A multi-spectral channel gas classification and quantitative detection method and system

Abstract

The present invention belongs to the field of optical gas imaging detection, and discloses a multi-spectral channel gas classification and quantitative detection method and system thereof, including: dividing an incident light beam into a plurality of sub-beams and filtering them to obtain N-1 sub-beams of different narrow-band spectral bands and a broadband all-pass band sub-beam; converting the sub-beams of N bands into digital image signals of simultaneous imaging of multi-band channels; performing image registration and alignment of the digital image signals of simultaneous imaging of multi-band channels with images of different channels, and outputting a spectral image cube; performing classification detection on the spectral image cube to obtain pixel coordinate areas and type information of gas distribution; performing quantitative solution of concentration equations for the pixel coordinate areas and type information of gas distribution, and performing pseudo-color enhancement on the gas, and outputting the spatial distribution of gas concentration. The present invention can significantly improve the application level of optical gas detection technology, and provide more accurate and reliable solutions for fields such as environmental monitoring.

Description

Technical Field

[0001] The present invention relates to the field of optical gas imaging detection, and in particular to a multi-spectral channel gas classification and quantitative detection method and system thereof. Background Art

[0002] Optical gas imaging detection technology has been widely used in environmental monitoring, industrial process control, and medical health in recent years due to its non-invasive, high sensitivity, and the ability to image and respond in real time. Traditional optical gas detection methods are mainly based on the light absorption characteristics of gas molecules, and the presence and concentration of gases are inferred by measuring the absorption intensity at a single specific wavelength. However, these methods have exposed many problems in practical applications, especially when applied in chemical sites, it is difficult to accurately distinguish the type of leaked gas and quantify its concentration. When the leaked gas is of unknown type, traditional single-wavelength or narrow-band light absorption technology is powerless to deal with such complex situations. Since the absorption spectra of different gases may overlap at certain wavelengths, it is difficult to distinguish the various gas components with a single wavelength detection. In this case, the accumulation of errors leads to inaccurate detection results, and even missed detection or false alarms may occur. In addition to the identification of gas types, accurately measuring gas concentration is also a major problem for optical gas detection technology. At present, there is no optical gas imaging detection technology in the industry that can achieve concentration quantification. Summary of the invention

[0003] The object of the present invention is to provide a multi-spectral channel gas classification and quantitative detection method and system thereof to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A multi-spectral channel gas classification and quantitative detection method comprises the following steps:

[0006] Step 1, using a multi-spectral spectrometer module, splitting the incident light beam into multiple sub-beams and filtering them to obtain N-1 sub-beams with different narrow-band spectral bands and one broadband all-pass band sub-beam;

[0007] Step 2, converting the N-band sub-beams obtained in step 1 into digital image signals simultaneously imaged by multi-band channels through an infrared detector;

[0008] Step 3, through the image distortion correction and registration unit, the digital image signal of the multi-band channels simultaneously imaged by the infrared detector is subjected to distortion correction and image registration and alignment with the images of different channels, and the spectral image cube is output;

[0009] Step 4, through the gas classification unit, the registered spectral image cube output in step 3 is subjected to pixel-level gas classification detection to obtain the pixel coordinate area and type information of the gas distribution;

[0010] Step 5, using a gas quantitative unit, quantitatively solves the concentration equation for the pixel coordinate area and type information of the gas distribution obtained in step 4, performs pseudo-color enhancement on the gas based on the gas concentration, and outputs the spatial distribution of the gas concentration.

[0011] Furthermore, the step 1 specifically includes the following process:

[0012] Step 1.1, splitting an incident light beam into N sub-beams by an aperture array module, where the aperture array module is a lens array composed of N millimeter-level lenses;

[0013] Step 1.2, retaining one of the N sub-beams, and performing sub-beam filtering on the remaining N-1 sub-beams through a filter array module to obtain N-1 sub-beams with different narrowband spectral bands and one broadband all-pass band sub-beam, wherein the filter array module is a filter array composed of N-1 narrowband bandpass filters with different central wavelengths.

