WO2022016884A1 - Method for extracting sea surface wind speed on basis of k-means clustering algorithm - Google Patents
Method for extracting sea surface wind speed on basis of k-means clustering algorithm Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/91—Radar or analogous systems specially adapted for specific applications for traffic control
- G01S13/92—Radar or analogous systems specially adapted for specific applications for traffic control for velocity measurement
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
- G01S13/62—Sense-of-movement determination
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
Definitions
- the invention relates to the technical field of sea surface wind speed remote sensing using X-band nautical radar images for sea surface wind speed inversion calculation, in particular to a sea surface wind speed method based on K-means clustering algorithm.
- the sea surface wind field is an important factor in the study of ocean dynamics and an important guarantee for the safety of navigation operations. It plays a vital role in understanding ocean changes and predicting sea surface risks.
- the sea surface wind field information mainly includes the sea surface wind direction and the sea surface wind speed.
- the present invention is a method for extracting the sea surface wind speed information based on the nautical radar image.
- the traditional way of extracting wind speed information on the sea surface is mainly anemometers, which are installed on ships, shores or buoys to measure the wind speed. At the same time, this method is susceptible to the influence of sea weather or sea traffic, and lacks continuity in time and space.
- X-band marine radar has the advantages of being unaffected by light, capable of real-time continuous feedback and high resolution, and has become one of the important means of marine environmental monitoring at this stage.
- the X-band marine radar at home and abroad has realized the monitoring of sea surface waves, currents, and rainfall, and the measurement of the oil spill area on the sea surface, but the sea surface wind field measurement based on marine radar images is still in the primary research stage.
- Jia Ruicai of Harbin Engineering University used the double-hidden-layer unipolar S-function BP neural network method to invert the sea surface wind speed information, which improved the convergence speed of the neural network and the network extension ability.
- the application of neural network has inherent shortcomings.
- the main problem is the poor applicability of the model. For different environmental locations and different types of marine radars, a large amount of data is required for retraining, and marine environmental factors have a great impact on the method, so the accuracy cannot be achieved. ensure.
- Bueno et al. used the linear integration method to obtain the relationship between the radar echo intensity level and the sea surface wind speed function to obtain the sea surface wind speed information.
- Liu Y et al. proposed to use hyperbolic fitting to extract sea surface wind speed information from the measured marine radar data for two radars, Decca and Furono.
- Huang W et al. proposed the use of RCS spectral analysis algorithm, RCS and sea surface wind speed empirical mode decomposition method for Decca radar, and established a function model to obtain sea surface wind speed.
- the present invention discloses a method for retrieving sea surface wind speed from marine radar images based on K-means clustering algorithm.
- the radar image is recognized by rainfall image to remove the influence of rainfall on the extraction of sea surface wind speed;
- secondly combined with sensors and radar image information to classify the sea surface influencing factor data, eliminate the influence of heterogeneous data on the sea surface wind speed extraction model, and improve the robustness of the model;
- inheriting the nonlinear relationship between sea surface wind speed and echo intensity proposed by Lund the Eliminate heterogeneous data and apply nonlinear quadratic function to determine the sea surface wind speed extraction model, which ensures the accuracy of model extraction of sea surface wind speed.
- the measured data proves the engineering feasibility of the method to extract the sea surface wind speed information from the nautical radar image.
- the invention discloses a sea surface wind speed method based on the K-means clustering algorithm.
- the method is based on the K-means clustering algorithm, and specifically includes the following steps:
- Step 1 radar image data preprocessing.
- the marine radar monitoring system is used to collect the sea surface radar image sequence data
- the wind meter is used to collect the synchronized sea surface wind direction and wind speed information.
- ZPP Zero Intensity Percentage
- Step 2 data classification based on K-means clustering algorithm.
- the data normalization is performed on the radar image echo intensity, sea surface wind direction information, sea surface wind speed information and the calculated image signal-to-noise ratio, so that the data are in the same coordinate range;
- the K-means clustering algorithm is applied to the radar image
- the image echo intensity, sea surface wind direction information and image signal-to-noise ratio data are classified according to the Euclidean distance, and the error of the centroid distance is used as the judgment basis to obtain heterogeneous data; finally, the radar data and sea surface wind field information data are all excluded.
- Corresponding information data the cluster data of radar data and sea surface wind field information are obtained.
- Step 3 the sea surface wind speed extraction model is determined. Using clustered radar data and sea surface wind speed data, nonlinear quadratic fitting of sea surface wind speed was performed to obtain the extraction model of sea surface wind speed, and SSE was used to verify the accuracy of the model.
- Step 4 extraction of sea surface wind speed information. Select some images of the test marine radar image, carry out normalized mapping, and input them into the sea surface wind speed extraction model to obtain the sea surface wind speed information.
- the steps 2 and 3 of the sea surface wind speed extraction method based on the K-means clustering algorithm include the following steps:
- Step 2.1 normalized data processing of the average value of radar echo intensity, sea surface wind direction, wind speed information and image signal-to-noise ratio information
- f(x, y) is the selected radar image intensity value
- N x and N y are the number of pixels along x and y of the selected image
- i is the corresponding radar image number. Normalize f' i to get the radar image normalized value F i :
- Step 2.2 radar data classification based on K-means clustering algorithm
- F i, R i, D i and S i of all the data is divided into two sections, one for determining the wind speed based clustering algorithm sea Model K-means, one for the data of the test model; and
- the data F i , R i , and D i used in the determination of the model form a data set, which is used as a set of factors affecting the sea surface wind speed;
- the cluster centroids are initialized, and K data points in the ⁇ i region are randomly selected as the initial centroids.
- the Euclidean distance between the data point and the centroid is recalculated according to formula (6), and a new cluster is formed at the same time.
- Step 2.3 remove radar data of heterogeneous data
- the centroid whose centroid position is farthest from other centroids is determined as the heterogeneous centroid C d , and all the data points in its area are also determined as the heterogeneous data set ⁇ d , and the data is removed.
- heterogeneous data sets in ⁇ i, S i the simultaneous removal of heterogeneous data corresponding to a position of surface wind speed S d, to give the final removal of heterogeneous radar data F f, R f, D f , S f:
- Step 3.1 determine the sea surface wind speed extraction model; first release the original characteristics of F f , S f data to obtain the corresponding radar image echo intensity average f f , and the training sea surface wind speed information s f :
- Step 3.2 fitting the sea surface wind speed extraction model; applying the nonlinear quadratic function to fit the data f f and s f to obtain the sea surface wind speed prediction model:
- Step 3.3 test the sea surface wind speed model; select the variance function SSE as the error test index for the model application test data, where SSE is the error square sum of the sea surface wind speed and the test wind speed obtained by inputting the mean value of the test radar echo intensity to the model, and the calculation formula is as follows:
- a K-means clustering algorithm is designed to classify the data of the influencing factors of sea surface wind speed, and obtain heterogeneous data, and remove the influence of interference data on the sea surface model;
- the designed K-means clustering algorithm uses Euclidean distance to determine the distance of data points, and uses the distance mean as the determination condition for updating the centroid position, which has the advantages of fast convergence speed and good clustering effect;
- the fitted model selects the variance function SSE as the error inspection index, which improves the accuracy of the wind speed extraction model and the inversion accuracy of the algorithm in engineering.
- the model is obtained by training the measured X-band marine radar echo image, sea surface wind speed and sea surface wind speed, and has strong engineering applicability.
- Fig. 1 is the specific embodiment flow chart of the present invention
- Figure 3a is the radar map before filtering
- Figure 3b is the radar chart after filtering
- Figure 4 shows the relationship between sea surface wind speed and echo intensity
- Figure 5 is the distribution curve of the number of centroids and the sum of squares of errors
- Figure 6 is the K-means clustering distribution result of the influencing factors of sea surface wind speed
- Fig. 7 is the K-means clustering algorithm wind speed model fitting curve
- Fig. 8 is the fitting curve of the exponential function model wind speed model
- Figure 9 is a comparison result of the inversion results of the two groups of algorithms and the measured sea surface wind speed
- Figure 10 is a comparison chart of the error between the inversion results of the two groups of algorithms and the measured sea surface wind speed
- Figure 11 is a graph of the error statistics between the inversion results of the two groups of algorithms and the measured sea surface wind speed
- the flowchart of the specific embodiment of the present invention is shown in Figure 1, which is divided into four major blocks: marine radar image preprocessing, radar data classification based on K-means clustering algorithm, sea surface wind speed extraction model determination, and sea surface wind speed information extraction.
- the specific implementation steps are divided into sixteen steps.
- the first to third steps are data preprocessing;
- the fourth to eleventh steps are radar data classification based on K-means clustering algorithm;
- the fifth step is to determine the sea surface wind speed extraction model;
- the sixteenth step is to extract and analyze the sea surface wind speed information. Specific steps are as follows:
- the first step is to collect 1722 sets of marine radar images from October 22-29, November 13-21, December 14-26, January 1-10, 2010 by self-made wave monitoring equipment Sequence, each group of image sequences contains 32 marine radar images, and the time for each image is 2.5s.
- the wind meters at the same position synchronously collect the corresponding sea surface wind direction information ⁇ w and wind speed information U w .
- the equipment collection and image sequence are as follows: shown in Figure 2.
- the present invention returns the echo intensity value whose voltage value of the pixel point is less than 0.3V (according to the storage protocol, the echo intensity value after linear normalization of 0.3V voltage is 983) to zero intensity, and calculates the zero intensity pixel point. proportion.
