CN114894737A - Spectral reflectivity reconstruction method based on infrared image - Google Patents
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Abstract
The invention relates to a spectral reflectivity reconstruction method based on an infrared image, which comprises the following steps: according to the temperature T of black bodies to be measured of three different wave bands B And obtaining response function matrixes from the gray response values of the thermal images of the three different wave bands, wherein the three different wave bands comprise a near infrared wave band, a middle infrared wave band and a far infrared wave band, and the response function matrixes are matrixes of integral spectral response functions comprising a target intrinsic radiation brightness distribution function and an infrared imager spectral sensitivity function; based on the reflectivity reconstruction function, three different wave bands are obtained according to the matrix of the gray response value and the response matrixThe spectral reflectance of (a); and obtaining the full-waveband spectral reflectivity according to the spectral reflectances of the three different wavebands. The invention can restore the infrared full-band spectral reflectivity by adopting three frames of actual measurement images with different infrared bands by utilizing a pseudo-inverse matrix method, saves the cost consumed in the process of measuring the infrared spectral reflectivity and improves the application range of the spectral reflectivity reconstruction technology in the infrared bands.
Description
Technical Field
The invention belongs to the technical field of multispectral image processing, and relates to a spectral reflectivity reconstruction method based on an infrared image.
Background
Since the 19 th century infrared radiation came out, many scholars developed their research, but it was difficult to detect infrared radiation due to the lack of testing techniques and instrumentation. Until the early 20 th century, the infrared absorption spectra of hundreds of organic and inorganic compounds were studied more systematically and correlations between certain absorption bands and molecular groups were found. Research on infrared spectroscopy is flourishing at this time, and chemists began to consider infrared spectroscopy as a possible tool for analyzing chemical components of substances in the late 20 th 30 s and started to develop infrared spectrometers. Infrared spectroscopy analysis has become one of the more mature means in organic structure analysis, and the application field of the infrared spectroscopy analysis is also widely expanded, for example, in the field of ancient object identification, the storage life of a cultural relic is deduced by judging the use amount of a pigment of a surface artwork, and meanwhile, the research on the spectral characteristics of the pigment can repair and maintain the cultural relic to different degrees; in the fields of advanced visual systems such as video monitoring and the like, the target characteristics of an actual scene can be restored in a corresponding simulation scene by using a spectrum reconstruction technology, in short, an infrared spectrum analysis technology becomes a core technology applied to current image processing technology and computer graphics, and the characteristic of 'map integration' can be realized.
In the world we live, most of the time is surrounded by various types of "light", which come almost without exception from hotter source objects. The most common sources of heat are the sun, incandescent lamps, etc. In fact, objects with temperatures above absolute zero (-273 c) all radiate heat, and thermal ir imaging is a technique that relies on the thermal radiation of objects to obtain their thermal map, whose properties are largely similar to those of visible light, and which allows humans to see and understand the thermal image they detect, and thermal ir imagers are based on the principle that thermal imagers detect a different grayscale or different color photograph, which helps us to "see" thermal changes, but also a technique that quantifies these changes.
In recent years, the infrared radiation characteristic principle is widely applied to military and civil fields, and in the image simulation process, radiation information of each surface element is obtained by establishing an infrared radiation calculation model, so that radiation data of the whole scene is obtained, and the real-time scene can be restored with high precision. The acquisition of the infrared image is more convenient, and compared with the problems that the visible image is interfered by a large amount of noise in the acquisition process and the color information is lost under the condition of insufficient light at night or weak light, the infrared image can still generate clear texture details under the same environmental condition. Aiming at the conditions that the existing infrared spectrum test cost is high and the demand on environmental factors is large, the reconstruction of the infrared spectrum reflectivity of a scene by using an infrared image is a hot point of the current research, the defects of a visible light image reflectivity reconstruction technology can be effectively balanced, the spectral reflectivity of a target at an infrared waveband can be obtained, and the high-precision texture restoration of the scene in the real-time rendering and large environment simulation processes is facilitated.