[0014] Furthermore, the step 3 specifically includes the following process:

[0015] Step 3.1, cutting the digital image signal of the multi-band channel simultaneous imaging into N sub-images, including N-1 different narrow-band spectral band sub-images and one broadband all-pass band sub-image;

[0016] Step 3.2, use the ORB algorithm to register the cut images;

[0017] Step 3.3, arrange the registered multi-channel images into a spectral image cube with N channels and output it.

[0018] Furthermore, the step 4 specifically includes the following process:

[0019] Step 4.1, import the normalized gas absorption spectrum database into the gas classification unit;

[0020] Step 4.2, the registered spectral image cube output from step 3 is calculated as follows:

[0021] The spectral image cube is denoted as I s , then I s (x,y,c) represents the point with x as the horizontal coordinate, y as the vertical coordinate, and c as the channel. Its value is the coordinate DN value. s (x,y) represents a s (x,y,1),Is (x,y,2)...I s (x, y, n) is a vector composed of n DN values. After normalizing the vector, the vector is classified using a neural network based on Bayesian optimization. The neural network based on Bayesian optimization outputs the gas type i to which each pixel finally belongs and the probability p of belonging to the i-th gas;

[0022] Step 4.3, output pixel coordinate area and type information marked as gas distribution.

[0023] Furthermore, the neural network has a five-layer network structure, including an input layer, three hidden layers, and an output layer, wherein the number of neurons in the input layer is the number of spectral channels, the number of neurons in the output layer is the number of gas types I to be classified, and the Bayesian optimizer optimizes the hyperparameters of the neural network based on the initial values, and the hyperparameters after optimization are as follows: the size of the first hidden layer is 71, and the size of the second hidden layer is 164; the regularization strength is 7.99425566291672e-06; the activation function is tanh, and the loss function is the cross entropy loss function.

[0024] Furthermore, the step 5 specifically includes the following process:

[0025] Step 5.1, extracting the DN value corresponding to each channel area point according to the pixel coordinate area of ​​the gas distribution output in step 4;

[0026] Step 5.2: Subtract the DN value of the gas pixel from the DN value of the surrounding background pixels to obtain △DN(λ), and select at least three narrow-band spectral wavelength channels L a , L b , L c , and the following gas concentration equations are obtained respectively. The inverted gas concentration is calculated by solving the following three equations:

[0027]

[0028] Among them, e is a natural constant, λ a , b , c They are narrowband spectral wavelength channels L a , L b , L c The wavelength, τ s (λ a ), τ s (λ b ), τ s (λ c ) are narrowband spectral wavelength channels L a , L b , L c The total response rate of the band, τ2(λa ),τ2(λ b ),τ2(λ c ) are wavelengths λ a , b , c The atmospheric transmittance can be obtained by querying the Modtran radiation transfer model, K(λ a )、K(λ b )、K(λ c ) are narrowband spectral wavelength channels L a , L b , L c The absorption rate of the gas to be tested can be obtained by querying the Hitran database, ε(λ a ), ε(λ b ), ε(λ c ) are narrowband spectral wavelength channels L a , L b , L c The background emissivity, α(λ a ), α(λ b ), α(λ c ) through wavelength λ a , b , c Planck formula is used to calculate, β(λ a ), β(λ b ), β(λ c ) through wavelength λ a , b , c Calculated by Planck's formula, the unknown quantities are the gas column density c·l and the background temperature T bg , gas temperature T gas , after substituting the specific values, the column concentration of gas, background temperature and gas temperature can be solved by solving the three-variable transcendental equations;

[0029] Step 5.3, according to the gas concentration, the gas area of ​​the broadband all-pass band sub-image is subjected to pseudo-color enhancement processing, and the spatial distribution of the gas concentration is output.

[0030] The present invention also provides a multi-spectral channel gas classification and quantitative detection system, which is used to implement the multi-spectral channel gas classification and quantitative detection method as described above, comprising:

[0031] A multi-spectral spectrometer module, which is used to subdivide an incident light beam into sub-beams of multiple wavelength bands;

[0032] An infrared detector, which is used to convert the sub-beams obtained by the multi-spectral spectrometer into digital image signals visible to the naked eye;

[0033] A gas classification and quantification module, the gas classification and quantification module includes an image distortion correction and registration unit, a gas classification unit and a gas quantification unit, the image distortion correction and registration unit is used to perform distortion correction on the digital image signal output by the infrared detector and perform image registration and alignment with images of different channels, and output a multi-channel spectral image cube; the gas classification unit is used to perform pixel-level gas classification detection on the registered spectral image cube output by the image distortion correction and registration unit, and obtain the pixel coordinate area and type information of the gas distribution; the gas quantification unit is used to perform quantitative concentration equation solution on the pixel coordinate area and type information of the gas distribution obtained from the output of the gas classification unit, and perform pseudo-color enhancement on the gas based on the gas concentration, and output the spatial distribution of the gas concentration.

