A method and system for spectral standardization under multi-dimensional environmental influences

Abstract

The application discloses a kind of methods and systems for spectral standardization under the influence of multi-dimensional environment, it is related to spectral data standardization field.The method comprises first to the target object is carried out single point spectral collection, for acquisition position, the apparent radiance spectral data of target object and the temperature data of target object surface are acquired, the geometric parameter when target object is carried out spectral collection is acquired;Target object is replaced by standard reference plate, for the center position of standard reference plate, the apparent radiance spectral data of standard reference plate is acquired;Then using the thermal radiation-reflectivity decoupling method based on iterative calculation, obtain the reflectivity spectral data of the surface of target object after preliminary correction;Finally, BRDF model is acquired and parameter is determined, using the model and reflectivity spectral data obtain geometric standardization reflectivity spectral data, complete spectral standardization.The application can cope with various temperature, illumination, zenith angle and so on imaging condition.

Description

Technical Field

[0001] This invention belongs to the field of spectral analysis technology, specifically relating to a spectral standardization method and system for multidimensional environmental influences. Background Technology

[0002] Spectroscopic measurement is a key non-destructive testing technology in fields such as remote sensing, agriculture, and environmental monitoring. Its application effectiveness highly depends on the accuracy and comparability of the data. However, in field (in-situ) measurements outside the laboratory, spectral data is simultaneously affected by multi-dimensional environmental factors such as light source conditions, observation geometry (angle), and target temperature. This results in a lack of consistency in spectral curves obtained from the same target, severely limiting its quantitative application.

[0003] To address this issue, several correction methods have been developed. The most basic method uses a standard reference plate to calculate reflectivity to correct for changes in the light source, but this does not consider other factors. For observation geometry, existing methods either strictly limit the measurement angle (lacking flexibility) or involve complex multi-angle BRDF (Bidirectional Reflectance Distribution Function) modeling (costly and time-consuming). More often, the target is simplified to an ideal Lambertian body assumption, sacrificing accuracy. Correction methods are even more inadequate for temperature effects. Temperature is often ignored in the visible-near-infrared band, and thermal radiation subtraction methods in the thermal infrared band are difficult to implement due to their reliance on precise emissivity and other parameters, and they fail to correct for the effect of temperature on the material's reflectivity itself.

[0004] In summary, existing spectral correction techniques generally suffer from a single correction dimension and a fragmented approach to various factors. These methods lack a comprehensive consideration of the coupled effects of multiple factors such as illumination, geometry, and temperature, often relying on overly idealized assumptions, leading to error accumulation and insufficient accuracy and robustness of the correction results. Therefore, there is an urgent need in this field for a new spectral standardization method that can integrate and systematically address the combined effects of multidimensional environments to improve the accuracy, consistency, and comparability of in-situ spectral data. Summary of the Invention

[0005] To address the problems in the prior art, this invention proposes a spectral normalization method and system for multidimensional environmental influences.

[0006] The technical solution adopted in this invention is as follows:

[0007] In a first aspect, the present invention provides a spectral normalization method for multidimensional environmental influences, comprising the following steps:

[0008] Step 1): Perform single-point spectral acquisition on the target object. For the acquisition location, obtain the apparent radiance spectral data and surface temperature data of the target object, as well as the geometric parameters when performing spectral acquisition on the target object; replace the target object with a standard reference plate, and obtain the apparent radiance spectral data of the standard reference plate for the center position of the standard reference plate.

[0009] Step 2): Based on the results obtained in Step 1), the reflectance spectrum data of the target object surface after preliminary correction is obtained by using the thermal radiation-reflectance decoupling method based on iterative calculation.

[0010] Step 3): Obtain a semi-empirical or physical BRDF model that can describe the anisotropic reflection characteristics of the Earth's surface, and determine the parameters of the semi-empirical or physical BRDF model; calculate the results of the semi-empirical or physical BRDF model under standard geometric parameters.

[0011] Step 4): Calculate the results of the semi-empirical or physical BRDF model with the geometric parameters obtained in Step 1);

[0012] Step 5): Based on the results obtained in Step 3) and Step 4), and the reflectance spectral data obtained in Step 2), geometrically normalized reflectance spectral data is obtained, and spectral normalization is completed.