- the formula for calculating the zero intensity percentage is as follows:
- n is the total number of pixel points in the radar image
- n 0 is the number of pixel points in the radar image whose echo intensity values return to zero.
- the zero-intensity percentage is 0.542 when it is not raining
- the average zero-intensity percentage is 0.207 when it is raining. Therefore, the present invention determines radar images with zero intensity percentage lower than 0.207 as images with severe rainfall interference, and directly rejects them, and other images are images without rainfall interference, which are used for sea surface wind speed information extraction. Finally, 100 sets of images were removed by this method, and 1622 sets of images were reserved for subsequent research on sea surface wind speed information extraction technology.
- the third step is to perform median filtering on the marine radar image g(x, y) after rainfall identification to suppress the influence of the same frequency signal on the extraction of sea surface wind speed.
- a 2D nonlinear smooth median filter of 3 ⁇ 3 template is applied to each radar image in the marine radar image sequence, and the filtered image gray value f'(x,y) is:
- f(x, y) is the echo intensity value of the radar image
- f'(x, y) is the gray value after filtering
- (i, j) is the 8 pixel points adjacent to the center of the template W .
- W is the template window, as follows:
- the center of the median filter W is coincident with the center of the image, and the echo intensity value of the surrounding 8 adjacent pixel points is compared, and the middle value of the echo intensity is selected to update the echo intensity value of the image.
- the template is in step unit 1. Traverse the polar coordinate marine radar image, and finally obtain the marine radar image after median filtering.
- Figure 3a is the radar image before filtering
- Figure 3b is the radar image after filtering.
- the fourth step is normalized data processing of the average value of radar echo intensity
- f(x, y) is the selected radar image intensity value
- N x and N y are the number of pixels along x and y of the selected image
- i is the corresponding radar image number. Normalize f' i to get the radar image normalized value F i :
- the fifth step is to perform normalized data processing on the image signal-to-noise ratio information
- the sixth step is to perform normalized data processing on the sea surface wind direction and wind speed information
- the sea surface wind direction information d i and the sea surface wind speed information s i collected by the anemometer are normalized according to the radar image sequence, and the normalized values D i and S i of the sea surface wind direction and wind speed information are obtained:
- a seventh step a third and fourth data set 1622 obtained in five steps S i, R i, D i and F i is divided into two portions, a data set for 1081 K-means clustering algorithm is trained surface wind speed experience Model, the remaining 541 sets of data are used for the test of the sea surface wind speed extraction model.
- FIG. 1081 An eighth step, the statistical relationship between the wind speed data group 1081 and the echo intensity S i F i, as shown, can be seen in FIG.
- Surface wind speed and the echo intensity is proportional to 4, it can be seen with the radar image SSW
- the echo signals are closely correlated, and the sea surface wind field information can be retrieved using the marine radar image.
- the present invention uses the echo intensity data F i , the signal-to-noise ratio information R i , and the sea surface wind speed information D i to form a data set, as a set of factors affecting the sea surface wind speed, as follows:
- the ninth step is to divide the data points according to the initialized centroid
- the tenth step update the cluster centroids; for each ⁇ k
- the mean value is used as the centroid of the next update, and the calculation formula is as follows:
- the Euclidean distance between the data point and the centroid is recalculated according to formula (9), and a new cluster is formed at the same time.
- the relationship between the number of centroids and the sum of squares of errors is obtained through experiments. As shown in Figure 5, when the number of cluster points is 5, it is the turning point of the sum of squares of errors, from which it decreases slowly, and the number of clusters is 5.
- the eleventh step is to remove the radar data of heterogeneous data; the K-means clustering distribution results of the factors affecting the sea surface wind speed according to the number of clusters are shown in Figure 6, and the centroid with the farthest centroid position relative to other centroids is determined as heterogeneous
- the centroid C d all the data points in the region where it is located are also determined as heterogeneous data sets ⁇ d , such as the cluster set corresponding to the green data in Fig. 6 .
- step 3.1 the sea surface wind speed extraction model is determined; first, the original characteristics of F f , S f data must be released to obtain the corresponding radar image echo intensity average f f , and the training sea surface wind speed information s f :
- the thirteenth step fitting the sea surface wind speed extraction model; applying the nonlinear quadratic function to fit the data f f and s f to obtain the sea surface wind speed prediction model:
- the quadratic function coefficient is -9, ⁇ is 325.9, ⁇ is -637.6, and the fitting curve is shown in Figure 7.
- the fourteenth step test the sea surface wind speed model; select the variance function SSE as the error test index for the model application test data, where SSE is the sum of squares of errors between the sea surface wind speed and the test wind speed obtained by inputting the mean value of the test radar echo intensity to the model, the calculation formula as follows:
- the fifteenth step applying the exponential function relationship between the radar echo intensity and the sea surface wind speed proposed by Dankert, establishes the sea surface wind speed model as:
- F i is the radar echo intensity
- S i is the sea surface wind speed information
- a, b, and c are the function coefficients, which are -0.7, -0.5, and 1.7, respectively.
- the fitting curve is shown in Figure 8. After experimental calculation, it is obtained that the SSE of the training data is 2.765, which is greater than the error function index of the algorithm of the present invention.
- the K-means clustering algorithm sea surface wind speed model and the exponential function sea surface wind speed model designed by the present invention are respectively applied to 541 groups of data, and the comparison result between the two groups of results and the measured sea surface wind speed is shown in Figure 9. It can be directly seen from Figure 9 that the sea surface wind speed model obtained by the K-means clustering algorithm is more consistent with the measured wind speed information, especially when the sea surface wind and rain are 15m/s, most of the wind speed information extracted by the exponential function sea surface wind speed model There is a problem that the wind speed is less than the measured wind speed.
- the error range of the K-means clustering algorithm model inversion results is smaller, and about 50% of the data errors are concentrated in -0.1 ⁇ 0.1; while the exponential function The error range of the model inversion results is more scattered, and 62% of the data errors are concentrated in -1 ⁇ 1m/s. It can be concluded that the K-means clustering algorithm model inversion results are more accurate and stable than the exponential function model inversion results.
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Abstract
Disclosed is a method for extracting a sea surface wind speed from a navigation radar image, wherein the method is based on a K-means clustering algorithm and belongs to the field of using a remote sensing means to perform inversion on a sea surface wind speed. The present invention comprises four parts, i.e. navigation radar image data preprocessing, radar data classification based on a K-means clustering algorithm, sea surface wind speed extraction model determination and sea surface wind speed information extraction. Heterogeneous data is obtained by means of a sea surface wind speed inversion process, and the influence of interference data on a sea surface model is removed, thereby improving the robustness of the model; and for rejected heterogeneous data, a nonlinear quadratic function is used to determine a sea surface wind speed extraction model, thereby improving the precision and speed of sea surface wind speed extraction by the model. Actual measured data is used for verifying the present invention, and in the present invention, a correlation coefficient of a sea surface wind speed and a reference wind speed reaches 0.99, a standard deviation is 0.38 m/s, and a deviation is -0.04 m/s, which are enough to meet engineering and environment monitoring requirements.
Description
本发明涉及的是利用X-band航海雷达图像进行海面风速反演计算的海面风速遥感技术领域,具体地说,是一种基于K-means聚类算法的海面风速方法。The invention relates to the technical field of sea surface wind speed remote sensing using X-band nautical radar images for sea surface wind speed inversion calculation, in particular to a sea surface wind speed method based on K-means clustering algorithm.
海面风场是海洋动力学研究的重要因素,也是航海作业安全的重要保障,对于了解海洋变化与预知海面风险起到至关重要的作用。海面风场信息主要包括海面风向和海面风速两个方面,本发明是基于航海雷达图像提取海面风速信息的一种方法研究。The sea surface wind field is an important factor in the study of ocean dynamics and an important guarantee for the safety of navigation operations. It plays a vital role in understanding ocean changes and predicting sea surface risks. The sea surface wind field information mainly includes the sea surface wind direction and the sea surface wind speed. The present invention is a method for extracting the sea surface wind speed information based on the nautical radar image.
传统提取海面风速信息的方式主要为测风仪,将测风仪安装在船上、岸边或浮标上测量风速大小,但由于船体上固定物或岸基环境等产生的湍流效应影响导致测量精度较低,同时该方法易受海上天气或是海上交通的影响,在时间上和空间上缺乏连续性。The traditional way of extracting wind speed information on the sea surface is mainly anemometers, which are installed on ships, shores or buoys to measure the wind speed. At the same time, this method is susceptible to the influence of sea weather or sea traffic, and lacks continuity in time and space.
现有遥感测风手段主要有散射计、机载或星载合成孔径雷达(SAR)、卫星高度计及航海雷达等,但散射计存在分辨率较低的问题,卫星遥感重复采样率低,且受到云层干扰的问题,导致测量数据可能不是海表面所要探测的风速信息。X波段航海雷达具有不受光线影响、能够实时连续反馈及高分辨率等优点,成为现阶段海洋环境监测的重要手段之一。目前,国内外X-band航海雷达已实现了海面浪、流,降雨量的监测,及海面漏油面积的测量,但基于航海雷达图像的海面风场测量还处于初级研究阶段。Existing remote sensing wind measurement methods mainly include scatterometers, airborne or spaceborne synthetic aperture radar (SAR), satellite altimeters and marine radars, etc. However, scatterometers have the problem of low resolution, low repeated sampling rate of satellite remote sensing, and are subject to The problem of cloud interference means that the measurement data may not be the wind speed information to be detected at the sea surface. X-band marine radar has the advantages of being unaffected by light, capable of real-time continuous feedback and high resolution, and has become one of the important means of marine environmental monitoring at this stage. At present, the X-band marine radar at home and abroad has realized the monitoring of sea surface waves, currents, and rainfall, and the measurement of the oil spill area on the sea surface, but the sea surface wind field measurement based on marine radar images is still in the primary research stage.