[0034] Furthermore, the multi-spectral spectrometry module includes the following sub-modules:

[0035] An aperture array module, the aperture array module is used to divide the incident infrared light beam into N sub-beams, and the aperture array module is a lens array composed of N millimeter-level lenses;

[0036] A narrowband bandpass filter array module, which is used to filter N-1 sub-beams to obtain N-1 sub-beams with different narrowband spectral bands. The filter array module is a filter array composed of N-1 narrowband bandpass filters with different central wavelengths.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: compared with the traditional single-wavelength imaging detection, the multi-spectral channel detection has the advantages of being able to identify the type of gas and detect the gas concentration. The main innovation of the multi-spectral channel detection is that by analyzing the absorption characteristics at different wavelengths, the presence of different gases can be more accurately distinguished. Even in the case of overlapping absorption spectra, the comprehensive use of multi-wavelength information can significantly improve the accuracy of recognition. The redundant information of the multi-spectral channel makes the system more robust in the face of environmental noise and interference, and can effectively filter out the influence of non-target gases.

[0038] Through these innovations, the multi-spectral channel gas classification and quantitative detection method and system proposed in the present invention can significantly improve the application level of optical gas detection technology and provide more accurate and reliable solutions for environmental monitoring, industrial control, public safety and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The present invention is a flow chart of a multi-spectral channel gas classification and quantitative detection method.

[0040] Figure 2It is a schematic diagram of the working process of the gas classification and quantification module in the present invention.

[0041] Figure 3 The figure is a schematic diagram of the structure of a multi-spectral channel gas classification and quantitative detection system of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] See also Figure 1-Figure 2 , a multi-spectral channel gas classification and quantitative detection method, comprising the following steps:

[0044] Step 1: Split the incident light beam into multiple sub-beams and filter them through a multi-spectral spectrometer to obtain N-1 sub-beams with different narrow-band spectral bands and one broadband all-pass band sub-beam, specifically including:

[0045] Step 1.1, split the incident light beam into N sub-beams through an aperture array module, the aperture array module is a lens array composed of N millimeter-level lenses, and the N millimeter-level lenses correspond to the N sub-beams.

[0046] Step 1.2, retain one of the N sub-beams, and filter the remaining N-1 sub-beams through the filter array module to obtain N-1 sub-beams with different narrow-band spectral bands and one broadband all-pass band sub-beam. The filter array module is a filter array composed of N-1 narrow-band pass filters with different central wavelengths, and the remaining N-1 sub-beams are filtered one by one by N-1 narrow-band pass filters with different central wavelengths.

[0047] Step 2, converting the N-band sub-beams obtained in step 1 into digital image signals simultaneously imaged by multi-band channels through an infrared detector.

[0048] Step 3: Through the image distortion correction and registration unit, the digital image signal of the multi-band channels simultaneously imaged by the infrared detector is subjected to distortion correction and image registration and alignment with the images of different channels, and the spectral image cube is output, which specifically includes the following processes:

[0049] Step 3.1, cutting the digital image signal of the multi-band channel simultaneous imaging into N sub-images, including N-1 different narrow-band spectral band sub-images and one broadband all-pass band sub-image.

[0050] In step 3.2, the cut images are registered using the ORB (Oriented FAST and Rotated BRIEF) algorithm.

[0051] Step 3.3, arrange the registered multi-channel images into a spectral image cube with N channels and output it.

[0052] Step 4, through the gas classification unit, the registered spectral image cube output in step 3 is subjected to pixel-level gas classification detection to obtain the pixel coordinate area and type information of the gas distribution, which specifically includes the following processes:

[0053] Step 4.1, import the normalized gas absorption spectrum database into the gas classification unit.