[0013] Further, step 2) includes:

[0014] 21) Based on the apparent radiance spectrum data of the target object and the apparent radiance spectrum data of the standard reference plate in step 1), the zero-order reflectance spectrum data of the target object surface is obtained.

[0015] 22) Based on the reflectance spectrum data of the target object surface obtained in the (n-1)th iteration, the emissivity spectrum data of the target object surface at the (n-1)th iteration is obtained according to Kirchhoff's thermal radiation law; then, combined with the temperature data of the target object surface in step 1), the brightness spectrum data of the thermal radiation emitted by the target object itself at the (n-1)th iteration is obtained using Planck's law.

[0016] 23) Subtract the thermal radiation spectrum data emitted by the target object itself at the (n-1)th iteration from the apparent radiation spectrum data of the target object in step 1) to obtain the reflected radiation spectrum data of the target object at the nth iteration; then obtain the reflectance spectrum data of the target object surface at the nth iteration based on the reflected radiation spectrum data.

[0017] 24) Repeat steps 22)-23) until the average absolute difference between the reflectance spectral data of the target object surface at the nth iteration and the reflectance spectral data of the target object surface at the (n-1)th iteration is less than a preset threshold. At this point, the reflectance spectral data of the target object surface at the nth iteration is used as the reflectance spectral data of the target object surface after preliminary correction; where n is a positive integer, and the reflectance spectral data of the target object surface obtained at the 0th iteration is the zero-order reflectance spectral data of the target object surface.

[0018] Further, in step 3), the semi-empirical or physical BRDF model is the Ross-Li model;

[0019] The method for determining the parameters of a semi-empirical or physical BRDF model includes:

[0020] Obtain the feature type-BRDF parameter database, retrieve the feature type-BRDF parameter data that is the same as the feature type of the target object from the feature type-BRDF parameter database, and then use the BRDF parameters of the feature type-BRDF parameter data as the parameters of the semi-empirical or physical BRDF model to complete the determination of the semi-empirical or physical BRDF model parameters.

[0021] Secondly, the present invention also provides a spectral normalization system for implementing the method under multidimensional environmental influences, comprising:

[0022] The multi-parameter spectral data acquisition module is used to perform single-point spectral acquisition on a target object. For the acquisition location, it obtains the apparent radiance spectral data and surface temperature data of the target object, obtains the geometric parameters when performing spectral acquisition on the target object, and replaces the target object with a standard reference plate, obtaining the apparent radiance spectral data of the standard reference plate at the center position of the standard reference plate.

[0023] The thermal radiation-reflectivity decoupling module is used to obtain the reflectivity spectral data of the target object surface after preliminary correction based on the results obtained by the multi-parameter spectral data acquisition module and the thermal radiation-reflectivity decoupling method based on iterative calculation.

[0024] The BRDF-based geometric normalization module is used to acquire a semi-empirical or physical BRDF model that can describe the anisotropic reflectance characteristics of the Earth's surface, determine the parameters of the semi-empirical or physical BRDF model, calculate the results of the semi-empirical or physical BRDF model under standard geometric parameters, and calculate the results of the semi-empirical or physical BRDF model under geometric parameters obtained by the multi-parameter spectral data acquisition module. Then, it combines the reflectance spectral data obtained by the thermal radiation-reflectance decoupling module to obtain geometrically normalized reflectance spectral data, thus completing spectral normalization.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] 1) This invention proposes for the first time a systematic method that integrates the three major environmental factors of light, geometry and temperature into a unified framework for integrated correction, which overcomes the defect of error accumulation caused by the "fragmented" processing of the prior art and takes into account the coupling effect between the factors.

[0027] 2) This invention uses an innovative iterative calculation method to accurately decouple the thermal radiation of the target itself from the total signal and introduces the BRDF model to physically correct the angle effect, which is far superior to the traditional Lambertian body assumption and significantly improves the accuracy of the normalized spectrum.

[0028] 3) After processing by this method, the spectral data obtained from different times, locations and devices have good consistency and comparability, providing a reliable data foundation for the subsequent establishment of a general and high-precision quantitative analysis model, and greatly enhancing the application value of spectral data. Attached Figure Description

[0029] Figure 1 This is the spectral diagram corresponding to the apparent radiance spectral data of the target object in this invention;

[0030] Figure 2 This is the spectral diagram corresponding to the reflectance spectral data that has had its thermal radiation effects deducted and has been preliminarily corrected in this invention.

