A spectral line correction method and device for a multi-slit hyperspectral imager

Abstract

The present application relates to hyperspectral imager technical field, specifically provide a kind of for the spectral line correction method, equipment of multislit hyperspectral imager, the spectral line correction method includes the following steps: S10, the coupling model between the off-axis slit parameter of the multislit array and the structure parameter of the spectrometer element is established;S20, based on the coupling model, construct with multislit spectral line bending summation minimization and dispersion linearity minimization as target double-target optimization function;S30, using multi-objective genetic algorithm, with the off-axis slit parameter and the structure parameter of spectrometer element as synchronous optimization variable, the initial structure is obtained by solving the double-target optimization function.The present application can effectively correct the spectral line bending of each slit of multislit hyperspectral imager, while taking into account the uniformity of all slit dispersion.

Description

Technical Field

[0001] This invention belongs to the field of hyperspectral imaging technology, and particularly relates to a spectral line correction method, device and computer-readable storage medium for a multi-slit hyperspectral imager. Background Technology

[0002] Hyperspectral imagers are powerful tools in remote sensing, atmospheric observation, and greenhouse gas monitoring, capable of accurately capturing target images and spectra, i.e., obtaining two-dimensional spatial information and one-dimensional spectral information of matter simultaneously. Among them, dispersive spectrometers have long attracted international attention due to their simple structure and wide range of applications. These instruments typically consist of a slit, a collimating lens, one or more spectroscopic elements, a focusing lens, and a detector.

[0003] Prisms and gratings, as core components in spectroscopic elements, both produce spectral line bending, which significantly affects image quality. In multi-slit hyperspectral imagers, the multi-order diffraction effect of the grating leads to the overlap of the dispersive spectra across the slits. Therefore, multi-slit spectrometers often employ a dual-material prism assembly as the spectroscopic element.

[0004] In a multi-slit hyperspectral imager, each slit will cause spectral line bending, which will not only cause the same row of detector pixels to receive spectral energy from multiple channels within the same slit field of view, but also cause spectral crosstalk between the multiple slit fields of view, increasing the difficulty of spectral information extraction and calibration.

[0005] Current research on spectral line bending correction mainly focuses on the correction of single-slit spectral line bending, generally utilizing the property that prisms and gratings produce spectral line bending in opposite directions for compensation. Research on spectral line bending correction for multi-slit dual-material prism assemblies is relatively scarce. Summary of the Invention

[0006] In view of this, the present invention aims to provide a spectral line correction method, apparatus and computer-readable storage medium for a multi-slit hyperspectral imager, so as to effectively correct the spectral line curvature of each slit of the multi-slit hyperspectral imager while taking into account the uniformity of the dispersion of all slits.

[0007] To achieve the above objectives, the technical solution created by this invention is implemented as follows:

[0008] In a first aspect, the present invention provides a spectral line correction method for a multi-slit hyperspectral imager, the multi-slit hyperspectral imager comprising a multi-slit array, a collimation system, a beam splitter, a focusing lens, and a photodetector sequentially distributed along the Z-axis; the spectral line correction method includes the following steps:

[0009] S10. Establish a coupling model between the off-axis slit parameters of the multi-slit array and the structural parameters of the beam splitter;

[0010] S20. Based on the coupling model, construct a bi-objective optimization function with the objectives of minimizing the sum of the bending of the multi-slit spectral lines and minimizing the dispersion linearity;

[0011] S30. Using a multi-objective genetic algorithm, with the off-axis slit parameters and the structural parameters of the beam splitter as synchronous optimization variables, the initial structure is obtained by solving the bi-objective optimization function.

[0012] Furthermore, step S30 is followed by the following step:

[0013] S40. The structural parameters of the spectrophotometer are used as variables to optimize the initial structure in the optical design software ZEMAX. The distortion of the focusing imaging system at the front end of the spectrometer is used to compensate for the spectral line bending and color distortion caused by the spectrophotometer to obtain the optimal structure.