基于航海雷达图像反演海面风速现阶段主要算法有两种:一种是神经网络法,一种为模型函数法。2002年Dankert首先提出神经网络法,根据雷达散射截面积与风速之间存在的关系,利用海面风向信息和NRCS作为输入量,应用BP神经网络反演出海面风速。2006年,Dankert考虑了湿度、温度、信噪比等海洋因素作为BP神经网络的输入量,以提高海面风速的适用性。哈尔滨工程大学贾瑞才采用双隐层单极型S函数BP神经网络法反演出海面风速信息,提高了神经网络的收敛速度及网络推广能力。但应用神经网络存在固有的不足之处,主要问题就 是模型的适用性差,对于不同环境位置、不同型号的航海雷达需要大量数据重新训练,而且海洋环境因素对该方法的影响也较大,精度无法保证。At present, there are two main algorithms for inversion of sea surface wind speed based on marine radar images: one is the neural network method, and the other is the model function method. In 2002, Dankert first proposed the neural network method. According to the relationship between the radar scattering cross-sectional area and wind speed, the sea surface wind direction information and NRCS were used as inputs, and the BP neural network was used to invert the sea surface wind speed. In 2006, Dankert considered marine factors such as humidity, temperature, and signal-to-noise ratio as the input of BP neural network to improve the applicability of sea surface wind speed. Jia Ruicai of Harbin Engineering University used the double-hidden-layer unipolar S-function BP neural network method to invert the sea surface wind speed information, which improved the convergence speed of the neural network and the network extension ability. However, the application of neural network has inherent shortcomings. The main problem is the poor applicability of the model. For different environmental locations and different types of marine radars, a large amount of data is required for retraining, and marine environmental factors have a great impact on the method, so the accuracy cannot be achieved. ensure.
2005年Horstmann首次提出应用地球物理模型函数(GMF),在输入SAR回波强度及海面风向信息时,可得到海面风速信息,虽不能直接应用于航海雷达但足已证明海面风场与雷达回波强度具有一定的指数模型函数关系。2007年Dankert对雷达回波强度与海面风速成指数函数模型应用实测数据进行验证,但反演精度无法达到工程要求。2012年Lund等针对FurunoFAR2117BB型号航海雷达,得到RCS与海面风速之间存在三次多项式非线性关系,并利用计算出风速,反演精度有很大提升。2013年Bueno等针对Furuno2117BB型号雷达,利用线性积分法得到雷达回波强度水平与海面风速函数关系获得海面风速信息。2015年Liu Y等针对Decca和Furono两种雷达,提出应用双曲线拟合应用实测航海雷达数据提取出海面风速信息。2017年Huang W等针对Decca雷达,提出了利用RCS谱分析算法、RCS与海面风速经验模态分解方法,建立函数模型得到海面风速。2015年陈忠彪等针对9.3GHz Furuno雷达,将RCS、有效波高与海面风速拟合成线性概率分布函数,由此获得海面风速信息。以上方法没有考虑降雨和海洋环境因素的影响,在雷达图像受降雨影响时,模型的反演精度和数据适用性则无法保证。海面风速反演模型函数普遍存在提取精度地,海况适用性差的问题,制约了该方法的发展前景。In 2005, Horstmann first proposed the application of geophysical model function (GMF), when inputting the SAR echo intensity and sea surface wind direction information, the sea surface wind speed information can be obtained. Although it cannot be directly applied to marine radar, it has been proved that the sea surface wind field and radar echo The intensity has a certain exponential model function relationship. In 2007, Dankert used the measured data to verify the exponential function model of radar echo intensity and sea surface wind speed, but the inversion accuracy could not meet the engineering requirements. In 2012, Lund et al. aimed at the FurunoFAR2117BB marine radar and found that there is a cubic polynomial nonlinear relationship between the RCS and the sea surface wind speed, and used the calculated wind speed to greatly improve the inversion accuracy. In 2013, for the Furuno2117BB radar, Bueno et al. used the linear integration method to obtain the relationship between the radar echo intensity level and the sea surface wind speed function to obtain the sea surface wind speed information. In 2015, Liu Y et al. proposed to use hyperbolic fitting to extract sea surface wind speed information from the measured marine radar data for two radars, Decca and Furono. In 2017, Huang W et al. proposed the use of RCS spectral analysis algorithm, RCS and sea surface wind speed empirical mode decomposition method for Decca radar, and established a function model to obtain sea surface wind speed. In 2015, Chen Zhongbiao and others fit the RCS, effective wave height and sea surface wind speed into a linear probability distribution function for the 9.3GHz Furuno radar, thereby obtaining the sea surface wind speed information. The above methods do not consider the influence of rainfall and marine environmental factors. When the radar image is affected by rainfall, the inversion accuracy and data applicability of the model cannot be guaranteed. The sea surface wind speed inversion model functions generally have problems of high extraction accuracy and poor applicability of sea conditions, which restrict the development prospect of this method.
针对以上问题,本发明公开了一种基于K-means聚类算法的航海雷达图像反演海面风速方法,首先,对雷达图像进行降雨图像识别,去除降雨对海面风速提取的影响;其次,结合传感器及雷达图像信息对海面影响因素数据进行分类,剔除异类数据对海面风速提取模型的影响,提高了模型的鲁棒性;最终,继承了Lund提出的海面风速与回波强度的非线性关系,对剔除异类数据应用非线性二次函数确定海面风速提取模型,保证了模型提取海面风速的精度。通过实测数据证明该方法从航海雷达图像中提取出海面风速信息结果的工程可行性。In view of the above problems, the present invention discloses a method for retrieving sea surface wind speed from marine radar images based on K-means clustering algorithm. First, the radar image is recognized by rainfall image to remove the influence of rainfall on the extraction of sea surface wind speed; secondly, combined with sensors and radar image information to classify the sea surface influencing factor data, eliminate the influence of heterogeneous data on the sea surface wind speed extraction model, and improve the robustness of the model; finally, inheriting the nonlinear relationship between sea surface wind speed and echo intensity proposed by Lund, the Eliminate heterogeneous data and apply nonlinear quadratic function to determine the sea surface wind speed extraction model, which ensures the accuracy of model extraction of sea surface wind speed. The measured data proves the engineering feasibility of the method to extract the sea surface wind speed information from the nautical radar image.
发明内容SUMMARY OF THE INVENTION
本发明公开了一种基于K-means聚类算法的海面风速方法,此方法是基于K-means聚类算法的,具体包括步骤如下:The invention discloses a sea surface wind speed method based on the K-means clustering algorithm. The method is based on the K-means clustering algorithm, and specifically includes the following steps:
步骤1,雷达图像数据预处理。应用航海雷达监测系统采集海面雷达图像序 列数据,同步应用风力计采集同步海面风向、风速信息。对雷达图像序列应用零强度百分比(ZPP)对降雨噪声较大的图像数据进行识别、剔除;对雨雪干扰较小的图像,应用图像中值滤波抑制噪声和同频信号对海面风向提取的干扰。 Step 1, radar image data preprocessing. The marine radar monitoring system is used to collect the sea surface radar image sequence data, and the wind meter is used to collect the synchronized sea surface wind direction and wind speed information. Apply Zero Intensity Percentage (ZPP) to radar image sequences to identify and eliminate image data with large rainfall noise; for images with less rain and snow interference, image median filtering is applied to suppress the interference of noise and same-frequency signals on the extraction of sea surface wind direction .
步骤2,基于K-means聚类算法的数据分类。首先,对雷达图像回波强度、海面风向信息、海面风速信息和计算得到的图像信噪比进行数据归一化处理,使数据在同一坐标范围内;其次,应用K-means聚类算法对雷达图像回波强度、海面风向信息和图像信噪比数据依据欧式距离对数据进行分类,并应用质心距离误差作为判定依据,得到异类数据;最终,将雷达数据和海面风场信息数据都剔除异类数据相对应的信息数据,得到雷达数据和海面风场信息的聚类数据。 Step 2, data classification based on K-means clustering algorithm. First, the data normalization is performed on the radar image echo intensity, sea surface wind direction information, sea surface wind speed information and the calculated image signal-to-noise ratio, so that the data are in the same coordinate range; secondly, the K-means clustering algorithm is applied to the radar image The image echo intensity, sea surface wind direction information and image signal-to-noise ratio data are classified according to the Euclidean distance, and the error of the centroid distance is used as the judgment basis to obtain heterogeneous data; finally, the radar data and sea surface wind field information data are all excluded. Corresponding information data, the cluster data of radar data and sea surface wind field information are obtained.
步骤3,海面风速提取模型确定。利用聚类雷达数据和海面风速数据对海面风速进行非线性二次拟合,得到海面风速提取模型,应用SSE验证模型的准确性。 Step 3, the sea surface wind speed extraction model is determined. Using clustered radar data and sea surface wind speed data, nonlinear quadratic fitting of sea surface wind speed was performed to obtain the extraction model of sea surface wind speed, and SSE was used to verify the accuracy of the model.
