Infrared wave band multi-modal calibration method and device

Abstract

The embodiments of the present disclosure disclose an infrared waveband multi-modal calibration method and device. The method comprises: calculating a point spread function (PSF) of an original infrared image, and generating a spatial response matching filter function (SRMF) according to the PSF; performing two-dimensional convolution calculation on the SRMF to obtain an infrared image after effective spatial calibration; generating a possible spectral response function affected by ice pollution, and evaluating an optimal ice pollution spectral response by a cross-calibration method; generating a possible spectral response function after possible spectral shift according to the optimal ice pollution spectral response, and evaluating an optimal possible spectral shift amount by the cross-calibration method, and obtaining a spectral response function after spectral calibration by optimal sampling; calculating a linear coefficient, a nonlinear coefficient and a constant coefficient of radiation calibration; and calculating an infrared image radiation after multi-modal calibration according to the coefficients of radiation calibration and. The method can overcome existing problems and promote quantitative satellite infrared data and multi-source fusion application.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of satellite remote sensing, and in particular to an infrared band multi-modal calibration method and device. BACKGROUND

[0002] Satellite remote sensing technology is the main means for humans to obtain earth information, and more than 80% of earth cognition data comes from this. In the field of quantitative remote sensing, strict calibration is regarded as the core to realize accurate application of data, and the calibration accuracy directly determines the reliability of remote sensing data. Calibration is essentially a process of converting the digital quantity output by the remote sensor into physical radiation quantity, which provides a foundation for quantitative application, and the improvement of its accuracy has become an urgent need for the development of current remote sensing technology.

[0003] Existing infrared calibration technologies include radiation calibration, spatial calibration and spectral calibration. Among them, in terms of radiation calibration, emphasis is placed on linear response measurement of a single mode, and the linear response coefficient is determined through on-board blackbody and cold space observation, but the nonlinear effects caused by changes in on-orbit working conditions of the remote sensor are generally ignored.

[0004] In terms of spatial calibration, the "continuous and uniform step edge" relied on by the classical spatial calibration method is difficult to obtain in actual observation. In order to improve the measurement accuracy, the spatial characteristics are characterized by a point spread function, and a classical Wiener filter is used to suppress the adjacent effect, but this method is prone to 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 temperature in space environment cause significant brightness temperature deviation of typical infrared wide-band remote sensors at home and abroad.

[0006] In summary, the three calibration methods in the prior art all have certain limitations, which limit the quantitative and multi-source fusion application of satellite infrared data, and a new calibration method is urgently needed. SUMMARY

[0007] Therefore, the present disclosure provides an infrared band multi-modal calibration method and device, which can overcome the problems existing in the prior art and promote the quantitative and multi-source fusion application of satellite infrared data.

[0008] In a first aspect, the present disclosure provides an infrared band multi-modal calibration method, which adopts the following technical scheme:

[0009] An infrared band multi-modal calibration method comprises:

[0010] S1, calculating a point spread function PSF of an original infrared image , and generating a spatial response matched filter function SRMF according to the point spread function PSF.

[0011] S2, calculating a point spread function (PSF) of the original infrared image , and generating a spatial response matched filter function (SRMF) according to the PSF ;

[0012] S3, calculating a possible ice transmittance spectrum parameter, generating a possible spectrum response function contaminated by ice, and evaluating an optimal ice contamination spectrum response by a cross-calibration method

[0013] S4, generating a possible spectrum shifted spectrum response function according to the optimal ice contamination spectrum response, evaluating an optimal possible spectrum shift by a cross-calibration method, and obtaining a spectrum calibrated spectrum response function by optimal sampling

[0014] S5, calculating a linear coefficient, a nonlinear coefficient and a constant coefficient of radiation calibration according to a blackbody observation result, a cold space observation result, an infrared remote sensor nonlinear parameter measured by a ground test and the spectrum calibrated spectrum response function

[0015] S6, calculating a multi-modal calibrated infrared image radiation quantity according to the linear coefficient, the nonlinear coefficient and the constant coefficient of radiation calibration, and the effective spatial calibrated infrared image .

