Satellite infrared channel radiation calibration method and system

Abstract

The invention discloses a satellite infrared channel radiation calibration method and system, and belongs to the technical field of equipment calibration, and the method comprises the steps: scanning a deep space scene and black bodies at different temperatures through a satellite scanner, and obtaining a deep space DN value and a black body DN value; in combination with a matrix decomposition algorithm, determining radiation bias of a satellite infrared channel; a radiation transfer equation used for reflecting the relation between the DN value obtained through satellite infrared channel scanning and the radiation brightness is established in combination with radiation bias; based on the obtained black body DN value, determining the maximum order of a radiation transfer equation in combination with a ridge regression algorithm; updating the radiation transfer equation based on the maximum order, and determining each order correction coefficient of the updated radiation transfer equation through a least square method; updating the radiation transfer equation with a scan angle polarization factor caused by the satellite scanner to correct the radiation intensity; and outputting the updated radiation transfer equation, and ending the radiation calibration of the satellite infrared channel. And the satellite observation precision is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of equipment calibration, and in particular relates to a satellite infrared channel radiation calibration method and system. Background Art

[0002] DN (Digital Number) is the abbreviation of digital count value. It is the digital output value obtained by the sensor after receiving the light or radiation signal through the electronic device. It is the value converted by the sensor in satellite remote sensing from the actual physical quantity (such as radiation intensity) through the quantization and digitization process. Radiation intensity refers to the density of radiation energy flow per unit area within a unit solid angle. It is usually used to describe the intensity of light or electromagnetic waves. It is the real physical quantity received by the satellite infrared channel and reflects the radiation characteristics of the target object in a certain band. Satellite infrared channel radiation calibration refers to the accurate determination of the relationship between the digital count value (DN value) received by the satellite infrared detector and the actual radiation brightness through mathematical modeling and experimental calibration. This process is a key link to ensure the reliability and accuracy of satellite observation data, especially for fields that require high-precision observation, such as weather forecasting, environmental monitoring, remote sensing detection, etc.

[0003] Radiometric calibration is a key step in ensuring the accuracy and consistency of measurement data in satellite remote sensing. Since the sensor will be affected by factors such as electronic offset, temperature change, and aging during long-term operation, the measurement data will be biased. Through radiometric calibration, the relationship between the DN value and the actual radiation brightness can be accurately established, thereby eliminating system errors, ensuring that satellite observation data is authentic and reliable, and providing high-precision support for applications such as environmental monitoring and weather forecasting.

[0004] In the prior art, during the radiation calibration process, the fitting order between the DN value and the actual radiation brightness is often determined subjectively by humans, or a low-order fitting model is directly selected, resulting in the inability of the fitting model to accurately describe the relationship between the DN value and the actual radiation brightness. The fitting accuracy is low, and the detailed description of the radiation relationship by high-order terms is ignored, resulting in a large deviation in the radiation brightness of the final scanned area, which in turn leads to low accuracy of satellite observation data and affects the normal operation of the satellite. Summary of the invention

[0005] In order to solve the technical problem in the prior art that, in the radiation calibration process, the fitting order between the DN value and the actual radiation brightness is often determined subjectively by humans, or a low-order fitting model is directly selected, resulting in the fitting model being unable to accurately describe the accurate relationship between the DN value and the actual radiation brightness, and the fitting accuracy is low, and the fine description of the radiation relationship by high-order terms is ignored, resulting in a large deviation in the radiation brightness of the final scanned area, which in turn leads to low accuracy of satellite observation data and affects the normal operation of the satellite, the present invention provides a satellite infrared channel radiation calibration method and system.

[0006] First aspect

[0007] The present invention provides a satellite infrared channel radiation calibration method, comprising:

[0008] S1: Use a satellite scanner to scan deep space scenes and black bodies at different temperatures to obtain multiple deep space DN values ​​and multiple black body DN values;

[0009] S2: Combined with the matrix decomposition algorithm, the radiation bias of the satellite infrared channel is determined based on the obtained deep space DN value;

[0010] S3: Combined with the radiation bias, a radiation transfer equation is established to reflect the relationship between the DN value obtained by the satellite infrared channel scanning and the radiation brightness;

[0011] S4: Based on the obtained blackbody DN value, the maximum order of the radiation transfer equation is determined in combination with the ridge regression algorithm;

[0012] S5: updating the radiation transfer equation based on the maximum order, and determining the correction coefficients of each order of the updated radiation transfer equation by the least square method;

[0013] S6: Update the radiation transfer equation using the scan angle polarization factor caused by the satellite scanner to correct the radiation intensity;

[0014] S7: Output the updated radiation transfer equation and end the radiation calibration of the satellite infrared channel.