Therefore, a spectrum reflectivity reconstruction technique applicable to infrared images is urgently needed.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a spectral reflectance reconstruction method based on an infrared image. The technical problem to be solved by the invention is realized by the following technical scheme:
the embodiment of the invention provides a spectral reflectivity reconstruction method based on an infrared image, which comprises the following steps:
according to the temperature T of black bodies to be measured of three different wave bands B And obtaining a response function matrix from the gray response values of the thermal images of the three different wave bands, wherein the three different wave bands comprise a near infrared wave band, a middle infrared wave band and a far infrared wave band, and the response function matrix is a matrix of an overall spectral response function containing a target intrinsic radiation brightness distribution function and an infrared imager spectral sensitivity function;
based on a reflectivity reconstruction function, obtaining spectral reflectivities of three different wave bands according to the matrix of the gray response values and the response matrix;
and obtaining the full-waveband spectral reflectivity according to the spectral reflectivities of the three different wavebands.
In one embodiment of the invention, the temperature T of the black body to be measured is determined according to three different wave bands B And three different bands of thermal image gray scale response values to obtain a response function matrix, comprising:
at a temperature T B Under the condition, obtaining the gray response value of the black body to be detected by utilizing the thermal images of three different wave bands;
temperature T based on the black body to be detected B Obtaining the radiation exitance according to a blackbody Planck calculation formula;
and obtaining the response function matrixes of three different wave bands according to the gray response value and the radiation emittance.
In one embodiment of the invention, at temperature T B Under the condition, the gray scale response value of the black body to be detected is obtained by utilizing the thermal images of three different wave bands, and the method comprises the following steps:
at a temperature T B And under the condition, obtaining the gray response value of each wave band according to the gray average value of each pixel point of the N black body thermal images.
In one embodiment of the present invention, the blackbody planckian calculation formula is:
wherein M is bb Indicating the degree of radiation exitance, T B Indicating the temperature of the black body to be measured, c 1 ＝3.7418×10 8 W·m -2 ·μm 4 ，c 2 ＝1.4388×10 4 ·μm·K，λ 1 And lambda 2 Respectively representing the lower limit and the upper limit of the working waveband range of the thermal imager.
In an embodiment of the present invention, obtaining the response function matrix of three different bands according to the gray response value and the radiation emittance includes:
converting the radiant exitance into radiant brightness;
and obtaining the response function matrixes of three different wave bands according to the gray response value and the radiation brightness.
In one embodiment of the invention, the reflectivity reconstruction function is:
wherein R is NIR 、R MIR And R FIR Respectively representing the spectral reflectivities of a near infrared band, a middle infrared band and a far infrared band, Q NIR 、Q MIR And Q FIR Respectively representing the response functions, Q, of the near-infrared, mid-infrared and far-infrared bands + Pseudo-inverse of the Q matrix, I [a,b] 、I [b,c] And I [c,d] Respectively represent the gray response values of near infrared band, middle infrared band and far infrared band, [ a, b]Indicates the band range of the near infrared band, [ b, c]Indicates the band range of the mid-infrared band, [ c, d]Indicating the band range of the far infrared band.
In an embodiment of the present invention, after obtaining the full-band spectral reflectances according to the spectral reflectances of three different bands, the method further includes:
and under different temperature conditions, establishing a three-dimensional database of the full-wave-band spectral reflectivity and the temperature.
In an embodiment of the present invention, after obtaining the full-band spectral reflectances according to the spectral reflectances of three different bands, the method further includes:
and evaluating the precision of the full-waveband spectral reflectivity according to the error fitting optimization coefficient.
In one embodiment of the present invention, the formula of the error fitting optimization coefficient is:
where ε represents the error fit optimization coefficient, R M (λ i ) A measurement value, R (lambda), representing the spectral reflectance i ) The spectral reflectivity of the full-wave band is shown, and a and d respectively represent the upper limit and the lower limit of the reconstruction wave band.
Compared with the prior art, the invention has the beneficial effects that:
firstly, the spectral reflectance reconstruction research technology based on the RGB camera is popular at home and abroad in recent years, but the research on the infrared spectral reflectance reconstruction based on the thermal infrared imager is rare, the full-infrared-band spectral reflectance can be restored by using a pseudo-inverse matrix method and three frames of actual measurement images with different infrared bands, the cost consumed in the infrared spectral reflectance measurement process is saved, and the application range of the spectral reflectance reconstruction technology in the infrared bands is improved.
Secondly, the problems of noise interference, insufficient light at night or color information loss under the condition of weak light easily occur to a common camera, clear texture details can be generated by a thermal image acquired by an infrared camera under the same environmental condition, and the defects of RGB images are effectively overcome.
Other aspects and features of the present invention will become apparent from the following detailed description, which proceeds with reference to the accompanying drawings. It is to be understood, however, that the drawings are designed solely for purposes of illustration and not as a definition of the limits of the invention, for which reference should be made to the appended claims. It should be further understood that the drawings are not necessarily drawn to scale and that, unless otherwise indicated, they are merely intended to conceptually illustrate the structures and procedures described herein.