步骤4,海面风速信息提取。选取测试航海雷达图像部分图像,对其进行归一化映射,输入到海面风速提取模型中,得到海面风速信息。 Step 4, extraction of sea surface wind speed information. Select some images of the test marine radar image, carry out normalized mapping, and input them into the sea surface wind speed extraction model to obtain the sea surface wind speed information.
基于K-means聚类算法的海面风速提取方法所述步骤2、3包括以下步骤:The steps 2 and 3 of the sea surface wind speed extraction method based on the K-means clustering algorithm include the following steps:
步骤2.1,雷达回波强度平均值、海面风向、风速信息及图像信噪比信息进行归一化数据处理;Step 2.1, normalized data processing of the average value of radar echo intensity, sea surface wind direction, wind speed information and image signal-to-noise ratio information;
①对经过预处理的航海雷达图像选取适当部分雷达图像,沿x和y轴进行归一化映射,得到雷达图像均值f'
i:
①Select an appropriate part of the radar image for the preprocessed marine radar image, and perform normalized mapping along the x and y axes to obtain the average value of the radar image f' i :
其中,f(x,y)为选取的雷达图像强度值,N
x、N
y为选取图像沿x,y像元数,i是对应的雷达图像数。对f'
i进行归一化得到雷达图像归一化值F
i:
Among them, f(x, y) is the selected radar image intensity value, N x and N y are the number of pixels along x and y of the selected image, and i is the corresponding radar image number. Normalize f' i to get the radar image normalized value F i :
②获得选取雷达图像信噪比r
t,以雷达图像时间序列进行归一化,得到海况信息归一化值R
i:
② Obtain the signal-to-noise ratio r t of the selected radar image, normalize it with the radar image time series, and obtain the normalized value R i of the sea state information:
其中
为二维波数谱经校正后的海浪谱,
为雷达图像海浪信号以外的噪声谱。
in is the corrected ocean wave spectrum of the two-dimensional wavenumber spectrum, is the noise spectrum other than the wave signal in the radar image.
③对采集风力计的海面风向信息d
i、海面风速信息s
i,按雷达图像序列进行归一化,得到海面风向、风速信息归一化值D
i及S
i:
③ Normalize the sea surface wind direction information d i and the sea surface wind speed information s i collected by the anemometer according to the radar image sequence, and obtain the normalized values D i and S i of the sea surface wind direction and wind speed information:
步骤2.2,基于K-means聚类算法的雷达数据分类;Step 2.2, radar data classification based on K-means clustering algorithm;
①初始化K个初始类簇质心;① Initialize K initial cluster centroids;
对步骤2.1获得的F
i、R
i、D
i和S
i的所有数据分成两个部分,一部分用于基于K-means聚类算法海面风速模型的确定,另一部分用于模型的数据测试;将用于模型确定中的数据F
i、R
i、D
i组成数据集合,作为海面风速影响因素集合;
Obtained in step 2.1 F i, R i, D i and S i of all the data is divided into two sections, one for determining the wind speed based clustering algorithm sea Model K-means, one for the data of the test model; and The data F i , R i , and D i used in the determination of the model form a data set, which is used as a set of factors affecting the sea surface wind speed;
Ω
i={F
i,R
i,D
i} (5)
Ω i ={F i ,R i ,D i } (5)
初始化类簇质心,随机选取Ω
i区域中K个数据点作为初始化质心。
The cluster centroids are initialized, and K data points in the Ω i region are randomly selected as the initial centroids.
②依据初始化质心划分数据点;② Divide the data points according to the initialized centroid;
在确定K个海面风速影响因素质心后,在数据集Ω
i中找出距离质心最近的数据点,由此形成簇。这里应用欧氏距离进行度量,计算Ω
i中所有海面风速影响因素特征的数据点X
i(x
1,x
2,x
3)与选定K个质心C
k(c
1,c
2,c
3)之间的欧氏距离,公式如下:
After determining the centroids of the K sea surface wind speed influencing factors, find the data points closest to the centroids in the data set Ω i, thereby forming clusters. Here, the Euclidean distance is used for measurement, and the data points X i (x 1 ,x 2 ,x 3 ) of the characteristics of all the factors affecting the sea surface wind speed in Ω i are calculated and the selected K centroids C k (c 1 ,c 2 ,c 3 ), the formula is as follows:
各点找到相聚最近的质心后,就归属于该簇,数据集Ω
i被划分为K个子区域空间Τ
k。
After each point finds the closest centroid, it belongs to the cluster, and the data set Ω i is divided into K sub-region spaces Τ k .
③更新聚类质心;③ Update the cluster centroids;
对每个Τ
k中的
进行均值化,作为下一个更新的质心,计算公式如下:
for each Τ k The mean value is used as the centroid of the next update, and the calculation formula is as follows:
依据更新的质心按照公式(6)重新计算数据点与质心的欧氏距离,同时形成新的簇。According to the updated centroid, the Euclidean distance between the data point and the centroid is recalculated according to formula (6), and a new cluster is formed at the same time.
④质心停止更新判断依据;④The judgment basis for the centroid to stop updating;
根据原始质心C
k和更新质心C
j的距离判定质心是否需要进一步更新,判定条件如下:
According to the distance between the original centroid C k and the updated centroid C j , it is determined whether the centroid needs to be further updated. The judgment conditions are as follows:
||C
k-C
j||<<γ (8)
||C k -C j ||<<γ (8)
其中,γ=0.1,当满足上述条件时表示质心趋于收敛,则分类算法终止;若不满足上述条件,则不断重复步骤2.3~2.5,直到满足公式(8),得到聚类质心C
f(f=1,2,…f),及每个质心对应的聚类数据集Τ
f。
Among them, γ=0.1, when the above conditions are met, it means that the centroids tend to converge, and the classification algorithm is terminated; if the above conditions are not met, steps 2.3 to 2.5 are repeated continuously until formula (8) is satisfied, and the cluster centroids C f ( f = 1,2, ... f), and each cluster centroid corresponding data set Τ f.
步骤2.3去除异类数据的雷达数据;Step 2.3 remove radar data of heterogeneous data;
根据获得的海面风速影响因素的聚类分布,将质心位置相对其他质心最远的质心被判定为异类质心C
d,其所在区域内的所有数据点也被判定为异类数据集Τ
d,去除数据集Ω
i中的异类数据,同时去除S
i中异类数据对应的位置的海面风速S
d,最终得到去除异类雷达数据F
f,R
f,D
f,S
f:
According to the obtained cluster distribution of the influencing factors of sea surface wind speed, the centroid whose centroid position is farthest from other centroids is determined as the heterogeneous centroid C d , and all the data points in its area are also determined as the heterogeneous data set Τ d , and the data is removed. heterogeneous data sets in Ω i, S i the simultaneous removal of heterogeneous data corresponding to a position of surface wind speed S d, to give the final removal of heterogeneous radar data F f, R f, D f , S f:
Ω
f={F
f,R
f,D
f}={Ω
i-Τ
d} S
f={S
i-S
d} (9)
Ω f = {F f , R f , D f } = {Ω i -Τ d } S f ={S i -S d } (9)
步骤3.1,海面风速提取模型确定;先要释放F
f,S
f数据原有特性,得到对应的雷达图像回波强度均值f
f,及训练海面风速信息s
f:
Step 3.1, determine the sea surface wind speed extraction model; first release the original characteristics of F f , S f data to obtain the corresponding radar image echo intensity average f f , and the training sea surface wind speed information s f :
f
f=F
f*max(f
i'),s
f=S
f*max(s
i) (10)
f f = F f * max ( f i '), s f = S f * max (s i) (10)
步骤3.2,海面风速提取模型拟合;应用非线性二次函数对数据f
f、s
f进行拟合,得到海面风速预估模型:
Step 3.2, fitting the sea surface wind speed extraction model; applying the nonlinear quadratic function to fit the data f f and s f to obtain the sea surface wind speed prediction model:
通过数据实验验证,最终得到二次函数系数
β=325.9,δ=637.6。
Through data experimental verification, the quadratic function coefficients are finally obtained β=325.9, δ=637.6.
步骤3.3,海面风速模型测试;对模型应用测试数据选取方差函数SSE作为误差检验指标,这里SSE为对模型输入测试雷达回波强度均值得到的海面风速与测试风速的误差平方和,计算公式如下:Step 3.3, test the sea surface wind speed model; select the variance function SSE as the error test index for the model application test data, where SSE is the error square sum of the sea surface wind speed and the test wind speed obtained by inputting the mean value of the test radar echo intensity to the model, and the calculation formula is as follows:
其中,
为加权系数,m为数据的个数,s
i为实测海面风速,
为模型提取出的海面风速。SSE越接近0,则模型越精准,海面风速反演精度越高。
in, is the weighting coefficient, m is the number of data, s i is the measured sea surface wind speed, The sea surface wind speed extracted for the model. The closer the SSE is to 0, the more accurate the model and the higher the accuracy of the sea surface wind speed inversion.