[0015] Second aspect

[0016] The present invention provides a satellite infrared channel radiation calibration system for executing the satellite infrared channel radiation calibration method in the first aspect.

[0017] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0018] In the present invention, a deep space scene with a radiation intensity of 0 and a black body at different temperatures are first scanned by a satellite scanner to obtain a deep space DN value and a black body DN value. Then, based on the determined data and in combination with a matrix decomposition algorithm, the radiation bias of the satellite infrared channel is determined based on the obtained deep space DN value, and the influence of electronic offset and abnormal noise is accurately removed, thereby improving the accuracy and stability of the satellite infrared channel radiation calibration and ensuring data reliability. The maximum order of the radiation transfer equation is determined in combination with the ridge regression algorithm, which effectively avoids the problem of excessive fitting error caused by subjective selection of the order, improves the accuracy and robustness of the radiation calibration, and solves the overfitting or underfitting phenomenon, ensuring that the satellite infrared channel radiation brightness calculation is more accurate. And the correction coefficients of each order of the updated radiation transfer equation are accurately determined by the least squares method. Then, the scanning angle polarization factor caused by the satellite scanner was used to correct the radiation intensity. Finally, the radiation transfer equation reflecting the relationship between DN value and radiation intensity was accurately fitted, which can accurately and efficiently obtain the radiation intensity of the satellite scanning area. By combining deep space scenes, blackbody data, matrix decomposition algorithm and ridge regression algorithm, the influence of electronic offset and noise can be accurately removed. At the same time, the optimal order of the radiation transfer equation is adaptively determined to avoid fitting errors, improve the accuracy and stability of radiation calibration, and combine the least squares method with scanning angle correction to ensure that the radiation intensity calculation of the satellite infrared channel is accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The preferred implementation modes will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0020] Figure 1 It is a schematic flow chart of a satellite infrared channel radiation calibration method provided by the present invention;

[0021] Figure 2 The present invention is a schematic structural diagram of a satellite infrared channel radiation calibration system. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0023] In order to simplify the drawings, only the parts related to the invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0024] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] In this document, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] In addition, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0027] Example 1

[0028] In one embodiment, the reference specification Figure 1 , showing a schematic flow chart of the satellite infrared channel radiation calibration method provided by the present invention.

[0029] The present invention provides a satellite infrared channel radiation calibration method, comprising:

[0030] S1: Use a satellite scanner to scan deep space scenes and black bodies at different temperatures to obtain multiple deep space DN values ​​and multiple black body DN values.

[0031] Among them, satellite scanners are remote sensing sensor devices installed on satellites, which are used to scan the earth, deep space or other target scenes, by receiving electromagnetic radiation and converting it into digital signals (DN values) for scientific measurement and analysis. The deep space scene refers to the empty universe background without stars or other radiation sources. Its radiation intensity is close to zero and contains almost no thermal radiation. It is an ideal reference "zero radiation" scene for calibrating the electronic offset of the sensor. A black body is an ideal radiator that can completely absorb and emit thermal radiation, and its radiation intensity depends only on its own temperature. The radiation of a black body at different temperatures has a known mathematical relationship (such as Planck's law), so it is a standard reference source in radiation calibration.

[0032] It should be noted that the satellite scanner obtains the DN value of deep space and the DN value of black body by scanning deep space scenes with radiation intensity close to zero and black bodies at different temperatures, and determines the electronic offset (radiation bias) of the sensor through deep space data, while providing a standard reference for temperature and radiation intensity through black body data. Deep space data provides a "zero reference" for calibration, while black body data provides a known physical standard for subsequent radiation brightness calculations. This process is the basis for radiation calibration and ensures data reliability and accuracy.

[0033] S2: Combined with the matrix decomposition algorithm, the radiation bias of the satellite infrared channel is determined based on the obtained deep space DN value.

[0034] Among them, the matrix decomposition algorithm is a mathematical method to decompose a matrix into multiple sub-matrices with specific properties. The satellite infrared channel refers to a specific channel in the infrared band through which the satellite remote sensing sensor receives radiation signals from the earth's surface or atmosphere. The infrared channel can monitor the temperature and radiation intensity of objects and is widely used in meteorology, environmental monitoring and other fields. Radiation bias refers to the non-zero basic signal introduced by the noise or hardware characteristics of the electronic system itself during the observation process of the sensor, which will still generate a certain digital count value (DN value) even in the absence of radiation input (such as deep space scenes). Radiation bias needs to be removed through calibration to ensure the accuracy of the observation data.

[0035] Specifically, based on the deep space DN values ​​obtained by the satellite scanner, a matrix decomposition algorithm (such as RPCA) is used to decompose the deep space DN value matrix into a low-rank matrix and a sparse matrix. The low-rank matrix represents the radiation bias of the satellite infrared channel, that is, the reference offset introduced by the electronics system, while the sparse matrix reflects the influence of abnormal noise. Through this decomposition method, the radiation bias can be accurately determined while removing noise interference, ensuring the accuracy and stability of subsequent data processing. This step provides an accurate electronics offset reference for radiation calibration, laying the foundation for high-precision calibration.

[0036] In a possible implementation, the matrix decomposition algorithm is specifically an RPCA algorithm.

[0037] Among them, the RPCA algorithm (Robust Principal Component Analysis) is a mathematical method for processing data decomposition. It decomposes the original data matrix into two parts: a low-rank matrix and a sparse matrix. It is suitable for removing noise and outliers and is widely used in signal processing.

[0038] S2 specifically includes:

[0039] S201: Combining the deep space DN values ​​into a deep space DN value matrix:

[0040]

[0041] Where D represents the deep space DN value matrix, N SV,k,m It represents the DN value corresponding to the mth field of view of the kth scan, k=1,2,…,N, m=1,2,…,M, N represents the total number of scans, and M represents the total number of fields of view scanned.

[0042] S202: Decomposing the deep space DN value matrix by RPCA algorithm:

[0043] min L,S ‖L‖ * +λ‖S‖ 1

[0044]

[0045] Among them, L represents the low-rank matrix reflecting the radiation bias, S represents the coefficient matrix reflecting the abnormal noise, ‖L‖ * represents the nuclear norm of L, i.e. the sum of the singular values ​​of L, ‖S‖ 1 represents the L1 norm of S, λ represents the regularization parameter that balances the weights of L and S, min L,S ‖L‖ * +λ‖S‖ 1 Denotes the value such that ‖L‖ * +λ‖S‖ 1 Take the minimum value of L and S.

[0046] It should be noted that the deep space DN value matrix is ​​decomposed into a low-rank matrix L and a sparse matrix S through the RPCA algorithm, where L represents the normal part of the radiation bias and S represents the abnormal noise component, which effectively separates the normal radiation bias from the noise and improves the data accuracy.

[0047] In a possible implementation, the regularization parameter is calculated as follows:

[0048]

[0049] |λ (t+1) -λ (t) |<

[0050]

[0051] Among them, λ (t+1) and λ (t) They represent the regularization parameters obtained at the t-th iteration and the t+1-th iteration, ∈ represents the termination threshold, η represents the learning rate, represents partial derivatives, represents the RPCA decomposition and reconstruction error with respect to λ, express The gradient of , ← represents an update.

[0052] It should be noted that through the iterative optimization process, the parameter values ​​are dynamically adjusted to ensure that the optimal solution is gradually approached in each update. The introduction of learning rate and termination threshold can control the convergence speed and avoid too fast or too slow adjustments, thereby improving computational efficiency and stability. Partial derivatives and gradient calculations make the parameter update process more accurate, adaptively balance the reconstruction error and regularization terms, reduce the complexity of manual parameter adjustment, and ensure that the algorithm has good robustness and stability under different data scales.

[0053] Optionally, ∈ can be specifically 10 -5 or 10 -6 The learning rate η can be set to 0.01 at the beginning, and then decays exponentially to 0.001. The decomposition and reconstruction error is:

[0054]

[0055] Among them, |||| F represents the Frobenius norm, μ represents the control reconstruction error and the regularization term ‖L‖ * +λ‖S‖ 1 The balance parameter of the weight between is calculated as follows: Adaptive adjustment is performed through the Frobenius norm and the maximum dimension of the data matrix, so that the parameters can automatically balance the weight of the reconstruction error and the regularization term according to the data scale. This adaptive calculation avoids the complexity brought by manual parameter adjustment, improves the robustness and generalization ability of the algorithm, and ensures stable results under different data conditions, effectively improving the accuracy and efficiency of radiation bias extraction.

[0056] S203: Take the average of the radiation bias, i.e., the low-rank matrix, under each field of view to calculate the radiation bias:

[0057]

[0058] Among them, N SV Represents the radiation bias, L k,m Represents the element obtained from the mth field of view of the kth scan in the low-rank matrix L.

