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| 基于BP人工神经网络的水体遥感测深方法研究 | |
| 引用本文： | 王艳姣,张培群,董文杰,张鹰. 基于BP人工神经网络的水体遥感测深方法研究[J]. 海洋通报(英文版), 2007, 9(1): 26-35 |
| 作者姓名： | 王艳姣 张培群 董文杰 张鹰 |
| 作者单位： | 1. 国家气候中心,北京,100081;中科院大气物理研究所,北京,100029 2. 国家气候中心,北京,100081 3. 南京师范大学地理科学学院,江苏,南京,210097 |
| 摘 要： | 利用Landsat7 ETM 遥感图像反射率和实测水深值之间的相关性,建立了动量BP人工神经网络水深反演模型,并对长江口南港河段水深进行了反演。结果表明:具有较强非线性映射能力的动量BP神经网络模型能较好地反演出长江口南港河段的水深分布情况;由于受长江口水体高含沙量的影响,模型对小于5m的水深值反演精度较高,而对大于10m的水深值反演精度较低。 |
| 关 键 词： | 长江口 BP神经网络 水深遥感 反演模型 |
| 修稿时间： | 2006-09-13 |
Study on Remote Sensing of Water Depths Based on BP Artificial Neural Network |
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| WANG Yanjiao,ZHAOG Peiqun,DONG Wenjie,ZHANG Ying. Study on Remote Sensing of Water Depths Based on BP Artificial Neural Network[J]. Marina Science Bulletin, 2007, 9(1): 26-35 | |
| Authors: | WANG Yanjiao ZHAOG Peiqun DONG Wenjie ZHANG Ying |
| Affiliation: | 1. Laboratory for Climate Studies of China Meteorological Administration, National Climate Center, Beijing 100081 China; 2. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; 3. College of Geographical Science of Nanjing Normal University, Nanjing 210097, Jiangsu, China |
| Abstract: | A momentum BP neural network model (MBPNNM) was constructed to retrieve the water depth information for the South Channel of the Yangtze River Estuary using the relationship between the reflectance derived from Landsat 7 satellite data and the water depth information. Results showed that MBPNNM,which exhibited a strong capability of nonlinear mapping,allowed the water depth information in the study area to be retrieved at a relatively high level of accuracy. Affected by the sediment concentration of water in the estuary,MBPNNM enabled the retrieval of water depth of less than 5 meters accurately. However,the accuracy was not ideal for the water depths of more than 10 meters. |
| Keywords: | Yangtze River Estuary BP neural network water-depth remote sensing retrieval model |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |