CN118094117A - Construction method of shadow removal vegetation index, shadow removal method and shadow removal system - Google Patents
Construction method of shadow removal vegetation index, shadow removal method and shadow removal system Download PDFInfo
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Abstract
The invention discloses a construction method of a shadow removal vegetation index, a shadow removal method and a shadow removal system, wherein linear polarization degrees of vegetation detection optical signals under different preset polarization angles are obtained; and adding the linear polarization degree as an adjustment factor into a normalized vegetation index or a simple ratio vegetation index, constructing to obtain a shadow removal vegetation index, and improving the accuracy of vegetation health monitoring by using the constructed shadow removal vegetation index. Because the method and the system provided by the embodiment combine the spectrum and the polarization data, and the shadow is constructed based on the polarization spectrum combination to remove the vegetation index, the comprehensiveness of detection information is increased, and the vegetation detection precision is improved.
Description
Technical Field
The invention relates to the technical field of vegetation remote sensing, in particular to a construction method of a shadow removal vegetation index, a shadow removal method and a shadow removal system.
Background
Shadow interference is often caused when vegetation health monitoring is carried out, so that chlorophyll content inversion accuracy is reduced, and vegetation shadow removal is a challenging problem in vegetation remote sensing. In the existing vegetation shadow removing method, the vegetation shadow removing method is mainly based on spectrum, only the intensity information of each wave band is acquired, and the polarization information is ignored, so that the vegetation detection is inaccurate, and the requirement of vegetation detection precision cannot be met.
Thus, there is a need for further improvements in the methods of the prior art.
Disclosure of Invention
In view of the shortcomings in the related art, the invention aims to provide a construction method, a shadow removal method and a shadow removal system for a shadow removal vegetation index, which overcome the defect that in the prior art, the vegetation shadow is eliminated only based on the acquired intensity information of each wave band, and the detection result is inaccurate due to incomplete information.
The technical scheme adopted for solving the technical problems is as follows:
in a first aspect, the present embodiment provides a method for constructing a shadow-removed vegetation index, including:
acquiring linear polarization degrees of vegetation detection optical signals under different preset polarization angles;
And adding the linear polarization degree as an adjustment factor into a normalized vegetation index or a simple ratio vegetation index, and constructing to obtain the shade-removed vegetation index.
Optionally, the step of obtaining the linear polarization degree of the vegetation detection optical signal under different preset polarization angles includes:
calculating incident Stokes parameters corresponding to vegetation detection optical signals under different preset polarization angles by using a Stokes method;
And determining the linear polarization degree according to the Stokes parameters obtained through calculation.
Optionally, adding linear polarization degree as an adjustment factor into a normalized vegetation index or a simple ratio vegetation index, and constructing to obtain a shadow-removed vegetation index; the wavelength corresponding to the reflectivity adopted by the normalized vegetation index or the simple ratio vegetation index is a near infrared wavelength and a red light wavelength respectively.
Optionally, adding the linear polarization degree as an adjustment factor to the normalized vegetation index, and constructing the shadow-removed vegetation index includes:
Taking the reflectivity corresponding to the near infrared wavelength as a first reflectivity value, taking the reflectivity corresponding to the red wavelength as a second reflectivity value, taking the product of a preset first factor and the linear polarization degree as a regulating parameter for the first reflectivity value, and taking the product of the preset second factor and the linear polarization degree as a regulating parameter for the second reflectivity value, so as to construct and obtain a first shade-removed vegetation index.
Optionally, adding the linear polarization degree as an adjustment factor to the normalized vegetation index, and constructing the shadow-removed vegetation index includes:
And replacing a third reflectivity value with a difference value between the reflectivity corresponding to the near infrared wavelength and the product of the preset first factor and the linear polarization degree, and replacing a fourth reflectivity value with a difference value between the reflectivity corresponding to the red wavelength and the product of the preset second factor and the linear polarization degree, so as to construct and obtain a second shade-removed vegetation index.
Optionally, according to the fact that the change rates of the sunlight area and the shadow area in the vegetation spectrum data image corresponding to the near infrared band and the red light band are the same, the preset first factor and the preset second factor are obtained through calculation.
