CN104573816B - The neural network clustering method of microwave radiometer remote sensing atmosphere parameter - Google Patents
The neural network clustering method of microwave radiometer remote sensing atmosphere parameter Download PDFInfo
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Abstract
The present invention relates to a kind of neural network clustering method of microwave radiometer remote sensing atmosphere parameter, it is characterised in that：Comprise the following steps, first by history sounding data, quantitative calculating is carried out to the correlation between air different layers knot, the big layer knot of coefficient correlation is elected, then artificial neural network training is substituted into by cluster, inverting exports the atmospheric parameter of different clusters respectively.Artificial neural network algorithm is by constantly adjusting the weight and deviation of network, to reduce by the deviation between the output water-vapo(u)r density vector that input vector is calculated and the training objective output water-vapo(u)r density vector of reality.After training is completed, the weights and deviation of network also it is determined that, therefore in microwave radiometer actual observation, it is only necessary to call its weight matrix and deviation, its arithmetic speed comparatively fast and stably, has higher inversion accuracy.
Description
Technical field
The present invention relates to a kind of Atmospheric Microwave remote sensing fields, more particularly to a kind of Neural Network Inversion skill of atmospheric parameter
Art.
Background technology
Artificial neural network is at a kind of novel information proposed based on human brain tissue structure, the Preliminary study of active mechanism
Reason system, there is the functions such as associative memory, Nonlinear Mapping, classification and identification, optimization calculating.For setting for artificial neural network
Meter and parameter setting include：Number of training, initial weight, network structure, network training and test etc..Artificial neuron
Network method is widely used in microwave radiometer Inverting Terrestrial Atmospheric Parameters, it is common practice that microwave radiometer is observed into bright temperature and substitutes into god
Through network, by below 10km atmospheric parameter overall outputs, because the characteristic of each layer of knot of air is different, the correlation between each layer
Also it is different, therefore the inner link rule between input and output is different, overall inverting output may result in inversion error
It is larger.
The content of the invention
The problem to be solved in the present invention is to provide a kind of nerve net of the smaller microwave radiometer remote sensing atmosphere parameter of error
Network clustering method.
In order to solve the above technical problems, the neural network clustering method of the microwave radiometer remote sensing atmosphere parameter of the present invention,
It is characterized in that：Comprise the following steps, first by history sounding data, the correlation between air different layers knot is determined
Amount is calculated, and the big layer knot of coefficient correlation is elected, and then substitutes into artificial neural network training by cluster, and inverting exports respectively
The atmospheric parameter of difference cluster.
The history sounding data is the atmospheric radiation bright temperature data that microwave radiometer receives.
The history sounding data for the temperature and humidity pressure and microwave radiometer on ground the bright temperature of measurement, by the temperature and humidity pressure on ground and
The bright temperature of measurement of microwave radiometer is as input parameter, using atmosphere vapour density profile as output parameter.Input vector X length
Spend for L, output vector Y length is M.The output function of any single neuron is：
In formula：F is the output valve of neuron, and ω is the input weight matrix of neuron, and p is the input value of neuron, and b is
The bias vector of neuron.Using logarithm tangent type function：
The set of frequency of the microwave radiometer：From 22.24 to steam and the K-band of liquid water sensitive, 23.04,
23.84th, 25.44,26.24,27.84,30,31.4GHz and to the 51.26 of temperature and the v wave bands of liquid water sensitive, 52.28,
53.86th, 54.94,55.5,56.66,57.3,58GHz and infrared thermometer (wavelength is 8~14 microns).The observation elevation angle is set
Put：90 degree.Neutral net input is set：The bright temperature and infrared temperature of ground temperature and humidity pressure and 16 passages.Neutral net exports
Vertical resolution：100 meters one layer between 0~2km, 250 meters one layer between 2~10km, amount to 53 layers plus ground floor,
The water-vapo(u)r density correlation of the different layers knot of air is analyzed using history sounding data, coefficient R
Calculation formula is：
In formula, ρ1、ρ2Respectively the water-vapo(u)r density of the different layers knot of air, C are covariance matrix, are calculated by following formula：
C(ρ1,ρ2)=E ((ρ1-μ1)(ρ2-μ2))
In formula：μ1、μ2Respectively air ρ1、ρ2Average value, E represent expectation computing.By the way that conclusion is calculated：Air
Layer knot distance is nearer, and its coefficient correlation is bigger, i.e., correlation is stronger；
According to the characteristics of neutral net, the strong data input neutral net of correlation can be more accurately fitted defeated
Enter the relation of output, then choose three groups of higher layer knots of coefficient correlation, such data are put together and cluster output, Ran Houtong
Cross neutral net to be trained respectively, obtain three groups of inverting coefficient matrixes, be combined into unified coefficient matrix：
ω=[ω1；ω2；ω3]
In formula：ω1、ω2、ω3The inverting coefficient matrix that respectively three group cluster data are obtained by neural computing,
During actual observation, by the unified output of three system number inversion results, so that it may obtain the water-vapo(u)r density profile under 10km height.