[0016] Optionally, the point spread function (PSF) of the original infrared image is calculated, and the spatial response matched filter function (SRMF) is generated according to the PSF, and the method comprises the following steps:

[0017] S1.1, extracting a high-contrast target in the original infrared image along horizontal and vertical directions;

[0018] S1.2, calculating a slope parameter corresponding to the high-contrast target by a high-order variation method;

[0019] S1.3, calculating a line spread function (LSF) in the horizontal direction and the vertical direction respectively by an inverse Fourier transform method according to the obtained slope parameter and the corresponding high-contrast target;

[0020] S1.4, generating the point spread function (PSF) by a vector composition method by using the two line spread functions (LSFs);

[0021] S1.5, converting the point spread function (PSF) into a spatial response matched filter function (SRMF) of the target PSF by a residue analysis method according to a set target PSF.

[0022] Optionally, the point spread function (PSF) of 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. ,include:

[0023] 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. ;

[0024] 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 .

[0025] 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:

[0026]

[0027] 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.

[0028] 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:

[0029] S3.1, Based on the possible ice thickness Calculate the infrared transmittance spectrum of ice at different infrared wavelengths. ;

[0030] 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. ;

[0031] 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 , which is the optimal ice contamination spectral response.

[0032] Optionally, the method further comprises: generating a possible spectral response function after spectral shift according to the optimal ice contamination spectral response; and evaluating an optimal possible spectral shift amount by cross-calibration method, and obtaining a spectral response function after spectral calibration by optimal sampling.

[0033] S4.1, sampling the spectral response function with different wavelength shift amounts to obtain a possible spectral response function after shift .

[0034] S4.2, selecting continuous infrared images in a certain time period, calculating monthly calibration deviations under different wavelength shift amounts by cross-calibration with a high-precision infrared hyperspectral instrument, and obtaining a wavelength shift amount corresponding to the minimum fluctuation of monthly calibration deviations and a spectral response function thereof .

[0035] S4.3, under the conditions of the original spectral range and a spectral interval of , optimally sampling the spectral response function to obtain a spectral response function after spectral calibration .

[0036] Optionally, the method further comprises: under the conditions of the original spectral range and a spectral interval of , optimally sampling the spectral response function to obtain a spectral response function after spectral calibration , comprising:

[0037]

[0038]

[0039]

[0040] wherein, is a rounding function; is a modulo function; , , , , and when or , .

[0041] Optionally, the linear coefficient, the nonlinear coefficient and the constant coefficient of the radiation calibration are calculated according to the blackbody observation result of the infrared remote sensor, the cold space observation result, the nonlinear parameter of the infrared remote sensor measured by the ground test and the spectral response function after the spectral calibration, and the calculation includes:

[0042] S5.1, the linear coefficient of the radiation calibration is determined by using a linear regression method according to the blackbody observation result and the cold space observation result of the infrared remote sensor and the spectral response function after the spectral calibration .

[0043] S5.2, the noise variance value is calculated according to the cold space observation result of the infrared remote sensor , the constant coefficient of the radiation calibration is calculated according to the noise variance value . , ;

[0044] S5.3, the nonlinear coefficient of the radiation calibration is calculated by multiple iterations according to the nonlinear parameter of the infrared remote sensor measured by the ground test , the blackbody observation result of the infrared remote sensor and the linear coefficient of the radiation calibration . .

[0045] Optionally, the infrared image radiation after the multi-modal calibration is calculated according to the linear coefficient, the nonlinear coefficient and the constant coefficient of the radiation calibration and the effective spatially calibrated infrared image , and the calculation includes:

[0046] .

[0047] wherein, , represents the row number and the column number of the original infrared image.

[0048] In a second aspect, the embodiments of the present disclosure further provide an infrared waveband multi-modal calibration device, which adopts the following technical scheme:

[0049] The infrared waveband multi-modal calibration device includes:

[0050] A filter function generation module is configured to calculate a point spread function (PSF) of an original infrared image , and generate a spatial response matched filter function (SRMF) according to the point spread function (PSF);

[0051] A spatial calibration module is configured to perform two-dimensional convolution calculation on the original infrared image and the spatial response matched filter function (SRMF), and obtain an effective spatially calibrated infrared image according to the two-dimensional convolution calculation result.​

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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. .

[0056] 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.

[0057] 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

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0059] Figure 1 The flow chart of the infrared waveband multi-modal calibration method provided by the embodiments of the present disclosure is shown in FIG. 1.

[0060] Figure 2 The specific flow chart of step S1 provided by the embodiments of the present disclosure is shown in FIG. 2.

