| 黄河三角洲地区GF-3雷达数据与Sentinel-2多光谱数据湿地协同分类研究 |
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| 引用本文： | 李鹏,黎达辉,李振洪,王厚杰.黄河三角洲地区GF-3雷达数据与Sentinel-2多光谱数据湿地协同分类研究[J].武汉大学学报(信息科学版),2019,44(11):1641-1649. |
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| 作者姓名： | 李鹏 黎达辉 李振洪 王厚杰 |
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| 作者单位： | 1.中国海洋大学海底科学与探测技术教育部重点实验室, 山东 青岛, 266100 |
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| 基金项目： | 国家自然科学基金41806108国家重点研发计划2017YFE0133500国家重点研发计划2016YFA0600903山东省自然科学基金ZR2016DB30中国博士后科学基金2016M592248青岛市自主创新计划应用基础研究项目16-5-1-25-jch中央高校基本科研业务费专项资金201713039 |
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| 摘 要： | 黄河三角洲湿地的动态变化监测对湿地资源合理利用、开发保护具有重要意义。采用C波段全极化高分三号（GF-3）合成孔径雷达数据与欧洲空间局哨兵二号（Sentinel-2B）多光谱数据，分析了黄河三角洲湿地7类地物的光谱、指数、极化散射以及纹理等特征信息，分别基于最大似然法（maximum likelihood，ML）、决策树（decision tree，DT）、支持向量机（support vector machine，SVM）方法实现了有监督分类，评估了两者协同与单独应用于湿地地物分类与识别的能力，结果表明，两者协同分类时，其总体精度分别可达90.4%、95.4%、95.7%，均明显高于两者单独分类的结果，证明了GF-3雷达数据与多光谱数据在湿地协同分类方面的可靠性和应用潜力。
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| 关 键 词： | 黄河三角洲 GF-3 Sentinel-2B 湿地分类 监督分类 |
| 收稿时间： | 2018-11-04 |
Wetland Classification Through Integration of GF-3 SAR and Sentinel-2B Multispectral Data over the Yellow River Delta |
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| Affiliation: | 1.Key Laboratory of Submarine Geosciences and Prospecting Techniques, MOE, Ocean University of China, Qingdao 266100, China2.Laboratory of Marine Geology, Pilot National Laboratory for Marine Science and Technology(Qingdao), Qingdao 266061, China3.School of Geographic Sciences, East China Normal University, Shanghai 200241, China4.School of Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK |
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| Abstract: | It is of great significance to monitor dynamic change of wetland over the Yellow River Delta for rational utilization, development and protection of wetland resources. Both Gaofen-3 (GF-3) SAR data and Sentinel-2B multispectral data were used to analyze the spectral, index, polarization scatter and texture feature information of seven types of ground objects over the Yellow River Delta wetland, and then supervised classification was implemented with maximum likelihood (ML), decision tree (DT) and support vector machine (SVM) classifier. The performances of both the joint and the individual classifications with GF-3 and Sentinel-2B data were also evaluated. The results of three algorithms show that the overall accuracy of the joint classification can reach 90.4%, 95.4%, 95.7%, significantly higher than that of the individual classifications, showing the promising potential of GF-3 SAR and Sentinel-2B multi-spectral images in joint wetland classification. |
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