[0054] Step 4.2, the registered spectral image cube output from step 3 is calculated as follows:

[0055] The spectral image cube is denoted as I s , then I s (x,y,c) represents the point with x as the horizontal coordinate, y as the vertical coordinate, and c as the channel. Its value is the coordinate DN value. s (x,y) represents a s (x,y,1),I s (x,y,2)...I s (x, y, n) is a vector composed of n DN values. After the vector is normalized, it is classified by using a neural network based on Bayesian optimization. The neural network based on Bayesian optimization outputs the gas type i to which each pixel finally belongs and the probability p of belonging to the i-th gas.

[0056] The neural network has a five-layer network structure, including an input layer, three hidden layers, and an output layer. The number of neurons in the input layer is the number of spectral channels, and the number of neurons in the output layer is the number of gas types I required to be classified. The Bayesian optimizer optimizes the hyperparameters of the neural network based on the initial values. The optimized hyperparameters are as follows: the size of the first hidden layer is 71, and the size of the second hidden layer is 164; the regularization strength (lambda) is 7.99425566291672e-06; the activation function is tanh, and the loss function is the cross entropy loss function.

[0057] Step 4.3, output the pixel coordinate area and type information marked as gas distribution, the output result is as follows Figure 2 shown.

[0058] Step 5, through the gas quantitative unit, the pixel coordinate area and type information of the gas distribution obtained in step 4 are quantitatively solved by the concentration equation, and the gas is pseudo-colored based on the gas concentration to output the spatial distribution of the gas concentration, which specifically includes the following processes:

[0059] Step 5.1, extracting the DN value corresponding to each channel area point according to the pixel coordinate area of ​​the gas distribution output in step 4.

[0060] Step 5.2: Subtract the DN value of the gas pixel from the DN value of the surrounding background pixels to obtain △DN(λ), and select at least three narrow-band spectral wavelength channels L a , L b , L c , and the following gas concentration equations are obtained respectively. The inverted gas concentration is calculated by solving the following three equations:

[0061]

[0062] Among them, e is a natural constant, its value is about 2.718281828459045, λ a , b , c They are narrowband spectral wavelength channels L a , L b , L c The wavelength, τ s (λ a ), τ s (λ b ), τ s (λ c ) are narrowband spectral wavelength channels L a , L b , L c The total response rate of the band, τ2(λ a ),τ2(λ b ),τ2(λ c ) are wavelengths λ a , b , c The atmospheric transmittance can be obtained by querying the Modtran radiation transfer model, K(λ a )、K(λ b )、K(λ c ) are the narrowband spectral wavelength channels L a , L b , L c The absorption rate of the gas to be tested can be obtained by querying the Hitran database, ε(λ a ), ε(λ b ), ε(λ c ) are narrowband spectral wavelength channels La , L b , L c The background emissivity, α(λ a ), α(λ b ), α(λ c ) through wavelength λ a , b , c Planck formula is used to calculate, β(λ a ), β(λ b ), β(λ c ) through wavelength λ a , b , c Calculated by Planck's formula, the unknown quantities are the gas column density c·l and the background temperature T bg , gas temperature T gas , after substituting the specific values, we can solve the gas column concentration, background temperature and gas temperature by solving the three-variable transcendental equation group.

[0063] Step 5.3, according to the gas concentration, the gas area of ​​the broadband all-pass band sub-image is subjected to pseudo-color enhancement processing, and the spatial distribution of the gas concentration is output.

[0064] See also Figure 3 A multi-spectral channel gas classification and quantitative detection system is used to implement the multi-spectral channel gas classification and quantitative detection method as described above, including a multi-spectral spectrometer module, an infrared detector, and a gas classification and quantitative module.

[0065] The multi-spectral spectrometer module is used to subdivide the incident light beam into sub-beams of multiple bands, and includes an aperture array module and a narrow-band pass filter array module. The aperture array module is used to divide the incident infrared light beam into N sub-beams, and the narrow-band pass filter array module is used to filter the N-1 sub-beams to obtain N-1 sub-beams of different narrow-band spectral bands and one broadband all-pass band sub-beam. The aperture array module is a lens array composed of N millimeter-level lenses. The filter array module is a filter array composed of N-1 narrow-band pass filters with different central wavelengths.