[0059] It should be noted that by minimizing the weighted sum of the nuclear norm and the L1 norm, RPCA can effectively separate normal radiation bias from abnormal noise and improve data accuracy and stability. In S201, the deep space DN values ​​are organized into a matrix. In S202, the matrix is ​​decomposed by the RPCA algorithm. In S203, the low-rank matrix is ​​averaged to obtain an accurate radiation bias value, thereby providing a stable and reliable data basis for subsequent radiation calibration, where the radiation bias is a digital count value of the space field of view used to remove electronic offsets.

[0060] S3: Combined with the radiation bias, a radiation transfer equation is established to reflect the relationship between the DN value obtained by scanning the satellite infrared channel and the radiation brightness.

[0061] Among them, the radiation transfer equation is a mathematical model that describes the relationship between the digital count value (DN value) and the actual physical radiation brightness. Based on the radiation bias determined in the previous step, the DN value obtained by scanning the satellite infrared channel is linked to the actual radiation brightness by establishing the radiation transfer equation. This equation can compensate for the bias information of the sensor system, and through the introduction of multi-order correction coefficients, it can more accurately fit the nonlinear relationship between the DN value and the radiation brightness, ensure the accuracy and reliability of the calibration data, and provide a basis for subsequent radiation intensity calculations.

[0062] In a possible implementation, the radiation transfer equation is specifically:

[0063] R = a 0 +a 1 ·D N +a 2 ·D N 2 +…+a n ·D N n

[0064] a 0 =N SV

[0065] Among them, N sc represents the radiation brightness obtained by scanning, a v represents the v-order correction coefficient, v=1,2,…,n, n represents the maximum order, D N Indicates the DN value obtained by scanning, R indicates the same as D N The corresponding radiation brightness.

[0066] It should be noted that by introducing the polynomial form, an accurate relationship is established between the scanned digital count value and the actual radiation brightness. The order correction coefficient and the search for the maximum order can better fit the nonlinear relationship, compensate for the system deviation of the sensor, and improve the precision and accuracy of the radiation calibration. In addition, the flexible polynomial order design makes the model more adaptable and generalizable, suitable for different observation scenarios and data, and ensures that the observation results are more stable and reliable.

[0067] S4: Based on the obtained blackbody DN value, the maximum order of the radiation transfer equation is determined in combination with the ridge regression algorithm.

[0068] Among them, Ridge Regression is a regression analysis method used to solve the problem of multicollinearity. By introducing the L2 regularization term (penalty term) in the loss function, it effectively limits the model coefficients from being too large, avoids overfitting, and improves the generalization ability of the model. The maximum order refers to the highest order of the polynomial in the radiation transfer equation, which is used to describe the complex nonlinear relationship between the DN value and the radiation brightness. The purpose of determining the maximum order is to balance the fitting accuracy and model complexity to prevent overfitting or underfitting.

[0069] It should be noted that based on the scanned blackbody DN value and the corresponding temperature, the ridge regression algorithm is used to fit the radiation transfer equations of different orders and calculate the fitting error. By introducing the regularization penalty term, ridge regression can effectively control the complexity of the model, avoid the overfitting problem caused by high-order fitting, and determine the optimal maximum order. Finally, the performance of models of different orders is evaluated by the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), and the maximum order with the smallest fitting error and the highest stability is selected, thereby improving the accuracy and robustness of radiation calibration and ensuring more accurate calculation of radiation brightness.

[0070] In a possible implementation, S4 specifically includes:

[0071] S401: Calculate the real radiation brightness corresponding to each blackbody DN value by combining the blackbody DN value and the temperature corresponding to the blackbody DN value:

[0072]

[0073] Among them, T i and R i They represent the black body temperature and the real radiation brightness corresponding to the black body temperature, exp represents the natural exponential function, C 1 and C 2 denote the first radiation constant and the second radiation constant, λ 波长 Represents the observation wavelength of the black body emitted by the satellite's infrared channel.

[0074] It should be noted that previous calibrations were often based on the maximum order selected based on subjective experience or directly using a second-order function to fit the radiation transfer equation, ignoring the large fitting error, which ultimately led to a large deviation in the satellite infrared channel radiation calibration, and the inability to accurately calculate the radiation brightness, which in turn led to inaccurate satellite detection of various parameters. This scheme uses the ridge regression algorithm to fit the acquired blackbody DN value with known temperature, and evaluates the maximum order necessary to establish the radiation transfer equation before establishing the radiation transfer equation, avoiding excessive radiation transfer equation fitting errors caused by artificially selected orders.

[0075] S402: Substitute the blackbody DN value into the radiation transfer equation at different orders to obtain the fitted radiation brightness at different orders.

