A Stacking Ensemble Learning Method to Classify the Patterns of Complex Road Junctions

被引:8
|
作者
Yang, Min [1 ]
Cheng, Lingya [1 ]
Cao, Minjun [1 ]
Yan, Xiongfeng [2 ]
机构
[1] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China
[2] Tongji Univ, Coll Surveying & Geoinformat, 1239 Siping Rd, Shanghai 200092, Peoples R China
基金
中国国家自然科学基金;
关键词
complex road junction; pattern classification; convolutional neural network; stacking ensemble; area coverage; CLASSIFICATION; NETWORK;
D O I
10.3390/ijgi11100523
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recognizing the patterns of road junctions in a road network plays a crucial role in various applications. Owing to the diversity and complexity of morphologies of road junctions, traditional methods that rely heavily on manual settings of features and rules are often problematic. In recent years, several studies have employed convolutional neural networks (CNNs) to classify complex junctions. These methods usually convert vector-based junctions into raster representations with a predefined sampling area coverage. However, a fixed sampling area coverage cannot ensure the integrity and clarity of each junction, which inevitably leads to misclassification. To overcome this drawback, this study proposes a stacking ensemble learning method for classifying the patterns of complex road junctions. In this method, each junction is first converted into raster images with multiple area coverages. Subsequently, several CNN-based base-classifiers are trained using raster images, and they output the probabilities of the junction belonging to different patterns. Finally, a meta-classifier based on random forest is used to combine the outputs of the base-classifiers and learn to arrive at the final classification. Experimental results show that the proposed method can improve the classification accuracy for complex road junctions compared to existing CNN-based classifiers that are trained using raster representations of junctions with a fixed sampling area coverage.
引用
收藏
页数:15
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