基于航海雷达图像的海面风场反演算法研究
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摘要
全方位、实时监测海洋动力环境对于人们认识、开发海洋具有重要的指导价值。航海雷达能够获取高时空分辨率的海杂波图像，应用相关的算法可以从中提取出海洋参数，实现对海面状态的监测，具有其它测量工具无法比拟的优势，引起了国内外学者的广泛关注。本文结合实验室在研项目，开展了海面风场反演算法的研究，为航海雷达在海洋动力环境监测领域的应用和推广奠定了基础。
在分析总结了国内外航海雷达海洋遥感理论成果的基础上，本文对海浪噪声抑制，海面风向、风速计算等关键技术进行深入研究，形成了一套完整的海面风场反演技术方案，并应用模拟图像和真实雷达图像验证了其可行性。
1、针对噪声干扰问题，根据重力波符合的色散关系方程，设计了基于主导波长的色散关系带通滤波器(LDRBF)。LDRBF的频带由主导波波长和最大相对速度确定，与传统色散关系带通滤波器相比，它不依赖实时海流信息，能够独立工作；与普通数字图像滤波器相比，它根据信号特性抑制噪声，不破坏海杂波的原始结构。应用两次较大波浪过程期间获取的航海雷达图像实验，将LDRBF计算的有效波高与参考波高比对，结果显示误差低于0.26m，符合应用要求，证明了该方法的有效性。
2、应用实时色散关系带通滤波器可抑制海浪噪声对海面风场反演的干扰，而海流是滤波器的关键参数。针对传统算法计算的海流存在较大误差的问题，提出了海浪谱加权最小二乘法的海流反演算法(WS-LSM)。WS-LSM算法与传统算法相比在噪声抑制、最小二乘法权值选取方面进行了改进。通过长时间的实测实验，结果显示：改进流速精度为0.11m/s，提高了64.5%，改进流向精度为16.33o，提高了57.87%。改进结果满足应用要求，验证了WS-LSM算法的可行性。
3、针对航海雷达图像噪声多、非线性强以及时间间隔长的特点，提出了基于图像灰度和梯度不变模型的海面风场反演算法。海面风向计算方面，在图像灰度值和梯度值不变模型的基础上，考虑平滑项的影响，建立能量函数；然后应用正则化法求解函数，得到欧拉-拉格朗日非线性系统；再根据迭代方法计算出海面风向。海面风速计算方面，当雷达单独测量时，根据雷达散射截面、实测风向和海浪信噪比，设计了具有三个输入的BP网络结构，计算风速；当已知海气边界层参数时，将它们作为附加输入，设计了超过三个输入的BP网络结构，计算风速。
4、在理想情况下，为了研究海面风场反演与各种环境要素的相关性以及检验海面风场反演技术方案的可行性，为航海雷达遥测海面风场提供依据，应用模拟雷达图像开展海面风场反演实验。首先根据海浪谱基本原理、雷达几何成像原理与调制机制，模拟出航海雷达图像，值得一提的是本文首次在模拟图像过程中加入天线旋转的影响；然后详细设计了海面风场反演技术方案和流程，并应用模拟的图像，在理想情况下，通过实验分析了风速、测量面积、天线转速等因素对于风场结果的影响；最后应用模拟图像开展实验，结果显示风速误差低于0.26m/s，风向误差为2.76o，初步验证了海面风场反演技术方案的可行性。
5、2010年下半年开展了岸基航海雷达遥测实验，获取了大量的航海雷达图像和参考风速、风向资料。应用上面设计的海面风场反演技术方案处理实测的航海雷达图像。将实测风向与参考风向进行比对，结果显示实测风向误差为11.3o。将实测风速与参考风速进行比对：当仅考虑雷达散射截面与风速、实测风向与海浪信噪比的关系，应用BP网络计算的风速误差低于0.50m/s；当考虑海气边界层的稳定性对风速的影响时，应用BP网络计算的风速误差低于0.26m/s。实验结果进一步验证了海面风场反演技术方案的可行性。
Overall sea dynamic environment monitoring in real time has significant meaning tounderstand the ocean and exploit its resources. Marine radar can obtain sea clutter imageswith high resolution in space and time. By analyzing the spatial and temporal radar imagesequences with related algorithm, the ocean surface dynamic parameters can be extracted, andthen the sea state is known. Compared with other methods, marine radar has an incomparableadvantage and arouses wide interest of scholars all over the world. In this thesis, based on aresearch project of laboratory, ocean wind field retrieval algorithm is investigated, whichestablishes a foundation for further application and popularization of marine radar in seadynamic environment monitoring area.
