A method for calculating infrared radiation intensity of aerial point source targets

Abstract

The present invention discloses a method for calculating the infrared radiation intensity of an aerial point source target. The method comprises the following steps: calibrating an infrared detector, determining a response gain coefficient and a pixel offset of each pixel of an infrared image through the calibration, and constructing a response gain coefficient matrix and a pixel offset matrix; determining the radiation brightness of each pixel point of an image of a projection area of ​​an aerial point source target by the response gain coefficient and the pixel offset; and integrating the radiation brightness of each pixel point of the target projection area to obtain the infrared radiation intensity of the target area. The method comprises the following steps: determining the projection area of ​​the aerial point source target is determining the effective projection area of ​​the aerial point source target. The method improves the method for calculating the infrared radiation intensity of a point source target, improves the accuracy of the calculation method, has the characteristics of strong operability and wide applicability, and can be used as a universal method for calculating the infrared radiation intensity of an aerial point source target.

Description

Technical Field

[0001] The invention relates to a method for calculating the infrared radiation intensity of an aerial point source target. Background Art

[0002] In areas such as long-range target detection and limiting range estimation, small infrared targets are often encountered. In this case, the target is far away from the infrared measurement system. Theoretically, the target's solid angle to the infrared system is smaller than the solid angle of a single detector pixel. However, due to the influence of diffraction from the optical system and random atmospheric aberrations, the target appears as a small, diffuse light spot on the detector surface. In this case, the target is also called a point source target. The infrared characteristics of a point source target are generally described using radiation intensity. Radiant intensity refers to the radiation flux emitted by a point source per unit solid angle in a specific direction. It is usually used to describe the strength of the infrared radiation energy of a point source target in a specific direction.

[0003] The traditional process for calculating the radiation intensity of a point source target generally consists of three steps. First, the infrared radiation calibration coefficient is calculated by calibration to invert the grayscale values ​​of the infrared system's output image into radiance values. The projected area of ​​the point source target on the detector surface is then calculated to determine the number of effective imaging pixels. Finally, the radiance corresponding to each effective imaging pixel is integrated to obtain the radiation intensity of the point source target. Two factors primarily influence the final calculation result: the infrared system calibration coefficient, including the response gain coefficient and bias. Traditional methods assume that each sensor pixel has an equal response, ignoring minor individual differences caused by factors such as the production process. Furthermore, experiments have found that the sensor bias coefficient varies linearly with temperature, and real-time correction is often not implemented in actual measurements. Second, the number of effective imaging pixels of the target. Because the energy projected by a point source onto the sensor is weak and easily obscured by the background radiation of the sky, it is difficult to accurately determine the target's true imaging size. Traditional methods, such as grayscale threshold selection, also fail to account for systematic errors caused by the sensor. Summary of the Invention

[0004] The purpose of the present invention is to propose a method for calculating the infrared radiation intensity of an aerial point source target. First, the infrared radiation is calibrated for each pixel on the detector target surface, and a response gain coefficient matrix and a bias matrix are established. The grayscale image generated by the detector is converted into a radiation brightness image. Then, the effective imaging pixel judgment of the point target based on the 3σ principle of radiation brightness is carried out. Then, the radiation brightness of the target area is obtained by using background cancellation, eliminating the influence of temperature on the bias. Finally, the target area is integrated to obtain the target infrared radiation intensity.

[0005] In order to achieve the above object, the technical solution of the present invention is:

[0006] A method for calculating the infrared radiation intensity of an aerial point source target, used for measuring infrared radiation characteristics, includes an infrared detector. The calculation comprises: calibrating the infrared detector, determining the response gain coefficient and pixel offset of each pixel of the infrared image through calibration, and constructing a response gain coefficient matrix and a pixel offset matrix; determining the radiation brightness of each pixel point in the projection area of ​​the aerial point source target measured based on the response gain coefficient and the pixel offset; and integrating the radiation brightness of each pixel point in the target projection area to obtain the infrared radiation intensity of the target area. The method further comprises: determining the projection area of ​​the aerial point source target measured is determining the effective projection area of ​​the aerial point source target measured. The process is as follows:

[0007] The measured point source target is stably tracked to obtain an infrared grayscale image that includes both the measured target and the pure sky background. The target grayscale image is converted pixel by pixel into a radiance image using a response gain coefficient matrix and a pixel bias matrix. The target area in the radiance image is divided into area A, area B, and area C. Area A includes all point source targets and has a total of N1 pixels. Area B is an area that extends outward from area A by multiple pixels and has a total of N2 pixels. The matrix ring area C within area B and outside area A is the sky background and has a total of N2-N1 pixels. Pixels in area A of the infrared radiance image whose radiance value is greater than the mean of the background radiance by at least three times the sum of the standard deviations of the background radiance are defined as target effective imaging pixels. The area where the target effective imaging pixels exist is the target effective projection area D. The infrared radiation intensity of the target area is obtained by integrating the radiance of each pixel in the target effective projection area using the constructed target area infrared radiation intensity model.

