Infrared band multi-mode calibration method and device

Abstract

The embodiment of the invention discloses an infrared band multi-mode calibration method and device. The method comprises the following steps: calculating a point spread function (PSF) of an original infrared image, and generating a spatial response matching filter function (SRMF) according to the PSF; carrying out two-dimensional convolution calculation on the SRMF to obtain an infrared image after effective space calibration; generating a possible spectral response function of ice pollution, and evaluating an optimal ice pollution spectral response through a cross calibration method; according to the optimal ice pollution spectral response, a spectral response function after possible spectral offset is generated, the optimal possible spectral offset is evaluated through a cross calibration method, and a spectral response function after spectral calibration is obtained through optimal sampling; calculating a linear coefficient, a nonlinear coefficient and a constant coefficient of radiometric calibration; and according to each coefficient of radiometric calibration, calculating the radiation quantity of the infrared image after multi-modal calibration. The method can overcome the existing problems and promote the quantification and multi-source fusion application of the satellite infrared data.

Description

Technical Field

[0001] This disclosure relates to the field of satellite remote sensing technology, and in particular to an infrared band multimodal calibration method and apparatus. Background Technology

[0002] Satellite remote sensing technology is the primary means by which humanity acquires information about the Earth, with over 80% of Earth-related data originating from it. In the field of quantitative remote sensing, rigorous calibration is considered the core of achieving accurate data application, and calibration accuracy directly determines the reliability of remote sensing data. Calibration is essentially the process of converting the digital quantities output by remote sensors into physical radiometric quantities, providing fundamental support for quantitative applications. Improving its accuracy has become an urgent need for the current development of remote sensing technology.

[0003] Existing infrared calibration techniques include radiometric calibration, spatial calibration, and spectral calibration. Among them, radiometric calibration focuses on measuring the linear response of a single mode, determining the linear response coefficient through observations of spaceborne blackbodies and cold space, but generally ignores the nonlinear effects caused by changes in the on-orbit operating conditions of remote sensors.

[0004] In terms of spatial calibration, the "continuous and uniform step edges" relied upon by classical spatial calibration methods are difficult to obtain in actual observations. To improve measurement accuracy, spatial characteristics are characterized by a point spread function, and classical Wiener filtering is used to suppress proximity effects. However, this method is prone to producing local outliers in high-contrast scenes.

[0005] In terms of spectral calibration, factors such as residual water vapor volatilization, organic matter condensation pollution, and changes in optical components caused by deep cryogenic temperatures in the space environment have led to significant brightness temperature deviations in typical infrared broadband remote sensors both domestically and internationally.

[0006] In summary, the three existing calibration methods all have certain limitations, restricting the quantification and multi-source fusion application of satellite infrared data, and there is an urgent need to develop a new calibration method. Summary of the Invention

[0007] In view of this, the present disclosure provides an infrared multi-mode calibration method and apparatus that can overcome the problems of existing calibration methods and promote the quantification and multi-source fusion application of satellite infrared data.

[0008] In a first aspect, embodiments of this disclosure provide an infrared band multimodal calibration method, employing the following technical solution: An infrared multimodal calibration method includes: S1. Calculate the original infrared image. The point spread function (PSF) is used to generate the spatial response matched filter (SRMF). S2, On the original infrared image Two-dimensional convolution calculation is performed using the spatial response matched filter function SRMF, and the infrared image with effective spatial calibration is obtained based on the result of the two-dimensional convolution calculation. ; S3. Calculate the possible ice transmittance spectral parameters, generate the possible spectral response functions of ice contamination, and evaluate the optimal ice contamination spectral response through cross-calibration method; S4. Based on the optimal ice pollution spectral response, generate possible spectral shifted spectral response functions, evaluate the optimal possible spectral shift using the cross-calibration method, and obtain the spectral calibrated spectral response function through optimal sampling. S5. Based on the blackbody observation results, cold air observation results, nonlinear parameters of the infrared remote sensor obtained from ground tests, and the spectral response function after spectral calibration, calculate the linear coefficient, nonlinear coefficient, and constant coefficient of the radiometric calibration. S6. Based on the linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration, and the infrared image after effective spatial calibration. Calculate the radiance of the infrared image after multimodal calibration. .

[0009] Optionally, the calculation of the original infrared image The point spread function (PSF) is used to generate the spatial response matched filter (SRMF), which includes: S1.1 Extracting the original infrared image High-contrast targets along the horizontal and vertical directions; S1.2 Calculate the slope parameters corresponding to the high-contrast target using higher-order variational methods; S1.3. Based on the obtained slope parameters and the corresponding high contrast target, calculate the line spread function (LSF) in the horizontal and vertical directions respectively using the inverse Fourier transform method. S1.4. Generate a point spread function (PSF) from the two line spread functions (LSF) using the vector synthesis method; S1.5. Based on the set target PSF, use residue analysis to convert the point spread function PSF into the spatial response matched filter function SRMF of the target PSF.

[0010] Optionally, the raw infrared image Two-dimensional convolution calculation is performed using the spatial response matched filter function SRMF, and the infrared image with effective spatial calibration is obtained based on the result of the two-dimensional convolution calculation. ,include: S2.1, Transfer the original infrared image Two-dimensional convolution is performed using the spatial response matched filter function SRMF to generate a convolution-processed infrared image. ; S2.2. Based on the characteristics of two-dimensional convolution processing, the infrared image after convolution processing... Obtain the infrared image after effective spatial calibration .

[0011] Optionally, the infrared image after convolution processing is used based on the characteristics of two-dimensional convolution processing. Obtain the infrared image after effective spatial calibration ,include: in, , Represents the row and column numbers of the image, the original infrared image. The image size is , For the number of rows, Given the number of columns, the functional dimension of the Line Diffusion Function (LSF) is... , It is half the width length.