[0014] Furthermore, the coupling model outputs the spectral line bending length generated by the off-axis slit after dispersion of a single wavelength by the spectrometer.

[0015] Furthermore, the beam-splitting element is a dual-material prism assembly.

[0016] Furthermore, the bi-objective optimization function includes a first objective function that sums the absolute values ​​of the spectral line bending lengths over all wavelengths and slits; the spectral line bending of each slit at all wavelengths is optimized by selecting the optimal combination of the prism apex angle and off-axis spacing of the bimaterial prism group.

[0017] Furthermore, the bi-objective optimization function includes a second objective function that uses the angular dispersion rate to express spectral dispersion, which is obtained by differentiating the prism principal section deflection angle of the bimaterial prism group.

[0018] Furthermore, step S30 includes the following steps:

[0019] S31. Define the constraints on the decision variables and determine the constant parameters;

[0020] S32. Randomly generate an initial population within the constraints, where each individual in the population represents a potential solution that simultaneously satisfies the biobjective function;

[0021] S33. To determine the degree to which the values ​​of the two objective functions simultaneously approach 0, sort all individuals in the population using a non-dominated hierarchical ranking.

[0022] S34. For individuals in the same level of non-dominated layer, calculate their crowding distance;

[0023] S35. Based on the non-dominated sorting results and crowding distance, adopt a binary tournament selection strategy;

[0024] S36. Generate a new offspring population through crossover and mutation operations; merge the parent and offspring generations and perform non-dominated sorting and crowding calculations again, select individuals to become the new generation population, and iterate until the termination condition is met.

[0025] Furthermore, the decision variables are the off-axis distances of the multiple slits and the tilt angles of each facet of the main section of the prism group, which are included in the bi-objective optimization function.

[0026] In a second aspect, the present invention provides a computer device comprising:

[0027] At least one processor; and

[0028] A memory communicatively connected to the at least one processor; wherein,

[0029] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the spectral correction method for a multi-slit hyperspectral imager as described above.

[0030] Thirdly, the present invention provides a non-transient computer-readable storage medium storing computer instructions for causing the computer to execute the spectral correction method for a multi-slit hyperspectral imager as described in any of the preceding claims.

[0031] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0032] (1) By adding the off-axis distance of the multi-slit as the variable to be optimized and co-optimizing the total bending of the multi-slit spectral lines with the structural parameters of the prism group, the spectral dispersion uniformity of each slit is satisfied at the same time.

[0033] (2) The distortion of the converging imaging system at the front end of the spectrometer is used to compensate for the bending of the remaining spectral lines and the chromatic distortion of the dispersive element, thereby further effectively correcting the bending of the spectral lines of the multi-slit hyperspectral imager.

[0034] (3) The method for correcting the spectral line curvature of a multi-slit hyperspectral imager provided by the present invention is simple to operate, effective, and has a certain degree of universality. Attached Figure Description

[0035] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0036] Figure 1 This is a schematic diagram of the structure of a multi-slit hyperspectral imager.

[0037] Figure 2 A schematic diagram of the first process of the spectral line correction method for a multi-slit hyperspectral imager as described in an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of the light rays from the multi-slit array to the dual-material prism group as described in the embodiment of the present invention;

[0039] Figure 4 A schematic diagram of the second process of the spectral line correction method for a multi-slit hyperspectral imager as described in an embodiment of the present invention;

[0040] Figure 5 A schematic flowchart of step S30 in the spectral line correction method for a multi-slit hyperspectral imager described in the embodiments of the present invention;

[0041] Figure 6 A schematic diagram of the structure of the multi-slit hyperspectral imager system described in the embodiment of the present invention;

[0042] Figure 7 The spectral curvature diagrams of the 0th order slit and ±1st order slits in a multi-slit array are shown.

[0043] Figure 8 The full-band spectral curvature plot of the edge of each slit in a multi-slit array, including the 0th-order slit and the ±1st-order slits;

[0044] Figure 9 The spectral distribution of the spectrometer exhibits linear characteristics after employing the spectral line correction method for a multi-slit hyperspectral imager described in the embodiments of the present invention.

[0045] Figure 10 A schematic diagram of the structure of the computer device described in the embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and do not constitute a limitation thereof. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0047] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined to form various implementations. Furthermore, the order of the steps or actions in the method description can be changed or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various orders in the specification and drawings are merely for the clear description of a particular embodiment and do not imply a mandatory order, unless otherwise stated that a particular order must be followed.

[0048] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. The term "based on" should be understood as "at least partially based on." Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more, and the term "including" means "including but not limited to." Various embodiments of the present invention may exist in the form of a range; it should be understood that the description in the form of a range is merely for convenience and brevity and should not be construed as a rigid limitation on the scope of the invention; therefore, it should be considered that the range description has specifically disclosed all possible sub-ranges and single numerical values ​​within that range; for example, it should be considered that the range description from 1 to 6 has specifically disclosed sub-ranges, such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and single numbers within the range, such as 1, 2, 3, 4, 5, and 6, regardless of the range. Furthermore, whenever a numerical range is referred to herein, it means including any referenced number (fraction or integer) within the range referred to.

[0049] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0050] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] Example 1

[0052] The spectral line correction method provided by this invention is used in multi-slit hyperspectral imagers, such as... Figure 1As shown, the multi-slit hyperspectral imager includes a multi-slit array 11 consisting of ±1st and 0th order slits arranged sequentially along the Z-axis, a collimation system 12, a beam splitter 13, a focusing lens 14, and a photodetector 15. Polychromatic light from the multi-slit array 11 is collimated into parallel light at multiple angles by the collimation system 12 and then incident on the beam splitter 13. The beam splitter 13 splits the incident parallel light at different angles into multiple monochromatic beams arranged in wavelength order within the polychromatic light at each angle. The monochromatic beams of different wavelengths converge on the photodetector 15 after passing through the focusing lens 14. On the image plane of the photodetector 15, each slit presents a slit image of a different wavelength, thus obtaining hyperspectral information from all observation angles.

[0053] Please refer to the following at the same time Figure 2 The spectral line correction method for a multi-slit hyperspectral imager provided by this invention specifically includes the following steps:

[0054] S10. Establish a coupling model between the off-axis slit parameters of the multi-slit array and the structural parameters of the beam splitter;

[0055] The coupling model outputs the bending length of the spectral line generated by the off-axis slit after dispersion of a single wavelength by the beam splitter. In this embodiment, the beam splitter is a dual-material prism assembly.

[0056] The following is a reference. Figure 3 The schematic diagram of the polychromatic light rays from the multi-slit array to the dual-material prism group further illustrates step S10 above. Wherein, O is the starting point of the principal ray from the center of the on-axis (0th order) slit; O1 is the starting point of the principal ray from the edge of the on-axis (0th order) slit; O2 is the starting point of the principal ray from the center of the off-axis (-1st order) slit; O3 is the starting point of the principal ray from the edge of the off-axis slit; M is the intersection point of the principal rays from the centers of the 0th and -1st order slits with the principal dispersion section of the prism; N is the intersection point of the principal rays from the edges of the on-axis and off-axis slits with the secondary dispersion section of the prism; H is the intersection point of the principal rays from the center and edge of the on-axis slit; K is the intersection point of the principal rays from the center and edge of the off-axis slit; G is the intersection point of the principal dispersion section and the secondary dispersion section of the prism. PM is the normal to the principal section of the prism. QN is the normal to the secondary section of the prism. L represents the lens.

[0057] θ 1 represents the angle between the center of the on-axis slit and the principal ray at the edge of the slit; θ ω2 is the angle between the principal rays from the center and edge of the off-axis slit; ω1 is the angle between the principal rays from the center of the off-axis slit and the principal rays from the center of the on-axis slit; ω2 is the angle between the principal rays from the edge of the off-axis slit and the principal rays from the edge of the on-axis slit. i 1 is the angle of incidence of the central ray OM from the on-axis slit into the first face of the prism assembly; j The central ray emitted from the edge of the slit on axis 1 The angle of incidence of the light entering the first facet of the prism assembly.

[0058] m1, m2, m3 are the tilt angles of each face of the main section of the prism group; η1, η2, η3 are the tilt angles of each face of the secondary section of the prism group. Let be the refractive index of material 1; Let be the refractive index of material 2.

[0059] More specifically, Figure 3 The image shown is a slit on the axis off-axis slit The emitted light, after being collimated by lens L, is incident at the apex angle. , (Not labeled in the image) The prism assembly. From the center point of the slit on the axis The principal ray of the emitted parallel beam intersects the prism at a point. The normal is The angle of incidence is The incident surface in the prism has a vertex angle of... , plane (Referred to as the principal section). For the slit on the axis edge The principal ray of a parallel beam of light emitted from a point intersects the prism at that point. The normal is The angle of incidence is The incident surface in the prism has a vertex angle of... , (Unlabeled in the diagram) Plane (Referred to as a secondary section). and The included angle is Similarly, off-axis slits Principal ray at the center of the slit Intersects the prism at a point The angle of incidence is In the main section Transmission. Main ray at the slit edge. Intersects the prism at a point The angle of incidence is In the secondary section Transmission. That is: light. With light Using the angle difference as Incident light enters the principal section; light rays With light Using the angle difference as The incident light enters the subsection. and The included angle is .

[0060] Because the direction of the slit is parallel to the edge of the prism, the plane containing the optical axis of the slit and the collimating objective lens is also parallel to the edge of the prism, and this plane is perpendicular to the principal section of the prism. Therefore, and Both are located within the plane containing the optical axis of the slit and the collimating objective lens. and connection Parallel to the edge of the prism, and and main cross section Vertical. Simultaneously passing through... Dot and Draw a perpendicular line from point A to the plane of incidence, with the intersection point being... ,straight line Main cross section and sub-section The intersection line, the angle between the principal section and the secondary section is... .therefore, , , ;so , , , They are all right triangles. Because... ,so , , , .

[0061] Principal section apex angle , Inclination angles with each plane of the main section , , Relationship:

[0062] ;

[0063] ;

[0064] sub-section apex angle , Inclination angles of the sub-sections , , Relationship:

[0065] ;

[0066] .

[0067] angle This represents the angle between the principal dispersive section and the secondary dispersive section of the prism. The principal dispersive section ABC of the prism is where light rays emitted from off-axis and on-axis slit centers are dispersed; the secondary dispersive section DEF of the prism is where light rays emitted from off-axis and on-axis slit edges are dispersed. Therefore, point ABC is the intersection of the principal dispersive section and the prism; point DEF is the intersection of the secondary dispersive section and the prism.

[0068] .

[0069] The principal ray representing the center of the off-axis slit The principal ray from the center of the slit on the axis Angular relationship between them:

[0070] (1)

[0071] in, For the focal length of the collimation system, The off-axis distance of the multi-slit structure.

[0072] The angle between the center and edge principal rays of the off-axis slit Represented as:

[0073] (2)

[0074] in, It is the height of the slit edge point relative to the center point on the slit axis.

[0075] Off-axis slit edge principal ray principal ray at the edge of the slit on the axis Angle between Represented as:

[0076] (3)

[0077] The relationship between the incident angles of the off-axis slit center and the edge is as follows:

[0078] (4)

[0079] The inclination angles of each facet of the principal section of the prism assembly are as follows: , , Inclination angles of each surface of the sub-section , , The relationship is:

[0080] (5)

[0081] in, This indicates the serial number of the tilted surface of the prism assembly.

[0082] Angle of refraction of principal section and the angle of refraction of the sub-section They are represented as follows:

[0083] (6)

[0084] (7)

[0085] in, Let be the refractive index of material 1; Let be the refractive index of material 2.

[0086] focal length is For a converging lens group, the spectral line bending length of the off-axis slit bimaterial prism group at a single wavelength can be expressed as:

[0087] (8)

[0088] S20. Based on the coupling model, construct a bi-objective optimization function with the objectives of minimizing the sum of the bending of the multi-slit spectral lines and minimizing the dispersion linearity;

[0089] The dual-objective optimization function includes a first objective function that aims to minimize the sum of the bending of the multi-slit spectral lines and a second objective function that aims to minimize the dispersion linearity.

[0090] In the first objective function, to ensure effective correction of spectral line bending across the entire wavelength range, the absolute values ​​of spectral line bending over all wavelengths and slits are summed:

[0091] (9)

[0092] Subscript Indicates wavelength; subscript This represents the slit. The absolute value summation method is used because the positive and negative values ​​of spectral line bending have the same effect on pixels. As the primary objective function, the optimal combination of prism apex angle and off-axis spacing is selected to optimize the spectral line curvature of the ±1st order slit and the 0th order slit at all wavelengths.

[0093] In the second objective function, the spectral dispersion is expressed using the angular dispersion rate. More specifically, the prism principal section deflection angle is:

[0094] (10)

[0095] For the deflection angle Differential to obtain character dispersion rate Its constant value indicates that the spectral dispersion is uniform and linear; assuming and These are the upper and lower limits of the operating frequency band. The center wavelength, Let the dispersive linearity equation be used as the second objective function, and its expression is:

[0096] (11)

[0097] S30. Using a multi-objective genetic algorithm, with the off-axis slit parameters and the structural parameters of the beam splitter as synchronous optimization variables, the initial structure is obtained by solving the bi-objective optimization function.

[0098] In this invention, two objective functions need to be optimized simultaneously, and their implicit expressions are:

[0099] (12)

[0100] Both objective functions contain four decision variables. ;

[0101] Calculated using a multi-objective genetic algorithm This is the initial structure.

[0102] Example 2

[0103] like Figure 4 As shown, this invention also provides a spectral line correction method for a multi-slit hyperspectral imager, comprising the following steps:

[0104] S10. Establish a coupling model between the off-axis slit parameters of the multi-slit array and the structural parameters of the beam splitter;

[0105] S20. Based on the coupling model, construct a bi-objective optimization function with the objectives of minimizing the sum of the bending of the multi-slit spectral lines and minimizing the dispersion linearity;

[0106] S30. Using a multi-objective genetic algorithm, with the off-axis slit parameters and the structural parameters of the beam splitter as synchronous optimization variables, the initial structure is obtained by solving the bi-objective optimization function.

[0107] S40. The structural parameters of the spectrophotometer are used as variables to optimize the initial structure in the optical design software ZEMAX. The distortion of the focusing imaging system at the front end of the spectrometer is used to compensate for the spectral line bending and color distortion caused by the spectrophotometer to obtain the optimal structure.

[0108] Steps S10 to S30 are the same as those in Embodiment 1 above, and will not be repeated here.

[0109] In step S40, the calculated initial structure is used and output to the Zemax software. In the software, image quality is used as the evaluation metric to further optimize the lens curvature radius and air gap of the alignment and converging systems. Simultaneously, spectral line curvature and dispersion linearity are used as evaluation metrics, and parameters related to the prism assembly structure are further optimized in the optical design software. The converging imaging system and image plane are tilted and eccentrically adjusted to change their coaxiality. The distortion of the converging imaging system is used to further compensate for the residual spectral line curvature and chromatic aberration generated by the dispersive elements. Finally, the optimal structure for the multi-slit hyperspectral imager is obtained, satisfying the requirements of small spectral line curvature correction for each slit, uniform spectral dispersion, and good image quality.

[0110] Example 3

[0111] In the spectral line correction method for a multi-slit hyperspectral imager provided in Embodiment 1 or 2 above, step S30 includes the following steps:

[0112] S31. Define the constraints on the decision variables and determine the constant parameters;

[0113] The decision variables are the off-axis distances of the multiple slits and the tilt angles of each facet of the prism assembly's main section, which are included in the bi-objective optimization function. The constant parameters include... .

[0114] S32. Randomly generate an initial population within the constraints, where each individual in the population represents a potential solution that simultaneously satisfies the biobjective function;

[0115] Randomly generated initial population Its population size is The potential solution represented by each individual in the population is the system structure parameter. Initial population size With evolutionary algebra The appropriate approach depends on the complexity of the problem. For each individual in the initialized population, the values ​​of two objective functions are calculated simultaneously.

[0116] S33. To determine the degree to which the values ​​of the two objective functions simultaneously approach 0, sort all individuals in the population using a non-dominated hierarchical ranking.

[0117] The set of all non-dominated solutions in the objective function space is called the Pareto front. A non-dominated solution is one in which no other solution in the solution set is superior to the previous one on all objective functions.

[0118] S34. For individuals in the same level of non-dominated layer, calculate their crowding distance;

[0119] Different individuals have different crowding distances; when individuals are of the same level, those with larger crowding distances are given priority to enter the new population. (Crowding distance) Calculation formula:

[0120] (13)

[0121] This is the individual's serial number within the population. These refer to the first and last individuals in the population.

[0122] S35. Based on the non-dominated sorting results and crowding distance, adopt a binary tournament selection strategy;

[0123] Between two individuals, the individual with the lower non-dominated level is preferred; if the two individuals are in the same non-dominated level, the individual with the greater crowding distance is selected.

[0124] S36. Generate a new offspring population through crossover and mutation operations; merge the parent and offspring generations and perform non-dominated sorting and crowding calculations again, select individuals to become the new generation population, and iterate until the termination condition is met.

[0125] Termination conditions are typically reaching the maximum number of iterations or the objective function value converging to a certain threshold. If the termination condition is met, the Pareto front solution set in the current population is output; the selection is based on satisfying the constraints of the decision variables, i.e., satisfying... And also satisfy the off-axis distance Combinations of multiple decision variables that will not introduce significant redundancy to the detector Otherwise, repeat the above steps.

[0126] In specific implementation, step S36 above can be adopted as follows: Figure 5 The flowchart shown:

[0127] S51, Begin;

[0128] S52. Has a first-generation subgroup been generated? If yes, proceed to step S53; if no, proceed to step S521.

[0129] S521. Evaluate the values ​​of the first objective function and the second objective function for each individual in the population;

[0130] S522, Perform non-assigned sorting and crowding calculation;

[0131] S523. After performing selection, crossover, and mutation operations, return to step S52.

[0132] S53, Generation number GEN=2;

[0133] S54. Merging of parent and child individuals;

[0134] S55. Does a new parent population need to be generated? If yes, proceed to step S56; if no, proceed to step S551.

[0135] S551. Evaluate the values ​​of the first objective function and the second objective function for each individual in the population;

[0136] S552. Perform non-assigned sorting and crowding calculation;

[0137] S553. After selecting suitable individuals to form a new parent population, return to step S55.

[0138] S56. Perform selection, crossover, and mutation operations;

[0139] S57. Determine if GEN is equal to the maximum algebra; if yes, proceed to step S58; if no, set GEN = GEN + 1 and return to step S54.

[0140] S58, End.

[0141] Example 4

[0142] To further verify the correction effect of the spectral line correction method for multi-slit hyperspectral imagers, it was applied to applications such as... Figure 6 The multi-slit hyperspectral imager system shown includes a multi-slit array 81, a collimation system 82, a spectrophotometer 83, a focusing mirror 84, and a photodetector 85, all arranged sequentially along the Z-axis. The operating spectral band of this multi-slit hyperspectral imager system is 410–900 nm, therefore it can be determined that… =900nm, =410nm, =650nm.