[0076] S403: Establish a loss function for the true radiance and the fitted radiance through the ridge regression algorithm:

[0077]

[0078] Among them, L oss represents the loss function, O represents the total number of blackbody temperature changes, R i represents the fitted radiant brightness obtained by fitting the blackbody DN value collected at the i-th blackbody temperature, and γ=0.5 represents the penalty term coefficient.

[0079] S404: With the goal of minimizing the loss function, determine the residual sum of squares of the true radiance and the fitted radiance:

[0080] min RSS L oss

[0081]

[0082] Among them, RSS represents the residual sum of squares, min RSS L oss Indicates taking the minimum L oss RSS below.

[0083] S405: Calculate the Akaike information criterion value and the Bayesian information criterion value for the residual sum of squares:

[0084]

[0085]

[0086] Among them, AIC and BIC represent the Akaike information criterion value and the Bayesian information criterion value, respectively, and log represents the logarithmic function.

[0087] S406: Calculate the ratio of the Akaike information criterion value to the Bayesian information criterion value, and output the order under the minimum ratio as the maximum order of the radiation transfer equation:

[0088]

[0089] in, Indicates the minimum The following n.

[0090] Specifically, the temperature and DN value of the black body are used to calculate the true radiation brightness of the black body based on the Planck formula, providing a physical standard reference value. The radiation transfer equations of different orders are used to fit the relationship between the black body DN value and the true radiation brightness, and the fitting results at different orders are obtained. The loss function is introduced by the ridge regression algorithm, and the regularization term is combined to balance the fitting accuracy and complexity of the model to prevent overfitting. By minimizing the loss function, the residual sum of squares (RSS) is determined to further evaluate the error size of the model. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are calculated to evaluate the advantages and disadvantages of models of different orders. By comparing the ratio of AIC to BIC, the order corresponding to the minimum ratio is selected as the optimal maximum order output, and the optimal model of the radiation transfer equation is finally determined. This method avoids the fitting error caused by human subjective factors through automatic order selection, improves the accuracy, stability and robustness of radiation calibration, and ensures the accurate calculation of radiation brightness.

[0091] S5: updating the radiation transfer equation based on the maximum order, and determining correction coefficients of each order of the updated radiation transfer equation by the least square method.

[0092] Among them, the least squares method is a mathematical optimization method that solves the model parameters by minimizing the sum of squares of the errors between the observed values ​​and the model predicted values. The correction coefficient is the coefficient of each order polynomial in the radiation transfer equation, which is used to describe the relationship between the digital count value (DN value) and the radiation brightness. Based on the determined maximum order, the radiation transfer equation is converted into a matrix equation form, and the equation is solved by the least squares method to obtain the correction coefficients of each order. The least squares method can ensure that the model fitting error is minimized, so that the radiation transfer equation can more accurately reflect the corresponding relationship between the DN value and the actual radiation brightness, thereby improving the calibration accuracy.

[0093] In a possible implementation, S5 specifically includes:

[0094] S501: Transform the radiation transfer equation into a matrix equation:

[0095]

[0096] Among them, a represents the correction coefficient vector, R represents the real radiation brightness vector, and X represents the vector including D NThe design matrix for each order, represents the DN value associated with the i-th temperature and order v of the blackbody.

[0097] S502: Solve the matrix equation by the least square method to obtain the correction coefficient vector, i.e., the correction coefficients of each order:

[0098]

[0099] The superscript T indicates transposition. Indicates taking The smallest a, Indicates launch.

[0100] It should be noted that the radiation transfer equation is converted into a matrix equation, and the polynomial fitting problem is simplified to a linear algebra problem. Among them, the true radiation brightness is taken as a vector, the design matrix contains DN value items of different orders, and the correction coefficient to be calculated is represented as a vector. The matrix equation is solved by the least squares method to minimize the error between the model prediction value and the true radiation brightness, so as to obtain the correction coefficients of each order. The least squares method can accurately fit the data with the minimum error, and improve the accuracy and stability of the radiation transfer equation. Through this method, the correction coefficients of each order of the radiation transfer equation are finally determined, which makes the relationship between the DN value and the radiation brightness more accurately described, which helps to improve the reliability of the calibration results.

[0101] S6: Update the radiation transfer equation using the scan angle polarization factor caused by the satellite scanner to correct the radiation intensity.