[0031] Figure 3 This is the spectral diagram corresponding to the geometrically normalized reflectance spectral data in this invention;

[0032] Figure 4 This is a flowchart of the spectral normalization method for multidimensional environmental influences according to the present invention;

[0033] Figure 5 This is a schematic diagram illustrating the spectral normalization effect under the multidimensional environmental influences of the present invention. Detailed Implementation

[0034] The present invention will be further described and illustrated below with reference to specific embodiments. The embodiments described are merely examples of the content of this disclosure and do not limit the scope of the invention. The technical features of each embodiment in the present invention can be combined accordingly, provided that there is no mutual conflict.

[0035] To address the common problems of existing spectral correction techniques, such as a single correction dimension and fragmented treatment of various factors, this invention provides a spectral standardization method and system for multidimensional environmental influences. The method of this invention is a novel spectral standardization approach that can comprehensively and systematically address the combined effects of multidimensional environments, thereby improving the accuracy, consistency, and comparability of in-situ spectral data.

[0036] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0037] like Figure 4 As shown, the spectral normalization method for multidimensional environmental influences of the present invention mainly includes the following steps:

[0038] Step 1): Perform single-point spectral acquisition on the target object. For the acquisition location, acquire the apparent radiance spectral data and the temperature data of the target object surface (i.e., acquire the apparent radiance spectral data and the temperature data at the acquisition location on the target object), and acquire the geometric parameters at the acquisition location when performing spectral acquisition on the target object (i.e., acquire the geometric parameters at the acquisition location when performing spectral acquisition on the target object); replace the target object with a standard reference plate, and acquire the apparent radiance spectral data of the standard reference plate at its center position (i.e., acquire the apparent radiance spectral data at the center position of the standard reference plate).

[0039] This embodiment focuses on a scenario where a single-point sampler is used to acquire spectra. At the measurement site, the single-point sampler is used to acquire spectra of the target object, and the following four types of data are acquired and recorded simultaneously:

[0040] a): Apparent radiance spectral data of the target object ,like Figure 1 As shown;

[0041] b): Keeping the imaging angle and illumination conditions unchanged when acquiring the spectrum of the target object, replace the target object with a standard reference plate, and acquire the apparent radiance spectrum data of the standard reference plate for the central region (position). The reflectance spectrum of the laboratory-calibrated standard reference plate, wherein the standard reference plate is described. It is known;

[0042] c): Temperature data of the target object's surface Temperature data This refers to a temperature value at the data collection location;

[0043] d): Geometric parameters for spectral acquisition of the target object, including the incident zenith angle and incident azimuth angle of the light source, as well as the observation zenith angle and observation azimuth angle of the imaging spectrometer.

[0044] Step 2): Based on the results obtained in Step 1), the reflectance spectrum data of the target object surface is obtained by using the thermal radiation-reflectance decoupling method based on iterative calculation.

[0045] Due to the apparent radiance spectrum data of the target object obtained in step 1). In this process, the reflected radiation of the target object's surface to ambient light is coupled with the target object's own thermal radiation. Therefore, this step is designed based on iterative calculation of thermal radiation-reflectivity decoupling, aiming to decouple and separate the two to obtain reflectivity spectral data that has had its thermal radiation effect deducted and has been preliminarily corrected. The specific working process is as follows:

[0046] Step 21): Initialization parameters and calculation of zero-order reflectance spectral data:

[0047] Set the iteration counter It also presets a convergence threshold to determine whether the calculation has converged. In this embodiment .

[0048] First, without considering the effects of thermal radiation, calculate the zero-order reflectance spectrum of the target object's surface. The calculation formula is as follows:

[0049]

[0050] The zero-order reflectance spectrum data of the target object surface were calculated. The reflectivity data of the target object's surface obtained in the 0th iteration, i.e., the result at the initial moment, is used for calculations in subsequent iterations.

[0051] Step 22): Iterative calculation:

[0052] (221) Preliminary emissivity spectral data estimation: based on the reflectivity data of the target object surface obtained in the (n-1)th iteration. Based on Kirchhoff's law of thermal radiation, the emissivity of the target object's surface at various wavelengths is estimated for the (n-1)th iteration, thus obtaining the emissivity spectral data of the target object's surface for the (n-1)th iteration. :

[0053]

[0054] (222) Calculation of thermal radiation brightness spectrum data: based on emissivity spectrum data and the temperature data of the target object surface measured in step 1). The brightness spectrum data of the thermal radiation emitted by the target object itself during the (n-1)th iteration are calculated using Planck's law. :

[0055]

[0056] in, Let be the Planck blackbody radiation function, and its expression is: In the formula Let be Planck's constant. At the speed of light, Boltzmann's constant, λ is the wavelength.

[0057] (223) Correction of reflected radiance spectral data: from the apparent radiance spectral data of the target object Subtract the brightness spectrum data of the thermal radiation emitted by the target object itself at the (n-1)th iteration. This yields thermally corrected spectral data of the target object's reflected radiance, which is closer to the true value. That is, to obtain the reflected radiance spectrum data of the target object at the nth iteration:

[0058]

[0059] To ensure the correctness of the physical meaning, constraints can be imposed on the calculation results, i.e., if If it is less than 0, then set it to 0.

[0060] (224) Update reflectivity: using the corrected reflectance spectral data of the target object Apparent radiance spectral data compared with standard plate reference The updated calculation yields the first... The reflectance spectral data of the target object surface in the next iteration :

[0061]

[0062] Step 23) Convergence check: Determine whether the iteration process has ended. Calculate the... Next and first The mean absolute difference between the reflectance spectral data of the target object surface obtained in each iteration :

[0063]

[0064] in, This refers to the number of wavelengths.

[0065] The calculated mean absolute difference With the preset convergence threshold Comparison:

[0066] (a) If If the calculation result is converged, the iteration process is terminated. The result from the last calculation is then considered convergent. As the final output of step 2), it is used as reflectance spectral data. Then, it is passed to the next step (3) for processing.

[0067] (b) If If the calculation has not yet converged, then the iteration counter is considered to have failed. Increase by 1, and add reflectance spectral data Substitute into the next iteration (i.e., the (n+1)th iteration), return, and repeat step 22) until the convergence condition is met.

[0068] Through the above iterative process, this invention can effectively solve the cyclic dependence problem between reflectivity and emissivity, thereby achieving accurate subtraction of thermal radiation and significantly improving the accuracy of subsequent spectral analysis; the spectral diagram corresponding to the preliminarily corrected reflectivity spectral data after subtracting the influence of thermal radiation is shown below. Figure 2 As shown.

[0069] Step 3): Obtain a semi-empirical or physical BRDF model that can describe the anisotropic reflectance characteristics of the Earth's surface, and determine the parameters of the semi-empirical or physical BRDF model; then calculate the results of the semi-empirical or physical BRDF model under standard geometric parameters, and calculate the results of the semi-empirical or physical BRDF model under the geometric parameters in step 1). Based on the results of the semi-empirical or physical BRDF model under standard geometric parameters and the geometric parameters in step 1), and the reflectance spectral data mentioned in step 2), obtain geometrically normalized reflectance spectral data, and complete spectral normalization.

[0070] This step aims to resolve the reflectance spectral data obtained in step 2) of the previous step. The process still involves information on the observation geometry (i.e., sensor attitude) and illumination geometry (i.e., sun position) at a specific measurement moment. By introducing the bidirectional reflectance distribution function (BRDF) model, this step effectively eliminates the influence of this angle effect, normalizing the reflectance spectral data to a unified standard observation geometry independent of the specific measurement angle. This ensures the direct comparability of spectral data acquired at different times and attitudes. The specific working process is as follows:

[0071] Step 31): Selection and definition of the BRDF model: Select a semi-empirical or physical BRDF model that can describe the anisotropic reflectance characteristics of the Earth's surface. The model selection includes, but is not limited to, the Ross-Li model, which is widely used in remote sensing. The expression for the Ross-Li model is:

[0072]

[0073] in, , , These are the weighting coefficients for the isotropic scattering component, the volume scattering component, and the geometric optical scattering component, respectively. and These are the volume scattering kernel function and the geometric optics scattering kernel function, respectively; their specific forms are well known in the art. The zenith angle of the incident light source; To observe the zenith angle for the imaging spectrometer; To observe the azimuth angle for the imaging spectrometer; The incident azimuth angle of the light source.