Drawings
Fig. 1 is a schematic flowchart of a spectral reflectance reconstruction method based on an infrared image according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a real-time target infrared image acquisition test provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a calibration test of a thermal infrared imager according to an embodiment of the present invention;
fig. 4 is a schematic view of an operating principle of a thermal infrared imager according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1, fig. 1 is a schematic flowchart of a spectral reflectance reconstruction method based on an infrared image according to an embodiment of the present invention. The invention provides a spectral reflectivity reconstruction method based on an infrared image, which comprises the following steps:
step 1, according to the temperature T of black bodies to be detected in three different wave bands B And obtaining a response function matrix by the gray response values of the thermal images of the three different wave bands, wherein the three different wave bands comprise a near infrared wave band, a middle infrared wave band and a far infrared wave band, and the response function matrix is a matrix of an integral spectral response function containing a target intrinsic radiance distribution function and an infrared imager spectral sensitivity function.
Preferably, the band range of the near infrared band is [0.78, 2.5], the band range of the mid infrared band is [2.5, 8], and the band range of the far infrared band is [8, 14], in microns.
In a specific embodiment, step 1 may specifically include:
step 1.1, at temperature T B Under the condition, obtaining the gray response value of the black body to be detected by utilizing the thermal images of three different wave bands, wherein the gray response value is a gray valueOr a pseudo-color value.
In this embodiment, three thermal infrared imagers in different wavelength ranges are selected to ensure that the wavelength span thereof includes: near infrared band, mid infrared band, and far infrared band. And considering that the wave bands of some thermal infrared imagers may have repeated overlapping conditions, a plurality of groups of infrared filters (such as silicon, germanium and the like) with different specifications can be prepared in the early stage so as to accurately extract the radiation energy of a specific wave band region obtained by the test.
Specifically, as shown in fig. 2, the temperature of the black body to be measured is set to be T 0 Three thermal infrared imagers are respectively adopted to collect black body infrared temperature images (namely thermal images) of three wave band near infrared wave bands, middle infrared wave bands and far infrared wave bands, and gray information can be determined through the thermal images. According to the wave band range of the actual thermal infrared imager, an infrared filter can be arranged at the front end of the thermal infrared imager to accurately intercept the wave band to be detected, as shown in the attached drawing 3, in order to eliminate the influence of noise, temperature difference and atmospheric effect introduced by environmental factors on the thermal infrared imager, the distance L between the black body to be detected and the thermal infrared imager is required to be as small as possible, and the distances L between the three thermal infrared imagers are required to be consistent.
Preferably, the distance L between the black body to be detected and the thermal infrared imager ranges from 1cm to 20 cm.
Further, step 1.1 may specifically include:
at a temperature T B And under the condition, obtaining the gray response value of each wave band according to the gray average value of each pixel point of the N black body thermal images.
Specifically, in consideration of the inevitable error caused by other environmental radiation in the acquisition process, the thermal images of the black body to be detected acquired by the single thermal infrared imager are N, preferably N is more than or equal to 10, the gray value of the black body to be detected can be approximately read by the digital display function during real-time detection, the gray value is calculated by combining the final N thermal images, and finally the black body to be detected on the near, middle and far infrared bands at T is acquired 0 Infrared thermal image at temperature. By varying the temperature T of the black body a number of times B Recording each temperature T B And infrared thermal images of the black body to be detected in the lower three wave bands.
Step 1.2, based on the temperature T of the black body to be measured B The radiation exitance is obtained according to a blackbody planck calculation formula, which is as follows:
wherein M is bb Indicating the degree of radiation exitance, T B Indicating the temperature of the black body to be measured, c 1 ＝3.7418×10 8 W·m -2 ·μm 4 ，c 2 ＝1.4388×10 4 ·μm·K，λ 1 And lambda 2 Respectively representing the lower limit and the upper limit of the working waveband range of the thermal imager.
And 1.3, obtaining response function matrixes of three different wave bands according to the gray response value and the radiation emittance.
Further, step 1.3 may specifically include:
and step 1.31, converting the radiation exitance into radiation brightness.
The relationship formula between the lambertian body radiation brightness and the radiation emittance is as follows: radiation exitance is the radiance circumference ratio.
And step 1.32, obtaining response function matrixes of three different wave bands according to the gray response value and the radiation brightness.
