as challenges in threshold setting,complex rule formulations,and limited pattern recognition capabilities.Techniques based on machine learning and deep learning also have difficulties,such as substantial data requirements and complicated feature selection procedures.In response to these challenges,researchers have developed the directional entropy as a novel approach for multi-pattern detection of building groups.The directional entropy is a derivative measure of information entropy,which has been utilized in spatial analysis to evaluate the uncertainty of directional random variables.It assists in describing the prevalence,characteristics,and regularities of spatial phenomena.The study procedure for utilizing directional entropy in developing group pattern recognition is as follows:First,a minimal spanning tree geometric model is created by clustering building data from Lanzhou City using an artificial visual technique;Then,the building groups are split into sample set 1 and sample set 2,in a 7:3 ratio.Classification thresholds for straight,grid,and irregular building groups are calculated based on the training set and validated using the validation set.The experimental results show that directional entropy achieves a classification accuracy of above 97%for all three different building group types.The classification criteria established on the training set are further applied to building data from Shanghai,which yields expected results.These results demonstrate the effectiveness of directional entropy in classifying various building group modes and highlights the potential of directional entropy in identifying building group patterns.Compared to conventional and machine learning techniques,directional entropy overcomes several limitations and produces satisfactory classification results,presenting a novel strategy and technique for establishing group pattern recognition.