[0008] The solution further includes: the infrared radiation intensity model of the target area is:

[0009]

[0010] in,

[0011]

[0012] H target (x, y) is the grayscale value of the target area D at (x, y);

[0013] is the average gray value of the sky background area C image;

[0014] is the average bias of the sky background area C;

[0015] B(x,y) Bias coefficients corresponding to pixels at different positions

[0016] τ atm is the measured atmospheric transmittance;

[0017] A d is the detector pixel size;

[0018] M s is the magnification of the infrared system;

[0019] G(x, y) is the response gain coefficient and pixel bias of the pixel at (x, y) in the sky background area C;

[0020] Δ is the apparent radiation intensity of the atmosphere outside the target slant path:

[0021]

[0022] N0 is the target effective pixel number;

[0023] L path is the atmospheric radiation;

[0024] L back is the measured sky background radiation;

[0025] is the calibrated average response gain coefficient of the sky background area C.

[0026] The solution is further that: the region B is a region that extends outward by multiple pixels from region A, and is a region that extends outward by at least 3 pixels.

[0027] The solution is further as follows: the response gain coefficient and pixel offset of each pixel of the infrared image are determined by calibration, and the response gain coefficient matrix and pixel offset matrix are constructed as follows:

[0028] The first step is to obtain the standard surface source blackbody image and measure the standard surface source blackbody at n different temperature points T i Carry out measurement and record the corresponding images H at different temperature points i , n≥2, i is an integer and 1≤i≤n;

[0029] The second step is to use the linear regression method to calculate the linear relationship between the grayscale value of each pixel response of the infrared image and the target infrared radiation brightness at different temperatures, and establish the response gain coefficient matrix G and the pixel bias matrix B;

[0030]

[0031]

[0032] in:

[0033] x and y represent the image H i Pixel position coordinates along the x and y directions;

[0034] L i is the temperature Ti Theoretical radiation brightness of standard surface black body under ;

[0035] is L at n different temperature points i mean;

[0036] H i (x, y) is the temperature T i The grayscale value of the pixel at (x, y) in the infrared mean image under ;

[0037] G(x, y) and B(x, y) are the response gain coefficient and pixel bias of the pixel at (x, y), respectively.

[0038] The solution is further that: the radiant brightness of each pixel point in the target effective projection area is obtained by constructing a brightness model, and the brightness model is:

[0039]

[0040] in:

[0041] L target (x, y) is the radiation brightness value of the target area at (x, y), (x, y)∈D;

[0042] H target (x, y) is the grayscale value of the target area D at (x, y);

[0043] is the average gray value of the sky background area C image;

[0044] τ atm is the measured atmospheric transmittance;

[0045] L path is the atmospheric radiation;

[0046] L back is the measured sky background radiation;

[0047]

[0048] is the calibrated average response gain coefficient of the sky background area C;

[0049] is the average bias of the sky background area C;

[0050] B(x,y) is the bias coefficient corresponding to pixels at different positions;

[0051] G(x, y) and B(x, y) are the response gain coefficient and pixel bias of the pixel at (x, y) in the sky background area C, respectively.

[0052] The main advantages of the scheme of the present invention are: proposing a method for calculating the infrared radiation intensity of aerial point source targets, obtaining a linear relationship between the grayscale response of a single pixel and a standard infrared radiation source by performing infrared radiation calibration on each pixel of the detector, improving the whole-frame grayscale mean calibration method commonly used in infrared characteristic testing at this stage, solving the problem of large error in the results of traditional calculation methods due to the non-uniformity of the response degree between different pixels of the detector, judging the effective imaging pixels of the point target based on the principle of 3 times the radiation brightness, calculating the target projection area, and finally eliminating the influence of temperature drift on the bias through background cancellation, improving the method for calculating the infrared radiation intensity of point source targets, and improving the accuracy of the calculation method. It has the characteristics of strong operability and wide applicability, and can be used as a universal method for calculating the infrared radiation intensity of aerial point source targets.

[0053] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the process steps of the present invention;

[0055] Figure 2 Schematic diagram of the area where the infrared image target is located. DETAILED DESCRIPTION

[0056] A method for calculating the infrared radiation intensity of an aerial point source target, used for measuring infrared radiation characteristics, includes an infrared detector. The calculation comprises: calibrating the infrared detector, determining the response gain coefficient and pixel offset of each pixel of the infrared image through calibration, and constructing a response gain coefficient matrix and a pixel offset matrix; determining the radiation brightness of each pixel point in the projection area of ​​the aerial point source target measured based on the response gain coefficient and the pixel offset; and integrating the radiation brightness of each pixel point in the target projection area to obtain the infrared radiation intensity of the target area. The method further comprises: determining the projection area of ​​the aerial point source target measured is determining the effective projection area of ​​the aerial point source target measured. The process is as follows:

[0057] The measured point source target is stably tracked to obtain an infrared grayscale image that includes both the measured target and the pure sky background. The target grayscale image is converted pixel by pixel into a radiance image using the response gain coefficient matrix and the pixel bias matrix. Figure 2As shown in the figure, the target area in the radiation brightness image is divided into area A, area B and area C. Area A contains all point source targets and has a total of N1 pixels. Area B is the area extending outward from area A by multiple pixels and has a total of N2 pixels. The matrix ring area C inside area B and outside area A is the sky background and has a total of N2-N1 pixels. The pixels in area A of the infrared radiation brightness image whose radiation brightness value is greater than the mean of the background radiation brightness by at least 3 times the sum of the standard deviation of the background radiation brightness are defined as target effective imaging pixels. The area where the target effective imaging pixels exist is the target effective projection area D. The infrared radiation intensity of the target area is obtained by integrating the radiation brightness of each pixel in the target effective projection area using the constructed target area infrared radiation intensity model.

[0058] The infrared radiation intensity model of the target area is:

[0059]

[0060] in,

[0061]

[0062] H target (x, y) is the grayscale value of the target area D at (x, y);

[0063] is the average gray value of the sky background area C image;

[0064] is the average bias of the sky background area C;

[0065] B(x,y) is the bias coefficient corresponding to pixels at different positions;

[0066] τ atm is the measured atmospheric transmittance;

[0067] A d is the detector pixel size;

[0068] M s is the magnification of the infrared system;

[0069] G(x, y) is the response gain coefficient and pixel bias of the pixel at (x, y) in the sky background area C;

[0070] Δ is the apparent radiation intensity of the atmosphere outside the target slant path:

[0071]

[0072] N0 is the target effective pixel number;

[0073] L path is the atmospheric radiation;

[0074] L back is the measured sky background radiation;

[0075] is the calibrated average response gain coefficient of the sky background area C.

[0076] The region B is a region extending outwards from the region A by multiple pixels, and is a region extending outwards by at least 3 pixels.

[0077] The response gain coefficient and pixel offset of each pixel of the infrared image are determined by calibration, and the response gain coefficient matrix and pixel offset matrix are constructed as follows:

[0078] The first step is to obtain the standard surface source blackbody image and measure the standard surface source blackbody at n different temperature points T i Carry out measurement and record the corresponding images H at different temperature points i , n≥2, i is an integer and 1≤i≤n;

[0079] The second step is to use the linear regression method to calculate the linear relationship between the grayscale value of each pixel response of the infrared image and the target infrared radiation brightness at different temperatures, and establish the response gain coefficient matrix G and the pixel bias matrix B;

[0080]

[0081]

[0082] in:

[0083] x and y represent the image H i Pixel position coordinates along the x and y directions;

[0084] L i is the temperature T i Theoretical radiation brightness of standard surface black body under ;

[0085] is L at n different temperature points i mean;

[0086] H i (x, y) is the temperature T i The grayscale value of the pixel at (x, y) in the infrared mean image under ;

[0087] G(x, y) and B(x, y) are the response gain coefficient and pixel bias of the pixel at (x, y), respectively.

[0088] The radiance of each pixel in the target effective projection area is obtained by constructing a brightness model, which is:

[0089]

[0090] in:

[0091] L target (x, y) is the radiation brightness value of the target area at (x, y), (x, y)∈D;

[0092] H target (x, y) is the grayscale value of the target area D at (x, y);

[0093] is the average gray value of the sky background area C image;

[0094] τ atm is the measured atmospheric transmittance;

[0095] L path is the atmospheric radiation;

[0096] L back is the measured sky background radiation;

[0097]

[0098] is the calibrated average response gain coefficient of the sky background area C;

[0099] is the average bias of the sky background area C;

[0100] B(x,y) is the bias coefficient corresponding to pixels at different positions;

[0101] G(x, y) and B(x, y) are the response gain coefficient and pixel bias of the pixel at (x, y) in the sky background area C, respectively.