与传统的曲线拟合提取海面风速方法相比,本发明的优点在于:Compared with the traditional method of curve fitting to extract the sea surface wind speed, the advantages of the present invention are:
1、设计了一种K-means聚类算法对海面风速影响因素数据进行分类,得到异类数据,去除了干扰数据对海面模型的影响;1. A K-means clustering algorithm is designed to classify the data of the influencing factors of sea surface wind speed, and obtain heterogeneous data, and remove the influence of interference data on the sea surface model;
2、对剔除异类数据应用非线性二次函数确定海面风速提取模型,提高海面风速的提取精度;2. Apply a nonlinear quadratic function to remove heterogeneous data to determine the extraction model of sea surface wind speed to improve the extraction accuracy of sea surface wind speed;
3、设计的K-means聚类算法采用欧氏距离判定数据点距离,利用距离均值作为更新质心位置判定条件,具有收敛速度快、聚类效果好的优点;3. The designed K-means clustering algorithm uses Euclidean distance to determine the distance of data points, and uses the distance mean as the determination condition for updating the centroid position, which has the advantages of fast convergence speed and good clustering effect;
4、拟合的模型选取方差函数SSE作为误差检验指标,提高了风速提取模型的精准性,提高了算法在工程中的反演精度。4. The fitted model selects the variance function SSE as the error inspection index, which improves the accuracy of the wind speed extraction model and the inversion accuracy of the algorithm in engineering.
5、模型应用实测X-band航海雷达回波图像、海面风速、海面风速训练获得,具有很强的工程适用性。5. The model is obtained by training the measured X-band marine radar echo image, sea surface wind speed and sea surface wind speed, and has strong engineering applicability.
图1是本发明的具体实施方式流程图;Fig. 1 is the specific embodiment flow chart of the present invention;
图2设备采集及图像序列图;Figure 2 equipment acquisition and image sequence diagram;
图3a是滤波前雷达图;Figure 3a is the radar map before filtering;
图3b是滤波后雷达图;Figure 3b is the radar chart after filtering;
图4是海面风速与回波强度关系;Figure 4 shows the relationship between sea surface wind speed and echo intensity;
图5是质心数与误差平方和分布曲线;Figure 5 is the distribution curve of the number of centroids and the sum of squares of errors;
图6是海面风速影响因素K-means聚类分布结果;Figure 6 is the K-means clustering distribution result of the influencing factors of sea surface wind speed;
图7是K-means聚类算法风速模型拟合曲线;Fig. 7 is the K-means clustering algorithm wind speed model fitting curve;
图8是指数函数模型风速模型拟合曲线;Fig. 8 is the fitting curve of the exponential function model wind speed model;
图9是两组算法反演结果与实测海面风速的对比结果图;Figure 9 is a comparison result of the inversion results of the two groups of algorithms and the measured sea surface wind speed;
图10是两组算法反演结果与实测海面风速的误差对比图;Figure 10 is a comparison chart of the error between the inversion results of the two groups of algorithms and the measured sea surface wind speed;
图11是两组算法反演结果与实测海面风速的误差统计结果图;Figure 11 is a graph of the error statistics between the inversion results of the two groups of algorithms and the measured sea surface wind speed;
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
本发明具体实施方式流程图见图1,分为航海雷达图像预处理、基于K-means聚类算法的雷达数据分类、海面风速提取模型确定和海面风速信息提取这四大块。具体实施步骤共分为十六步,第一步到第三步为数据预处理;第四步到第十一步是基于K-means聚类算法的雷达数据分类;第十二步到第十五步为海面风速提取模型确定;第十六步为海面风速信息提取及分析。具体步骤如下:The flowchart of the specific embodiment of the present invention is shown in Figure 1, which is divided into four major blocks: marine radar image preprocessing, radar data classification based on K-means clustering algorithm, sea surface wind speed extraction model determination, and sea surface wind speed information extraction. The specific implementation steps are divided into sixteen steps. The first to third steps are data preprocessing; the fourth to eleventh steps are radar data classification based on K-means clustering algorithm; the twelfth to tenth steps The fifth step is to determine the sea surface wind speed extraction model; the sixteenth step is to extract and analyze the sea surface wind speed information. Specific steps are as follows:
第一步,由自制海浪监测设备采集2010年10月22日-29日,11月13日-21日,12月14日-26日,1月1日-10日,共1722组航海雷达图像序列,每组图像序列中包含32幅航海雷达图像,每幅图像经历的时间为2.5s,相同位置的风力计同步采集相应海面风向信息θ
w、风速信息U
w,设备采集及图像序列如图2所示。
The first step is to collect 1722 sets of marine radar images from October 22-29, November 13-21, December 14-26, January 1-10, 2010 by self-made wave monitoring equipment Sequence, each group of image sequences contains 32 marine radar images, and the time for each image is 2.5s. The wind meters at the same position synchronously collect the corresponding sea surface wind direction information θ w and wind speed information U w . The equipment collection and image sequence are as follows: shown in Figure 2.
第二步,对于雷达图像任一像元点,雷达直接接收到的回波信号强弱为0~2.5伏的电压值。因此,本发明将像元点电压值小于0.3V(根据存储协议,0.3伏电压线性归化后的回波强度值983)的回波强度值归为零强度,并计算零强度像元点所占的比例。零强度百分比的计算公式如下所示:In the second step, for any pixel point of the radar image, the strength of the echo signal directly received by the radar is a voltage value of 0 to 2.5 volts. Therefore, the present invention returns the echo intensity value whose voltage value of the pixel point is less than 0.3V (according to the storage protocol, the echo intensity value after linear normalization of 0.3V voltage is 983) to zero intensity, and calculates the zero intensity pixel point. proportion. The formula for calculating the zero intensity percentage is as follows:
其中,n为雷达图像中的像元点总个数,n
0为雷达图像中回波强度值归为零的像元点个数。
Among them, n is the total number of pixel points in the radar image, and n 0 is the number of pixel points in the radar image whose echo intensity values return to zero.
统计332组在无降雨天气,小雨天气和大雨天气时的零强度百分比,发现在降雨情况下,雨水散射对雷达影响更大,无效信号就越少,零强度百分比会越小。最终,得到未降雨天气时的零强度百分比为0.542;而在降雨天气时,零强度百分比平均值为0.207。因此,本发明将零强度百分比低于0.207的雷达图像判定为降雨干扰严重图像,直接剔除,其他图像为不受降雨干扰图像,用于海面风速信息提取。最终,应用此方法剔除100组图像,保留1622组图像用于后续海面 风速信息提取技术研究。Statistical analysis of the zero-intensity percentages of 332 groups in no rain, light rain, and heavy rain shows that under rainfall conditions, rain scattering has a greater impact on the radar, resulting in fewer invalid signals and a smaller zero-intensity percentage. In the end, the zero-intensity percentage is 0.542 when it is not raining, and the average zero-intensity percentage is 0.207 when it is raining. Therefore, the present invention determines radar images with zero intensity percentage lower than 0.207 as images with severe rainfall interference, and directly rejects them, and other images are images without rainfall interference, which are used for sea surface wind speed information extraction. Finally, 100 sets of images were removed by this method, and 1622 sets of images were reserved for subsequent research on sea surface wind speed information extraction technology.
第三步,对降雨识别后的航海雷达图像g(x,y)进行中值滤波,抑制同频信号对海面风速提取的影响。对航海雷达图像序列中每幅雷达图像都应用3×3模板的2D非线性平滑中值滤波,滤波后图像灰度值f'(x,y)为:The third step is to perform median filtering on the marine radar image g(x, y) after rainfall identification to suppress the influence of the same frequency signal on the extraction of sea surface wind speed. A 2D nonlinear smooth median filter of 3×3 template is applied to each radar image in the marine radar image sequence, and the filtered image gray value f'(x,y) is:
f'(x,y)=median{g(x-i,y-j),(i,j)∈W} (2)f'(x,y)=median{g(x-i,y-j),(i,j)∈W} (2)
式(1)中f(x,y)为雷达图像回波强度值;f'(x,y)为滤波后灰度值,(i,j)是模板W中心相邻的8个像元点。W为模板窗口,具体如下:In formula (1), f(x, y) is the echo intensity value of the radar image; f'(x, y) is the gray value after filtering, and (i, j) is the 8 pixel points adjacent to the center of the template W . W is the template window, as follows:
11 | 11 | 11 |
11 | 11 | 11 |
11 | 11 | 11 |
将中值滤波器W中心与图像中心重合,通过与周围8个相邻像元点的回波强度值比较,选取回波强度中间值来更新图像的回波强度值,模板以步长单位1遍历极坐标航海雷达图像,最终获得中值滤波后的航海雷达图像,中值滤波前后对比如图3a为滤波前雷达图,图3b为滤波后雷达图。The center of the median filter W is coincident with the center of the image, and the echo intensity value of the surrounding 8 adjacent pixel points is compared, and the middle value of the echo intensity is selected to update the echo intensity value of the image. The template is in step unit 1. Traverse the polar coordinate marine radar image, and finally obtain the marine radar image after median filtering. Figure 3a is the radar image before filtering, and Figure 3b is the radar image after filtering.
第四步,雷达回波强度平均值归一化数据处理;The fourth step is normalized data processing of the average value of radar echo intensity;
对经过预处理的航海雷达图像选取适当部分雷达图像,沿x和y轴进行归一化映射,得到雷达图像均值f'
i:
Select an appropriate part of the radar image for the preprocessed marine radar image, and perform normalized mapping along the x and y axes to obtain the average value of the radar image f' i :
其中,f(x,y)为选取的雷达图像强度值,N
x、N
y为选取图像沿x,y像元数,i是对应的雷达图像数。对f'
i进行归一化得到雷达图像归一化值F
i:
Among them, f(x, y) is the selected radar image intensity value, N x and N y are the number of pixels along x and y of the selected image, and i is the corresponding radar image number. Normalize f' i to get the radar image normalized value F i :
第五步,对图像信噪比信息进行归一化数据处理;The fifth step is to perform normalized data processing on the image signal-to-noise ratio information;
获得选取雷达图像信噪比r
t,以雷达图像时间序列进行归一化,得到海况 信息归一化值R
i:
Obtain the signal-to-noise ratio r t of the selected radar image, normalize it with the radar image time series, and obtain the normalized value R i of the sea state information:
其中
为二维波数谱经校正后的海浪谱,
为雷达图像海浪信号以外的噪声谱。
in is the corrected ocean wave spectrum of the two-dimensional wavenumber spectrum, is the noise spectrum other than the wave signal in the radar image.