By summarizing the outcome of marine radar on ocean remote sensing in domestic andinternational, this dissertation makes a thorough study of wave noise suppression, oceansurface wind vector calculation. And a relatively complete technical scheme on ocean surfacewind field retrieval is formed, which was validated to be feasible by experiments usingsimulated and real image sequences.
In order to remove the noise disturbance, a dispersion relationship band filter based onmain wave number is designed according to the dispersion relationship of gravity waves,named LDRBF. The bandwidth of LDRBF is decided by the main wave number andmaximum relative speed. Compared with traditional dispersion relationship band filter,LDRBF could work independently without real-time ocean current. In contrast to ordinarydigital image filter, LDRBF doesn’t destroy the original sea clutter structure by working onthe basis of sea clutter feature. Radar images recorded during two high sea state processes areapplied for experiment. The error of measured significant wave height derived from LDRBFis below0.26m compared with reference significant wave height, which shows the highperformance and feasibility of LDRBF.
The traditional dispersion relationship band filter can be used for removing wave noisedisturbance of wind field retrieval. Ocean current is the key parameter of the traditional filter,while the current error is large calculated by traditional ocean current algorithm. In order toovercome the problem, a new method for retrieving ocean surface current is presented, namedWS-LSM. Compared with traditional algorithm, WS-LSM algorithm is significantlyimproved on noise suppression and weight selection of the least squared method. Long timeexperiment has been carried out. The comparison of ocean current speed resulted in an accuracy of0.11m/s with an increase of64.5%, and ocean current direction resulted in anaccuracy of16.33°with an increase of57.87%. It can be seen that the WS-LSM algorithm hashigh performance and feasibility.
A new ocean surface wind field retrieval algorithm based on brightness and gradientconstancy model was presented aiming at the nautical radar image features that is, the imagehas much noise, strong nonlinearity and long interval. One is wind direction calculation.Firstly, with the constancy assumptions of brightness, gradient and considering thesmoothness effect, the energy function is established. By using the regularization method tosolve the energy function, the nonlinear Euler-Lagrange equations are deduced. Finally, oceansurface wind direction can be achieved by use of iterative method to solve Euler-Lagrangeequations. Another is wind speed calculation. With the radar cross section, the measured winddirection and the wave signal and noise ratio as the input, the structure of BP neutral networkis designed for calculating wind speed when radar works alone. When the parameters ofmarine and air boundary layer are known, another structure of BP neutral network is designedfor calculating wind speed with the parameters as additional input.
In order to study the correlation between wind vector retrieval results and environmentalfactors as well as the feasibility of ocean surface wind retrieval technical scheme, whichprovide a basis for marine radar remote sensing ocean surface wind, ocean wind retrievalexperiments are carried out by simulated image sequences. Firstly, based on the basicprinciples of wave spectrum and radar geometric imaging, radar images are simulated. Aproblem worthy to be pointed out is that the effect of antenna rotation is taken into account inthe simulation process. Secondly, ocean surface wind field retrieval technical scheme isdesigned in detail. And then the effect of wind speed, measured area and antenna rotation onwind vector retrieval is analyzed by experiments under ideal condition. Finally experiment iscarried out with simulated images, and the results show that the wind direction resulted in anerror of2.76°, wind speed error of0.26m/s, which verify the feasibility of wind vectorretrieval technical scheme.
Remote sensing experiment with shore based marine radar was carried out in2010. Agreat number of radar images were recorded simultaneously with observations of wind vectorby in-situ sensor. Marine radar images are analyzed by wind vector retrieval technical scemedescribed above. The comparison of wind direction results in an error of11.3o. Thecomparison of wind speed results in an error of0.50m/s when considering radar cross section,wind direction and wave signal noise ratio. While considering marine and air boundary layerparameters, the wind speed error is0.26m/s. The feasibility of ocean surface wind field retrieval technical scheme is further verified by the experimental results.