[0012] Optionally, the calculation of possible ice transmittance spectral parameters, the generation of possible spectral response functions for ice contamination, and the evaluation of the optimal ice contamination spectral response using a cross-calibration method include: S3.1, Based on the possible ice thickness Calculate the infrared transmittance spectrum of ice at different infrared wavelengths. ; S3.2, The original spectral response function of the infrared remote sensor Infrared transmittance spectra at different ice thicknesses d Calculate the possible spectral response function after ice contamination. ; S3.3 Select continuous infrared images within a certain time period, and calculate the monthly calibration deviation for different ice thicknesses through cross-calibration with a high-precision infrared hyperspectral instrument. Obtain the ice thickness corresponding to the minimum monthly calibration deviation fluctuation. and its spectral response function This represents the optimal spectral response to ice pollution.

[0013] Optionally, the step of generating a possible spectral shifted spectral response function based on the optimal ice pollution spectral response, evaluating the optimal possible spectral shift using a cross-calibration method, and obtaining the spectrally calibrated spectral response function through optimal sampling includes: S4.1, Spectral Response Function Apply different wavelength offsets The possible spectral response function after the shift is obtained. ; S4.2 Select continuous infrared images within a certain time period, and calculate the monthly calibration deviation under different wavelength offsets through cross-calibration with a high-precision infrared hyperspectral instrument. Obtain the wavelength offset corresponding to the minimum monthly calibration deviation fluctuation. and its spectral response function ; S4.3, within the existing spectral range and spectral interval is Under the condition, the spectral response function Optimal sampling is performed to obtain the spectral response function after spectral calibration. .

[0014] Optionally, the statement within the original spectral range and spectral interval is Under the condition, the spectral response function Optimal sampling is performed to obtain the spectral response function after spectral calibration. ,include: in, It is a rounding function; It is the modulo function; , , , , and when or hour, .

[0015] Optionally, the step of calculating the linear coefficients, nonlinear coefficients, and constant coefficients of radiometric calibration based on the blackbody observation results, cold air observation results, nonlinear parameters of the infrared remote sensor obtained from ground tests, and the spectral response function after spectral calibration includes: S5.1. Based on the blackbody observation results and cold space observation results from the infrared remote sensor, and the spectral response function after spectral calibration, the linear coefficients of the radiometric calibration are determined using the linear regression method. ; S5.2 Calculate the noise variance value based on the cold air observation results from the infrared remote sensor. Based on noise variance Calculate the constant coefficients for radiation calibration , ; S5.3, Based on the nonlinear parameters of the infrared remote sensor measured by ground tests And the linear coefficient between the blackbody observations from the infrared remote sensor and the radiometric calibration. The nonlinear coefficients for radiation calibration were obtained through multiple iterative calculations. .

[0016] Optionally, the method involves using the linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration, as well as the infrared image after effective spatial calibration. Calculate the radiance of the infrared image after multimodal calibration. ,include: ; in, , The row and column numbers represent the original infrared image.

[0017] Secondly, this disclosure also provides an infrared multi-mode calibration device, which adopts the following technical solution: The infrared multimode calibration device includes: The filter function generation module is used to calculate the original infrared image. The point spread function (PSF) is used to generate the spatial response matched filter (SRMF). Spatial calibration module, used for calibration of raw infrared images Two-dimensional convolution calculation is performed using the spatial response matched filter function SRMF, and the infrared image with effective spatial calibration is obtained based on the result of the two-dimensional convolution calculation. ; The ice contamination spectral response calculation module is used to calculate possible ice transmittance spectral parameters, generate possible spectral response functions for ice contamination, and evaluate the optimal ice contamination spectral response through a cross-calibration method. The spectral response function determination module is used to generate possible spectral shifted spectral response functions based on the optimal ice pollution spectral response, evaluate the optimal possible spectral shift through cross-calibration method, and obtain the spectral calibrated spectral response function through optimal sampling. The coefficient calculation module is used to calculate the linear coefficients, nonlinear coefficients, and constant coefficients of radiometric calibration based on the blackbody observation results, cold air observation results, nonlinear parameters of the infrared remote sensor measured by ground tests, and the spectral response function after spectral calibration. The radiation calculation module is used to calculate the radiation based on the linear coefficients, nonlinear coefficients, and constant coefficients of the radiation calibration, as well as the infrared image after effective spatial calibration. Calculate the radiance of the infrared image after multimodal calibration. .

[0018] In the infrared multimodal calibration method and apparatus provided in this disclosure, the original infrared image is first... Spatial calibration was performed to obtain an infrared image with effective spatial calibration. This approach addresses the issue of inaccurate radiation measurements caused by differences in the amount of radiation observed by different remote sensing instruments when observing the same target due to inconsistent spatial concentration. Further spectral calibration is then performed to obtain the calibrated spectral response function, which is equivalent to determining the actual spectral response function of the remote sensing instrument during on-orbit operation. This addresses the impact of spectral shifts caused by uncertainties such as ice contamination and irradiation under space conditions on the spectral response function. Finally, radiometric calibration is implemented to accurately calculate the radiometric response characteristics under precise spatial and spectral characteristics, thereby obtaining the accurate radiation amount of the observed target.

[0019] The above description is merely an overview of the technical solution disclosed herein. In order to better understand the technical means of this disclosure and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart of the infrared band multimodal calibration method provided in this embodiment of the disclosure; Figure 2 A detailed flowchart of step S1 provided in this embodiment of the disclosure; Figure 3 A detailed flowchart of step S2 provided in this embodiment of the disclosure; Figure 4 A detailed flowchart of step S3 provided in this embodiment of the disclosure; Figure 5 A detailed flowchart of step S4 provided in this embodiment of the disclosure; Figure 6 A detailed flowchart of step S5 provided in this embodiment of the disclosure; Figure 7 A schematic diagram of the infrared multi-mode calibration device provided in the embodiments of this disclosure. Detailed Implementation

[0022] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0023] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0024] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0025] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0026] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0027] This disclosure provides an infrared multi-mode calibration method, such as... Figure 1 As shown, this infrared multi-mode calibration method includes: S1. Calculate the original infrared image. The point spread function (PSF) is used to generate the spatial response matched filter (SRMF). Optionally, such as Figure 2 As shown, step S1 calculates the original infrared image. The point spread function (PSF) is used to generate the spatial response matched filter (SRMF), which specifically includes: S1.1 Extracting the original infrared image High-contrast targets along the horizontal and vertical directions; The above original infrared images The image size is , For the number of rows, For column numbers.

[0028] In the original infrared image Extracting high-contrast targets along the horizontal and vertical directions is a crucial prerequisite for accurate spatial calibration. Step S1.1, based on a slope profile model (rather than the traditional step model), is accomplished by analyzing brightness-temperature transition regions in the image. First, natural boundaries with significant radiation differences need to be scanned and identified across the entire image range, focusing on typical scenes such as cloud-free land-sea boundaries (e.g., the Red Sea region) and desert-water boundaries. These targets will exhibit obvious brightness-temperature gradients in the horizontal or vertical directions, and the length of their high- and low-level regions (i.e., uniform regions on both sides of the boundary) must be no less than the LSF radius D (typically 3-5 pixels) to ensure sufficient representation of spatial features.

[0029] The targets initially screened need to undergo rigorous feature verification. By extracting the grayscale profiles of the target region in the horizontal and vertical directions, the non-uniformity index of its surrounding area is calculated to ensure that the high and low level components are approximately constant and only affected by additive noise.

[0030] The slope profile target that meets the requirements will enter the precise feature extraction stage: the sequence of cumulative sums from both the left and right directions is calculated using the Directional Partial Summation Average (DPSA) operator, and the boundary of the transition interval is determined by detecting the first non-monotonic point (FNMP). Specifically, the first feature in the sequence from left to right that meets the requirements is selected as the boundary. The position is denoted as n L-FNMP The first sequence from right to left that satisfies The position is denoted as n R-FNMP Together, they define the starting position P of the slope. slope and the number of ramp points N slope Meanwhile, by calculating the average grayscale value of D pixels before and after the transition interval, the radiation values ​​of the high level H and the low level L are accurately estimated.

[0031] Final validity confirmation requires two core conditions to be met: first, the length of the transition interval |n L-FNMP - n R-FNMP The first requirement is that the difference between high and low levels Δ = |HL| must not exceed a preset threshold (usually ≤5 pixels), and the second requirement is that the difference between high and low levels Δ = |HL| must reach a significant proportion of the image's dynamic range.

[0032] S1.2 Calculate the slope parameters corresponding to the high-contrast target using higher-order variational methods; The above slope parameters may include the slope starting position P. slope and the number of ramp points N slope .

[0033] For example, calculating the slope parameters corresponding to a high-contrast target using a higher-order variational method specifically includes: (1) For a given high-contrast target (horizontal or vertical direction), obtain its discrete observation grayscale sequence. The theoretical model of this sequence is the result of convolving an ideal slope profile signal with an LSF and then superimposing noise.

[0034] (2) Calculate the second difference of the observed signal to approximate its second derivative in the continuous domain. The second derivative curve of the ideal slope signal convolved with the LSF will show a clear negative peak and a positive peak. The positions of these two peaks directly correspond to the start and end points of the slope transition zone.

[0035] (3) Find the minimum and maximum points on the calculated second-order difference sequence. Theoretically, for a ramp (Type I) transitioning from high level H to low level L, the minimum point corresponds to the vicinity of the beginning of the transition region, and the maximum point corresponds to the vicinity of the end of the transition region.

[0036] (4) Based on the located extreme points, the slope parameters are initially calculated.

[0037] (5) To overcome the possibility of misjudgment of extreme points caused by noise, the Directional Partial Summation Average (DPSA) operator is introduced for verification.

[0038] (6) The parameters are finally determined by combining the results of second-order difference and DPSA.

[0039] S1.3. Based on the obtained slope parameters and the corresponding high contrast target, calculate the line spread function (LSF) in the horizontal and vertical directions respectively using the inverse Fourier transform method. The function dimensions of the line spread function (LSF) in both the horizontal and vertical directions are 1. , This is called the half-width length, and it is generally taken as 3-5.

[0040] Optionally, based on the obtained slope parameters and the corresponding high-contrast target, the line spread function (LSF) in the horizontal and vertical directions is calculated using the inverse Fourier transform method, specifically including: (1) Construct the estimated ideal input ramp signal: Based on the slope parameters P determined in step S1.2 slope N slopeH, L, reconstruct an ideal ramp profile signal in the discrete domain that is not degraded by the PSF. The signal is at a constant level H or L outside the transition region, and exhibits a linear change within the transition region.

[0041] (2) Signal expansion to match convolution dimension: Since the LSF has a certain spatial range (radius D), the value of the i-th pixel in the observed signal is the weighted sum of the LSF coefficients of its surrounding 2D+1 input signal pixels. Therefore, in order to establish an accurate equation, the previously constructed... Extend the input signal by D pixels to both the left and right. The pixel values ​​of the extended portions are filled with either the constant voltage level H or L at the two ends of the ramp, respectively. At this point, the extended input signal... The length becomes .