第六步,对海面风向、风速信息进行归一化数据处理;The sixth step is to perform normalized data processing on the sea surface wind direction and wind speed information;
对采集风力计的海面风向信息d
i、海面风速信息s
i,按雷达图像序列进行归一化,得到海面风向、风速信息归一化值D
i及S
i:
The sea surface wind direction information d i and the sea surface wind speed information s i collected by the anemometer are normalized according to the radar image sequence, and the normalized values D i and S i of the sea surface wind direction and wind speed information are obtained:
第七步,对第三、四、五步获得的1622组S
i、R
i、D
i和F
i的数据分成两个部分,1081组数据用于K-means聚类算法训练得到海面风速经验模型,剩余的541组数据用于海面风速提取模型的测试。
A seventh step, a third and fourth data set 1622 obtained in five steps S i, R i, D i and F i is divided into two portions, a data set for 1081 K-means clustering algorithm is trained surface wind speed experience Model, the remaining 541 sets of data are used for the test of the sea surface wind speed extraction model.
第八步,统计1081组数据风速S
i与回波强度F
i的关系,如图4所示,图中可以看出海面风速与回波强度成正比关系,由此可知海面风场与雷达图像回波信号具有紧密的相关性,可以应用航海雷达图像反演海面风场信息。
An eighth step, the statistical relationship between the wind speed data group 1081 and the echo intensity S i F i, as shown, can be seen in FIG. Surface wind speed and the echo intensity is proportional to 4, it can be seen with the radar image SSW The echo signals are closely correlated, and the sea surface wind field information can be retrieved using the marine radar image.
Wilson提出的深水充分成长风浪风速和有效波高的关系如式所示:The relationship between the wind speed and the effective wave height of fully grown wind waves in deep water proposed by Wilson is as follows:
其中,g表示重力加速度,H
sw表示充分成长风浪有效波高,U表示风速。由上式说明海面风场与海况信息具有紧密的相关性,为雷达图像信噪比与海浪成正比。因此,本发明应用回波强度数据F
i、信噪比信息R
i、海面风速信息D
i组成数据集合,作为海面风速影响因素集合,如下式:
Among them, g is the acceleration of gravity, H sw is the effective wave height of fully grown wind waves, and U is the wind speed. The above formula shows that the sea surface wind field and sea state information are closely related, and the signal-to-noise ratio of the radar image is proportional to the sea wave. Therefore, the present invention uses the echo intensity data F i , the signal-to-noise ratio information R i , and the sea surface wind speed information D i to form a data set, as a set of factors affecting the sea surface wind speed, as follows:
Ω
i={F
i,R
i,D
i} (8)
Ω i ={F i ,R i ,D i } (8)
第九步,依据初始化质心划分数据点;The ninth step is to divide the data points according to the initialized centroid;
①初始化类簇质心,随机选取Ω
i区域中K个数据点作为初始化质心。
① Initialize the cluster centroids, and randomly select K data points in the Ω i region as the initialized centroids.
②在确定K个海面风速影响因素质心后,在数据集Ω
i中找出距离质心最近的数据点,由此形成簇。这里应用欧氏距离进行度量,计算Ω
i中所有海面风速影响因素特征的数据点X
i(x
1,x
2,x
3)与选定K个质心C
k(c
1,c
2,c
3)之间的欧氏距离,公式如下:
(2) After determining the centroids of the K sea surface wind speed influencing factors, find the data points closest to the centroids in the dataset Ω i to form clusters. Here, the Euclidean distance is used for measurement, and the data points X i (x 1 ,x 2 ,x 3 ) of the characteristics of all the factors affecting the sea surface wind speed in Ω i are calculated and the selected K centroids C k (c 1 ,c 2 ,c 3 ), the formula is as follows:
各点找到相聚最近的质心后,就归属于该簇,数据集Ω
i被划分为K个子区域空间Τ
k。
After each point finds the closest centroid, it belongs to the cluster, and the data set Ω i is divided into K sub-region spaces Τ k .
第十步,更新聚类质心;对每个Τ
k中的
进行均值化,作为下一个更新的质心,计算公式如下:
The tenth step, update the cluster centroids; for each Τ k The mean value is used as the centroid of the next update, and the calculation formula is as follows:
依据更新的质心按照公式(9)重新计算数据点与质心的欧氏距离,同时形成新的簇。According to the updated centroid, the Euclidean distance between the data point and the centroid is recalculated according to formula (9), and a new cluster is formed at the same time.
④质心停止更新判断依据;④The judgment basis for the centroid to stop updating;
根据原始质心C
k和更新质心C
j的距离判定质心是否需要进一步更新,判定条件如下:
According to the distance between the original centroid C k and the updated centroid C j , it is determined whether the centroid needs to be further updated. The judgment conditions are as follows:
||C
k-C
j||<<γ (11)
||C k -C j ||<<γ (11)
其中,γ=0.1,当满足上述条件时表示质心趋于收敛,则分类算法终止;若不满足上述条件,则不断重复步骤八和九,直到满足公式(11),得到聚类质心C
f(f=1,2,…f),及每个质心对应的聚类数据集Τ
f。通过实验得到质心数与误差平方和的关系,如图5所示,当聚点数为5时为误差平方和转折点,由此处下降缓慢,由此得到聚类数为5。
Among them, γ=0.1, when the above conditions are met, it means that the centroids tend to converge, and the classification algorithm is terminated; if the above conditions are not met, repeat steps 8 and 9 until formula (11) is satisfied, and the cluster centroids C f ( f = 1,2, ... f), and each cluster centroid corresponding data set Τ f. The relationship between the number of centroids and the sum of squares of errors is obtained through experiments. As shown in Figure 5, when the number of cluster points is 5, it is the turning point of the sum of squares of errors, from which it decreases slowly, and the number of clusters is 5.
第十一步,去除异类数据的雷达数据;根据聚类数的到海面风速影响因素的K-means聚类分布结果如图6所示,将质心位置相对其他质心最远的质心被判定 为异类质心C
d,其所在区域内的所有数据点也被判定为异类数据集Τ
d,如图6中绿色数据对应的聚类集合。去除数据集Ω
i中的异类数据,同时去除S
i中异类数据对应的位置的海面风速S
d,最终得到去除异类雷达数据F
f,R
f,D
f,S
f:
The eleventh step is to remove the radar data of heterogeneous data; the K-means clustering distribution results of the factors affecting the sea surface wind speed according to the number of clusters are shown in Figure 6, and the centroid with the farthest centroid position relative to other centroids is determined as heterogeneous The centroid C d , all the data points in the region where it is located are also determined as heterogeneous data sets Τ d , such as the cluster set corresponding to the green data in Fig. 6 . Removing the heterogeneous data in a data set Ω i, S i the simultaneous removal of heterogeneous data corresponding to a position of surface wind speed S d, to give the final removal of heterogeneous radar data F f, R f, D f , S f:
Ω
f={F
f,R
f,D
f}={Ω
i-Τ
d} S
f={S
i-S
d} (12)
Ω f ={F f ,R f ,D f }={Ω i -Τ d } S f ={S i -S d } (12)
第十二步,步骤3.1,海面风速提取模型确定;先要释放F
f,S
f数据原有特性,得到对应的雷达图像回波强度均值f
f,及训练海面风速信息s
f:
The twelfth step, step 3.1, the sea surface wind speed extraction model is determined; first, the original characteristics of F f , S f data must be released to obtain the corresponding radar image echo intensity average f f , and the training sea surface wind speed information s f :
f
f=F
f*max(f
i'),s
f=S
f*max(s
i) (13)
f f = F f * max ( f i '), s f = S f * max (s i) (13)
第十三步,海面风速提取模型拟合;应用非线性二次函数对数据f
f、s
f进行拟合,得到海面风速预估模型:
The thirteenth step, fitting the sea surface wind speed extraction model; applying the nonlinear quadratic function to fit the data f f and s f to obtain the sea surface wind speed prediction model:
其中,二次函数系数
为-9,β为325.9,δ为-637.6,拟合曲线如图7所示。
Among them, the quadratic function coefficient is -9, β is 325.9, δ is -637.6, and the fitting curve is shown in Figure 7.
第十四步,海面风速模型测试;对模型应用测试数据选取方差函数SSE作为误差检验指标,这里SSE为对模型输入测试雷达回波强度均值得到的海面风速与测试风速的误差平方和,计算公式如下:The fourteenth step, test the sea surface wind speed model; select the variance function SSE as the error test index for the model application test data, where SSE is the sum of squares of errors between the sea surface wind speed and the test wind speed obtained by inputting the mean value of the test radar echo intensity to the model, the calculation formula as follows:
其中,
为加权系数,m为数据的个数,s
i为实测海面风速,
为模型提取出的海面风速。经过实验计算,得到训练数据结果SSE为0.44,接近0,说明该海面风速提取模型精准,可用于工程应用。
in, is the weighting coefficient, m is the number of data, s i is the measured sea surface wind speed, The sea surface wind speed extracted for the model. After experimental calculation, it is found that the SSE of the training data is 0.44, which is close to 0, indicating that the sea surface wind speed extraction model is accurate and can be used for engineering applications.
