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.
关键词
方向熵/建筑物群组模式/最小生成树/模式识别/阈值分类/直线模式/格网模式
Key words
directional entropy/building group mode/minimal spanning tree/pattern recognition/threshold classification/straight line mode/grid mode
维普
万方数据