[0102] Among them, the scanning angle polarization factor is a polarization effect caused by the structure and optical characteristics of the satellite scanner, which affects the measurement results of the radiation intensity as the scanning angle changes. This polarization effect causes systematic errors in the observation data at different scanning angles and needs to be corrected. Considering the polarization effect caused by the change in the scanning angle of the satellite scanner, the radiation transfer equation is updated and corrected by introducing the scanning angle polarization factor. This step can effectively eliminate the radiation intensity error at different scanning angles, ensure that the final calculated radiation intensity remains consistent and accurate at each scanning angle, and further improve the data quality.

[0103] In a possible implementation manner, the corrected radiation intensity is specifically:

[0104] R′=R·[1+P(θ)cos 2 (θ)]

[0105] Where R′ represents the corrected radiation intensity, θ represents the scanning angle of the satellite scanner, P(θ) represents the scanning angle polarization factor related to the satellite structure, and cos represents the cosine function.

[0106] Among them, the scanning angle polarization factor is obtained by testing the radiation response of the satellite scanner under ground laboratory conditions using a standard radiation source and a test device with a controllable scanning angle. By changing the scanning angle, recording the radiation signal received by the scanner, and comparing it with the actual radiation intensity of the standard radiation source, the polarization factor is calculated. It is understandable that since changes in the scanning angle may lead to radiation measurement errors, the introduction of the cosine function and the polarization factor to correct the observed data effectively eliminates the influence of the angle deviation, ensures the consistency and accuracy of the radiation intensity at different scanning angles, and improves the reliability of the measurement results.

[0107] S7: Output the updated radiation transfer equation and end the radiation calibration of the satellite infrared channel.

[0108] In a possible implementation manner, after S7, the method further includes:

[0109] After a preset time interval, steps S1 to S7 are executed again.

[0110] It is understandable that the calibration process will be repeated periodically to continuously update the radiation bias and correction coefficients, ensure the accuracy of the radiation calibration of the satellite's infrared channel, adapt to possible performance drift of the sensor over time, and ensure the long-term stability and accuracy of the observation data.

[0111] It should be noted that those skilled in the art can set the preset duration according to actual needs, and the present invention is not limited thereto.

[0112] In a possible implementation, it further includes:

[0113] Scan the target scene through the satellite scanner to obtain the target DN value.

[0114] Substitute the target DN value into the radiation transfer equation to determine the radiant brightness of the target scene.

[0115] It should be noted that the target scene is scanned by a satellite scanner to obtain the target DN value, which is then substituted into the calibrated radiation transfer equation to calculate the radiation brightness of the target scene. This process establishes an accurate correspondence between satellite observation data and actual physical quantities, ensures the accuracy of radiation brightness, and provides reliable data support for the analysis and application of radiation characteristics of the target scene, which is widely used in environmental monitoring and remote sensing analysis.

[0116] In the actual application process, high-precision radiation calibration of satellite infrared channels is achieved through systematic satellite infrared channel radiation calibration. First, by scanning deep space scenes and black bodies of different temperatures, deep space DN values ​​and black body DN values ​​are obtained, which are used for electronic offset correction and radiation reference standards respectively. The matrix decomposition algorithm is used to accurately determine the radiation bias and remove noise interference. The radiation transfer equation of the relationship between DN value and radiation brightness is established to compensate for the system bias information. The ridge regression algorithm is used to determine the optimal order to avoid excessive fitting errors and improve model accuracy. The correction coefficient of the equation is solved by the least squares method to ensure that the fitting error is minimized. The scanning angle polarization factor is introduced to correct the radiation intensity and eliminate the scanning angle error. Finally, an accurate radiation transfer equation is output. This method effectively improves the accuracy, stability and robustness of radiation calibration through scientific algorithms and parameter optimization, and ensures the reliability of observation data.

[0117] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0118] In the present invention, a deep space scene with a radiation intensity of 0 and a black body at different temperatures are first scanned by a satellite scanner to obtain a deep space DN value and a black body DN value. Then, based on the determined data and in combination with a matrix decomposition algorithm, the radiation bias of the satellite infrared channel is determined based on the obtained deep space DN value, and the influence of electronic offset and abnormal noise is accurately removed, thereby improving the accuracy and stability of the satellite infrared channel radiation calibration and ensuring data reliability. The maximum order of the radiation transfer equation is determined in combination with the ridge regression algorithm, which effectively avoids the problem of excessive fitting error caused by subjective selection of the order, improves the accuracy and robustness of the radiation calibration, and solves the overfitting or underfitting phenomenon, ensuring that the satellite infrared channel radiation brightness calculation is more accurate. And the correction coefficients of each order of the updated radiation transfer equation are accurately determined by the least squares method. Then, the scanning angle polarization factor caused by the satellite scanner was used to correct the radiation intensity. Finally, the radiation transfer equation reflecting the relationship between DN value and radiation intensity was accurately fitted, which can accurately and efficiently obtain the radiation intensity of the satellite scanning area. By combining deep space scenes, blackbody data, matrix decomposition algorithm and ridge regression algorithm, the influence of electronic offset and noise can be accurately removed. At the same time, the optimal order of the radiation transfer equation is adaptively determined to avoid fitting errors, improve the accuracy and stability of radiation calibration, and combine the least squares method with scanning angle correction to ensure that the radiation intensity calculation of the satellite infrared channel is accurate and reliable.