Artificial neural network algorithm is calculated by constantly adjusting the weight and deviation of network to reduce by input vector
Deviation between the output water-vapo(u)r density vector and actual training objective output water-vapo(u)r density vector that arrive.Although neutral net is calculated
Method iterates for a long time, but after training is completed during training, the weights and deviation of network also it is determined that,
Therefore in microwave radiometer actual observation, it is only necessary to call its weight matrix and deviation, its arithmetic speed it is very fast and
It is stable, there is higher inversion accuracy.Clustering neural network method proposed by the present invention, be first by quantitative calculating air not
With the correlation between layer knot, the big layer knot of coefficient correlation is found out, neural network structure is then substituted into respectively, obtains more accurately
Inverting coefficient matrix.
Brief description of the drawings
The present invention is further detailed explanation with reference to the accompanying drawings and detailed description.
Fig. 1 is neutral net vector model.
Fig. 2 is the coefficient correlation of atmosphere first layer knot (ground floor) and other 52 atmospheres, different atmospheric stratification steam
The coefficient correlation of density and surface vapor concentration.
Fig. 3 is the simulation result that error calculation is carried out using multiple checking samples, and Neural Network Inversion water-vapo(u)r density is square
Root error.
Fig. 4 is that the profile of Neural Network Inversion water-vapo(u)r density compares.
Fig. 5 is neural network clustering inverting flow process figure.
Embodiment
Using the bright temperature of the measurement of the temperature and humidity pressure on ground and microwave radiometer as input parameter, atmosphere vapour density profile is made
For output parameter.Input vector X length is L, and output vector Y length is M.The output function of any single neuron is：
In formula：F is the output valve of neuron, and ω is the input weight matrix of neuron, and p is the input value of neuron, and b is
The bias vector of neuron.Using logarithm tangent type function：
Specifically, the set of frequency of the microwave radiometer：From 22.24 to steam and the K-band of liquid water sensitive,
23.04th, 23.84,25.44,26.24,27.84,30,31.4GHz and to the 51.26 of temperature and the v wave bands of liquid water sensitive,
52.28th, 53.86,54.94,55.5,56.66,57.3,58GHz and infrared thermometer (wavelength is 8~14 microns).Observation is faced upward
Angle is set：90 degree.Neutral net input is set：The bright temperature and infrared temperature of ground temperature and humidity pressure and 16 passages.Neutral net
Export vertical resolution：100 meters one layer between 0~2km, 250 meters one layer between 2~10km, amount to 53 layers plus ground floor,
The water-vapo(u)r density correlation of the different layers knot of air is analyzed using history sounding data, coefficient R
Calculation formula is：
In formula, ρ1、ρ2Respectively the water-vapo(u)r density of the different layers knot of air, C are covariance matrix, are calculated by following formula：
C(ρ1,ρ2)=E ((ρ1-μ1)(ρ2-μ2))
In formula：μ1、μ2Respectively air ρ1、ρ2Average value, E represent expectation computing.By the way that conclusion is calculated：Air
Layer knot distance is nearer, and its coefficient correlation is bigger, i.e., correlation is stronger；
According to the characteristics of neutral net, the strong data input neutral net of correlation can be more accurately fitted defeated
Enter the relation of output, then choose three groups of higher layer knots of coefficient correlation, such data are put together and cluster output, Ran Houtong
Cross neutral net to be trained respectively, obtain three groups of inverting coefficient matrixes, be combined into unified coefficient matrix：
ω=[ω1；ω2；ω3]
In formula：ω1、ω2、ω3The inverting coefficient matrix that respectively three group cluster data are obtained by neural computing,
During actual observation, by the unified output of three system number inversion results, so that it may obtain the water-vapo(u)r density profile under 10km height.
The water-vapo(u)r density correlation of air different layers knot is analyzed using the somewhere history sounding data of 10 years, schemed
2 be the coefficient correlation of atmosphere first layer knot (ground floor) and other 52 atmospheres, as can be seen from Figure, atmospheric stratification distance
Nearer, its coefficient correlation is bigger, i.e., correlation is stronger.Therefore according to the characteristics of neutral net, it have chosen the high layer of coefficient correlation
Knot, the cluster that such data can be put together output.Cluster standard will be made a concrete analysis of for particular problem, choose 1 here
~5 layers, 6~15 layers, 16~53 layers as an example.Then it is respectively trained using neutral net, obtains three groups of inverting coefficients
Matrix.