[0066] The infrared detector is used to convert the sub-beams obtained by the multi-spectral spectrometer module into digital image signals visible to the naked eye.

[0067] The gas classification and quantification module is a computer program stored and run in a host computer, which includes an image distortion correction and registration unit, a gas classification unit, and a gas quantification unit.

[0068] The image distortion correction and registration unit is used to perform distortion correction on the digital image signal output by the infrared detector and perform image registration and alignment with images of different channels, and output a spectral image cube. The image distortion correction and registration unit is a computer program, and its specific implementation can be found in step 3 above, which will not be repeated here.

[0069] The gas classification unit is used to perform pixel-level gas classification detection on the registered spectral image cube output by the image distortion correction and registration unit to obtain the pixel coordinate area and type information of the gas distribution. The gas classification unit is a computer program, and its specific implementation can be found in the above step 4, which will not be repeated here.

[0070] The gas quantification unit is used to quantitatively solve the concentration equation for the pixel coordinate area and type information of the gas distribution obtained from the output of the gas classification unit, and to perform pseudo-color enhancement on the gas based on the gas concentration, and output the spatial distribution of the gas concentration. The gas quantification unit is a computer program, and its specific implementation can be found in the above step 5, which will not be repeated here.

[0071] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-spectral channel gas classification and quantitative detection method, characterized in that: The following steps are involved: Step 1, using a multi-spectral spectrometer module, splitting the incident light beam into a plurality of sub-beams and filtering them to obtain N-1 sub-beams with different narrow-band spectral bands and one broadband all-pass band sub-beam, where N is a positive integer greater than 2; Step 2, converting the N-band sub-beams obtained in step 1 into digital image signals simultaneously imaged by multi-band channels through an infrared detector; Step 3, through the image distortion correction and registration unit, the digital image signal of the multi-band channels simultaneously imaged by the infrared detector is subjected to distortion correction and image registration and alignment with the images of different channels, and the spectral image cube is output; Step 4, through the gas classification unit, the registered spectral image cube output in step 3 is subjected to pixel-level gas classification detection to obtain the pixel coordinate area and type information of the gas distribution, which specifically includes the following processes: Step 4.1, import the normalized gas absorption spectrum database into the gas classification unit; Step 4.2, the registered spectral image cube output from step 3 is calculated as follows: The spectral image cube is denoted as I s， Then I s (x,y,c) represents the point with x as the horizontal coordinate, y as the vertical coordinate, and c as the channel. Its value is the coordinate DN value. s (x,y) represents a s (x,y,1),I s (x,y,2)...I s (x, y, n) is a vector composed of n DN values. After normalizing the vector, the vector is classified using a neural network based on Bayesian optimization. The neural network based on Bayesian optimization outputs the gas type i to which each pixel finally belongs and the probability p of belonging to the i-th gas; Step 4.3, output pixel coordinate area and type information marked as gas distribution; The neural network has a five-layer network structure, including an input layer, three hidden layers, and an output layer, wherein the number of neurons in the input layer is the number of spectral channels, the number of neurons in the output layer is the number of gas types I required to be classified, and the Bayesian optimizer optimizes the hyperparameters of the neural network based on the initial values. The optimized hyperparameters are as follows: the size of the first hidden layer is 71, the size of the second hidden layer is 164; the regularization strength is 7.99425566291672e-06; the activation function is tanh, and the loss function is the cross entropy loss function; Step 5, through the gas quantitative unit, the pixel coordinate area and type information of the gas distribution obtained in step 4 are quantitatively solved by the concentration equation, and the gas is pseudo-colored based on the gas concentration to output the spatial distribution of the gas concentration, which specifically includes the following processes: Step 5.1, extracting the DN value corresponding to each channel area point according to the pixel coordinate area of ​​the gas distribution output in step 4; Step 5.2: Subtract the DN value of the gas pixel from the DN value of the surrounding background pixels to obtain △DN(λ), and select at least three narrow-band spectral wavelength channels L a , L b , L c , and the following gas concentration equations are obtained respectively. The inverted gas concentration is calculated by solving the following three equations: ， Among them, e is a natural constant, λ a , b , c They are narrowband spectral wavelength channels L a , L b , L c The wavelength, τ s (λ a ), τ s (λ b ), τ s (λ c ) are narrowband spectral wavelength channels L a , L b , L c The total response rate of the band, τ2(λ a ),τ2(λ b ),τ2(λ c ) are wavelengths λ a , b , c The atmospheric transmittance can be obtained by querying the Modtran radiation transfer model, K(λ a )、K(λ b )、K(λ c ) are the narrowband spectral wavelength channels L a , L b , L c The absorption rate of the gas to be tested can be obtained by querying the Hitran database, ε(λ a ), ε(λ b ), ε(λ c ) are narrowband spectral wavelength channels L a , L b , L c The background emissivity, α(λ a ), α(λ b ), α(λ c ) through wavelength λ a , b , c Planck formula is used to calculate, β(λ a ), β(λ b ), β(λ c ) through wavelength λ a , b , c Calculated by Planck's formula, the unknown quantities are the gas column density c·l and the background temperature T bg , gas temperature T gas , after substituting the specific values, the column density of the gas, the background temperature and the gas temperature can be solved by solving the three-variable transcendental equations; Step 5.3, according to the gas concentration, the gas area of ​​the broadband all-pass band sub-image is subjected to pseudo-color enhancement processing, and the spatial distribution of the gas concentration is output.