[0119] Example 2

[0120] Reference Manual Attached Figure 2 , showing a schematic structural diagram of a satellite infrared channel radiation calibration system provided by the present invention.

[0121] The present invention also provides a satellite infrared channel radiation calibration system 20, which is applied to the above-mentioned satellite infrared channel radiation calibration method, comprising:

[0122] Processor 201.

[0123] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the satellite infrared channel radiation calibration method of the method embodiment is implemented.

[0124] The satellite infrared channel radiation calibration system 20 provided by the present invention can execute the above-mentioned satellite infrared channel radiation calibration method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0125] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0126] In the present invention, a deep space scene with a radiation intensity of 0 and a black body at different temperatures are first scanned by a satellite scanner to obtain a deep space DN value and a black body DN value. Then, based on the determined data and in combination with a matrix decomposition algorithm, the radiation bias of the satellite infrared channel is determined based on the obtained deep space DN value, and the influence of electronic offset and abnormal noise is accurately removed, thereby improving the accuracy and stability of the satellite infrared channel radiation calibration and ensuring data reliability. The maximum order of the radiation transfer equation is determined in combination with the ridge regression algorithm, which effectively avoids the problem of excessive fitting error caused by subjective selection of the order, improves the accuracy and robustness of the radiation calibration, and solves the overfitting or underfitting phenomenon, ensuring that the satellite infrared channel radiation brightness calculation is more accurate. And the correction coefficients of each order of the updated radiation transfer equation are accurately determined by the least squares method. Then, the scanning angle polarization factor caused by the satellite scanner was used to correct the radiation intensity. Finally, the radiation transfer equation reflecting the relationship between DN value and radiation intensity was accurately fitted, which can accurately and efficiently obtain the radiation intensity of the satellite scanning area. By combining deep space scenes, blackbody data, matrix decomposition algorithm and ridge regression algorithm, the influence of electronic offset and noise can be accurately removed. At the same time, the optimal order of the radiation transfer equation is adaptively determined to avoid fitting errors, improve the accuracy and stability of radiation calibration, and combine the least squares method with scanning angle correction to ensure that the radiation intensity calculation of the satellite infrared channel is accurate and reliable.

[0127] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A satellite infrared channel radiation calibration method, characterized in that: include: S1: Use a satellite scanner to scan deep space scenes and black bodies at different temperatures to obtain multiple deep space DN values ​​and multiple black body DN values; S2: combining a matrix decomposition algorithm, determining the radiation bias of the satellite infrared channel based on the obtained deep space DN value; S3: establishing a radiation transfer equation for reflecting the relationship between the DN value obtained by scanning the satellite infrared channel and the radiation brightness in combination with the radiation bias; S4: Based on the obtained blackbody DN value, a maximum order of the radiation transfer equation is determined in combination with a ridge regression algorithm; S5: updating the radiation transfer equation based on the maximum order, and determining correction coefficients of each order of the updated radiation transfer equation by a least square method; S6: updating the radiation transfer equation using the scanning angle polarization factor caused by the satellite scanner to correct the radiation intensity; S7: Outputting the updated radiation transfer equation, and ending the radiation calibration of the satellite infrared channel.

2. The satellite infrared channel radiation calibration method according to claim 1, characterized in that: The matrix decomposition algorithm is specifically the RPCA algorithm; S2 specifically includes: S201: Combining the deep space DN values ​​into a deep space DN value matrix: Where D represents the deep space DN value matrix, N SV,k,m represents the DN value corresponding to the mth field of view of the kth scan, k = 1, 2, ..., N, m = 1, 2, ..., M, N represents the total number of scans, and M represents the total number of fields of view scanned; S202: Decomposing the deep space DN value matrix by RPCA algorithm: minutes L,S "L" * +λ‖S‖1 D=L+S Among them, L represents the low-rank matrix reflecting the radiation bias, S represents the coefficient matrix reflecting the abnormal noise, ‖L‖ * represents the nuclear norm of L, i.e., the sum of the singular values ​​of L, ‖S‖1 represents the L1 norm of S, λ represents the regularization parameter for balancing the weights of L and S, min L,S ‖L‖ * +λ‖S‖1 means taking the value such that ‖L‖ * +λ‖S‖1 takes the minimum value of L and S; S203: taking the average of the radiation bias, i.e., the low-rank matrix, under each field of view, and calculating the radiation bias: Among them, N SV Represents the radiation bias, L k,m Represents the element obtained from the mth field of view of the kth scan in the low-rank matrix L.

