珠江口海域叶绿素α质量浓度SAR反演模型

海洋学研究 ›› 2012, Vol. 30 ›› Issue (2): 66-73.

珠江口海域叶绿素α质量浓度SAR反演模型

李露锋, 刘湘南^*, 李致博, 弥永宏

中国地质大学(北京)信息工程学院,北京 100083

收稿日期:2011-06-29 修回日期:2012-03-07 出版日期:2012-06-15 发布日期:2023-04-24
通讯作者: * 刘湘南,教授,E-mail: Liuxncugb@163.com
作者简介:李露锋(1985-),男,湖南岳阳市人,硕士研究生,主要从事遥感建模及GIS应用等方面的研究。E-mail:lilufeng219@163.com
基金资助:
国家自然科学基金资助项目(U0933005)

Inversion models of chlorophyll α mass concentration in Pearl River Estuary using SAR image

Ll Lu-feng, LIU Xiang-nan^*, Ll Zhi-bo, MI Yong-hong

Faculty of Inf ormation Engineering, China University of Geosciences,Beijing 100083,China

Received:2011-06-29 Revised:2012-03-07 Online:2012-06-15 Published:2023-04-24

摘要/Abstract

摘要： 以珠江口海域的Radarsat-2全极化SAR数据和海域表层水面叶绿素α质量浓度实测数据为基础,利用微波散射原理及Cloude-Pottier理论对SAR图像进行分解,得到平均散射角α、散射嫡H及VH,VV、HH、HV等6个参数;采用BP人工神经网络模型建立上述6个参数与叶绿素质量装度的数学关系模型,并结合实测数据对叶绿素α质量浓度进行分类。结果表明:当隐含层节点数为9,输入层和隐含层传递函数分别为tansig 和 logsig,学习速率和动量系数均为0.2时的网络结构对叶绿素质量浓度反演取得了较好的效果,叶绿素α质量浓度实测值与预测值之间的决定系数(R²)为0.826。将模型应用于不同时期的2幅图像进行验证,效果良好,与实际情况基本相符。

关键词: Radarsat-2, SAR, BP人工神经网络, 叶绿素α质量浓度

Abstract: Base on the Radarsat-2 full polarimetric SAR data and the observed chlorophyll a data from the seasurface of the Pearl River Estuary, and using microwave scattering theory and the Cloude-Pottier theory, theSAR images were decomposed,which resulted in the average scattering angle a and scattering entropies H andVH, VV.HH and HV. A mathematical relationship model between the six parameters and the chlorophyll amass concentration was established using the BP artificial neural network model,and combined with themeasured data the chlorophyll a mass concentration was classified. The results shows that the net workstructure has a good result of the inversion to the chlorophyll a mass concentration when the hidden layernodes is 9,the transmit function of the input layer and the hidden layer is tansig and logsig respectively andthe learning rate and the momentum coefficient are both 0.2,namely, the determination coefficient betweenthe measured data and the predicted data of the chlorophyll a mass concentration (R²) is 0.826. When themodel was applied to two images of different period for verification,it worked well, and the results were ingood accordance with the actual situation.

Key words: Radarsat-2, SAR, BP artificial neural network, chlorophyll a mass concentration

中图分类号:

李露锋, 刘湘南, 李致博, 弥永宏. 珠江口海域叶绿素α质量浓度SAR反演模型[J]. 海洋学研究, 2012, 30(2): 66-73.

Ll Lu-feng, LIU Xiang-nan, Ll Zhi-bo, MI Yong-hong. Inversion models of chlorophyll α mass concentration in Pearl River Estuary using SAR image[J]. Journal of Marine Sciences, 2012, 30(2): 66-73.

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