| 基于随机森林算法的城市不透水面信息提取——以长春市为例 |
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| 引用本文： | 常翔宇,柯长青.基于随机森林算法的城市不透水面信息提取——以长春市为例[J].测绘通报,2020,0(11):43-49. |
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| 作者姓名： | 常翔宇 柯长青 |
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| 作者单位： | 南京大学地理与海洋科学学院, 江苏 南京 210023 |
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| 基金项目： | 江苏省自然科学基金（BK20180343） |
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| 摘 要： | 为了快速、准确地掌握不透水面的空间分布及满足动态变化信息现实需求，本文基于多分类器集成学习的思想，引入随机森林算法，以Landsat8影像为数据源，长春市为实验区，选取光谱特征、纹理测度、空间变换后的独立分量等25个特征变量进行分类研究，根据OOB误差进行重要性分析并试验得出最优的分类模型，实现高精度不透水面信息的提取，最后与传统参数分类法进行比较。结果表明：随机森林算法的总体精度可以达到94%，高出最大似然分类法5.9%，支持向量机算法0.77%，Kappa系数为0.914 3，均方根误差为0.104 3，不透水面的提取精度达95.54%，可以精确地得出所需信息，为城市建设与规划提供有效的专题数据。
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| 关 键 词： | 不透水面 随机森林 影像分类 特征提取 |
| 收稿时间： | 2019-12-09 |
| 修稿时间： | 2020-07-06 |
Urban impervious surface information extraction based on random forest algorithm: taking Changchun as an example |
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| CHANG Xiangyu,KE Changqing.Urban impervious surface information extraction based on random forest algorithm: taking Changchun as an example[J].Bulletin of Surveying and Mapping,2020,0(11):43-49. |
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| Authors: | CHANG Xiangyu KE Changqing |
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| Institution: | School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China |
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| Abstract: | In order to quickly and accurately grasp the spatial distribution and dynamic change information of impervious surface, based on the idea of multi-classifier ensemble learning, random forest algorithm is introduced in eliis paper. Landsat 8 image is used as data source and Changchun city as experimental area. 25 feature variables, such as spectral indices, texture measures and independent components after spatial transformation are selected to classify. The importance of variables calculated by out of bag error is analyzed and the optimal classification model is obtained through many experiments. The extraction of high-precision impervious surface is also realized. Finally, random forest algorithm is compared with the traditional parameter classifier. The result indicated that the overall accuracy of random forest algorithm can reach 94%, which is higher than 5.9% of maximum likelihood classification, 0.77% of support vector machine algorithm, 0.914 3 of Kappa coefficient and 0.104 3 of root mean square error. The extraction accuracy of impervious surface is 95.54%, which can not only accurately extract impervious surface but also provide effective thematic information for urban construction and planning. |
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| Keywords: | impervious surface random forest image classification feature extraction |
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