[0061] Figure 3 The specific flow chart of step S2 provided by the embodiments of the present disclosure is shown in FIG. 3.

[0062] Figure 4 The specific flow chart of step S3 provided by the embodiments of the present disclosure is shown in FIG. 4.

[0063] Figure 5 The specific flow chart of step S4 provided by the embodiments of the present disclosure is shown in FIG. 5.

[0064] Figure 6 The specific flow chart of step S5 provided by the embodiments of the present disclosure is shown in FIG. 6.

[0065] Figure 7 The principle block diagram of the infrared waveband multi-modal calibration device provided by the embodiments of the present disclosure is shown in FIG. 7. DETAILED DESCRIPTION

[0066] The embodiments of the present disclosure will be described in detail below with reference to the drawings.

[0067] It should be apparent that the following describes only some embodiments of the present disclosure and not all embodiments of the present disclosure. The present disclosure can be implemented or applied in other different specific embodiments, and each detail in the present disclosure can be modified or changed based on different views and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present disclosure.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] This disclosure provides an infrared multi-mode calibration method, such as... Figure 1 As shown, this infrared multi-mode calibration method includes:

[0072] S1. Calculate the original infrared image. The point spread function (PSF) is used to generate the spatial response matched filter (SRMF).

[0073] 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:

[0074] S1.1 Extracting the original infrared image High-contrast targets along the horizontal and vertical directions;

[0075] The above original infrared images The image size is , For the number of rows, For column numbers.

[0076] In the original infrared image The extraction of high-contrast targets along horizontal and vertical directions is a key prerequisite for achieving accurate spatial calibration. In step S1.1, based on a slope profile model (rather than a traditional step model), this is accomplished by analyzing the brightness temperature transition region in the image. First, a natural boundary with significant radiation difference needs to be scanned and identified in the entire image range, focusing on finding typical scenes such as the land-sea junction (such as the Red Sea area), the desert and water boundary, etc. These targets will exhibit a clear brightness temperature gradient in the horizontal or vertical direction, and the length of the high and low level regions (i.e. the uniform regions on both sides of the boundary) should not be less than the LSF radius D (usually 3-5 pixels) to ensure sufficient expression of spatial features.

[0077] The preliminary screening targets need to be strictly verified for features. By extracting the gray scale profile of the target region in the horizontal and vertical directions, the non-uniformity index of the peripheral region is calculated to ensure that the high and low level parts are approximately constant and only affected by additive noise.

[0078] The slope profile target that meets the requirements will enter the precise feature extraction stage: the Directional Partial Sum Average (DPSA) operator is used to calculate the cumulative sum sequence from both left and right directions, and the transition interval boundary is determined by detecting the first non-monotonic point (FNMP). Specifically, the first position that satisfies from left to right sequence is recorded as n L-FNMP , the first position that satisfies from right to left sequence is recorded as n R-FNMP , and both define the starting position P slope of the slope and the number of slope points N slope . At the same time, by calculating the gray scale average of the D pixels before and after the transition interval, the radiation values of the high level H and the low level L are accurately estimated.

[0079] The final effectiveness confirmation needs to meet two core conditions: one is that the transition interval length |n L-FNMP - n R-FNMP | does not exceed the preset threshold (usually ≤5 pixels), and the other is that the high and low level difference Δ = |H-L| needs to reach a significant proportion of the image dynamic range.

[0080] S1.2, calculating the slope parameters corresponding to the high-contrast target using high-order variational method;

[0081] The above slope parameters can include the slope starting position P slope and the number of slope points N slope .

[0082] Exemplarily, calculating the slope parameters corresponding to the high-contrast target using high-order variational method specifically includes:

[0083] (1) For a given high-contrast target (horizontal or vertical direction), obtain its discrete observed gray scale sequence. The theoretical model of this sequence is the result of the convolution of an ideal ramp profile signal with LSF, and then superimposed with noise.

[0084] (2) Calculate the second-order difference of the observed signal to approximate its second-order derivative in the continuous domain. The second-order derivative curve of the ideal ramp signal after convolution with LSF will present a clear negative peak and a positive peak, and the positions of the two peaks directly correspond to the start and end positions of the ramp transition region.

[0085] (3) Find the minimum and maximum points in the calculated second-order difference sequence. In theory, for a ramp (type I) that transitions from high level H to low level L, the minimum point corresponds to the vicinity of the start position of the transition region, and the maximum point corresponds to the vicinity of the end position.

[0086] (4) Preliminary calculation of ramp parameters based on the located extreme points.

[0087] (5) To overcome the possible misjudgment of extreme points caused by noise, introduce the Directional Partial Sum Average (DPSA) operator for verification.

[0088] (6) Finally determine the parameters by combining the results of the second-order difference and DPSA.

[0089] S1.3, according to the obtained ramp parameters and corresponding high-contrast targets, use the Fourier inverse transform method to calculate the line spread function LSF in the horizontal and vertical directions respectively;

[0090] The function size of the line spread function LSF in the horizontal and vertical directions is , called half-width length, generally taken as 3-5.

[0091] Optionally, according to the obtained ramp parameters and corresponding high-contrast targets, use the Fourier inverse transform method to calculate the line spread function LSF in the horizontal and vertical directions respectively, which specifically includes:

[0092] (1) Construct an estimated ideal input ramp signal:

[0093] According to the ramp parameters P slope , N slope , H, L determined in step S1.2, reconstruct an ideal, non-degraded by PSF, ramp profile signal in the discrete domain. The signal is constant at H or L outside the transition region, and presents linear variation in the transition region.

[0094] (2) Signal extension to match the convolution dimension:

[0095] Since 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 2D+1 input signal pixels around it and the LSF coefficients. Therefore, in order to establish an accurate equation, the input signal constructed in the last step must be extended by D pixels to the left and right ends. The pixel values of the extended part are filled with constant levels H or L at the ends of the ramp, respectively. At this point, the length of the extended input signal becomes . .

[0096] (3) Construct the matrix equation of spatial convolution:

[0097] Convert the discrete spatial convolution operation into the multiplication form of matrix and vector, and establish the following linear equation group:

[0098]

[0099] Wherein:

[0100] is a column vector with a size of , which is composed of the observed signal .

[0101] is a column vector to be solved with a size of , which represents the linear spread function to be solved.

[0102] is a matrix with a size of , and its construction method is as follows: for the i-th row, its elements are composed of the extended input signal . The dot product of each row of matrix and vector , completes the local convolution operation at position i.

[0103] is an additive noise vector.

[0104] (4) Solve the generalized inverse using singular value decomposition (SVD):

[0105] The above equation is a typical overdetermined linear equation group (usually ). The optimal estimate of is obtained by solving the least squares problem .

[0106] Specifically, the generalized inverse method based on singular value decomposition (SVD) is used to stably solve:

[0107]

[0108] Wherein is the SVD decomposition of the matrix , is the pseudo-inverse of the singular value matrix (typically truncated for small singular values to avoid noise amplification).

[0109] (5) Result normalization and output:

[0110] The obtained vector is normalized so that the sum of all its elements is 1 to satisfy the energy conservation property of the point spread function, and the normalized horizontal line spread function LSF and the vertical line spread function LSF are output.

[0111] S1.4, two line spread functions LSF are used to generate a point spread function PSF by vector synthesis method;

[0112] The function size of the point spread function PSF is .

[0113] Exemplarily, the two line spread functions LSF are used to generate a point spread function PSF by vector synthesis method, which 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 by vector outer product operation.

[0114] S1.5, according to the set target PSF, the point spread function PSF is converted into the spatial response matching filter function SRMF of the target PSF by the residue analysis method.

[0115] Exemplarily, the modulation transfer function value of the target PSF corresponding to the 0.5 normalized frequency can be set, which is usually 0.35-0.40, and the target PSF is generated accordingly.

[0116] The function size of the SRMF is .

[0117] Exemplarily, according to the set target PSF, the point spread function PSF is converted into the spatial response matching filter function SRMF of the target PSF by the residue analysis method, which specifically includes: (1) Z-transform is performed on the horizontal and vertical line spread functions (LSF) respectively to obtain their transfer functions; (2) the Z-domain expression of the spatial response matching filter (SRMF) is constructed; (3) the spatial domain expression of the SRMF is obtained by the residue analysis method, and the horizontal and vertical SRMFs are obtained; (4) the horizontal and vertical SRMFs are synthesized into a two-dimensional SRMF by outer product; (5) the obtained two-dimensional SRMF is normalized and output to obtain the spatial response matching filter function SRMF.