第十五步,应用Dankert提出的雷达回波强度与海面风速成指数函数关系,建立海面风速模型为:The fifteenth step, applying the exponential function relationship between the radar echo intensity and the sea surface wind speed proposed by Dankert, establishes the sea surface wind speed model as:
其中,F
i为雷达回波强度,S
i为海面风速信息,a、b、c为函数系数,分别为-0.7、-0.5、1.7,拟合曲线如图8所示。经过实验计算,得到训练数据结果SSE为2.765,大于本发明算法的误差函数指标。
Among them, F i is the radar echo intensity, S i is the sea surface wind speed information, a, b, and c are the function coefficients, which are -0.7, -0.5, and 1.7, respectively. The fitting curve is shown in Figure 8. After experimental calculation, it is obtained that the SSE of the training data is 2.765, which is greater than the error function index of the algorithm of the present invention.
第十六步,对541组数据分别应用本发明设计的K-means聚类算法海面风 速模型和指数函数海面风速模型,得到两组结果与实测海面风速的对比结果如图9所示。从图9中可以直接看出K-means聚类算法海面风速模型获得海面风速与实测风速信息更加吻合,尤其是在海面风大雨15m/s的时候,指数函数海面风速模型提取的风速信息大部分出现小于实测风速的问题。In the sixteenth step, the K-means clustering algorithm sea surface wind speed model and the exponential function sea surface wind speed model designed by the present invention are respectively applied to 541 groups of data, and the comparison result between the two groups of results and the measured sea surface wind speed is shown in Figure 9. It can be directly seen from Figure 9 that the sea surface wind speed model obtained by the K-means clustering algorithm is more consistent with the measured wind speed information, especially when the sea surface wind and rain are 15m/s, most of the wind speed information extracted by the exponential function sea surface wind speed model There is a problem that the wind speed is less than the measured wind speed.
通过实验结果计算得到两种模型反演风速结果与实测风速统计结果如表1所示,本发明风速反演结果与实测风向相关系数达到0.99,标准差0.38m/s,偏差-0.04,完全达到工程要求,并且结果完全优异于指数函数反演结果,反演精度提高了77%。Through the calculation of the experimental results, the inversion wind speed results of the two models and the measured wind speed statistical results are shown in Table 1. The correlation coefficient between the wind speed inversion results of the present invention and the measured wind direction reaches 0.99, the standard deviation is 0.38m/s, and the deviation is -0.04. Engineering requirements, and the results are completely superior to the exponential function inversion results, the inversion accuracy is improved by 77%.
表1海面风速误差统计Table 1 Error statistics of sea surface wind speed
K-means聚类算法模型反演结果与指数函数模型反演结果与真实值的误差对比如图10所示。由图10可以看出K-means聚类算法模型反演结果的误差范围大体在-1~+1m/s之间,而的指数函数模型反演结果误差范围在-4~+6m/s之间,说明K-means聚类算法模型反演结果精度更高。两种算法结果的误差统计结果如图11所示,可以看出K-means聚类算法模型反演结果的误差范围更小,数据50%左右的误差都集中在-0.1~0.1;而指数函数模型反演结果误差范围更加分布散,62%数据误差集中在-1~1m/s。可以得出,K-means聚类算法模型反演结果相对指数函数模型反演结果更加精准和稳定。The error comparison between the inversion results of the K-means clustering algorithm model and the inversion results of the exponential function model and the real value is shown in Figure 10. It can be seen from Figure 10 that the error range of the inversion results of the K-means clustering algorithm model is generally between -1 and +1m/s, while the error range of the exponential function model inversion results is between -4 and +6m/s. time, indicating that the K-means clustering algorithm model inversion results are more accurate. The error statistics of the results of the two algorithms are shown in Figure 11. It can be seen that the error range of the K-means clustering algorithm model inversion results is smaller, and about 50% of the data errors are concentrated in -0.1 ~ 0.1; while the exponential function The error range of the model inversion results is more scattered, and 62% of the data errors are concentrated in -1~1m/s. It can be concluded that the K-means clustering algorithm model inversion results are more accurate and stable than the exponential function model inversion results.
以上所述的仅是本发明的实施例,方案中公知的具体结构及特性等常识在此未作过多描述。对于本领域技术人员而言,显然本发明不限于上述示范性实施例的细节,而且在不背离本发明的精神或基本特征的情况下,能够以其他的具体形式实现本发明。因此,无论从哪一点来看,均应将实施例看作是示范性的,而且是非限制性的,本发明的范围由所附权利要求而不是上述说明限定,因此旨在将落在权利要求的等同要件的含义和范围内的所有变化囊括在本发明内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。The above descriptions are only embodiments of the present invention, and common knowledge such as well-known specific structures and characteristics in the solution are not described too much here. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, which are therefore intended to fall within the scope of the appended claims. All changes within the meaning and range of the equivalents of , are included in the present invention. Any reference signs in the claims shall not be construed as limiting the involved claim.
Claims (8)
- 一种基于K-means聚类算法的海面风速方法,其特征在于,此方法是基于K-means聚类算法的,其实施包含雷达图像数据预处理、基于K-means聚类算法的雷达数据分类、海面风速提取模型确定和海面风速信息提取四个部分,具体反演步骤如下:A sea surface wind speed method based on K-means clustering algorithm, characterized in that the method is based on K-means clustering algorithm, and its implementation includes radar image data preprocessing, radar data classification based on K-means clustering algorithm , the determination of the sea surface wind speed extraction model and the extraction of sea surface wind speed information. The specific inversion steps are as follows:步骤1,雷达图像数据预处理:应用航海雷达监测系统采集海面雷达图像序列数据,同步应用风力计采集同步海面风向、风速信息,对雷达图像序列应用零强度百分比(ZPP)对降雨噪声较大的图像数据进行识别、剔除;对雨雪干扰较小的图像,应用图像中值滤波抑制噪声和同频信号对海面风向提取的干扰;Step 1, radar image data preprocessing: use the marine radar monitoring system to collect the sea surface radar image sequence data, synchronously use the wind meter to collect the synchronous sea surface wind direction and wind speed information, and apply the zero intensity percentage (ZPP) to the radar image sequence. Image data is identified and eliminated; for images with less interference from rain and snow, image median filtering is applied to suppress the interference of noise and co-frequency signals on the extraction of sea surface wind direction;步骤2,基于K-means聚类算法的数据分类:首先,对雷达图像回波强度、海面风向信息、海面风速信息和计算得到的图像信噪比进行数据归一化处理,使数据在同一坐标范围内;其次,应用K-means聚类算法对雷达图像回波强度、海面风向信息和图像信噪比数据依据欧式距离对数据进行分类,并应用质心距离误差作为判定依据,得到异类数据;最终,将雷达数据和海面风场信息数据都剔除异类数据相对应的信息数据,得到雷达数据和海面风场信息的聚类数据;Step 2, data classification based on K-means clustering algorithm: First, perform data normalization on the radar image echo intensity, sea surface wind direction information, sea surface wind speed information and the calculated image signal-to-noise ratio, so that the data are in the same coordinate. Second, the K-means clustering algorithm is used to classify the data according to the Euclidean distance to the radar image echo intensity, sea surface wind direction information and image signal-to-noise ratio data, and the centroid distance error is used as the judgment basis to obtain heterogeneous data; finally , remove the information data corresponding to heterogeneous data from both the radar data and the sea surface wind field information data, and obtain the cluster data of the radar data and the sea surface wind field information;步骤3,海面风速提取模型确定:利用聚类雷达数据和海面风速数据对海面风速进行非线性二次拟合,得到海面风速提取模型,应用SSE验证模型的准确性;Step 3, determining the sea surface wind speed extraction model: using the clustered radar data and the sea surface wind speed data to perform nonlinear quadratic fitting on the sea surface wind speed to obtain the sea surface wind speed extraction model, and applying SSE to verify the accuracy of the model;步骤4,海面风速信息提取:选取测试航海雷达图像部分图像,对其进行归一化映射,输入到海面风速提取模型中,得到海面风速信息。Step 4, extraction of sea surface wind speed information: select some images of the test marine radar image, perform normalized mapping on them, and input them into the sea surface wind speed extraction model to obtain sea surface wind speed information.