[0074] Step 32): Determine the model parameters for the selected BRDF model: For the specific application scenario of this invention, a single-measurement estimation scheme based on prior knowledge is adopted. This is achieved by obtaining a single set of geometric angles from this measurement. and the corresponding reflectivity value This involves combining a pre-established database of typical BRDF prototype parameters for different land cover types (such as vegetation, soil, water bodies, and man-made structures). By determining the land cover type of the current target object, the database is used to retrieve the typical model parameters corresponding to that type as the parameters for this calibration, i.e., as the model parameters.

[0075] Step 33): Geometric normalization calculation: After determining the BRDF model and its parameters, calculate a geometric correction factor and apply it to the reflectance spectral data. .

[0076] First, based on the laboratory spectral measurement environment, standard geometric parameters are determined, namely, standardized observation and illumination geometry. In this embodiment, the imaging spectrometer observation zenith angle is one of the standard geometric parameters. The azimuth angle of the imaging spectrometer in the standard geometric parameters is 0°. The zenith angle of incidence of the light source in the standard geometric parameters is 0°. The incident azimuth angle of the light source in the standard geometric parameters is 45°. It is 0°.

[0077] Then, the results of the semi-empirical or physical BRDF model under standard geometric parameters are calculated. And the results of the semi-empirical or physical BRDF model with geometric parameters in step 1). The calculation formulas are as follows:

[0078]

[0079] in, The incident zenith angle of the light source in the standard geometric parameters; The incident azimuth angle of the light source in the standard geometric parameters; The zenith angle is observed by an imaging spectrometer within the standard geometric parameters; The azimuth angle for imaging spectrometer observations is given in the standard geometric parameters.

[0080]

[0081] in, The incident zenith angle of the light source in the geometric parameters of step 1); The incident azimuth angle of the light source in the geometric parameters of step 1); The imaging spectrometer observation zenith angle is one of the geometric parameters in step 1). The azimuth angle of the imaging spectrometer is one of the geometric parameters in step 1).

[0082] Finally, the geometrically normalized reflectance spectral data are calculated using the following formula. :

[0083]

[0084] The output of step 3) is geometrically normalized reflectance spectral data. This spectral data has eliminated variations introduced by different observation angles and solar positions, reflecting the inherent reflectance characteristics of the target under a uniform geometric standard; geometrically normalized reflectance spectral data. The corresponding spectrum is as follows Figure 3 As shown.

[0085] like Figure 5 As shown in the figure, the orange curve reflects the original radiation signal intensity of the measurement point acquired by the single-point spectrometer, the green curve reflects the reflectance of the measurement point obtained by the present invention based on thermal radiation-reflectance decoupling, and the blue curve is the final spectral curve obtained by standardizing the reflectance after thermal radiation correction based on imaging geometric parameters. Figure 5 This intuitively demonstrates that the method proposed in this invention can systematically and stepwise eliminate thermal radiation noise and geometric angle effects in the original spectral signal, and successfully correct and convert the original radiance collected on-site, which is contaminated by multidimensional factors, into a standard reflectance curve with high comparability and clear physical meaning.

[0086] In one specific embodiment of the present invention, the present invention also provides a spectral normalization system for multidimensional environmental influences to implement the method, comprising:

[0087] The multi-parameter spectral data acquisition module is used to perform single-point spectral acquisition on a target object. For the acquisition location, it obtains the apparent radiance spectral data and surface temperature data of the target object, obtains the geometric parameters when performing spectral acquisition on the target object, and replaces the target object with a standard reference plate, obtaining the apparent radiance spectral data of the standard reference plate at the center position of the standard reference plate.

[0088] The thermal radiation-reflectivity decoupling module is used to obtain the reflectivity spectral data of the target object surface after preliminary correction based on the results obtained by the multi-parameter spectral data acquisition module and the thermal radiation-reflectivity decoupling method based on iterative calculation.

[0089] The BRDF-based geometric normalization module is used to acquire a semi-empirical or physical BRDF model that can describe the anisotropic reflectance characteristics of the Earth's surface, determine the parameters of the semi-empirical or physical BRDF model, calculate the results of the semi-empirical or physical BRDF model under standard geometric parameters, and calculate the results of the semi-empirical or physical BRDF model under geometric parameters obtained by the multi-parameter spectral data acquisition module. Then, it combines the reflectance spectral data obtained by the thermal radiation-reflectance decoupling module to obtain geometrically normalized reflectance spectral data, thus completing spectral normalization.