在分析总结了国内外航海雷达海洋遥感理论成果的基础上，本文对海浪噪声抑制，海面风向、风速计算等关键技术进行深入研究，形成了一套完整的海面风场反演技术方案，并应用模拟图像和真实雷达图像验证了其可行性。
1、针对噪声干扰问题，根据重力波符合的色散关系方程，设计了基于主导波长的色散关系带通滤波器(LDRBF)。LDRBF的频带由主导波波长和最大相对速度确定，与传统色散关系带通滤波器相比，它不依赖实时海流信息，能够独立工作；与普通数字图像滤波器相比，它根据信号特性抑制噪声，不破坏海杂波的原始结构。应用两次较大波浪过程期间获取的航海雷达图像实验，将LDRBF计算的有效波高与参考波高比对，结果显示误差低于0.26m，符合应用要求，证明了该方法的有效性。
2、应用实时色散关系带通滤波器可抑制海浪噪声对海面风场反演的干扰，而海流是滤波器的关键参数。针对传统算法计算的海流存在较大误差的问题，提出了海浪谱加权最小二乘法的海流反演算法(WS-LSM)。WS-LSM算法与传统算法相比在噪声抑制、最小二乘法权值选取方面进行了改进。通过长时间的实测实验，结果显示：改进流速精度为0.11m/s，提高了64.5%，改进流向精度为16.33o，提高了57.87%。改进结果满足应用要求，验证了WS-LSM算法的可行性。
3、针对航海雷达图像噪声多、非线性强以及时间间隔长的特点，提出了基于图像灰度和梯度不变模型的海面风场反演算法。海面风向计算方面，在图像灰度值和梯度值不变模型的基础上，考虑平滑项的影响，建立能量函数；然后应用正则化法求解函数，得到欧拉-拉格朗日非线性系统；再根据迭代方法计算出海面风向。海面风速计算方面，当雷达单独测量时，根据雷达散射截面、实测风向和海浪信噪比，设计了具有三个输入的BP网络结构，计算风速；当已知海气边界层参数时，将它们作为附加输入，设计了超过三个输入的BP网络结构，计算风速。
4、在理想情况下，为了研究海面风场反演与各种环境要素的相关性以及检验海面风场反演技术方案的可行性，为航海雷达遥测海面风场提供依据，应用模拟雷达图像开展海面风场反演实验。首先根据海浪谱基本原理、雷达几何成像原理与调制机制，模拟出航海雷达图像，值得一提的是本文首次在模拟图像过程中加入天线旋转的影响；然后详细设计了海面风场反演技术方案和流程，并应用模拟的图像，在理想情况下，通过实验分析了风速、测量面积、天线转速等因素对于风场结果的影响；最后应用模拟图像开展实验，结果显示风速误差低于0.26m/s，风向误差为2.76o，初步验证了海面风场反演技术方案的可行性。
5、2010年下半年开展了岸基航海雷达遥测实验，获取了大量的航海雷达图像和参考风速、风向资料。应用上面设计的海面风场反演技术方案处理实测的航海雷达图像。将实测风向与参考风向进行比对，结果显示实测风向误差为11.3o。将实测风速与参考风速进行比对：当仅考虑雷达散射截面与风速、实测风向与海浪信噪比的关系，应用BP网络计算的风速误差低于0.50m/s；当考虑海气边界层的稳定性对风速的影响时，应用BP网络计算的风速误差低于0.26m/s。实验结果进一步验证了海面风场反演技术方案的可行性。
Overall sea dynamic environment monitoring in real time has significant meaning tounderstand the ocean and exploit its resources. Marine radar can obtain sea clutter imageswith high resolution in space and time. By analyzing the spatial and temporal radar imagesequences with related algorithm, the ocean surface dynamic parameters can be extracted, andthen the sea state is known. Compared with other methods, marine radar has an incomparableadvantage and arouses wide interest of scholars all over the world. In this thesis, based on aresearch project of laboratory, ocean wind field retrieval algorithm is investigated, whichestablishes a foundation for further application and popularization of marine radar in seadynamic environment monitoring area.