2. A multi-spectral channel gas classification and quantitative detection method according to claim 1, characterized in that: The step 1 specifically includes the following process: Step 1.1, splitting an incident light beam into N sub-beams by an aperture array module, where the aperture array module is a lens array composed of N millimeter-level lenses; Step 1.2, retaining one of the N sub-beams, and performing sub-beam filtering on the remaining N-1 sub-beams through a filter array module to obtain N-1 sub-beams with different narrowband spectral bands and one broadband all-pass band sub-beam, wherein the filter array module is a filter array composed of N-1 narrowband bandpass filters with different central wavelengths.

3. A multi-spectral channel gas classification and quantitative detection method according to claim 1, characterized in that: The step 3 specifically includes the following process: Step 3.1, cutting the digital image signal of the multi-band channel simultaneous imaging into N sub-images, including N-1 different narrow-band spectral band sub-images and one broadband all-pass band sub-image; Step 3.2, register the cut images using the ORB algorithm; Step 3.3, arrange the registered multi-channel images into a spectral image cube with N channels and output it.

4. A multi-spectral channel gas classification and quantitative detection system, characterized in that: A method for implementing the multi-spectral channel gas classification and quantitative detection method as claimed in any one of claims 1 to 3, comprising: A multi-spectral spectrometer module, which is used to subdivide an incident light beam into sub-beams of multiple wavelength bands; An infrared detector, which is used to convert the sub-beams obtained by the multi-spectral spectrometer into digital image signals visible to the naked eye; A gas classification and quantification module, the gas classification and quantification module includes an image distortion correction and registration unit, a gas classification unit and a gas quantification unit, the image distortion correction and registration unit is used to perform distortion correction on the digital image signal output by the infrared detector and perform image registration and alignment with images of different channels, and output a multi-channel spectral image cube; the gas classification unit is used to perform pixel-level gas classification detection on the registered spectral image cube output by the image distortion correction and registration unit, and obtain the pixel coordinate area and type information of the gas distribution; the gas quantification unit is used to perform quantitative concentration equation solution on the pixel coordinate area and type information of the gas distribution obtained from the output of the gas classification unit, and perform pseudo-color enhancement on the gas based on the gas concentration, and output the spatial distribution of the gas concentration.

5. A multi-spectral channel gas classification and quantitative detection system according to claim 4, characterized in that: The multi-spectral spectrometer module includes the following sub-modules: An aperture array module, the aperture array module is used to divide the incident infrared light beam into N sub-beams, and the aperture array module is a lens array composed of N millimeter-level lenses; A narrowband bandpass filter array module, which is used to filter N-1 sub-beams to obtain N-1 sub-beams with different narrowband spectral bands. The filter array module is a filter array composed of N-1 narrowband bandpass filters with different central wavelengths.

Images