基于小波的多极化机载合成孔径雷达海面风向反演

艾未华; 孔毅; 赵现斌

doi:10.7498/aps.61.148403

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摘要

为了实现从合成孔径雷达(SAR)图像本身提取高精度的海面风向信息, 提高SAR海面风场反演精度,研究了多极化机载SAR海面风向反演技术, 借助小波分析相对傅里叶分析和局部梯度更精细的时-频分析能力, 将二维连续小波变换与快速傅里叶变换(FFT)相结合,提出一种新的机载SAR海面风向反演方法. 为验证反演方法的有效性,通过海上同步飞行试验获取多极化机载SAR数据及同步调查船实测风向数据,用于反演试验的数据比对.采用本文提出的方法, 利用多种小波基对机载C波段SAR的同极化和交叉极化数据进行风向反演, 将反演结果与美国国家环境预报中心再分析资料以及调查船实测风向进行比对. 结果表明,本文提出的基于小波分析的海面风向反演方法适用于机载SAR探测数据, 反演精度优于二维FFT法和局部梯度方法;小波基的选择对反演结果影响较大, Mexican-Hat小波基是机载SAR海面风向反演的最优小波基, 且同极化与交叉极化机载SAR数据均可用于海面风向的反演.

关键词:

作者及机构信息

1.
解放军理工大学气象学院, 南京 211101

基金项目: 国家自然科学基金(批准号: 41005018)、解放军理工大学气象学院基础理论基金(批准号: 201001) 和中国气象局大气物理与大气环境重点开放实验室基金(批准号: KDW1105)资助的课题.

参考文献

[1]	Lin H, Xu Q, Zheng Q 2008 Prog. Nat. Sci. 18 913
[2]	Zecchetto S, Biasio D 2008 IEEE Trans. Geosci. Remote Sens. 46 2983
[3]	Horstmann J, Koch W, Lehner S, Tonboe R 2000 IEEE Trans. Geosci. Remote Sens. 38 2122
[4]	Thompson D R, Beal R C 2000 Johns Hopkins APL Tech. Dig. 21 58
[5]	Monaldo F 2000 Johns Hopkins APL Tech. Dig. 21 75
[6]	Cameron I, Lumsdon P, Walker N, Woodhouse I 2006 In Proceedings of SEASAR: Advances in SAR Oceanography from ENVISAT and ERS Missions Frascati, Italy, January 23-26, 2006 p1
[7]	Yang J S 2005 SAR Remote Sensing Techniques of Sea Surface Wind, Surface Wave and Internal Wave (Beijing: Ocean Press) pp31-32(in Chinese) [杨劲松 2005 合成孔径雷达海面风场、海浪和海洋内波遥感技术(北京:海洋出版社) 第31—32页]
[8]	Apel J R 1994 J. Geophys. Res. 99 16269
[9]	Levy G 1998 Proc. Int. Geosci. Remote Sens. Symp. 1437
[10]	Vachon P W, Dobson F W 1996 The Global Atmosphere and Ocean System 5 177
[11]	Ynag J S, Huang W G, Zhou C B 2001 J. Remote Sens. 5 13 (in Chinese) [杨劲松, 黄韦艮, 周长宝 2001 遥感学报 5 13]
[12]	Wolfgang K 2004 IEEE Trans. Geosci. Remote Sens. 42 702
[13]	Jiang Z H, Huang S X, Shi H Q, Zhang W, Wang B 2011 Acta Phys. Sin. 60 108402 (in Chinese) [姜祝辉, 黄思训, 石汉青, 张伟, 王彪 2011 物理学报 60 108402]
[14]	Jiang Z H, Huang S X, He R, Zhou C T 2011 Acta Phys. Sin. 60 068401 (in Chinese) [姜祝辉, 黄思训, 何然, 周晨腾 2011 物理学报 60 068401]
[15]	Zhang R W, Yan W, Ai W H, Ma S 2011 J. Mircowaves 27 79 (in Chinese) [张日伟, 严卫, 艾未华, 马烁 2011 微波学报 27 79]
[16]	Kong Y, Zhao X B, Ai W H, Han D, Xue J 2011 J. PLA Univ. Sci. Tech. 12 301 (in Chinese) [孔毅, 赵现斌, 艾未华, 韩丁, 薛剑 2011 解放军理工大学学报 12 301]
[17]	Gladeston C L, Daniela M U, Fátima N S 2010 Sensors 10 5994
[18]	Zhu H B, Wen B Y, Huang J 2005 J. Wuhan Univ. 51 375 (in Chinese) [朱华波, 文必洋, 黄坚 2005 武汉大学学报 51 375]
[19]	Almeida L B 1994 IEEE Trans. Signal Proc. 42 3084
[20]	Kutter M, Bhattacharjee S K, Ebrahimi T 1999 Proceedings of IEEE International Conference Image Processing Japan, Kobe, October 25-28, 1999 p320
[21]	Merete B C, Wolfgang K, Horstmann J 2006 Remote Sens. Environ. 105 68

