| 利用随机森林回归进行极化SAR土壤水分反演 |
| |
| 引用本文： | 李平湘,刘致曲,杨杰,孙维东,黎旻懿,任烨仙.利用随机森林回归进行极化SAR土壤水分反演[J].武汉大学学报(信息科学版),2019,44(3):405-412. |
| |
| 作者姓名： | 李平湘 刘致曲 杨杰 孙维东 黎旻懿 任烨仙 |
| |
| 作者单位： | 1.武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉, 430079 |
| |
| 基金项目： | 国家自然科学基金41771377国家自然科学基金41601355国家自然科学基金91438203国家自然科学基金41501382国家国防科技工业局高分项目03-Y20A10-9001-15/16 |
| |
| 摘 要： | 全极化合成孔径雷达影像能够提供地物丰富的极化信息，挖掘这些信息在地表参数反演中的作用是目前相关领域的研究趋势之一。针对冬小麦区域的不同植被覆盖情况，利用随机森林回归对常用极化特征在土壤水分反演中的重要性进行评估，并在此基础上进行特征选择，挑选优化的极化特征组合，构建了高精度的土壤水分反演模型。实验结果显示，由重要性评分较高的极化特征所组成的反演模型能得到均方根误差（root mean square error，RMSE）小于6%的反演精度，比只输入传统线极化后向散射系数的模型在不同时相、不同数据集的精度都有所提高。与支持向量回归和人工神经网络模型进行比较，利用随机森林回归进行重要性评分与土壤水分反演的效果更好。
|
| 关 键 词： | 极化SAR 土壤水分 随机森林回归 支持向量回归 人工神经网络 |
| 收稿时间： | 2018-03-06 |
Soil Moisture Retrieval of Winter Wheat Fields Based on Random Forest Regression Using Quad-Polarimetric SAR Images |
| |
| Affiliation: | 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China2.Deqing iSpatial Co. Ltd, Deqing 313200, China |
| |
| Abstract: | Soil moisture has great significance in the researches of hydrology, meteorology and agriculture yield estimation. The quad-polarimetric SAR images can provide a lot of polarimetric features, the significance of the features in surface parameter retrieval have attracted attentions in previous researches with no final conclusions because of the complexity of terrain scattering. In this paper, random forest regression (RFR) is used for both soil moisture retrieval and the importance evaluation of polarimetric features of Radarsat-2 images in winter wheat fields. According to the score of importance, feature selection and combination are done for modelling. We evaluate the retrieval accuracy of models with different feature combinations. The results show that models of important features selected by RFR have RMSE(root mean square error) less than 6% which are better results compared to traditional models; when compared with support vector regression and artifical neural networks, the RFR also shows best retrieval accuracies, which proves that RFR is suitable for soil moisture retrieval and feature selection. The high retrieval accuracies of LBC-CPD(linear backscatter coefficients-Cloude-Pottier decomposition) and LBC-CPR(linear backscatter coefficients-circular polarimetric ratio) indicates these features can improve the retrieval accuracy of soil moisture. |
| |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《武汉大学学报(信息科学版)》浏览原始摘要信息 |
|
点击此处可从《武汉大学学报(信息科学版)》下载全文 |