[0118] S2, the original infrared image performing two-dimensional convolution calculation on the original infrared image and the spatial response matched filter function, and obtaining the effective spatially calibrated infrared image according to the two-dimensional convolution calculation result ;

[0119] Optionally, as shown in Figure 3 , step S2 performs two-dimensional convolution calculation on the original infrared image and the spatial response matched filter function, and obtains the effective spatially calibrated infrared image according to the two-dimensional convolution calculation result , and specifically includes:

[0120] S2.1, performing two-dimensional convolution calculation on the original infrared image and the spatial response matched filter function, to generate a convolution-processed infrared image ;

[0121] The convolution-processed infrared image is denoted as , and the image size is , the number of rows, the number of columns.

[0122] Exemplarily, performing two-dimensional convolution calculation on the original infrared image and the spatial response matched filter function, to generate a convolution-processed infrared image specifically includes: performing extension padding on the boundary of the original infrared image by using a reflective padding method; and performing two-dimensional convolution calculation on the padded image and the spatial response matched filter function, to obtain the convolution-processed infrared image .

[0123] S2.2, obtaining the effective spatially calibrated infrared image from the convolution-processed infrared image according to the two-dimensional convolution processing characteristic

[0124] Optionally, obtaining the effective spatially calibrated infrared image from the convolution-processed infrared image according to the two-dimensional convolution processing characteristic, includes:

[0125]

[0126] wherein, , represent the row number and the column number of the image, the image size of the original infrared image is , 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.

[0127] 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.

[0128] 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;

[0129] 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:

[0130] S3.1, Based on the possible ice thickness Calculate the infrared transmittance spectrum of ice at different infrared wavelengths. ;

[0131] 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. .

[0132] 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. ;

[0133] Original spectral response function spectral range , the spectral interval is .

[0134] Exemplarily, the possible spectral response function after ice contamination is calculated by the original spectral response function of infrared remote sensor and the infrared transmittance spectrum under different ice thickness d . Specifically, the original spectral response function of infrared remote sensor defines the relative responsivity at each wavelength point, and each infrared transmittance spectrum under different ice thickness d has the same wavelength range and sampling interval as the original spectral response function , if the sampling intervals are inconsistent, the transmittance spectrum needs to be interpolated to ensure that all data are on a unified wavelength grid; for each ice thickness d to be evaluated, the corresponding infrared transmittance spectrum is multiplied point by point at each wavelength point with the original spectral response function , simulating the process of incident radiation being perceived by the detector after being attenuated by wavelength dependence after passing through the ice layer, to obtain the convolved spectral response function, which represents the actual spectral response characteristics of the instrument under specific ice contamination; the convolved spectral response function is normalized to obtain the possible spectral response function after ice contamination .

[0135] S3.3, select continuous infrared images in a certain time period, calculate the monthly calibration deviation under different ice thicknesses through cross-calibration with high-precision infrared hyperspectral instruments, and obtain the ice thickness corresponding to the minimum monthly calibration deviation fluctuation and its spectral response function , which is the optimal ice contamination spectral response.

[0136] Exemplarily, select continuous infrared images in a certain time period, calculate the monthly calibration deviation under different ice thicknesses through cross-calibration with high-precision infrared hyperspectral instruments, and obtain the ice thickness corresponding to the minimum monthly calibration deviation fluctuation and its spectral response function , which is the optimal ice contamination spectral response. Specifically, it includes:

[0137] (1) Select a continuous on-orbit data set with sufficient time length for analysis. Generally, data in the first half year to one year when the satellite is on orbit and the cold bias phenomenon is obvious should be selected to ensure the significance and stability of the statistics. At the same time, the reference sensor selected as the benchmark should have extremely high radiation calibration accuracy and spectral stability in the corresponding infrared band, for example, the IASI hyperspectral interferometer carried on the METOP-A satellite.

[0138] (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.

[0139] (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 .

[0140] 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.

[0141] 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:

[0142] S4.1, Spectral Response Function Apply different wavelength offsets The possible spectral response function after the shift is obtained. ;

[0143] For example, the spectral response function Apply different wavelength offsets The possible spectral response function after the shift is obtained. Specifically, it includes:

[0144] (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.

[0145] (2) Spectral response function Apply each candidate wavelength shift. The specific operation is: keep the shape of the spectral response function unchanged as a whole, and translate it on the wavelength axis. 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 shift).