[0090] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0091] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. For example, the image preprocessing module can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules can be combined or integrated into another unit. Furthermore, the connection between the modules shown or discussed can be a communication connection through some interfaces, which can be electrical or other forms. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort. The following uses a real hyperspectral image as an example to illustrate the specific implementation method to demonstrate the technical effects of the present invention; the specific steps in the embodiments will not be repeated.

[0092] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. Those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A spectral normalization method for use under multidimensional environmental influences, characterized in that, Includes the following steps: Step 1): Perform single-point spectral acquisition on the target object. For the acquisition location, obtain the apparent radiance spectral data and surface temperature data of the target object, as well as the geometric parameters when performing spectral acquisition on the target object; replace the target object with a standard reference plate, and obtain the apparent radiance spectral data of the standard reference plate for the center position of the standard reference plate. Step 2): Based on the results obtained in Step 1), the reflectance spectrum data of the target object surface after preliminary correction is obtained by using the thermal radiation-reflectance decoupling method based on iterative calculation. Step 3): Obtain a semi-empirical or physical BRDF model that can describe the anisotropic reflection characteristics of the Earth's surface, and determine the parameters of the semi-empirical or physical BRDF model; calculate the results of the semi-empirical or physical BRDF model under standard geometric parameters. Step 4): Calculate the results of the semi-empirical or physical BRDF model with the geometric parameters obtained in Step 1); Step 5): Based on the results obtained in Step 3) and Step 4), and the reflectance spectral data obtained in Step 2), geometrically normalized reflectance spectral data is obtained, and spectral normalization is completed.

2. The spectral normalization method for multidimensional environmental influences according to claim 1, characterized in that, Step 1), the geometric parameters include the incident zenith angle of the light source, the incident azimuth angle of the light source, the observation zenith angle of the imaging spectrometer, and the observation azimuth angle of the imaging spectrometer.

3. The spectral normalization method for multidimensional environmental influences according to claim 1, characterized in that, Step 2) includes: 21) Based on the apparent radiance spectrum data of the target object and the apparent radiance spectrum data of the standard reference plate in step 1), the zero-order reflectance spectrum data of the target object surface is obtained. 22) Based on the reflectance spectrum data of the target object surface obtained in the (n-1)th iteration, the emissivity spectrum data of the target object surface at the (n-1)th iteration is obtained according to Kirchhoff's thermal radiation law; then, combined with the temperature data of the target object surface in step 1), the brightness spectrum data of the thermal radiation emitted by the target object itself at the (n-1)th iteration is obtained using Planck's law. 23) Subtract the thermal radiation spectrum data emitted by the target object itself at the (n-1)th iteration from the apparent radiation spectrum data of the target object in step 1) to obtain the reflected radiation spectrum data of the target object at the nth iteration; then obtain the reflectance spectrum data of the target object surface at the nth iteration based on the reflected radiation spectrum data. 24) Repeat steps 22)-23) until the average absolute difference between the reflectance spectral data of the target object surface at the nth iteration and the reflectance spectral data of the target object surface at the (n-1)th iteration is less than a preset threshold. At this point, the reflectance spectral data of the target object surface at the nth iteration is used as the reflectance spectral data of the target object surface after preliminary correction; where n is a positive integer, and the reflectance spectral data of the target object surface obtained at the 0th iteration is the zero-order reflectance spectral data of the target object surface.

4. The spectral normalization method for multidimensional environmental influences according to claim 3, characterized in that, In step 21), the zero-order reflectance spectral data of the target object's surface. The calculation formula is: in, The apparent radiance spectrum data of the target object; The apparent radiance spectrum data for the standard reference plate; The reflectance spectrum of the standard reference plate; In step 22), the emissivity spectrum data of the target object surface during the (n-1)th iteration. The calculation formula is: in, The reflectance spectrum data of the target object surface obtained in the (n-1)th iteration; The brightness spectrum data of the thermal radiation emitted by the target object itself during the (n-1)th iteration. The calculation formula is: in, Let be the Planck blackbody radiation function. , Let be Planck's constant. At the speed of light, Boltzmann's constant, For wavelength, This refers to the temperature data of the target object's surface.

