基于不确定抽样的半监督城市土地功能分类方法

为提高分类准确率, 解决城市区域社会功能标签分类难的问题, 提出了一种基于不确定抽样选择策略的半监督城市土地功能分类方法。该算法从轨迹数据中提取城市区域的特征向量, 只需对少量区域进行标签, 根据不确定抽样的主动学习选择策略, 从未标注训练样本中选取具有较多信息的数据, 利用半监督学习算法进行标注, 得到新的标注训练样本添加到训练集, 反复迭代后得到分类结果。实验结果表明, 该方法对不同社会功能的城市区域分类准确率可达90. 2%, 与传统方法相比分类准确率高, 减少了大量标注工作, 在少数标签数据上仍有较好的分类效果。

Semi-Supervised Urban Land Using Classification Method Based on Uncertainty Sampling

In order to improve the classification accuracy and solve the problem of the difficulty of labeling social functions of the urban land, a semi-supervised urban land function classification method based on the uncertainty sampling selection strategy is proposed. The algorithm extracts the feature vector of the urban area from the trajectory data, and only a small number of areas need to be labeled. According to the uncertainty sampling active learning selection strategy, unlabeled training samples with more information data are selected and labeled by semi-supervised learning algorithm. The new labeled training samples are added to the training set. The classification results will come out after repeated iteration. Experimental results show that the proposed method can reach the accuracy rate of 90. 2% on classifying urban areas with different social functions. It has a high classification accuracy and reduces a lot of labeling work compared to traditional methods, showing a good performance on a small number of labeled data.