In a second aspect, the present embodiment discloses a vegetation shade removal method, including:
Acquiring vegetation spectrum data images when the central wave band is the near infrared wavelength and the red light wavelength, and acquiring polarization data images of vegetation under different preset polarization angles when the central wave band is the near infrared wavelength and the red light wavelength;
calculating the linear polarization degree of the vegetation detection optical signals according to the polarization data image;
calculating a first reflectivity value corresponding to the near infrared wavelength and a second reflectivity value corresponding to the red wavelength according to the spectrum data image;
substituting the linear polarization degree, the first reflectivity value and the second reflectivity value into a pre-constructed shadow removal vegetation index to realize vegetation shadow removal; the shadow-removed vegetation index is constructed based on the shadow-removed vegetation index construction method.
Optionally, an EMCCD micro-light polarization multispectral imaging device is used for shooting to obtain a polarization data image and a vegetation spectrum data image of vegetation.
Optionally, the calculation formula of the shadow removal vegetation index is:
；
Or (b) ；
Wherein NSR-SR is a first shade-removed vegetation index, NSR-NDVI is a second shade-removed vegetation index, R 760nm and R 680nm are respectively a reflectivity value corresponding to a wavelength of 760nm and a reflectivity value corresponding to 680nm, doLP is a linear polarization degree, and a and b are respectively a preset first factor and a preset second factor.
In a third aspect, the present embodiment provides a vegetation shade removal system, including:
The image acquisition module is used for acquiring vegetation spectrum data images when the central wave band is the near infrared wavelength and the red light wavelength and acquiring polarization data images of vegetation under different preset polarization angles when the central wave band is the near infrared wavelength and the red light wavelength;
the data processing module is used for detecting the linear polarization degree of the optical signal according to the polarization data image vegetation, and calculating a first reflectivity value corresponding to the near infrared wavelength and a second reflectivity value corresponding to the red light wavelength according to the spectrum data image;
substituting the linear polarization degree, the first reflectivity value and the second reflectivity value into a pre-constructed shadow removal vegetation index to realize vegetation shadow removal; the shadow-removed vegetation index is constructed based on the shadow-removed vegetation index construction method.
Advantageous effects
The embodiment discloses a construction method, a shadow removing method and a system for a shadow removing vegetation index, which are used for obtaining linear polarization degrees of vegetation detection optical signals under different preset polarization angles; and adding the linear polarization degree as an adjustment factor into a normalized vegetation index or a simple ratio vegetation index, constructing to obtain a shadow removal vegetation index, and improving the accuracy of vegetation health monitoring by using the constructed shadow removal vegetation index. Because the method and the system provided by the embodiment combine the spectrum and the polarization data, the comprehensiveness of detection information is increased, and the accuracy of vegetation health condition is improved.
Drawings
FIG. 1 is a flow chart of steps of a method for constructing a shade-removed vegetation index according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps of a method for removing shadows of vegetation according to an embodiment of the present invention;
FIG. 3 is a representation of vegetation detection processing images when implemented in accordance with an embodiment of the present invention;
Fig. 4 is a schematic diagram of the structural principle of the system according to the embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In the technical field of vegetation remote sensing, health detection and analysis of vegetation are often involved, and the growth condition of the vegetation is obtained according to the health detection and analysis result, so that the growth environment of the vegetation is adjusted.
And when the vegetation is detected and analyzed healthily, the vegetation is interfered by shadows, so that the chlorophyll content inversion accuracy is reduced. Because vegetation has stronger reflection in the near infrared band, has higher reflectivity value, has stronger absorption in the infrared band and has low reflectivity value, a method for effectively eliminating the influence of shadows based on a shadow elimination vegetation index is proposed in the prior art, and the vegetation growth condition is quantified by calculating the difference between the near infrared band and the infrared band. Jiang et al propose a Shadow Elimination Vegetation Index (SEVI) based on the Ratio Vegetation Index (RVI), shadow Vegetation Index (SVI), and adjustment factor (f (Δ)), which effectively eliminates the effects of terrain shadows.
In 2022, jiang et al developed an automatic calculation algorithm (BIE algorithm) using block information entropy in order to achieve the best adjustment factor for SEVI. Zhang et al analyzed the effect of shadows on spectral index and found that shadows had a significant effect on the estimation of single-leaf scale vegetation parameters. Yang et al propose a fusion strategy based on concatenation and addition using polarized reflectance information of vegetation and an improved semantic segmentation network. The method effectively improves the accuracy of vegetation segmentation under the shadow condition.
Although a series of methods have been proposed in the prior art to try to directly remove or mitigate the shadow effect of the leaves, none of the methods introduce polarization information into the spectrum index to propose a vegetation shadow removal vegetation index based on polarization spectrum, and the detection result is obtained by detection and analysis based on the intensity information of each band only, so that the accuracy of the obtained vegetation detection result is low.