[0042] (3) Construct the matrix equation for spatial convolution: The discrete spatial convolution operation is transformed into a matrix-vector multiplication form, and the following system of linear equations is established: in: It is a size of The column vector is formed by the observed signal. constitute.

[0043] It is a variable to be determined, with a size of The column vectors represent the line spread function to be solved.

[0044] It is a size of The matrix is ​​constructed as follows: For the i-th row, its elements are derived from the expanded input signal. Composition. Matrix Each row and vector The dot product is used to perform a local convolution operation at position i.

[0045] It is an additive noise vector.

[0046] (4) Solve for the generalized inverse using singular value decomposition (SVD): The above equations are a typical overdetermined linear system of equations (usually...) Solving the least squares problem To get The optimal estimate.

[0047] Specifically, a generalized inverse method based on Singular Value Decomposition (SVD) is used to stably solve the problem: in It is a matrix SVD decomposition, It is a singular value matrix The pseudo-inverse (usually truncated for small singular values ​​to avoid noise amplification).

[0048] (5) Result normalization and output: The vector obtained by solving Normalize the function so that the sum of all its elements is 1 to satisfy the energy conservation property of the point spread function. Output the normalized horizontal line spread function (LSF) and vertical line spread function (LSF).

[0049] S1.4. Generate a point spread function (PSF) from the two line spread functions (LSF) using the vector synthesis method; The function size of the point spread function PSF is .

[0050] For example, generating a point spread function (PSF) from two line spread functions (LSFs) using a vector synthesis method specifically includes: based on the reasonable assumption that the spatial response of the imaging system is separable in orthogonal directions, the normalized horizontal line spread function (LSF) and the vertical line spread function (LSF) are synthesized into a point spread function (PSF) through a vector cross product operation.

[0051] S1.5. Based on the set target PSF, use residue analysis to convert the point spread function PSF into the spatial response matched filter function SRMF of the target PSF.

[0052] For example, the modulation transfer function value at the 0.5 normalized frequency corresponding to the target PSF can be set, usually between 0.35 and 0.40, and the target PSF can be generated accordingly.

[0053] The function size of SRMF is .

[0054] For example, based on the set target PSF, the spatial response matched filter function (SRMF) for converting the point spread function PSF into the target PSF using residue analysis specifically includes: (1) performing Z-transform on the line spread functions (LSF) in the horizontal and vertical directions respectively to obtain their transfer functions; (2) constructing the Z-domain expression of the spatial response matched filter (SRMF); (3) using residue analysis to obtain the spatial domain expression of the SRMF to obtain the SRMF in the horizontal and vertical directions; (4) combining the SRMF in the horizontal and vertical directions into a two-dimensional SRMF through an outer product; (5) normalizing the obtained two-dimensional SRMF and outputting it to obtain the spatial response matched filter function (SRMF).

[0055] S2, On the original infrared image Two-dimensional convolution calculation is performed using the spatial response matched filter function SRMF, and the infrared image with effective spatial calibration is obtained based on the result of the two-dimensional convolution calculation. ; Optionally, such as Figure 3 As shown, step S2 processes the original infrared image. Two-dimensional convolution calculation is performed using the spatial response matched filter function SRMF, and the infrared image with effective spatial calibration is obtained based on the result of the two-dimensional convolution calculation. Specifically, it includes: S2.1, Transfer the original infrared image Two-dimensional convolution is performed using the spatial response matched filter function SRMF to generate a convolution-processed infrared image. ; The infrared image after convolution processing is denoted as Image size is , For the number of rows, For column numbers.

[0056] For example, the original infrared image Two-dimensional convolution is performed using the spatial response matched filter function SRMF to generate a convolution-processed infrared image. Specifically, this includes: the raw infrared image The boundaries are expanded using reflective padding; the padded image is then convolved with the spatial response matched filter (SRMF) function in two dimensions to obtain the convolved infrared image. .

[0057] S2.2. Based on the characteristics of two-dimensional convolution processing, the infrared image after convolution processing... Obtain the infrared image after effective spatial calibration .

[0058] Optionally, the infrared image after convolution processing is used based on the characteristics of two-dimensional convolution processing. Obtain the infrared image after effective spatial calibration ,include: in, , Represents the row and column numbers of the image, the original infrared image. The image size is , For the number of rows, Given the number of columns, the functional dimension of the Line Diffusion Function (LSF) is... , It is half the width length.

[0059] Because spatial convolution is used, it introduces translation (i.e., misalignment) in the row and column directions of the image. The amount of translation is related to the spatial size of the SRMF function. Step S2.2 can realize the spatial characteristic normalization process in spatial calibration, correct the above translation amount, and effectively use the spatial calibration results.

[0060] S3. Calculate the possible ice transmittance spectral parameters, generate the possible spectral response functions of ice contamination, and evaluate the optimal ice contamination spectral response through cross-calibration method; Optionally, such as Figure 4 As shown, step S3 calculates possible ice transmittance spectral parameters, generates possible spectral response functions for ice contamination, and evaluates the optimal ice contamination spectral response using a cross-calibration method. Specifically, this includes: S3.1, Based on the possible ice thickness Calculate the infrared transmittance spectrum of ice at different infrared wavelengths. ; Optionally, based on the possible ice thickness Calculate the infrared transmittance spectrum of ice at different infrared wavelengths. Specifically, this includes defining ice layer parameters (including ice thickness). And the ice particle radius r) and wavelength range, where ice thickness Typically, a range of 1.0–5.0 μm is used, with 1.0 μm intervals. The complex refractive index of the ice is obtained. For given ice layer parameters, the absorption efficiency, scattering efficiency, and asymmetry factor of the ice spherical particles are calculated using Mie theory based on the complex refractive index of the ice. The ice layer composed of discrete ice particles is considered as a scattering medium, and its volume absorption coefficient and scattering coefficient are calculated based on the absorption efficiency, scattering efficiency, asymmetry factor, and ice layer parameters. Using a one-dimensional multilayer radiative transfer model, the transmittance of the planar parallel ice layer is calculated based on the ice layer parameters, volume absorption coefficient, and scattering coefficient. For each ice thickness d, a spectral transmittance curve covering the entire infrared wavelength range is obtained, i.e., the infrared transmittance spectrum. .

[0061] S3.2, The original spectral response function of the infrared remote sensor Infrared transmittance spectra at different ice thicknesses d Calculate the possible spectral response function after ice contamination. ; Original spectral response function spectral range Spectral interval is .

[0062] For example, the raw spectral response function of an infrared remote sensor Infrared transmittance spectra at different ice thicknesses d Calculate the possible spectral response function after ice contamination. Specifically, this includes: the raw spectral response function of the infrared remote sensor. The relative responsivity at each wavelength point is defined, and the infrared transmittance spectrum for each ice thickness d is defined. Compared with the original spectral response function With the same wavelength range and sampling interval, if the sampling intervals are inconsistent, the transmittance spectrum needs to be interpolated to ensure that all data are on a uniform wavelength grid; for each ice thickness d to be evaluated, its corresponding infrared transmittance spectrum is... Compared with the original spectral response function Wavelength-by-wavelength point-to-point multiplication is performed to simulate the wavelength-dependent attenuation of incident radiation after passing through the ice layer before it is sensed by the detector, yielding the convolved spectral response function, which characterizes the actual spectral response characteristics of the instrument under specific ice contamination. Normalization of the convolved spectral response function yields the possible spectral response function after ice contamination. .

[0063] S3.3 Select continuous infrared images within a certain time period, and calculate the monthly calibration deviation for different ice thicknesses through cross-calibration with a high-precision infrared hyperspectral instrument. Obtain the ice thickness corresponding to the minimum monthly calibration deviation fluctuation. and its spectral response function This represents the optimal spectral response to ice pollution.

[0064] For example, continuous infrared images within a certain time period are selected, and the calibration deviation is calculated month by month for different ice thicknesses through cross-calibration with a high-precision infrared hyperspectral instrument. The ice thickness corresponding to the smallest monthly calibration deviation fluctuation is then obtained. and its spectral response function The optimal spectral response to ice pollution specifically includes: (1) Select a continuous on-orbit dataset with a sufficient time frame for analysis. Typically, data from the initial stage of satellite operation, within a period of six months to one year when cold polarization is evident, should be selected to ensure statistical significance and stability. At the same time, a reference sensor should be selected as the benchmark, which should have extremely high radiometric calibration accuracy and spectral stability in the corresponding infrared band, such as the IASI hyperspectral interferometer carried on the METOP-A satellite.

[0065] (2) Following the procedures recommended by the global space-based cross-calibration system, spatiotemporal matching of the observation data of the target sensor and the reference sensor is performed, including time window constraints, observation geometry screening, and improvement methods such as parallax correction and variable weight spatial matching are applied to obtain high-quality matching data pairs.

[0066] (3) For each preset ice thickness d and its corresponding spectral response function This data is used as the instrument's current spectral response function to reprocess all matched observations within the selected time period, calculating the average calibration deviation for each month. In other words, each candidate ice thickness value generates a time series composed of multiple monthly average deviations. When a candidate ice thickness value causes the monthly average deviation series to fluctuate around zero with minimal amplitude and dispersion (i.e., exhibiting optimal stability), this ice thickness is considered to best reflect the actual situation, thus yielding the ice thickness corresponding to the minimum monthly calibration deviation fluctuation. and its spectral response function .

[0067] S4. Based on the optimal ice pollution spectral response, generate possible spectral shifted spectral response functions, evaluate the optimal possible spectral shift using the cross-calibration method, and obtain the spectral calibrated spectral response function through optimal sampling. Optionally, such as Figure 5 As shown, step S4 generates possible spectral shifted spectral response functions based on the optimal ice pollution spectral response, evaluates the optimal possible spectral shift using a cross-calibration method, and obtains the spectral calibrated spectral response function through optimal sampling. Specifically, this includes: S4.1, Spectral Response Function Apply different wavelength offsets The possible spectral response function after the shift is obtained. ; For example, the spectral response function Apply different wavelength offsets The possible spectral response function after the shift is obtained. Specifically, it includes: (1) Determine a reasonable wavelength offset search range and step size. The offset range is usually set based on prior knowledge or preliminary simulation. For example, in the calibration of the IR2 band of the FY-2G satellite, the offset exploration range was set to ±0.20 micrometers, with a step size of 0.01 micrometers. A positive value indicates that the spectral response function shifts towards longer wavelengths, while a negative value indicates that it shifts towards shorter wavelengths.

[0068] (2) Spectral response function Apply a wavelength offset to each candidate wavelength. Specifically, maintain the spectral response function. The shape remains completely unchanged, and the function is translated along the wavelength axis as a whole. After translation, the response value of the function at each new wavelength point is equal to its response value at the original wavelength point (i.e., the new wavelength minus the offset).

[0069] (3) Due to the translation, the spectral response function A portion of the wavelength may shift outside the originally defined effective wavelength range. For these out-of-range bands, the response value is usually set to zero, indicating that the instrument does not have the ability to respond in that region. The portion that exceeds the original wavelength range after translation is processed to ensure that the new spectral response function obtained after translation has the same wavelength array definition and number of points as the original function.

[0070] Through the above steps, a corresponding possible spectral response function is generated for each set wavelength offset. .

[0071] S4.2 Select continuous infrared images within a certain time period, and calculate the monthly calibration deviation under different wavelength offsets through cross-calibration with a high-precision infrared hyperspectral instrument. Obtain the wavelength offset corresponding to the minimum monthly calibration deviation fluctuation. and its spectral response function ; For example, continuous infrared images within a certain time period are selected, and through cross-calibration with a high-precision infrared hyperspectral instrument, the monthly calibration deviation under different wavelength offsets is calculated to obtain the wavelength offset corresponding to the minimum monthly calibration deviation fluctuation. and its spectral response function Specifically, it includes: (1) Select an on-orbit dataset with a sufficient time span that is either connected to or independent of the ice pollution phase, typically covering continuous observations for six months to one year, to ensure that the characteristics of calibration bias variation under different seasons and scenarios can be captured. At the same time, continue to use high-precision infrared hyperspectral instruments such as IASI on the METOP-A satellite as a reference benchmark.

[0072] (2) Following the standardized procedures under the framework of the global space-based cross-calibration system, the data of the target sensor and the reference sensor are accurately matched in time and space and in spectral form.

[0073] (3) The possible spectral response functions corresponding to each set wavelength offset The data are used sequentially as the on-orbit spectral response function of the target sensor, and all matching data pairs in the entire time series are reprocessed. For each candidate offset, the average calibration deviation for each month is calculated, thus obtaining a time series composed of multi-month offset values.

[0074] (4) Analyze the statistical characteristics of these monthly deviation sequences. The optimal wavelength offset should satisfy two core conditions for its corresponding monthly deviation sequence: First, the mean of the sequence should be as close to zero as possible, indicating that systematic cold or hot biases have been eliminated to the greatest extent; second, the fluctuation range of the monthly deviation values ​​around zero should be as small as possible, that is, its standard deviation should be the smallest, which indicates that the calibration result has reached the highest stability. When a specific wavelength offset can simultaneously satisfy the conditions of the mean deviation being closest to zero and the fluctuation range being the smallest, it is determined as the optimal wavelength offset, and the wavelength offset corresponding to the minimum monthly calibration deviation fluctuation is obtained. and its spectral response function .

[0075] S4.3, within the existing spectral range and spectral interval is Under the condition, the spectral response function Optimal sampling is performed to obtain the spectral response function after spectral calibration. .

[0076] Optionally, the statement within the original spectral range and spectral interval is Under the condition, the spectral response function Optimal sampling is performed to obtain the spectral response function after spectral calibration. ,include: in, It is a rounding function; It is the modulo function; , , , , and when or hour, .

[0077] In spectral calibration, after spectral shifting, the shift amount does not match the original spectral interval (i.e., the shift amount is not an integer multiple of the spectral interval), which leads to the loss of information on the original spectral interval and makes the spectral system unusable. Step S4.3 can solve the above problem by obtaining the spectral response information at the original spectral position after spectral calibration.

[0078] S5. Based on the blackbody observation results, cold air observation results, nonlinear parameters of the infrared remote sensor obtained from ground tests, and the spectral response function after spectral calibration, calculate the linear coefficient, nonlinear coefficient, and constant coefficient of the radiometric calibration. Optionally, such as Figure 6 As shown, based on the blackbody observation results, cold air observation results, nonlinear parameters of the infrared remote sensor obtained from ground tests, and the spectral response function after spectral calibration, the linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration are calculated, specifically including: S5.1. Based on the blackbody observation results and cold space observation results from the infrared remote sensor, and the spectral response function after spectral calibration, the linear coefficients of the radiometric calibration are determined using the linear regression method. ; S5.2 Calculate the noise variance value based on the cold air observation results from the infrared remote sensor. Based on noise variance Calculate the constant coefficients for radiation calibration , ; S5.3, Based on the nonlinear parameters of the infrared remote sensor measured by ground tests And the linear coefficient between the blackbody observations from the infrared remote sensor and the radiometric calibration. The nonlinear coefficients for radiation calibration were obtained through multiple iterative calculations. .

[0079] Optionally, based on the nonlinear parameters of the infrared remote sensor measured by ground tests... And the linear coefficient between the blackbody observations from the infrared remote sensor and the radiometric calibration. The nonlinear coefficients for radiation calibration were obtained through multiple iterative calculations. Specifically, it includes: (1) Utilizing nonlinear parameters obtained from pre-launch laboratory calibration that are insensitive to changes in the working environment and linear coefficients Through formula Calculate the initial nonlinear coefficients .

[0080] (2) Use the currently estimated nonlinear coefficients The original observed radiance values ​​are corrected by subtracting the nonlinear contribution, resulting in a corrected radiance value. Using this corrected radiance value, the two-point calibration method is used again to recalculate an updated linear coefficient. .

[0081] (3) Calculate the new round of linear coefficients Compared with the previous round of linear coefficients The relative deviation between them. If this deviation is greater than the preset convergence threshold, the result is considered unstable and further iteration is needed. At this time, and The average value is used as the new initial linear coefficient. The above steps are repeated until the relative deviation of the linear coefficients is less than the preset convergence threshold. The iterative process then terminates, and the nonlinear coefficients are obtained. .

[0082] S6. Based on the linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration, and the infrared image after effective spatial calibration. Calculate the radiance of the infrared image after multimodal calibration. .

[0083] Optionally, step S6 involves using the linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration, as well as the infrared image after effective spatial calibration. Calculate the radiance of the infrared image after multimodal calibration. ,include: ; in, , The row and column numbers represent the original infrared image.