[0146] (3) Due to translation, part of the spectral response function may move out of the originally defined valid wavelength range. For these out-of-range bands, the response value is usually set to zero, indicating that the instrument has no response capability in this area. Process the part of the function that is out of the original wavelength range after translation to ensure that the new spectral response function obtained after translation has the same wavelength array definition and point number as the original function.

[0147] Through the above steps, for each set wavelength shift, a corresponding possible spectral response function after shift is generated.

[0148] S4.2, select continuous infrared images in a certain period of time, calculate the monthly calibration deviation under different wavelength shifts through cross-calibration with high-precision infrared hyperspectral instruments, and obtain the wavelength shift and its spectral response function corresponding to the minimum monthly calibration deviation fluctuation;

[0149] Exemplarily, select continuous infrared images in a certain period of time, calculate the monthly calibration deviation under different wavelength shifts through cross-calibration with high-precision infrared hyperspectral instruments, and obtain the wavelength shift and its spectral response function corresponding to the minimum monthly calibration deviation fluctuation. Specifically, it includes:

[0150] (1) Select a sufficient time span of in-orbit data set that connects with the ice pollution stage or is independent, usually covering continuous observation for half a year to a year, to ensure that the calibration deviation variation characteristics in different seasons and scenes can be captured. At the same time, high-precision infrared hyperspectral instruments such as IASI on METOP-A satellite are still selected as reference benchmarks.

[0151] (2) According to the standardized process under the framework of global space-based cross-calibration system, accurately match the time and space and spectrum of the data of the target sensor and the reference sensor.

[0152] (3) Corresponding to each set wavelength shift, the possible spectral response function , in turn, as the on-orbit working spectral response function of the target sensor, and re-process all the matched data pairs within the entire time series, for each candidate offset, calculate its monthly average calibration bias, thus obtaining a time series composed of multi-month bias values.

[0153] (4) Analyze the statistical properties of these monthly bias series. The optimal wavelength offset, whose corresponding monthly bias series should satisfy two core conditions: first, the mean of the series should be as close to zero as possible, which indicates that the systematic cold or hot bias has been eliminated to the greatest extent; second, the fluctuation range of each monthly bias value in the series around the zero value should be as small as possible, i.e., its standard deviation is the smallest, which marks that the calibration result has reached the highest stability. When a certain specific wavelength offset can simultaneously satisfy the conditions of the bias mean closest to zero and the fluctuation range the smallest, it is determined as the optimal wavelength offset, and the wavelength offset corresponding to the minimum monthly calibration bias fluctuation is obtained and its spectral response function .

[0154] S4.3, under the condition that the original spectral range and the spectral interval is , the spectral response function is optimally sampled to obtain the spectral response function after spectral calibration .

[0155] Optionally, under the condition that the original spectral range and the spectral interval is , the spectral response function is optimally sampled to obtain the spectral response function after spectral calibration , comprising:

[0156]

[0157]

[0158]

[0159] wherein, is the floor function; is the modulo function; , , , , and when or , .

[0160] 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.

[0161] 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.

[0162] 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:

[0163] 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. ;

[0164] 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 , ;

[0165] 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. .

[0166] 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:

[0167] (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 .

[0168] (2) using the current estimated non-linear coefficient The original observed radiance value is corrected, i.e. the non-linear contribution part is subtracted from the original radiance value, to obtain a round of corrected radiance value. Using the corrected radiance value, the two-point calibration method is used again to recalculate an updated linear coefficient .

[0169] (3) calculating the relative deviation between the new round of linear coefficient and the last round of linear coefficient . If the deviation is greater than the preset convergence threshold, it is considered that the result is not stable and iteration needs to be continued. At this time, the average value of and is taken as the new initial linear coefficient , and the above steps are repeated. When the relative deviation of the linear coefficient is less than the preset convergence threshold, the iteration process is terminated, and the non-linear coefficient is obtained.

[0170] S6, according to the radiometrically calibrated linear coefficient, non-linear coefficient and constant coefficient, and the effectively spatially calibrated infrared image , calculating the multi-modal calibrated infrared image radiance .

[0171] Optionally, step S6 calculates the multi-modal calibrated infrared image radiance according to the radiometrically calibrated linear coefficient, non-linear coefficient and constant coefficient, and the effectively spatially calibrated infrared image , including:

[0172] ;

[0173] wherein, , represent the row number and column number of the original infrared image.