In order to solve the problem, the present embodiment discloses a method for constructing a shadow removal vegetation index, a shadow removal method and a system, and proposes a vegetation shadow removal vegetation index based on polarization spectrum fusion, so as to establish a general solution to eliminate vegetation shadows. Specifically, the linear polarization degree corresponding to the vegetation detection optical signal is collected, the linear polarization degree is used as an adjustment factor, the normalized vegetation index or the simple ratio vegetation index is added, and the shadow removal vegetation index is constructed, so that shadow removal in vegetation detection is realized.
The following describes a method for constructing a vegetation index for removing shadows, a method for removing shadows, and a system thereof according to the present embodiment with reference to the accompanying drawings.
In a first aspect, this embodiment provides a method for constructing a shadow-removed vegetation index, as shown in fig. 1, including:
And S1, acquiring linear polarization degrees corresponding to vegetation detection optical signals under different preset polarization angles.
In the step, firstly, linear polarization degrees corresponding to vegetation detection light signals are obtained when different preset polarization angles are obtained, specifically, a camera device is utilized to adjust linear polarizers arranged on a camera so as to obtain polarized data images of vegetation under different preset polarization angles.
In one embodiment, the measurement of polarization data of vegetation is performed using EMCCD microimaging techniques. Specifically, the EMCCD is an electron multiplication CCD, is a high-end photoelectric detection product with extremely high sensitivity in the detection field, and can still acquire polarization and spectrum information of vegetation even in a low-illumination environment, so that vegetation shadows are removed.
Further, the following features are provided in the microlight imaging technique: the first, used light source is very weak, typically a weak light signal generated by fluorescence effects or spontaneous radiation, which can be captured and measured by a sensitive optical sensor, and the second, optical imaging: in order to improve resolution and sensitivity of low-light imaging, a high-definition optical imaging system is generally used to accurately control and image light. The imaging systems comprise optical elements such as films, objective lenses, lenses and the like, and capture and image of weak light signals are realized through focusing, reflection and refraction of light rays. Third, imaging processing: the collected weak signals need to be subjected to digital signal processing by imaging processing software, noise and interference are removed, and the signals are amplified and enhanced, so that clear images or videos are finally obtained. The imaging processing technology comprises algorithms such as digital filtering, enhancement, noise suppression, registration and the like, and can greatly improve resolution and definition of low-light imaging. Fourth, the application is extensive: the micro-light imaging technology has wide application in the fields of biomedicine, environmental monitoring, national defense safety and the like.
The EMCCD micro-light imaging technology adopted in the step is to use an electron multiplication CCD to carry out micro-light imaging so as to shoot the prepared spectrum data image and polarization data image. The EMCCD micro-light imaging technology can acquire polarization and spectrum information of vegetation under different illumination conditions, and also has better shadow elimination performance under a low illumination environment, so that the method has wide applicability. Specifically, an EMCCD camera and a filter with 680nm and 760nm central wave band are utilized to obtain vegetation spectrum data images, and a linear polarizer is utilized to obtain polarized data images with 0, 60 and 120 degrees of vegetation corresponding to polarization angles.
Further, the obtained polarization data image is processed to obtain the linear polarization degree. Specifically, stokes parameters corresponding to vegetation under different preset polarization angles are calculated by using a Stokes method, and the linear polarization degree is determined according to the calculated Stokes parameters.
First, the transmission characteristics of the polarization state of the light wave are described by a Mueller matrix M. For an EMCCD microoptical imaging system, assuming that the Stokes vector of the incident light is S in and the Stokes vector of the emergent light is S out, when the light beam passes through the linear system, the effect of the linear system on the light can be represented by a 4×4 Mueller matrix, i.e. formula (1):
(1)
the Mueller matrix of an ideal linear polarizer at an angle α to the reference direction can be expressed as formula (2):
(2)
since the intensity of the emitted light is a function of α, U can be expressed as formula (3) using I, Q:
(3)
Equation (3) represents the equivalent relationship of the outgoing light intensity Iout (α) and the incoming stokes vector. Secondly, substituting the angle to obtain the incident stokes parameter, wherein the common angle of 0 degree, 60 degree and 120 degree (0 degree, 45 degree, 90 degree and 135 degree can be considered) is selected in the study, and the formula (4):
(4)
Finally, after obtaining I, Q, U respective components, the linear polarization degree (DoLP) can be further calculated as formula (5):
（5）
therefore, the linear polarization degree corresponding to the vegetation detection light signal can be calculated by the formula (5).