Referring to fig. 3, in this embodiment, calibration tests of the temperature-controllable black body (i.e., black body to be measured) are respectively performed on the three thermal infrared imagers, i.e., the temperature T of the black body to be measured is changed B Recorded at different T B Gray scale information respectively acquired by three thermal infrared imagers under the value is determined, so that the gray scale response value of the thermal infrared imager corresponding to the gray scale information and the temperature T of the black body to be detected are determined B The fitted curve of (1).
Temperature T based on gray response value and black body to be detected B The fitting curve and the black body Planck calculation formula can obtain the mapping relation between the radiation exitance and the corresponding gray response value of the thermal infrared imager, and finally can fit and generate the corresponding gray response value and the radiation brightness of the thermal infrared imager at a certain temperature according to the conversion relation between the radiation exitance and the radiation brightnessThe response curve is the response function of three different wave bands, namely the response function Q of the near infrared wave band NIR Response function Q of middle infrared band MIR And response function Q of far infrared band FIR 。
In addition, a three-dimensional database between the gray response value and the radiation brightness of the thermal infrared imager under different temperature values is generated by changing different temperature values.
And 2, based on a reflectivity reconstruction function, obtaining spectral reflectances of three different wave bands according to the matrix of the gray response value and the response matrix, wherein the reflectivity reconstruction function is as follows:
wherein R is NIR 、R MIR And R FIR Respectively representing the spectral reflectivities of a near infrared band, a middle infrared band and a far infrared band, Q NIR 、Q MIR And Q FIR Respectively representing the response functions, Q, of the near-infrared, mid-infrared and far-infrared bands + Pseudo-inverse of the Q matrix, I [a,b] 、I [b,c] And I [c,d] Respectively represent the gray response values of near infrared band, middle infrared band and far infrared band, [ a, b]Indicates the band range of the near infrared band, [ b, c]Indicates the band range of the mid-infrared band, [ c, d]Indicating the band range of the far infrared band.
That is, the temperature T of the black body to be measured is acquired B And determining the final gray response value in each wave band according to the heat image of the black body to be detected in the three wave bands and the calculation of the gray average value in each wave band, thereby establishing a gray response value matrix I. According to step 1 at this temperature T B The response matrix Q of the lower thermal infrared imager corresponding to the gray level-radiance is known from the reflectivity reconstruction function at the temperature T B And reconstructing the infrared spectrum reflectivity of the black body to be detected in the full wave band.
Preferably, a is 0.78, b is 2.5, c is 8, and d is 14.
In order to facilitate understanding of the reflectivity reconstruction function of the present embodiment, the following description will be made of the process of establishing the reflectivity reconstruction function, that is:
and mapping infrared spectrum reflectivity distribution by using a scene radiant energy distribution image acquired by the thermal infrared imager. Because the image collected by the thermal infrared imager is a device for converting the target temperature distribution image into a visual image after optical-electrical signal conversion, the chromatic value corresponding to each pixel corresponds to a specified temperature value, the overall temperature distribution condition of the measured target can be observed by checking the thermal image collected by the thermal infrared imager, the heating condition of the target is researched, and the attached diagram 4 is the working principle diagram of the thermal infrared imager. Meanwhile, the visible image is a result which is shown by the response of the infrared detector to the self attribute of the target, the relation between the gray scale response value of the infrared detector and the spectral reflectivity representing the self attribute of the target can be established through the relation, the reflectivity value corresponding to the corresponding output channel is obtained, and the one-to-one mapping relation between the visible image, the target temperature and the spectral reflectivity is calculated, so that the spectral reflectivity reconstruction of the infrared image is realized.
Assuming that the photoelectric conversion function of the thermal infrared imager is an ideal linear model, and setting L 0 (lambda) is an intrinsic radiance distribution function of a target in a scene, R (lambda) is a spectral reflectivity of a target surface, C (lambda) is a spectral sensitivity function of the thermal infrared imager, the spectral sensitivity function comprises a transmissivity of an imaging optical system and a spectral sensitivity function of a detector optical element, the change range of lambda is selected to be 0.78-14 mu m by referring to a general working waveband range of the thermal infrared imager, and a gray scale response value of an ith channel of the infrared detector corresponding to a pixel point or a sample can be represented as the following integral when the gray scale response value is output:
in the above formula, b i And n i Respectively the dark current noise of the thermal infrared imager and the integral noise of the system. L in formula (2) 0 (λ) can be rewritten as:
L 0 (λ)＝L refl (λ)+L item (λ) (4)
wherein L is refl (lambda) is the radiance of the scene environment of the target, L item (lambda) is the target self thermal radiation value, both values are constant values under the condition that the scene and the self temperature are constant, and the target intrinsic radiance distribution function is a spectral function related to the scene. To simplify this process, equation (2) can be written as:
namely, the process of converting the infrared spectrum reflectivity of the object surface into the response of the thermal infrared imager, and dividing the wave band interval [ a, d ] into three wave band response intervals of near infrared, intermediate infrared and far infrared, namely three wave bands [ a, b ], [ b, c ] and [ c, d ], then the formula (4) can be expanded into:
let Δ λ be 0.01, and equation (5) be rewritten in a matrix form, it can be obtained:
wherein, the left side of the equation is the gray response value of the corresponding detector on the near, middle and far infrared wave bands, generally the gray value or the pseudo-color value; q NIR 、Q MIR And Q FIR Response functions corresponding to near, middle and far wave bands within 0.78-14 mu m of the full wave band are respectively obtained through a thermal infrared imager radiation black body calibration test; r NIR 、R MIR And R FIR The spectral reflectances respectively obtained for the corresponding bands. Equation (5) can be further simplified as:
in the above formula, Q is a matrix of the entire spectral response function of the thermal infrared imager including the target intrinsic radiance distribution function and the spectral sensitivity function of the infrared imaging system, and is a matrix of 3 × 1322 th order; i is the vector of response values of the pixels, which is a 3 × 1 matrix. The relation between the spectral reflectivity R (lambda) and the gray scale response value I of the thermal infrared imager is established through the formula, so that the target spectral reflectivity distribution R is obtained through the reconstruction of the gray scale response value I of the thermal infrared imager with low dimensionality through the following formula, namely:
in general:
Q + ＝Q T (QQ T ) -1 (9)
wherein Q is + Is a pseudo-inverse of the matrix Q, also referred to as the generalized inverse of Q.
And 3, obtaining the full-waveband spectral reflectivity according to the spectral reflectivities of the three different wavebands.
Specifically, the spectral reflectance in the full-band range is:
R general assembly ＝R NIR +R MIR +R FIR (10)
Wherein R is General assembly Is the full-band spectral reflectance.
And 4, establishing a three-dimensional database of the full-wave-band spectral reflectivity and the temperature under different temperature conditions.
In particular for different temperatures T B The values are all solved by the method, and a group of different temperatures T can be obtained B -three-dimensional data of R.
And 5, evaluating the precision of the full-waveband spectral reflectivity according to the error fitting optimization coefficient.
Specifically, in order to verify the accuracy of the method for reconstructing the infrared spectrum, the medium-wavelength infrared spectrometer and the long-wavelength infrared spectrometer are used for measuring different temperatures T in the step 1 B Collecting spectral information of the black body to be measured, and recording the spectral information as spectral reflectivityMeasured value R of M (λ i ) And the reconstructed spectral function is denoted as R (lambda) i ) Establishing an error fitting optimization coefficient, namely:
wherein all of the terms introduced into the above formula are wavelengths λ i And (i-a-d), wherein a and d are respectively an upper limit and a lower limit of a reconstruction waveband, and the accuracy of the infrared spectrum reflectivity reconstruction algorithm can be estimated according to the optimization coefficients, namely:
when epsilon is 1, the ideal state is the complete reconstruction state;
when 1 is larger than epsilon and is more than or equal to 0.9999, the reconstruction precision is higher;
when the epsilon is more than 0.9999 and is more than or equal to 0.99, the reconstruction precision is good;
when the epsilon is more than 0.99 and more than or equal to 0.9, the reconstruction precision is general.
The invention provides a spectral reflectivity reconstruction technology applicable to infrared images, aims to research the current image processing field and the spectral reflectivity registration problem, effectively solves the problems of texture loss, noise interference on imaging quality and the like of common images under the weak light condition, can obtain the spectral reflectivity distribution of a target in a multispectral section (visible and infrared) by combining RGB images collected by a visible light imaging detector, and is further applied to the fields of three-dimensional rendering, imaging and the like.
The spectral reflectance reconstruction research technology based on the RGB camera is popular at home and abroad in recent years, but the research on the infrared spectrum reflectance reconstruction based on the thermal infrared imager is rare, the infrared full-band spectral reflectance can be restored by adopting three frames of real-time images with different infrared bands by utilizing a pseudo-inverse matrix method, the cost consumed in the infrared spectrum reflectance measurement process is saved, and the application range of the spectral reflectance reconstruction technology in the infrared bands is improved.