[0143] The multi-slit array consists of ±1-order off-axis slits and 0-order on-axis slits; each slit is 60mm long and fixed. =30mm; Simultaneously, fixed parameters =167mm, =231mm, .by Minimize the two objective functions; use a multi-objective genetic algorithm to solve for the initial structural parameters of different material combinations. Based on experience, the main parameters of the algorithm are set as follows: initial population. =100, maximum number of iterations =10000, crossover probability =0.7, mutation probability =0.4.

[0144] In this embodiment, the dual-material prism assembly uses prism material one as H-LAK51A and material two as ZF6. The calculated lns off-axis distance is 3.91 mm. The tilt angles of the first facet of the prism assembly are 22.08°, the second facet is -24.77°, and the third facet is -8.7°. Parameters related to the prism assembly structure are further optimized using the optical design software ZEMAX. Simultaneously, the converging imaging system and the image plane are tilted and eccentrically adjusted to alter their coaxiality. The distortion of the converging imaging system is then used to further compensate for the spectral line bending and chromatic aberration caused by the dispersive element.

[0145] Figure 7 The figure illustrates the spectral line curvature of the 0th-order slit and ±1st-order slits. The full-field spectral line curvature shown in the figure is less than 6. Less than 0.4 pixels. Figure 8 The system's full-band spectral curvature is shown at the edges of each slit, including the 0th-order and ±1st-order slits; all of these spectral curvatures are less than 20. Less than 1 pixel. Figure 9 This demonstrates that the spectral distribution of the spectrometer exhibits a linear characteristic.

[0146] As can be seen, the method for correcting spectral line curvature in multi-slit hyperspectral imagers provided by this invention is simple to operate, effective, and has a certain degree of universality. When designing such a multi-slit hyperspectral imager, only the algorithm input parameters need to be modified according to the required specifications to quickly obtain the combination of structural parameters with the best imaging quality.

[0147] Example 5

[0148] Accordingly, according to embodiments of the present invention, the present invention also provides a computer device, a readable storage medium, and a computer program product.

[0149] Figure 10 This is a schematic diagram of the structure of a computer device 62 provided in an embodiment of the present invention. Figure 10 A block diagram of an exemplary computer device 62 suitable for implementing embodiments of the present invention is shown. Figure 10 The computer device 62 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0150] like Figure 10As shown, computer device 62 is represented in the form of a general-purpose computing device. Computer device 62 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0151] The components of computer device 62 may include, but are not limited to: one or more processors or processing units 66, system memory 78, and bus 68 connecting different system components (including system memory 78 and processing unit 66).

[0152] Bus 68 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0153] Computer device 62 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 62, including volatile and non-volatile media, removable and non-removable media.

[0154] System memory 78 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 80 and / or cache memory 82. Computer device 62 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 84 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 10 Not shown; usually referred to as a "hard drive"). Although Figure 10 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 68 via one or more data media interfaces. System memory 78 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0155] A program / utility 90 having a set (at least one) of program modules 92 may be stored, for example, in system memory 78. Such program modules 92 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 92 typically perform the functions and / or methods described in the embodiments of the present invention.

[0156] Computer device 62 can also communicate with one or more external devices 64 (e.g., keyboard, pointing device, display 74, etc.), and with one or more devices that enable a user to interact with computer device 62, and / or with any device that enables computer device 62 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 72. Furthermore, computer device 62 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 70. As shown, network adapter 70 communicates with other modules of computer device 62 via bus 68. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 62, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0157] The processing unit 66 executes various functional applications and data processing by running programs stored in the system memory 78, such as implementing the spectral line correction method for a multi-slit hyperspectral imager provided in the embodiments of the present invention.

[0158] This invention also provides a non-transitory computer-readable storage medium storing computer instructions, on which a computer program is stored, wherein when the program is executed by a processor, it implements the spectral line correction method for a multi-slit hyperspectral imager provided in all embodiments of this invention.

