最优特征选择下多层次分割的城市道路提取

针对道路提取过程中特征维数过高的问题，提出了一种基于ReliefF过滤式和Wrapper封装式的特征选择方法。将粒子群优化算法（PSO）作为Wrapper的搜索算法，优化过的随机森林算法（OPRF）作为Wrapper的分类器构成PSO_OPRF封装式子集评估器，对ReliefF预选后的特征子集进行评估，降低特征维度，选出最优特征集，根据选择的特征对影像进行多层次分割分类提取城市道路网。以山西省太原市部分城区GF 2遥感影像为数据源进行道路提取，利用提出的特征选择方法所得的道路提取质量与仅使用ReliefF算法选择的特征、以传统随机森林作为分类器和以J48决策树作为分类器特征选择方法的提取质量对比。结果表明该方法的三种类型道路的提取质量分别达到0.959、0.853、0.931。

Urban road extraction of multi-level segmentation based on optimal feature selection

In order to solve the problem that the feature dimension is too high in the process of road extraction, a kind of feature selection method based on ReliefF filtering and Wrapper encapsulation was put forward. The particle swarm optimization algorithm (PSO) was used as the search algorithm of Wrapper, and the optimized random forest algorithm (OPRF) was used as the classifier of Wrapper to form the PSO OPRF encapsulated subsets evaluator. The feature subset selected by ReliefF was evaluated, the feature dimension was reduced, and the optimal feature subset was selected. According to the selected features, the urban road network was extracted by multi level segmentation and classification. Taking GF-2 remote sensing images of some urban areas of Taiyuan city, Shanxi province as data sources, the road extraction quality obtained by using the proposed feature selection method was compared with that obtained by using only ReliefF algorithm, using traditional random forest as a classifier, and using J48 decision tree as a kind of method on classifier feature selection. The results show that the road extraction quality is the best for the proposed method, and the extraction qualitiy of three types of road are up to 0.959, 0.853 and 0.931, respectively.