[0084] This disclosure also provides an infrared band multimode calibration device, optionally, such as Figure 7 As shown, the infrared multimode calibration device includes: Filter function generation module 10 is used to calculate the original infrared image. The point spread function (PSF) is used to generate the spatial response matched filter (SRMF). Spatial calibration module 20 is used for calibration of the raw infrared image. Two-dimensional convolution calculation is performed using the spatial response matched filter function SRMF, and the infrared image with effective spatial calibration is obtained based on the result of the two-dimensional convolution calculation. ; The ice contamination spectral response calculation module 30 is used to calculate possible ice transmittance spectral parameters, generate possible spectral response functions for ice contamination, and evaluate the optimal ice contamination spectral response through a cross-calibration method. The spectral response function determination module 40 is used to generate possible spectral shifted spectral response functions based on the optimal ice pollution spectral response, evaluate the optimal possible spectral shift through the cross-calibration method, and obtain the spectral calibrated spectral response function through optimal sampling. The coefficient calculation module 50 is used to calculate the linear coefficients, nonlinear coefficients, and constant coefficients of radiometric calibration based on the blackbody observation results, cold air observation results, nonlinear parameters of the infrared remote sensor measured by ground tests, and the spectral response function after spectral calibration. The radiation calculation module 60 is used to calculate the radiation based on the linear coefficients, nonlinear coefficients, and constant coefficients of the radiation calibration, as well as the infrared image after effective spatial calibration. Calculate the radiance of the infrared image after multimodal calibration. .

[0085] It should be noted that the specific details of each step in the above infrared multimodal calibration method are applicable to the corresponding modules, and will not be repeated here.

[0086] In the infrared multimodal calibration method and apparatus provided in this disclosure, the original infrared image is first... Spatial calibration was performed to obtain an infrared image with effective spatial calibration. This approach addresses the issue of inaccurate radiometric measurements caused by differences in the radiometric amounts observed by different remote sensing instruments when observing the same target due to variations in spatial concentration (e.g., uncorrected radiometric measurements would introduce a 2-6K radiometric error when observing small-scale, low-temperature targets). Further spectral calibration is then performed to obtain the calibrated spectral response function, which is equivalent to determining the actual spectral response function of the remote sensing instrument during on-orbit operation. This addresses the impact of spectral shifts caused by uncertainties such as ice contamination and irradiation under space conditions on the spectral response function (e.g., uncorrected calibrated measurements would introduce a radiometric error as high as 1-2K). Finally, radiometric calibration is implemented, and under precise spatial and spectral characteristics, accurate calculations of the radiometric response characteristics are performed to obtain the precise radiometric amount of the observed target.

[0087] A computer device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0088] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory, causing the computer device to perform all or part of the steps of the infrared band multimodal calibration methods of the foregoing embodiments of this disclosure.

[0089] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0090] A computer device may include a processor (such as a central processing unit, graphics processing unit, etc.), which performs various appropriate actions and processes based on programs stored in read-only memory (ROM) or loaded from storage devices into random access memory (RAM). RAM also stores various programs and data required for the operation of the computer device. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0091] Typically, the following devices can be connected to the I / O interface: input devices, such as sensors or visual information acquisition devices; output devices, such as displays; storage devices, such as magnetic tapes or hard drives; and communication devices. Communication devices allow the computer device to communicate wirelessly or wiredly with other devices (such as edge computing devices) to exchange data. It should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0092] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the infrared band multimodal calibration method of embodiments of this disclosure are performed.

[0093] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0094] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the infrared band multimodal calibration methods described in the foregoing embodiments of the present disclosure are performed.

[0095] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0096] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0097] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0098] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0099] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0100] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0101] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0102] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0103] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. An infrared waveband multi-modal calibration method, characterized by, The method comprises the following steps: S1, calculating a point spread function PSF of the original infrared image S1, calculating a point spread function PSF of the original infrared image S2, generating a spatial response matching filter function SRMF according to the point spread function PSF; S2, to the original infrared image a two-dimensional convolution calculation is performed with a spatial response matching filter function SRMF, and an effective spatially calibrated infrared image is obtained according to the two-dimensional convolution calculation result ; S3, calculating possible ice transmittance spectrum parameters, generating possible spectral response functions contaminated by ice, and evaluating optimal ice contamination spectral response by a cross-calibration method; S4, generating possible spectral response functions after spectral shift according to the optimal ice contamination spectral response, evaluating optimal possible spectral shift by a cross-calibration method, and obtaining spectral response functions after spectral calibration by optimal sampling; S5, calculating linear coefficients, nonlinear coefficients and constant coefficients of radiation calibration according to blackbody observation results of the infrared remote sensor, cold air observation results, nonlinear parameters of the infrared remote sensor measured by ground tests, and the spectral response functions after spectral calibration; S6, calculate the radiation of the multi-modal calibrated infrared image according to the linear coefficient, the nonlinear coefficient and the constant coefficient of the radiation calibration, and the effective spatial calibrated infrared image , calculate the radiation of the multi-modal calibrated infrared image .

2. The infrared waveband multi-modal calibration method of claim 1, wherein, The computing the original infrared image A point spread function PSF of the original infrared image, generating a spatial response matched filter function SRMF according to the point spread function PSF, comprising: S1.1, extracting original infrared image high-contrast targets in the horizontal and vertical directions; S1.2, calculating slope parameters corresponding to high-contrast targets by a high-order variation method; S1.3, calculating line spread functions LSF in horizontal and vertical directions respectively by a Fourier inverse transform method according to the slope parameters and corresponding high-contrast targets; S1.4, generating a point spread function PSF by vector synthesis of the two line spread functions LSF; S1.5, converting the point spread function PSF into a spatial response matching filter function SRMF of the target PSF by a residue analysis method according to the set target PSF.