[0102] The above method is based on Figure 1 The process steps described perform the following operations:

[0103] First, step one: calibrate the infrared detector:

[0104] During the infrared calibration process, the standard surface source black body is measured at n different temperature points T i Carry out measurement and record the corresponding images H at different temperature points i (n≥2, i is an integer and 1≤i≤n);

[0105] The linear regression method is used to calculate the linear relationship between the grayscale value of each pixel response of the infrared image and the target infrared radiation brightness at different temperatures, and the response gain coefficient matrix G and the bias matrix B are established;

[0106]

[0107]

[0108] Among them, L i is the temperature T i The standard surface blackbody theoretical radiation brightness under is L at n different temperature points i Mean, H i (x, y) is the temperature T i The grayscale value of the pixel at (x, y) in the infrared mean image under [1] is shown in Figure 2. G(x, y) and B(x, y) are the response gain coefficient and bias of the pixel at (x, y), respectively.

[0109] Step 2: Stably track the measured point source target to obtain an infrared grayscale image that includes both the measured target and the pure sky background. Use the detection response gain coefficient matrix G and the bias matrix B to convert the target grayscale image pixel by pixel into a radiance image L0.

[0110] The target area in the infrared radiation brightness image of the detector is divided into area A, area B and area C. Area A contains all point source targets and part of the sky background, with a total of N1 pixels. Area B is the outer expansion area of ​​area A, generally expanding outward by 3 pixels, with a total of N2 pixels. Among them, the matrix ring area C inside area B and outside area A is the sky background, with a total of N2-N1 pixels, and its background radiation brightness average is and the standard deviation of background radiation brightness σ b Expressed as:

[0111]

[0112]

[0113] The infrared radiation brightness image area A has a radiation brightness value L0 (x, y) greater than the background radiation brightness mean and 3 times the standard deviation of background radiation brightness σ b The pixel of the sum is defined as the target effective imaging pixel, and F(x, y) is defined to indicate whether the pixel at (x, y) is a valid pixel. Valid is 1, invalid is 0, and:

[0114]

[0115] The target projection area is the area where the effective imaging pixels of the target exist, which is defined as area D. The number of effective pixels of the target is represented by N0:

[0116] N0=∑F(x,y)(x,y)∈A (6)

[0117] Step 3: Define H target(x, y) is the grayscale value of the target area D at (x, y), and is defined as is the average grayscale value of the sky background area C image, and are the calibrated average response gain coefficient and average bias of the sky background area C, respectively. H(x, y) is the grayscale value of the target infrared image recorded by the detector at (x, y):

[0118]

[0119]

[0120] The radiation brightness value L of the target at (x, y) target (x, y) can be expressed as, where (x, y)∈D:

[0121]

[0122] in, L target (x, y) is the radiation brightness value of the target at (x, y), τ atm is the measured atmospheric transmittance, L path is the atmospheric radiation, L back is the measured sky background radiation.

[0123] Integrate the pixels in the target area D to obtain the infrared radiation intensity I of the point source target target , where (x,y))∈D:

[0124]

[0125] Among them, A d is the detector pixel size, M s is the infrared system magnification, and Δ is the apparent radiation intensity of the atmosphere outside the target slant path:

[0126]

[0127] In the calibration calculation formula of the embodiment, the bias matrix B(x, y) is regarded as a quantity related to the performance of the infrared system pixel itself. The bias coefficients corresponding to pixels in different positions are different. However, in the actual calibration process, it is found that the system bias coefficient is affected by the ambient temperature and changes linearly with the change of the ambient temperature. Moreover, the change amount of the bias coefficient B(x, y) corresponding to different pixels is basically the same.

[0128] Therefore, the linear relationship between bias B and ambient temperature is set to B = aT t +b, a is the linear coefficient, b is the linear bias, then the relationship between the bias coefficient B(x, y) corresponding to the pixel at different positions and the ambient temperature is B(x, y)) = aTt +b(x,y)

[0129] This means that in a linear relationship, the linear coefficient a is independent of (x, y), and the linear bias b is related to (x, y). is the average bias of the sky background area C. The average here is the average of all (x, y), so it is reflected in the linear relationship with the ambient temperature as Among them, a has nothing to do with (x, y) so it remains unchanged, and b is related to (x, y) so the average is taken.