And S2, adding the linear polarization degree as an adjustment factor into a normalized vegetation index or a simple ratio vegetation index, and constructing to obtain a shadow removal vegetation index.
After calculating the linear polarization degree corresponding to the vegetation detection light signal in the step S1, adding the linear polarization degree as an adjustment factor to a normalized vegetation index (NDVI) or a simple ratio vegetation index (SR) to construct a shade-removed vegetation index.
Specifically, the calculation formula of the normalized vegetation index (NDVI) or the simple ratio vegetation index (SR) is as follows:
(6)
(7)
In formulas (6) and (7), R λ1 and R λ2 are reflectance values at wavelengths of λ 1 and λ 2, respectively.
Specifically, adding linear polarization degree as an adjustment factor into a normalized vegetation index or a simple ratio vegetation index, and constructing to obtain a shadow removal vegetation index; the wavelength corresponding to the reflectivity adopted by the normalized vegetation index or the simple ratio vegetation index is a near infrared wavelength and a red light wavelength respectively.
The vegetation shadows result in a reduction in reflectance in the red and near infrared bands and a greater reduction in the near infrared band than in the red band. The 760nm spectral image is insensitive to vegetation shadows, while the 680nm spectral image is more sensitive to vegetation shadows. The exponential anomaly is caused by the difference in the extent to which the near infrared band and the red band are affected by shadows. Thus, if this difference is eliminated, the shadow effect of vegetation will be removed. In addition, the DoLP of the target is inversely proportional to the surface reflectivity, and the value of the shadow area of the DoLP image is high. Therefore, the method provided by the embodiment regulates and controls the shadow areas of the near infrared band and the red light band images by utilizing the characteristics of the DoLP shadow areas, so that the change rates of the sunlight areas and the shadow areas of the near infrared band and the red light band images are consistent, and further the vegetation influence effect is eliminated.
Therefore, the step of adding the linear polarization degree as the adjustment factor to the normalized vegetation index to construct a shadow-removed vegetation index comprises:
Taking the reflectivity corresponding to the near infrared wavelength as a first reflectivity value, taking the reflectivity corresponding to the red wavelength as a second reflectivity value, taking the product of a preset first factor and the linear polarization degree as a regulating parameter for the first reflectivity value, and taking the product of the preset second factor and the linear polarization degree as a regulating parameter for the second reflectivity value, so as to construct and obtain a first shade-removed vegetation index.
The formula of the obtained NSR-NDVI shadow removal vegetation index is as follows:
(8)
Wherein R 760nm and R 680nm are respectively a reflectivity corresponding to a near infrared wavelength and a reflectivity corresponding to a red wavelength, in this embodiment, R 760nm is taken as a first reflectivity value, R 680nm is taken as a second reflectivity value, a and b are respectively a preset first factor and a preset second factor, and DoLP is a linear polarization degree.
In addition, the step of adding the linear polarization degree as an adjustment factor to the normalized vegetation index to construct a shadow-removed vegetation index comprises the following steps:
And replacing a third reflectivity value with a difference value between the reflectivity corresponding to the near infrared wavelength and the product of the preset first factor and the linear polarization degree, and replacing a fourth reflectivity value with a difference value between the reflectivity corresponding to the red wavelength and the product of the preset second factor and the linear polarization degree, so as to construct and obtain a second shade-removed vegetation index.
That is, the formula of the obtained NSR-SR shadow removal vegetation index is:
(9)
wherein, R 760nm and R 680nm are respectively the reflectivity corresponding to the near infrared wavelength and the reflectivity corresponding to the red wavelength, a and b are respectively a preset first factor and a preset second factor, and DoLP is the linear polarization degree.
By combining formulas (6), (7), (8) and (9), it can be obtained that only the factors contained in the Normalized Difference Vegetation Index (NDVI) and the Simple Ratio (SR) are reflectance values, and the improved NSR-NDVI shadow removal vegetation index and NSR-SR shadow removal vegetation index not only contain reflectance values, but also contain linear polarization values, preset first factors and preset second factors, so that the spectral data and the polarization data are fused, and detection analysis results are obtained based on more comprehensive information.
Further, according to the fact that the change rates of the sunlight area and the shadow area in the vegetation spectrum data image corresponding to the near infrared band and the infrared band are the same, the preset first factor and the preset second factor are obtained through calculation.
