基于自适应标注样本的高分辨率影像道路提取

针对现有道路提取算法中难以大规模人工标注样本类别标签的问题,提出了一种基于自适应标注样本提取遥感影像道路的方法.首先,通过改进的模糊C均值聚类算法提取道路区域,进行初步的样本标注;其次,利用基于二次投票的集成去噪算法定位标签噪声样本,更新样本数据集;再次,将更新后的样本集投入随机森林训练并预测影像的分类结果;最后,对道...

Road Extraction of High-spatial Resolution Remote Sensing Imagery Based on Automatic Sample Labeling Method

The existing road extraction algorithms cannot label large-scale samples manually. A method based on automatic sample labeling is proposed to extract road from remote sensing imagery. Firstly, the improved fuzzy c-means clustering algorithm was used to extract the road area and label the samples. Then the label noise samples were located and the sample data set was updated by using the integrated denoising algorithm based on second voting. Then, the updated sample set was put into random forest training and the classification result was predicted. Finally, the road extraction result was filtered by multi-directional morphological filtering to remove the non-road areas, and the accurate road extraction result was obtained. The experimental results of different resolutions, different scenes and different methods show that the proposed method can select and mark samples by itself. Compared with the traditional algorithm, it has higher extraction accuracy, and has better road extraction effect for linear and curved roads in high-resolution remote sensing images.