- 根据权利要求1所述的一种基于K-means聚类算法的海面风速方法,其特征在于:A kind of sea surface wind speed method based on K-means clustering algorithm according to claim 1, is characterized in that:海面风速反演所述步骤2包括以下步骤:The step 2 of the sea surface wind speed inversion includes the following steps:步骤2.1,雷达回波强度平均值、海面风向、风速信息及图像信噪比信息进行归一化数据处理;Step 2.1, normalized data processing of the average value of radar echo intensity, sea surface wind direction, wind speed information and image signal-to-noise ratio information;①对经过预处理的航海雷达图像选取适当部分雷达图像,沿x和y轴进行归一化映射,得到雷达图像均值f' i: ①Select an appropriate part of the radar image for the preprocessed marine radar image, and perform normalized mapping along the x and y axes to obtain the average value of the radar image f' i :其中,f(x,y)为选取的雷达图像强度值,N x、N y为选取图像沿x,y像元数,i是对应的雷达图像数;对f' i进行归一化得到雷达图像归一化值F i: Wherein, f (x, y) is the radar image intensity values selected, N x, N y is the selected image along the x, y number of pixels, i is the corresponding radar image data; of f 'i is normalized in radar Image normalization value F i :②获得选取雷达图像信噪比r t,以雷达图像时间序列进行归一化,得到海况信息归一化值R i: ② Obtain the signal-to-noise ratio r t of the selected radar image, normalize it with the radar image time series, and obtain the normalized value R i of the sea state information:其中 为二维波数谱经校正后的海浪谱, 为雷达图像海浪信号以外的噪声谱; in is the corrected ocean wave spectrum of the two-dimensional wavenumber spectrum, is the noise spectrum other than the radar image wave signal;③对采集风力计的海面风向信息d i、海面风速信息s i,按雷达图像序列进行归一化,得到海面风向、风速信息归一化值D i及S i: ③ Normalize the sea surface wind direction information d i and the sea surface wind speed information s i collected by the anemometer according to the radar image sequence, and obtain the normalized values D i and S i of the sea surface wind direction and wind speed information:步骤2.2,基于K-means聚类算法的雷达数据分类;Step 2.2, radar data classification based on K-means clustering algorithm;①初始化K个初始类簇质心;① Initialize K initial cluster centroids;对步骤2.1获得的F i、R i、D i和S i的所有数据分成两个部分;将用于模型确定中的数据F i、R i、D i组成数据集合,作为海面风速影响因素集合; Obtained in step 2.1 F i, R i, D i and S i of all the data divided into two parts; the data model for determination of F i, R i, D i consisting of a set of data as a set of surface wind speed factors ;Ω i={F i,R i,D i} (5) Ω i ={F i ,R i ,D i } (5)初始化类簇质心,随机选取Ω i区域中K个数据点作为初始化质心; Initialize the cluster centroids, and randomly select K data points in the Ω i region as the initialized centroids;②依据初始化质心划分数据点;② Divide the data points according to the initialized centroid;在确定K个海面风速影响因素质心后,在数据集Ω i中找出距离质心最近 的数据点,由此形成簇;计算Ω i中所有海面风速影响因素特征的数据点X i(x 1,x 2,x 3)与选定K个质心C k(c 1,c 2,c 3)之间的距离,公式如下: In determining the K surface wind speed affected by the quality of the heart, to find the distance from the centroid of the nearest data point in the data set Ω i, thereby forming clusters; Factor for all data points affect the calculation of surface wind speed Ω i of X i (x 1 ,x 2 ,x 3 ) and the distance between the selected K centroids C k (c 1 ,c 2 ,c 3 ), the formula is as follows:各点找到相聚最近的质心后,就归属于该簇,数据集Ω i被划分为K个子区域空间Τ k; After each point finds the closest centroid, it belongs to the cluster, and the data set Ω i is divided into K sub-region spaces Τ k ;③更新聚类质心;③ Update the cluster centroids;对每个Τ k中的 进行均值化,作为下一个更新的质心,计算公式如下: for each Τ k The mean value is used as the centroid of the next update, and the calculation formula is as follows:依据更新的质心按照公式(6)重新计算数据点与质心的欧氏距离,同时形成新的簇。According to the updated centroid, the Euclidean distance between the data point and the centroid is recalculated according to formula (6), and a new cluster is formed at the same time.④质心停止更新判断依据;④The judgment basis for the centroid to stop updating;根据原始质心C k和更新质心C j的距离判定质心是否需要进一步更新,判定条件如下: According to the distance between the original centroid C k and the updated centroid C j , it is determined whether the centroid needs to be further updated. The judgment conditions are as follows:||C k-C j||<<γ (8) ||C k -C j ||<<γ (8)当满足上述条件时表示质心趋于收敛,则分类算法终止;若不满足上述条件,则不断重复步骤2.3~2.5,直到满足公式(8),得到聚类质心C f(f=1,2,…f),及每个质心对应的聚类数据集Τ f; When the above conditions are met, it means that the centroids tend to converge, and the classification algorithm is terminated; if the above conditions are not met, repeat steps 2.3 to 2.5 until formula (8) is met, and the cluster centroids C f (f=1,2, ... f), and each cluster centroid corresponding data set Τ f;步骤2.3去除异类数据的雷达数据;Step 2.3 remove radar data of heterogeneous data;根据获得的海面风速影响因素的聚类分布,将质心位置相对其他质心最远的质心被判定为异类质心C d,其所在区域内的所有数据点也被判定为异类数据集Τ d,去除数据集Ω i中的异类数据,同时去除S i中异类数据对应的位置的海面风速S d,最终得到去除异类雷达数据F f,R f,D f,S f: According to the obtained cluster distribution of the influencing factors of sea surface wind speed, the centroid whose centroid position is farthest from other centroids is determined as the heterogeneous centroid C d , and all the data points in its area are also determined as the heterogeneous data set Τ d , and the data is removed. heterogeneous data sets in Ω i, S i the simultaneous removal of heterogeneous data corresponding to a position of surface wind speed S d, to give the final removal of heterogeneous radar data F f, R f, D f , S f:Ω f={F f,R f,D f}={Ω i-Τ d} S f={S i-S d} (9) Ω f = {F f , R f , D f } = {Ω i -Τ d } S f ={S i -S d } (9)步骤3.1,海面风速提取模型确定;先要释放F f,S f数据原有特性,得到对 应的雷达图像回波强度均值f f,及训练海面风速信息s f: Step 3.1, the sea surface wind speed extraction model is determined; first, the original characteristics of F f , S f data must be released to obtain the corresponding radar image echo intensity average value f f , and the training sea surface wind speed information s f :f f=F f*max(f i'),s f=S f*max(s i) (10) f f = F f * max ( f i '), s f = S f * max (s i) (10)步骤3.2,海面风速提取模型拟合;应用非线性二次函数对数据f f、s f进行拟合,得到海面风速预估模型: Step 3.2, fitting the sea surface wind speed extraction model; applying the nonlinear quadratic function to fit the data f f and s f to obtain the sea surface wind speed prediction model:步骤3.3,海面风速模型测试;对模型应用测试数据选取方差函数SSE作为误差检验指标,计算公式如下:Step 3.3, test the sea surface wind speed model; select the variance function SSE as the error test index for the model application test data, and the calculation formula is as follows:其中,ω i为加权系数,s i为实测海面风速, 为模型提取出的海面风速;SSE越接近0,则模型越精准,海面风速反演精度越高。 Among them, ω i is the weighting coefficient, s i is the measured sea surface wind speed, The sea surface wind speed extracted for the model; the closer the SSE is to 0, the more accurate the model and the higher the sea surface wind speed inversion accuracy.
- 根据权利要求2所述的一种基于K-means聚类算法的海面风速方法,其特征在于,所述步骤2.2中的初始化K个初始类簇质心,F i、R i、D i和S i的两个部分为:一部分用于基于K-means聚类算法海面风速模型的确定,另一部分用于模型的数据测试。 A method for sea surface wind speed based on K-means clustering algorithm according to claim 2, characterized in that, in the step 2.2, initializing K initial cluster centroids, F i , R i , D i and S i The two parts are: one part is used for the determination of the sea surface wind speed model based on the K-means clustering algorithm, and the other part is used for the data testing of the model.
- 根据权利要求2所述的一种基于K-means聚类算法的海面风速方法,其特征在于,所述步骤2.2的依据初始化质心划分数据点,所有海面风速影响因素特征的数据点X i(x 1,x 2,x 3)与选定K个质心C k(c 1,c 2,c 3)之间的距离为欧式距离。 A kind of sea surface wind speed method based on K-means clustering algorithm according to claim 2, it is characterized in that, in described step 2.2, according to the initialization centroid division data point, all data points X i (x The distance between 1 , x 2 , x 3 ) and the selected K centroids C k (c 1 , c 2 , c 3 ) is the Euclidean distance.
- 根据权利要求2所述的一种基于K-means聚类算法的海面风速方法,其特征在于,所述质心距离误差判定限制条件γ=0.1,得到聚类数为5。The sea surface wind speed method based on the K-means clustering algorithm according to claim 2, wherein the centroid distance error judgment limit condition γ=0.1, and the number of clusters obtained is 5.
- 根据权利要求2所述的一种基于K-means聚类算法的海面风速方法,其特征在于,所述步骤3.2中非线性二次函数对数据f f、s f进行拟合,所述的二次函数系数 为-9,β为325.9,δ为-637.6。 A sea surface wind speed method based on K-means clustering algorithm according to claim 2, characterized in that, in the step 3.2, the nonlinear quadratic function fits the data f f and s f , and the two Secondary function coefficients is -9, beta is 325.9, and delta is -637.6.
- 根据权利要求2所述的一种基于K-means聚类算法的海面风速方法,其特征在于,所述步骤3.3中海面风速模型测试误差检验指标的计算,所述SSE为对模型输入测试雷达回波强度均值得到的海面风速与测试风速的误差平方和。A sea surface wind speed method based on K-means clustering algorithm according to claim 2, wherein, in the step 3.3, in the calculation of the test error test index of the sea surface wind speed model, the SSE is to input the test radar feedback to the model. The sum of squares of errors between the sea surface wind speed obtained from the mean wave intensity and the test wind speed.
- 根据权利要求2所述的一种基于K-means聚类算法的海面风速方法,其特征 在于,所述步骤3.3中海面风速模型测试误差检验指标的计算,所述的方差函数SSE计算公式的系数 其中m为数据的个数。 A sea surface wind speed method based on K-means clustering algorithm according to claim 2, wherein, in the step 3.3, in the calculation of the test error test index of the sea surface wind speed model, the coefficient of the variance function SSE calculation formula where m is the number of data.
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