3. The satellite infrared channel radiation calibration method according to claim 2, characterized in that: The regularization parameter is calculated as follows: |l (t+1) -l (t) |< Among them, λ (t+1) and λ (t) They represent the regularization parameters obtained at the t-th iteration and the t+1-th iteration, respectively, represent the termination threshold, η represents the learning rate, represents partial derivatives, represents the RPCA decomposition and reconstruction error with respect to λ, express The gradient of , ← represents an update.

4. The satellite infrared channel radiation calibration method according to claim 2, characterized in that: The radiation transfer equation is specifically: R=a0+a1·D N +a2·D N 2 +…+a n ·D N n a0=N SV Among them, N sc represents the radiation brightness obtained by scanning, a v represents the v-order correction coefficient, v=1,2,…,n, n represents the maximum order, D N Indicates the DN value obtained by scanning, R indicates the same as D N The corresponding radiation brightness.

5. The satellite infrared channel radiation calibration method according to claim 1, characterized in that: The S4 specifically includes: S401: Calculate the real radiation brightness corresponding to each blackbody DN value by combining the blackbody DN value and the temperature corresponding to the blackbody DN value: Among them, T i and R i They represent the black body temperature and the real radiation brightness corresponding to the black body temperature, exp represents the natural exponential function, C1 and C2 represent the first radiation constant and the second radiation constant, respectively. 波长 It represents the wavelength of observation of black body emitted by the satellite infrared channel; S402: Substituting the blackbody DN value into the radiation transfer equation at different orders to obtain the fitted radiation brightness at different orders; S403: Establishing a loss function about the true radiance and the fitted radiance by using a ridge regression algorithm: Among them, L oss represents the loss function, O represents the total number of blackbody temperature changes, R i represents the fitted radiance obtained by fitting the blackbody DN value collected at the i-th blackbody temperature, and γ = 0.5 represents the penalty term coefficient; S404: With the goal of minimizing the loss function, determine the residual sum of squares of the true radiance and the fitted radiance: min RSS L oss Among them, RSS represents the residual sum of squares, min RSS L oss Indicates taking the minimum L oss RSS below; S405: Calculate the Akaike information criterion value and the Bayesian information criterion value for the residual sum of squares: Among them, AIC and BIC represent the Akaike information criterion value and the Bayesian information criterion value, respectively, and log represents the logarithmic function; S406: Calculate the ratio of the Akaike information criterion value to the Bayesian information criterion value, and output the order under the minimum ratio as the maximum order of the radiation transfer equation: in, Indicates taking the minimum The following n.

6. The satellite infrared channel radiation calibration method according to claim 1, characterized in that: The S5 specifically includes: S501: Transform the radiation transfer equation into a matrix equation: R=Xa Among them, a represents the correction coefficient vector, R represents the real radiation brightness vector, and X represents the vector including D N The design matrix for each order, represents the DN value associated with the i-th temperature and order v of the blackbody; S502: Solve the matrix equation by the least square method to obtain the correction coefficient vector, i.e., correction coefficients of each order: The superscript T indicates transposition. Indicates taking The smallest a, Indicates launch.

7. The satellite infrared channel radiation calibration method according to claim 1, characterized in that: The corrected radiation intensity is: R′=R·[1+P(θ)cos 2 (i)] Where R′ represents the corrected radiation intensity, θ represents the scanning angle of the satellite scanner, P(θ) represents the scanning angle polarization factor related to the satellite structure, and cos represents the cosine function.

8. The satellite infrared channel radiation calibration method according to claim 1, characterized in that: After S7, the method further includes: After a preset time interval, steps S1 to S7 are executed again.

9. The satellite infrared channel radiation calibration method according to claim 1, characterized in that: Also includes: Scan the target scene by the satellite scanner to obtain the target DN value; Substitute the target DN value into the radiation transfer equation to determine the radiation brightness of the target scene.

10. A satellite infrared channel radiation calibration system, characterized in that: Used to execute the satellite infrared channel radiation calibration method described in claims 1 to 9.

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