施引文献

[1]	Lin H, Xu Q, Zheng Q 2008 Prog. Nat. Sci. 18 913
[2]	Zecchetto S, Biasio D 2008 IEEE Trans. Geosci. Remote Sens. 46 2983
[3]	Horstmann J, Koch W, Lehner S, Tonboe R 2000 IEEE Trans. Geosci. Remote Sens. 38 2122
[4]	Thompson D R, Beal R C 2000 Johns Hopkins APL Tech. Dig. 21 58
[5]	Monaldo F 2000 Johns Hopkins APL Tech. Dig. 21 75
[6]	Cameron I, Lumsdon P, Walker N, Woodhouse I 2006 In Proceedings of SEASAR: Advances in SAR Oceanography from ENVISAT and ERS Missions Frascati, Italy, January 23-26, 2006 p1
[7]	Yang J S 2005 SAR Remote Sensing Techniques of Sea Surface Wind, Surface Wave and Internal Wave (Beijing: Ocean Press) pp31-32(in Chinese) [杨劲松 2005 合成孔径雷达海面风场、海浪和海洋内波遥感技术(北京:海洋出版社) 第31—32页]
[8]	Apel J R 1994 J. Geophys. Res. 99 16269
[9]	Levy G 1998 Proc. Int. Geosci. Remote Sens. Symp. 1437
[10]	Vachon P W, Dobson F W 1996 The Global Atmosphere and Ocean System 5 177
[11]	Ynag J S, Huang W G, Zhou C B 2001 J. Remote Sens. 5 13 (in Chinese) [杨劲松, 黄韦艮, 周长宝 2001 遥感学报 5 13]
[12]	Wolfgang K 2004 IEEE Trans. Geosci. Remote Sens. 42 702
[13]	Jiang Z H, Huang S X, Shi H Q, Zhang W, Wang B 2011 Acta Phys. Sin. 60 108402 (in Chinese) [姜祝辉, 黄思训, 石汉青, 张伟, 王彪 2011 物理学报 60 108402]
[14]	Jiang Z H, Huang S X, He R, Zhou C T 2011 Acta Phys. Sin. 60 068401 (in Chinese) [姜祝辉, 黄思训, 何然, 周晨腾 2011 物理学报 60 068401]
[15]	Zhang R W, Yan W, Ai W H, Ma S 2011 J. Mircowaves 27 79 (in Chinese) [张日伟, 严卫, 艾未华, 马烁 2011 微波学报 27 79]
[16]	Kong Y, Zhao X B, Ai W H, Han D, Xue J 2011 J. PLA Univ. Sci. Tech. 12 301 (in Chinese) [孔毅, 赵现斌, 艾未华, 韩丁, 薛剑 2011 解放军理工大学学报 12 301]
[17]	Gladeston C L, Daniela M U, Fátima N S 2010 Sensors 10 5994
[18]	Zhu H B, Wen B Y, Huang J 2005 J. Wuhan Univ. 51 375 (in Chinese) [朱华波, 文必洋, 黄坚 2005 武汉大学学报 51 375]
[19]	Almeida L B 1994 IEEE Trans. Signal Proc. 42 3084
[20]	Kutter M, Bhattacharjee S K, Ebrahimi T 1999 Proceedings of IEEE International Conference Image Processing Japan, Kobe, October 25-28, 1999 p320
[21]	Merete B C, Wolfgang K, Horstmann J 2006 Remote Sens. Environ. 105 68

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基于小波的多极化机载合成孔径雷达海面风向反演

1. 解放军理工大学气象学院, 南京 211101

基金项目:

国家自然科学基金(批准号: 41005018)、解放军理工大学气象学院基础理论基金(批准号: 201001) 和中国气象局大气物理与大气环境重点开放实验室基金(批准号: KDW1105)资助的课题.

收稿日期: 2011-10-26

修回日期: 2011-12-17

刊出日期: 2012-07-05

摘要: 为了实现从合成孔径雷达(SAR)图像本身提取高精度的海面风向信息, 提高SAR海面风场反演精度,研究了多极化机载SAR海面风向反演技术, 借助小波分析相对傅里叶分析和局部梯度更精细的时-频分析能力, 将二维连续小波变换与快速傅里叶变换(FFT)相结合,提出一种新的机载SAR海面风向反演方法. 为验证反演方法的有效性,通过海上同步飞行试验获取多极化机载SAR数据及同步调查船实测风向数据,用于反演试验的数据比对.采用本文提出的方法, 利用多种小波基对机载C波段SAR的同极化和交叉极化数据进行风向反演, 将反演结果与美国国家环境预报中心再分析资料以及调查船实测风向进行比对. 结果表明,本文提出的基于小波分析的海面风向反演方法适用于机载SAR探测数据, 反演精度优于二维FFT法和局部梯度方法;小波基的选择对反演结果影响较大, Mexican-Hat小波基是机载SAR海面风向反演的最优小波基, 且同极化与交叉极化机载SAR数据均可用于海面风向的反演.

关键词:

Ocean surface wind direction retrieval from multi-polarization airborne SAR based on wavelet

1.
Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China

Fund Project:

Project supported by the National Natural Science Foundation of China (Grant No. 41005018), the Institute of Meteorology of PLA University of Science and Technology Foundation of China (Grant No. 201001), and the Foundation of CMA Key Laboratory for Atmospheric Physics and Environment, China (Grant No. KDW1105).

Received Date: 26 October 2011

Accepted Date: 17 December 2011

Published Online: 05 July 2012

Abstract: An ocean surface wind direction retrieval method from multi-polarization airborne synthetic aperture radar images is presented in order to retrieve high precision wind direction SAR image and improve the accuracy of ocean surface wind. The method relies on the ability of two-dimensional (2D) continuous wavelet technique with better time-frequency than Fourier transform and local gradient, which combines 2D continuous wavelet and FFT to exact the ocean surface wind direction from airborne SAR images. The proposed method is executed by using several kinds of mother wavelet functions for the C-band co-polarization and cross-polarization airborne sounding images, and the retrieved ocean surface wind direction is compared with NCEP data and buoy data. The verification results show that the wavelet based ocean surface wind direction retrieval algorithm is suited to retrieve wind direction from airborne SAR sounding data. The choice of mother wavelet function has a certain influence on the results, and 2D Mexican-Hat is the best mother wavelet for wind retrieval. Both co-polarization and cross-polarization airborne sounding images are suited to retrieve ocean wind direction.

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