[0174] The present disclosure also provides an infrared band multi-modal calibration device. Optionally, as shown in Figure 7 , the infrared band multi-modal calibration device includes:

[0175] a filter function generation module 10 configured to calculate a point spread function PSF of an original infrared image , and generate a spatial response matched filter function SRMF according to the point spread function PSF;

[0176] a spatial calibration module 20 configured to perform two-dimensional convolution calculation on the original infrared image and the spatial response matched filter function SRMF, and obtain an effectively spatially calibrated infrared image according to the two-dimensional convolution calculation result ;

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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. .

[0181] 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.

[0182] 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.

[0183] A computer device according to an embodiment of the present disclosure includes a memory and a processor. The memory is configured to store non-transitory computer readable instructions. Specifically, the memory can include one or more computer program products that can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory, for example, can include random access memory (RAM) and / or cache memory, among others. The non-volatile memory, for example, can include read only memory (ROM), hard disk, flash memory, among others.

[0184] The processor can be a central processing unit (CPU) or other form of processing unit that has data processing and / or instruction execution capabilities, and can control other components in the computer device to perform desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory, so that the computer device performs all or part of the steps of the infrared waveband multi-modal calibration method according to the embodiments of the present disclosure.

[0185] Those skilled in the art will understand that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment can also include well-known structures such as a communication bus, an interface, and the like, which should also be included in the protection scope of the present disclosure.

[0186] The computer device can include a processor (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the computer device are also stored. The processor, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0187] Generally, the following devices can be connected to the I / O interface: input devices including, for example, a sensor or a visual information acquisition device; output devices including, for example, a display screen; storage devices including, for example, a magnetic tape, a hard disk, etc.; and communication devices. The communication devices can allow the computer device to perform wireless or wired communication with other devices (such as an edge computing device) to exchange data. It should be understood that all the illustrated devices are not required to be implemented or provided. More or less devices can be alternatively implemented or provided.

[0188] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present 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 through 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 waveband multi-modal calibration method of embodiments of the present disclosure are performed.

[0189] Detailed descriptions of the embodiments can refer to the corresponding descriptions of the previous embodiments, which will not be repeated here.

[0190] The computer-readable storage medium according to embodiments of the present disclosure has non-transitory computer-readable instructions stored thereon. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the infrared waveband multi-modal calibration method of embodiments of the present disclosure described above are performed.

[0191] The computer-readable storage medium described above includes, but is 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 mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0192] Detailed descriptions of the embodiments can refer to the corresponding descriptions of the previous embodiments, which will not be repeated here.

[0193] The above describes the basic principles of the present disclosure in combination with specific embodiments, but it should be noted that the advantages, advantages, effects, etc. mentioned in the present disclosure are only examples and are not limiting, and these advantages, advantages, effects, etc. cannot be considered as the must-have of each embodiment of the present disclosure. In addition, the above specific details are only for the purpose of example and for the purpose of understanding, and are not limiting, and the above details do not limit the present disclosure to the must-use of the above specific details to realize.

[0194] In this disclosure, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The block diagram of the devices, apparatus, equipment, systems referred to in this disclosure is merely illustrative and not intended to imply the necessity or arrangement of the connections, arrangement, configuration as shown in the block diagram. As will be appreciated by those skilled in the art, the devices, apparatus, equipment, systems can be connected, arranged, configured in any manner. The words comprising, including, having and the like are to be open ended. As used in this document, the conjunction "or" is to be interpreted in the inclusive sense, i.e. as meaning one or the other, or both. As used in this document, the words "and" and "or" are to be interpreted as having the meaning indicated in the phrase "and / or". As used in this document, the word "such as" is to be interpreted as meaning "such as, but not limited to". As used in this document, the word "for example" is to be interpreted as meaning "by way of example, not by way of limitation".

[0195] Also, as used in this document, the word "or" in the cases used to introduce list items is to be interpreted in the exclusive sense, i.e. as meaning one or the other, but not both. In addition, the phrase "example of" does not mean an example of the preferred or only example. Also, to the extent that there are listings of means, such as "means for" or "step for", such listings are intended to be construed in the manner set out in the above incorporated patent.