By summarizing the outcome of marine radar on ocean remote sensing in domestic andinternational, this dissertation makes a thorough study of wave noise suppression, oceansurface wind vector calculation. And a relatively complete technical scheme on ocean surfacewind field retrieval is formed, which was validated to be feasible by experiments usingsimulated and real image sequences.
In order to remove the noise disturbance, a dispersion relationship band filter based onmain wave number is designed according to the dispersion relationship of gravity waves,named LDRBF. The bandwidth of LDRBF is decided by the main wave number andmaximum relative speed. Compared with traditional dispersion relationship band filter,LDRBF could work independently without real-time ocean current. In contrast to ordinarydigital image filter, LDRBF doesn’t destroy the original sea clutter structure by working onthe basis of sea clutter feature. Radar images recorded during two high sea state processes areapplied for experiment. The error of measured significant wave height derived from LDRBFis below0.26m compared with reference significant wave height, which shows the highperformance and feasibility of LDRBF.
The traditional dispersion relationship band filter can be used for removing wave noisedisturbance of wind field retrieval. Ocean current is the key parameter of the traditional filter,while the current error is large calculated by traditional ocean current algorithm. In order toovercome the problem, a new method for retrieving ocean surface current is presented, namedWS-LSM. Compared with traditional algorithm, WS-LSM algorithm is significantlyimproved on noise suppression and weight selection of the least squared method. Long timeexperiment has been carried out. The comparison of ocean current speed resulted in an accuracy of0.11m/s with an increase of64.5%, and ocean current direction resulted in anaccuracy of16.33°with an increase of57.87%. It can be seen that the WS-LSM algorithm hashigh performance and feasibility.
A new ocean surface wind field retrieval algorithm based on brightness and gradientconstancy model was presented aiming at the nautical radar image features that is, the imagehas much noise, strong nonlinearity and long interval. One is wind direction calculation.Firstly, with the constancy assumptions of brightness, gradient and considering thesmoothness effect, the energy function is established. By using the regularization method tosolve the energy function, the nonlinear Euler-Lagrange equations are deduced. Finally, oceansurface wind direction can be achieved by use of iterative method to solve Euler-Lagrangeequations. Another is wind speed calculation. With the radar cross section, the measured winddirection and the wave signal and noise ratio as the input, the structure of BP neutral networkis designed for calculating wind speed when radar works alone. When the parameters ofmarine and air boundary layer are known, another structure of BP neutral network is designedfor calculating wind speed with the parameters as additional input.
In order to study the correlation between wind vector retrieval results and environmentalfactors as well as the feasibility of ocean surface wind retrieval technical scheme, whichprovide a basis for marine radar remote sensing ocean surface wind, ocean wind retrievalexperiments are carried out by simulated image sequences. Firstly, based on the basicprinciples of wave spectrum and radar geometric imaging, radar images are simulated. Aproblem worthy to be pointed out is that the effect of antenna rotation is taken into account inthe simulation process. Secondly, ocean surface wind field retrieval technical scheme isdesigned in detail. And then the effect of wind speed, measured area and antenna rotation onwind vector retrieval is analyzed by experiments under ideal condition. Finally experiment iscarried out with simulated images, and the results show that the wind direction resulted in anerror of2.76°, wind speed error of0.26m/s, which verify the feasibility of wind vectorretrieval technical scheme.
Remote sensing experiment with shore based marine radar was carried out in2010. Agreat number of radar images were recorded simultaneously with observations of wind vectorby in-situ sensor. Marine radar images are analyzed by wind vector retrieval technical scemedescribed above. The comparison of wind direction results in an error of11.3o. Thecomparison of wind speed results in an error of0.50m/s when considering radar cross section,wind direction and wave signal noise ratio. While considering marine and air boundary layerparameters, the wind speed error is0.26m/s. The feasibility of ocean surface wind field retrieval technical scheme is further verified by the experimental results.
引文
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