The change rates of the sunlight area and the shadow area based on the near infrared band and the red band images are consistent, and a formula (10) is obtained:
(10)
The first reflectance value R 760nm in the sunlight area, the second reflectance value R 680nm in the sunlight area, the linear polarization degree DoLP, the first reflectance value R 760nm Shadow in the shadow area, and the second reflectance values R 680nm Shadow and DoLP Shadow in the shadow area are all known, so the preset first factor a and the preset second factor b can be obtained.
The embodiment provides a polarized spectrum fusion vegetation shadow removing method based on an EMCCD, which shows the potential of removing vegetation shadows by utilizing polarization and spectrum, and provides a solution for removing vegetation shadows of different health conditions so as to realize accurate and efficient vegetation health condition detection.
On the premise of disclosing the shadow removal index, the embodiment discloses a vegetation shadow removal method, as shown in fig. 2, including:
and step H1, acquiring vegetation spectrum data images when the central wave band is near infrared wavelength and red light wavelength, and acquiring polarization data images of vegetation under different preset polarization angles.
Further, an EMCCD glimmer polarized multispectral imaging device is utilized to shoot and obtain a polarized data image of vegetation and a vegetation spectrum data image. The EMCCD micro-light imaging technology can acquire polarization and spectrum information of vegetation under different illumination conditions, and has excellent shadow elimination performance under a low illumination environment, so that the method is a universal solution suitable for wide application scenes.
And H2, calculating the linear polarization degree of vegetation according to the polarization data image.
Calculating polarization information of vegetation by using a Stokes method, specifically, calculating the polarization data image obtained in the step H1 to obtain an incident Stokes parameter, and then calculating to obtain linear polarization degree.
And step H3, calculating a first reflectivity value corresponding to the near infrared wavelength and a second reflectivity value corresponding to the red wavelength according to the spectrum data image.
And (3) calculating to obtain reflectance values corresponding to the red wavelength and the near infrared wavelength according to the spectrum data image obtained in the step (H1).
Step H4, substituting the linear polarization degree, the first reflectivity value and the second reflectivity value into a pre-constructed shadow removal vegetation index to realize vegetation shadow removal; the shadow-removed vegetation index is constructed based on the shadow-removed vegetation index construction method.
And substituting the data obtained by calculating the H2 and the H3 into the shadow removal vegetation index constructed in the embodiment so as to remove the vegetation shadow.
Specifically, the calculation formula of the shadow removal vegetation index is as follows:
；
Or (b) ；
Wherein NSR-SR is a first shade-removed vegetation index, NSR-NDVI is a second shade-removed vegetation index, R760nm and R680nm are respectively a reflectivity value corresponding to the wavelength of 760nm and a reflectivity value corresponding to 680nm, doLP is a linear polarization degree, and a and b are respectively a preset first factor and a preset second factor.
The vegetation indexes are removed through the NSR-SR and NSR-NDVI shadows, the vegetation shadow effect can be effectively eliminated, and the inversion accuracy of the vegetation health state is improved. Referring to fig. 3, the NDVI image of the vegetation in part (a) in fig. 3 is shown, wherein the black frame is displayed in the format of RGB image (i.e., the NDVI pseudo-color image of the vegetation in part (a) in fig. 3), and is shaded due to shading, but in the NDVI image, the partial area appears as bright color. Taking one of the black box portions on the leaves as an example, the area of the portion is shown as bright in the NDVI map due to the effect of shading, which interferes with vegetation monitoring. The vegetation detection result calculated by the method of the present embodiment is shown in part (C) of fig. 3, where the reflectivity values of the area of the black frame and the surrounding area are the same, and the shadow effect has been removed. As in fig. 3, part (d) is the NSR-NDVI pseudo-color map of part (c), i.e., the corresponding RGB image.
In a third aspect, the present embodiment provides a vegetation shade removal system, as shown in fig. 4, comprising:
The image acquisition module 410 is configured to acquire a vegetation spectrum data image when the central band is a near infrared wavelength and a red light wavelength, and acquire a polarization data image of vegetation at different preset polarization angles when the central band is the near infrared wavelength and the red light wavelength;
The data processing module 420 is configured to calculate a linear polarization degree of vegetation according to the polarization data image, and calculate a first reflectance value corresponding to a near infrared wavelength and a second reflectance value corresponding to a red light wavelength according to the spectrum data image; substituting the linear polarization degree, the first reflectivity value and the second reflectivity value into a pre-constructed shadow removal vegetation index to realize vegetation shadow removal; the shadow-removed vegetation index is constructed based on the shadow-removed vegetation index construction method.