Fig. 3 is the simulation result that error calculation is carried out using multiple checking samples, it can be seen that neutral net overall output
The inversion error of water-vapo(u)r density is maximum in the middle low layer (1~2km) of air, and clustering neural network method centering lower atmosphere layer
Inversion accuracy has preferable improvement.Bright temperature data is surveyed using QFW6000 microwave radiometers inverting is carried out to air, obtain two groups
Water-vapo(u)r density profile, and contrasted with sounding data, as shown in Figure 4, it can be seen that the inversion result of clustering neural network is in
Low layer more coincide with sounding, suitable with holistic approach precision in other layers of knot.
Claims (1)
- A kind of 1. neural network clustering method of microwave radiometer remote sensing atmosphere parameter, it is characterised in that：Comprise the following steps, it is first First usage history sounding data, quantitative calculating is carried out to the correlation between air different layers knot, by the big layer knot of coefficient correlation Elect, then substitute into artificial neural network training by cluster, inverting exports the atmospheric parameter of different clusters respectively；It is described to go through History sounding data is the atmospheric radiation bright temperature data that microwave radiometer receives；The history sounding data for ground temperature and humidity pressure and The bright temperature of measurement of microwave radiometer, using the bright temperature of the measurement of the temperature and humidity pressure on ground and microwave radiometer as input parameter, by air For water-vapo(u)r density profile as output parameter, input vector X length is L, and output vector Y length is M, any single neuron Output function be：<mrow> <mi>f</mi> <mo>=</mo> <mi>S</mi> <mrow> <mo>(</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>L</mi> </munderover> <msub> <mi>&omega;</mi> <mi>i</mi> </msub> <msub> <mi>P</mi> <mi>i</mi> </msub> <mo>+</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>In formula：F is the output valve of neuron, and ω is the input weight matrix of neuron, and p is the input value of neuron, and b is nerve The bias vector of member, using logarithm tangent type function：<mrow> <mi>S</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mi>n</mi> </mrow> </msup> <mo>+</mo> <mn>1</mn> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>The set of frequency of the microwave radiometer：From 22.24 to steam and the K-band of liquid water sensitive, 23.04,23.84, 25.44th, 26.24,27.84,30,31.4GHz and to the 51.26 of temperature and the v wave bands of liquid water sensitive, 52.28,53.86, 54.94th, 55.5,56.66,57.3,58GHz and infrared thermometer, wavelength are 8~14 microns, and the observation elevation angle is set：90 degree, Neutral net input is set：The bright temperature and infrared temperature of ground temperature and humidity pressure and 16 passages, neutral net output is vertical to differentiate Rate：100 meters one layer between 0~2km, 250 meters one layer between 2~10km, amount to 53 layers plus ground floor,The water-vapo(u)r density correlation of the different layers knot of air is analyzed using history sounding data, the calculating of coefficient R Formula is：<mrow> <mi>R</mi> <mrow> <mo>(</mo> <msub> <mi>&rho;</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>&rho;</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>C</mi> <mrow> <mo>(</mo> <msub> <mi>&rho;</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>&rho;</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> <msqrt> <mrow> <mi>C</mi> <mrow> <mo>(</mo> <msub> <mi>&rho;</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>&rho;</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mi>C</mi> <mrow> <mo>(</mo> <msub> <mi>&rho;</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>&rho;</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> </msqrt> </mfrac> </mrow>In formula, ρ1、ρ2Respectively the water-vapo(u)r density of the different layers knot of air, C are covariance matrix, are calculated by following formula：C(ρ1,ρ2)=E((ρ1-μ1)(ρ2-μ2))In formula：μ1、μ2Respectively air ρ1、ρ2Average value, E represent expectation computing, by the way that conclusion is calculated：Atmospheric stratification away from From nearer, its coefficient correlation is bigger, i.e., correlation is stronger；According to the characteristics of neutral net, by the strong data input neutral net of correlation, it is defeated can be more accurately fitted input The relation gone out, three groups of higher layer knots of coefficient correlation are then chosen, such data are put together and cluster output, then pass through god It is trained respectively through network, obtains three groups of inverting coefficient matrixes, be combined into unified coefficient matrix：ω=[ω1；ω2；ω3]In formula：ω1、ω2、ω3The inverting coefficient matrix that respectively three group cluster data are obtained by neural computing, it is actual During observation, by the unified output of three system number inversion results, so that it may obtain the water-vapo(u)r density profile under 10km height.
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