3. The infrared waveband multi-modal calibration method of claim 2, wherein, The original infrared image is matched with the spatial response matching filter function SRMF to obtain the effective spatial calibrated infrared image The original infrared image is matched with the spatial response matching filter function SRMF to obtain the effective spatial calibrated infrared image , comprising: S2.1, the original infrared image is filtered by a spatial response matching filter function SRMF to generate a filtered infrared image S2.2, the filtered infrared image is filtered by a spatial response matching filter function SRMF to generate a filtered infrared image ; S2.2, according to the two-dimensional convolution processing characteristics, the infrared image after convolution processing obtain the effective spatial calibrated infrared image .

4. The method of claim 3, wherein, The infrared image after the convolution processing is obtained according to the two-dimensional convolution processing characteristic The infrared image after the effective spatial calibration is obtained , comprising: wherein, , represent the row and column numbers of the image, the original infrared image The image size of the original infrared image is , is the number of rows, is the number of columns, and the function size of the line spread function LSF is , is the half-width length.

5. The infrared waveband multi-modal calibration method of claim 1, wherein, The method for calculating possible ice transmittance spectrum parameters, generating possible spectral response functions contaminated by ice, and evaluating optimal ice contamination spectral response by a cross-calibration method comprises the following steps: S3.1, according to possible ice thicknesses , calculate the infrared transmittance spectrum of the ice at different infrared wavelengths ; S3.2, the raw spectral response function of the infrared remote sensor and the infrared transmittance spectrum for different ice thicknesses d , the possible spectral response function after ice contamination is calculated ; S3.3, select continuous infrared images in a certain period of time, calculate the monthly calibration deviation under different ice thickness by cross calibration with high precision infrared hyperspectral instrument, get the ice thickness corresponding to the minimum fluctuation of monthly calibration deviation and its spectral response function , that is, the optimal ice pollution spectral response.

6. The infrared waveband multi-modal calibration method of claim 5, wherein, The method for generating possible spectral response functions after spectral shift according to the optimal ice contamination spectral response, evaluating optimal possible spectral shift by a cross-calibration method, and obtaining spectral response functions after spectral calibration by optimal sampling comprises the following steps: S4.1, the spectral response function Applying different wavelength offsets , resulting in possible spectral response functions after offset ; S4.2, select continuous infrared images in a certain period of time, calculate the monthly calibration deviation under different wavelength offsets through cross calibration with high-precision infrared hyperspectral instruments, and obtain the wavelength offset corresponding to the minimum monthly calibration deviation fluctuation and a spectral response function thereof ; S4.3, in the original spectral range and spectral interval under the condition that the spectral response function is optimally sampled, the spectrally calibrated spectral response function is obtained.

7. The infrared waveband multi-modal calibration method of claim 6, wherein, The original spectral range And the spectral interval is Under the condition, the spectral response function Optimal sampling is carried out on the spectral response function , comprising: wherein is a floor function; is a modulo function; , , , , and when or , .

8. The infrared waveband multi-modal calibration method of any one of claims 1-7, wherein, The method for calculating linear coefficients, nonlinear coefficients and constant coefficients of radiation calibration according to blackbody observation results of the infrared remote sensor, cold air observation results, nonlinear parameters of the infrared remote sensor measured by ground tests, and the spectral response functions after spectral calibration comprises the following steps: S5.1, According to the blackbody observation results and cold space observation results of the infrared remote sensor and the spectral response function after spectral calibration, the linear coefficient of radiation calibration is determined by using linear regression method ; S5.2, calculating noise variance value according to cold space observation result of infrared remote sensor , calculating constant coefficient of radiation calibration according to noise variance value , ;​ S5.3, the non-linear parameter of the infrared remote sensor measured by the ground test , and the linear coefficient of the blackbody observation result of the infrared remote sensor and the radiation calibration , the non-linear coefficient of the radiation calibration is obtained through multiple iteration calculations .

9. The infrared waveband multi-modal calibration method of claim 8, wherein, The linear coefficient, the nonlinear coefficient and the constant coefficient according to the radiation calibration, and the infrared image after the effective space calibration , calculate the radiation of the multi-modal calibrated infrared image , comprising: ； wherein , represent the row and column numbers of the original infrared image.

10. An infrared waveband multi-modal calibration device, characterized by, The method comprises the following steps: A filter function generating module is configured to calculate a point spread function (PSF) of the original infrared image, and generate a spatial response matched filter function (SRMF) according to the PSF. A filter function generating module is configured to calculate a point spread function (PSF) of the original infrared image, and generate a spatial response matched filter function (SRMF) according to the PSF. a spatial calibration module for calibrating the original infrared image a two-dimensional convolution calculation is performed with a spatial response matching filter function (SRMF), and an effective spatially calibrated infrared image is obtained according to the two-dimensional convolution calculation result ; An ice contamination spectral response calculation module is configured to calculate possible ice transmittance spectrum parameters, generate possible spectral response functions contaminated by ice, and evaluate optimal ice contamination spectral response by a cross-calibration method; A spectral response function determination module is configured to generate possible spectral response functions after spectral shift according to the optimal ice contamination spectral response, evaluate optimal possible spectral shift by a cross-calibration method, and obtain spectral response functions after spectral calibration by optimal sampling; A coefficient calculation module is configured to calculate linear coefficients, nonlinear coefficients and constant coefficients of radiation calibration according to blackbody observation results of the infrared remote sensor, cold air observation results, nonlinear parameters of the infrared remote sensor measured by ground tests, and the spectral response functions after spectral calibration. The radiation calculation module is used to calculate the radiation based on the linear coefficients, nonlinear coefficients, and constant coefficients of the radiation calibration, as well as the infrared image after effective spatial calibration. Calculate the radiance of the infrared image after multimodal calibration. .

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