5. The spectral normalization method for multidimensional environmental influences according to claim 3, characterized in that, In step 23), if the reflected radiance spectrum data of the target object at the nth iteration is less than 0, then set it to 0; The reflectance spectrum data of the target object surface at the nth iteration The calculation formula is: in, This represents the reflected radiance spectrum data of the target object at the nth iteration. The apparent radiance spectrum data for the standard reference plate; The reflectance spectrum of the standard reference plate; In step 24), the average absolute difference between the reflectance spectral data of the target object surface at the nth iteration and the reflectance spectral data of the target object surface at the (n-1)th iteration. The calculation formula is: in, The number of wavelengths; Wavelength; This represents the reflectance spectrum data of the target object's surface at the nth iteration. This represents the reflectance spectrum data of the target object's surface during the (n-1)th iteration.

6. The spectral normalization method for multidimensional environmental influences according to claim 1, characterized in that, In step 3), the semi-empirical or physical BRDF model is the Ross-Li model; The method for determining the parameters of a semi-empirical or physical BRDF model includes: Obtain the feature type-BRDF parameter database, retrieve the feature type-BRDF parameter data that is the same as the feature type of the target object from the feature type-BRDF parameter database, and then use the BRDF parameters of the feature type-BRDF parameter data as the parameters of the semi-empirical or physical BRDF model to complete the determination of the semi-empirical or physical BRDF model parameters.

7. The spectral normalization method for multidimensional environmental influences according to claim 1, characterized in that, In step 3), the results of the semi-empirical or physical BRDF model under standard geometric parameters. The calculation formula is: in, Functions for semi-empirical or physical BRDF models; For wavelength, The incident zenith angle of the light source in the standard geometric parameters; The incident azimuth angle of the light source in the standard geometric parameters; The zenith angle is observed by an imaging spectrometer within the standard geometric parameters; The azimuth angle of the imaging spectrometer in the standard geometric parameters; These are the weighting coefficients for the isotropic scattering components; These are the weighting coefficients for each body scattering component; For volume scattering kernel function; The weighting coefficients for the geometric optics scattering components; This is the geometric optics scattering kernel function.

8. The spectral normalization method for multidimensional environmental influences according to claim 1, characterized in that, In step 4), the results of the semi-empirical or physical BRDF model with geometric parameters from step 1) are used. The calculation formula is: in, The incident zenith angle of the light source in the geometric parameters of step 1); The incident azimuth angle of the light source in the geometric parameters of step 1); The imaging spectrometer observation zenith angle is one of the geometric parameters in step 1); The azimuth angle of the imaging spectrometer is one of the geometric parameters in step 1).

9. The spectral normalization method for multidimensional environmental influences according to claim 1, characterized in that, In step 5), the geometrically normalized reflectance spectral data The calculation formula is: in, The reflectance spectral data from step 2); The results are from a semi-empirical or physical BRDF model under standard geometric parameters; The result is the result of the semi-empirical or physical BRDF model with geometric parameters in step 1).

10. A spectral normalization system for multidimensional environmental influences that implements the method of claim 1, characterized in that, include: The multi-parameter spectral data acquisition module is used to perform single-point spectral acquisition on a target object. For the acquisition location, it obtains the apparent radiance spectral data and surface temperature data of the target object, obtains the geometric parameters when performing spectral acquisition on the target object, and replaces the target object with a standard reference plate, obtaining the apparent radiance spectral data of the standard reference plate at the center position of the standard reference plate. The thermal radiation-reflectivity decoupling module is used to obtain the reflectivity spectral data of the target object surface after preliminary correction based on the results obtained by the multi-parameter spectral data acquisition module and the thermal radiation-reflectivity decoupling method based on iterative calculation. The BRDF-based geometric normalization module is used to acquire a semi-empirical or physical BRDF model that can describe the anisotropic reflectance characteristics of the Earth's surface, determine the parameters of the semi-empirical or physical BRDF model, calculate the results of the semi-empirical or physical BRDF model under standard geometric parameters, and calculate the results of the semi-empirical or physical BRDF model under geometric parameters obtained by the multi-parameter spectral data acquisition module. Then, it combines the reflectance spectral data obtained by the thermal radiation-reflectance decoupling module to obtain geometrically normalized reflectance spectral data, thus completing spectral normalization.

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