The method has the advantages that the problems of noise interference, insufficient light at night or color information loss under the weak light condition easily occur to the common camera, clear texture details can be generated by the thermal image acquired by the infrared camera under the same environmental condition, and the defects of the RGB image are effectively overcome.
The reconstruction of the spectral reflectivity of the infrared image is an infrared spectral reflectivity reconstruction technology with low cost and convenient operation.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic data point described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples described in this specification can be combined and combined by those skilled in the art.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (9)
1. A spectral reflectivity reconstruction method based on infrared images is characterized by comprising the following steps:
according to the temperature T of black bodies to be measured of three different wave bands B And obtaining response function matrixes by the gray response values of the thermal images of the three different wave bands, wherein the three different wave bands comprise a near infrared wave band, a middle infrared wave band and a far infrared wave band, and the response function matrixes are matrixes of integral spectral response functions comprising a target intrinsic radiation brightness distribution function and an infrared imager spectral sensitivity function;
based on a reflectivity reconstruction function, obtaining spectral reflectivities of three different wave bands according to the matrix of the gray response values and the response matrix;
and obtaining the full-waveband spectral reflectivity according to the spectral reflectivities of the three different wavebands.
2. The method for reconstructing spectral reflectance based on infrared image according to claim 1, wherein the temperature T of the black body to be measured is determined according to three different wave bands B And three different bands of thermal image gray scale response values to obtain a response function matrix, comprising:
at a temperature T B Under the condition, obtaining the gray response value of the black body to be detected by utilizing the thermal images of three different wave bands;
temperature T based on black body to be detected B Obtaining the radiation exitance according to a blackbody Planck calculation formula;
and obtaining the response function matrixes of three different wave bands according to the gray response value and the radiation emittance.
3. Method for spectral reflectance reconstruction based on infrared images, according to claim 2, characterized in that at temperature T B Under the condition, the gray scale response value of the black body to be detected is obtained by utilizing the thermal images of three different wave bands, and the method comprises the following steps:
at a temperature T B And under the condition, obtaining the gray response value of each wave band according to the gray average value of each pixel point of the N black body thermal images.
4. The infrared image-based spectral reflectance reconstruction method according to claim 2, wherein the blackbody planckian calculation formula is:
wherein M is bb Indicating the degree of radiation exitance, T B Indicating the temperature of the black body to be measured, c 1 ＝3.7418×10 8 W·m -2 ·μm 4 ，c 2 ＝1.4388×10 4 ·μm·K，λ 1 And lambda 2 Respectively representing the lower limit and the upper limit of the working waveband range of the thermal imager.
5. The method of claim 2, wherein obtaining the response function matrix of three different bands according to the gray response value and the radiant exitance comprises:
converting the radiant exitance into radiant brightness;
and obtaining the response function matrixes of three different wave bands according to the gray response value and the radiation brightness.
6. The method of claim 1, wherein the reflectance reconstruction function is:
wherein R is NIR 、R MIR And R FIR Respectively representing the spectral reflectivities of a near infrared band, a middle infrared band and a far infrared band, Q NIR 、Q MIR And Q FIR Respectively representing the response functions, Q, of the near-infrared, mid-infrared and far-infrared bands + Pseudo-inverse of the Q matrix, I [a,b] 、I [b,c] And I [c,d] Respectively represent near infrared band and middleGray scale response values of infrared band and far infrared band, [ a, b ]]Indicates the band range of the near infrared band, [ b, c]Indicates the band range of the mid-infrared band, [ c, d]Indicating the band range of the far infrared band.
7. The method of claim 1, further comprising, after obtaining full-band spectral reflectances from the spectral reflectances of three different bands:
and under different temperature conditions, establishing a three-dimensional database of the full-waveband spectral reflectivity and the temperature.
8. The method of claim 1, further comprising, after obtaining full-band spectral reflectances from the spectral reflectances of three different bands:
and evaluating the precision of the full-waveband spectral reflectivity according to the error fitting optimization coefficient.
9. The method of claim 8, wherein the error fitting optimization coefficient is formulated as:
where ε represents the error fit optimization coefficient, R M (λ i ) A measurement value, R (lambda), representing the spectral reflectance i ) The spectral reflectivity of the full-wave band is shown, and a and d respectively represent the upper limit and the lower limit of the reconstruction wave band.
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