[0159] The computer storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0160] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0161] The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. The computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0162] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the spectral line correction method for a multi-slit hyperspectral imager described above.

[0163] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the spectral line correction method for a multi-slit hyperspectral imager described above.

[0164] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0165] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A spectral line correction method for a multi-slit hyperspectral imager, the multi-slit hyperspectral imager comprising a multi-slit array, a collimation system, a beam splitter, a focusing lens, and a photodetector sequentially distributed along the Z-axis; characterized in that, The spectral line correction method includes the following steps: S10. Establish a coupling model between the off-axis slit parameters of the multi-slit array and the structural parameters of the beam splitter; the off-axis slit parameters of the multi-slit array are the off-axis distances of the multi-slit array, and the structural parameters of the beam splitter are the tilt angles of each facet of the main cross-section of the prism group. S20. Based on the coupling model, construct a bi-objective optimization function with the objectives of minimizing the sum of the bending of the multi-slit spectral lines and minimizing the dispersion linearity; S30. Using a multi-objective genetic algorithm, with the off-axis slit parameters and the structural parameters of the beam splitter as synchronous optimization variables, the initial structure is obtained by solving the bi-objective optimization function.

2. The spectral line correction method for a multi-slit hyperspectral imager according to claim 1, characterized in that, The step S30 is followed by the following step: S40. The structural parameters of the spectrophotometer are used as variables to optimize the initial structure in the optical design software ZEMAX. The distortion of the focusing imaging system at the front end of the spectrometer is used to compensate for the spectral line bending and color distortion caused by the spectrophotometer to obtain the optimal structure.

3. The spectral line correction method for a multi-slit hyperspectral imager according to claim 1, characterized in that, The coupling model outputs the bending length of the spectral line generated by the off-axis slit after a single wavelength is dispersed by the spectrometer.

4. The spectral line correction method for a multi-slit hyperspectral imager according to claim 1, characterized in that, The beam splitter is a dual-material prism assembly.

5. The spectral line correction method for a multi-slit hyperspectral imager according to claim 4, characterized in that, The dual-objective optimization function includes a first objective function that sums the absolute values ​​of the spectral line bending lengths over all wavelengths and slits; the spectral line bending of each slit at all wavelengths is optimized by selecting the optimal combination of the prism apex angle and off-axis spacing of the dual-material prism set.

6. The spectral line correction method for a multi-slit hyperspectral imager according to claim 4, characterized in that, The bi-objective optimization function includes a second objective function that uses the angular dispersion rate to express spectral dispersion. The angular dispersion rate is obtained by differentiating the deflection angle of the principal section of the prism in the bimaterial prism assembly.

7. The spectral line correction method for a multi-slit hyperspectral imager according to claim 1, characterized in that, Step S30 includes the following steps: S31. Define the constraints on the decision variables and determine the constant parameters; S32. Randomly generate an initial population within the constraints, where each individual in the population represents a potential solution that simultaneously satisfies the bi-objective optimization function; S33. To determine the degree to which the values ​​of the two objective functions simultaneously approach 0, sort all individuals in the population using a non-dominated hierarchical ranking. S34. For individuals in the same level of non-dominated layer, calculate their crowding distance; S35. Based on the non-dominated sorting results and crowding distance, adopt a binary tournament selection strategy; S36. Generate a new offspring population through crossover and mutation operations; merge the parent and offspring generations and perform non-dominated sorting and crowding calculations again, select individuals to become the new generation population, and iterate until the termination condition is met.

8. The spectral line correction method for a multi-slit hyperspectral imager according to claim 7, characterized in that, The decision variables are the off-axis distances of the multiple slits and the tilt angles of each face of the main section of the prism group, which are included in the bi-objective optimization function.

9. A computer device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the spectral line correction method for a multi-slit hyperspectral imager as described in any one of claims 1 to 8.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the spectral line correction method for a multi-slit hyperspectral imager as described in any one of claims 1 to 8.

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