[0196] It is also important to note that the systems and methods of the present disclosure can be embodied in a variety of forms without departing from the teachings of the disclosure. Such forms include being implemented as a software routine running on a computer system, for example. In addition, the disclosure is amenable to various changes in the hardware components, software routines, and the methods for performing the functions of the disclosure. For example, the above-described embodiments of the present disclosure are directed to various forms of the methods and apparatus for performing functions of the disclosure. However, one of ordinary skill in the art will recognize that the scope of the disclosure is not limited to the embodiments described herein, but can be practiced with modification and alteration within the scope of the appended claims. Thus, the description is to be regarded as illustrative instead of restrictive on the scope of the disclosure.

[0197] Various changes, modifications and alterations in the teachings of the present disclosure will become apparent to those skilled in the art without departing from the teachings of the present disclosure. Moreover, the scope of the present disclosure is not to be interpreted as limited to the foregoing description but to be accorded the full scope consistent with the appended claims and their equivalents. Accordingly, the disclosure is not to be seen as being limited to the foregoing description but is to be accorded the full scope consistent with the principles and novel features disclosed herein.

[0198] The above description of the disclosed aspects is intended to be illustrative and not restrictive. Many embodiments of the present disclosure will suggest themselves to those skilled in the art having the benefit of this disclosure. Therefore, the disclosure is not to be taken as being limited on the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0199] The foregoing description has been presented for the purposes of illustration and description. Furthermore, the description is not intended to limit the embodiments of the disclosure to the forms disclosed herein. Although the various example aspects and embodiments have been described herein with regard to particular aspects and embodiments, those skilled in the art will recognize that certain modifications, changes, substitutions, additions and sub-combinations can be made without departing from the spirit of the disclosure.

Claims

1. A multi-mode calibration method for the infrared band, characterized in that, include: 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 ice transmittance spectral parameters, generate the spectral response function 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 the spectral response function after spectral shift, evaluate the optimal spectral shift using the cross-calibration method, and obtain the spectral response function after spectral calibration 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. ; 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. ,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 ; The calculation of the linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration is 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. This 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. ; The linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration, as well as the infrared image after effective spatial calibration, are used. Calculate the radiance of the infrared image after multimodal calibration. ,include: ； in, , The row and column numbers represent the original infrared image.

2. The infrared multi-mode calibration method according to claim 1, characterized in that, 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.

3. The infrared multi-mode calibration method according to claim 2, characterized in that, Based on the characteristics of two-dimensional convolution processing, the infrared image after 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.

4. The infrared multi-mode calibration method according to claim 1, characterized in that, The calculation of ice transmittance spectral parameters generates a spectral response function contaminated by ice, and the optimal ice-contaminated spectral response is evaluated using a cross-calibration method, including: S3.1, Based on 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 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.

5. The infrared multi-mode calibration method according to claim 4, characterized in that, The process of generating a spectrally shifted spectral response function based on the optimal ice contamination spectral response, evaluating the optimal 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 offset spectral response function 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. ; S4.2 includes: (1) Select an on-orbit dataset that is either connected to or independent of the ice pollution phase and has a sufficient time span; (2) Following the standardized procedures under the framework of the global space-based cross-calibration system, perform precise spatiotemporal and spectral matching of the data from the target sensor and the reference sensor; (3) The spectral response function corresponding to each set wavelength offset The spectral response function of the target sensor is used sequentially, and all matching data pairs in the entire time series are reprocessed. For each candidate wavelength offset, the average calibration deviation for each month is calculated, thus obtaining a time series composed of multiple months of deviation values. (4) Analyze the statistical characteristics of the monthly deviation sequence, determine the optimal wavelength offset, and obtain the wavelength offset corresponding to the minimum monthly calibration deviation fluctuation. and its spectral response function .

6. The infrared multi-mode calibration method according to claim 5, characterized in that, 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, .

7. An infrared multi-mode calibration device, characterized in that, include: 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 ice transmittance spectral parameters, generate spectral response functions affected by 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 a spectral shifted spectral response function based on the optimal ice pollution spectral response, evaluate the optimal spectral shift using a 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. ; 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. ,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 ; The calculation of the linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration is 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. This 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. ; The linear coefficients, nonlinear coefficients, and constant coefficients of the radiometric calibration, as well as the infrared image after effective spatial calibration, are used. Calculate the radiance of the infrared image after multimodal calibration. ,include: ； in, , The row and column numbers represent the original infrared image.

Images