[0130] The above-mentioned calculation method embodiment obtains the linear relationship between the grayscale response of a single pixel and the standard infrared radiation source by calibrating the infrared radiation of each pixel of the detector one by one, thereby improving the whole-frame grayscale mean calibration method commonly used in the current infrared characteristic test, and solving the problem of large error in the results of the traditional calculation method due to the non-uniformity of the response degree between different pixels of the detector. The effective imaging pixels of the point target are judged based on the principle of 3 times the radiation brightness, and the target projection area is calculated. Finally, the influence of temperature drift on the bias is eliminated through background cancellation, thereby improving the calculation method of the infrared radiation intensity of the point source target and improving the accuracy of the calculation method. It has the characteristics of strong operability and wide applicability, and can be used as a general calculation method for the infrared radiation intensity of the aerial point source target.

Claims

1. A method for calculating the infrared radiation intensity of an aerial point source target, used for measuring infrared radiation characteristics, including an infrared detector, the calculation comprising: Calibrate the infrared detector, determine the response gain coefficient and pixel offset of each pixel of the infrared image through calibration, and construct the response gain coefficient matrix and pixel offset matrix; The radiation brightness of each pixel of the image of the projection area of ​​the measured point source target in the air is determined by the response gain coefficient and the pixel offset; the radiation brightness of each pixel of the target projection area is integrated to obtain the infrared radiation intensity of the target area. It is characterized in that the determination of the projection area of ​​the measured point source target in the air is to determine the effective projection area of ​​the measured point source target in the air, and the process is: The measured point source target is stably tracked to obtain an infrared grayscale image that includes both the measured target and the pure sky background. The target grayscale image is converted pixel by pixel into a radiance image using a response gain coefficient matrix and a pixel bias matrix. The target area in the radiance image is divided into area A, area B, and area C. Area A includes all point source targets and has a total of N1 pixels. Area B is an area that extends outward from area A by multiple pixels and has a total of N2 pixels. The matrix ring area C within area B and outside area A is the sky background and has a total of N2-N1 pixels. The pixels in area A of the infrared radiance image whose radiance value is greater than the mean of the background radiance by at least 3 times the sum of the standard deviations of the background radiance are defined as target effective imaging pixels. The area where the target effective imaging pixels exist is the target effective projection area D. The infrared radiation intensity of the target area is obtained by integrating the radiance of each pixel in the target effective projection area using the constructed target area infrared radiation intensity model. The infrared radiation intensity model of the target area is: in, H target (x, y) is the grayscale value of the target area D at (x, y); is the average gray value of the sky background area C image; is the average bias of the sky background area C; B(x,y) Bias coefficients corresponding to pixels at different positions τ atm is the measured atmospheric transmittance; A d is the detector pixel size; M s is the magnification of the infrared system; G(x,y) is the response gain coefficient of the pixel at (x,y) in the sky background area C; Δ is the apparent radiation intensity of the atmosphere outside the target slant path: N0 is the target effective pixel number; L path is the atmospheric radiation; L back is the measured sky background radiation; is the calibrated average response gain coefficient of the sky background area C.

2. The calculation method according to claim 1, characterized in that The region B is a region extending outwards from the region A by multiple pixels, and is a region extending outwards by at least 3 pixels.

3. The calculation method according to claim 1, characterized in that The response gain coefficient and pixel offset of each pixel of the infrared image are determined by calibration, and the response gain coefficient matrix and pixel offset matrix are constructed as follows: The first step is to obtain the standard surface source blackbody image and measure the standard surface source blackbody at n different temperature points T i Carry out measurement and record the corresponding images H at different temperature points i , n≥2, i is an integer and 1≤i≤n; The second step is to use the linear regression method to calculate the linear relationship between the grayscale value of each pixel response of the infrared image and the target infrared radiation brightness at different temperatures, and establish the response gain coefficient matrix G and the pixel bias matrix B; in: x and y represent the image H i Pixel position coordinates along the x and y directions; L i is the temperature T i Theoretical radiation brightness of standard surface black body under ; is L at n different temperature points i mean; H i (x, y) is the temperature T i The grayscale value of the pixel at (x, y) in the infrared mean image under ; G(x, y) and B(x, y) are the response gain coefficient and pixel bias of the pixel at (x, y), respectively.

4. The calculation method according to claim 1, characterized in that The radiance of each pixel in the target effective projection area is obtained by constructing a brightness model, which is: in: L target (x, y) is the radiation brightness value of the target area at (x, y), (x, y)∈D; H target (x, y) is the grayscale value of the target area D at (x, y); is the average gray value of the sky background area C image; τ atm is the measured atmospheric transmittance; L path is the atmospheric radiation; L back is the measured sky background radiation; is the calibrated average response gain coefficient of the sky background area C; is the average bias of the sky background area C; B(x, y) is the bias coefficient corresponding to pixels at different positions; G(x, y) is the response gain coefficient of the pixel at (x, y) in the sky background area C.

Images