The method and the system mainly aim at eliminating the vegetation shadow effect and carrying out remote sensing monitoring on the health state of the plant, and the method for removing the vegetation shadow based on the polarization spectrum fusion of the EMCCD is provided, so that the interference of the vegetation shadow is eliminated and the detection precision of the health state of the vegetation is improved. Based on the improvement of SR and NDVI, NSR-SR and NSR-NDVI shadow removal vegetation indexes are established, and influence of shadows on vegetation health detection is evaluated. The invention shows the potential of removing vegetation shadows by using polarization and spectrum, and provides a solution for eliminating vegetation shadows of different health conditions so as to realize accurate and efficient vegetation health condition detection.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of constructing a shade-removed vegetation index, comprising:
acquiring linear polarization degrees of vegetation detection optical signals under different preset polarization angles;
And adding the linear polarization degree as an adjustment factor into a normalized vegetation index or a simple ratio vegetation index, and constructing to obtain the shade-removed vegetation index.
2. The method for constructing a shadow-removed vegetation index according to claim 1, wherein the step of obtaining the linear polarization degree of the vegetation detection light signal under different preset polarization angles comprises:
calculating incident Stokes parameters corresponding to vegetation detection optical signals under different preset polarization angles by using a Stokes method;
And determining the linear polarization degree according to the Stokes parameters obtained through calculation.
3. The method of constructing a shadow-removed vegetation index according to claim 1, wherein the step of constructing a shadow-removed vegetation index is performed by adding a linear polarization degree as an adjustment factor to a normalized vegetation index or a simple ratio vegetation index; the wavelength corresponding to the reflectivity adopted by the normalized vegetation index or the simple ratio vegetation index is a near infrared wavelength and a red light wavelength respectively.
4. The method of constructing a shadow-removed vegetation index according to claim 3, wherein the step of adding the linear polarization degree as the adjustment factor to the normalized vegetation index to construct the shadow-removed vegetation index comprises:
Taking the reflectivity corresponding to the near infrared wavelength as a first reflectivity value, taking the reflectivity corresponding to the red wavelength as a second reflectivity value, taking the product of a preset first factor and the linear polarization degree as a regulating parameter for the first reflectivity value, and taking the product of the preset second factor and the linear polarization degree as a regulating parameter for the second reflectivity value, so as to construct and obtain a first shade-removed vegetation index.
5. The method of constructing a shadow-removed vegetation index according to claim 3, wherein the step of adding the linear polarization degree as the adjustment factor to the normalized vegetation index to construct the shadow-removed vegetation index comprises:
And replacing a third reflectivity value with a difference value between the reflectivity corresponding to the near infrared wavelength and the product of the preset first factor and the linear polarization degree, and replacing a fourth reflectivity value with a difference value between the reflectivity corresponding to the red wavelength and the product of the preset second factor and the linear polarization degree, so as to construct and obtain a second shade-removed vegetation index.
6. The method for constructing a shadow-removed vegetation index according to claim 4 or 5, wherein the preset first factor and the preset second factor are calculated according to the same change rate of a sunlight area and a shadow area in a vegetation spectrum data image corresponding to a near infrared band and a red light band.
7. A vegetation shadow removal method, comprising:
Acquiring vegetation spectrum data images when the central wave band is near infrared wavelength and red light wavelength, and acquiring polarization data images of vegetation under different preset polarization angles;
calculating the linear polarization degree of the vegetation detection optical signals according to the polarization data image;
calculating a first reflectivity value corresponding to the near infrared wavelength and a second reflectivity value corresponding to the red wavelength according to the spectrum data image;
Substituting the linear polarization degree, the first reflectivity value and the second reflectivity value into a pre-constructed shadow removal vegetation index to realize vegetation shadow removal; wherein the shade-removed vegetation index is constructed based on the construction method of the shade-removed vegetation index as claimed in any one of claims 1 to 6.
8. The vegetation shadow removal method of claim 7, wherein the polarization data image and the vegetation spectrum data image of the vegetation are obtained by photographing with an EMCCD micro-polarization multispectral imaging device.
9. The vegetation shading removal method according to claim 7, wherein the calculation formula of the shading-removed vegetation index is:
；
Or (b) ；
Wherein NSR-SR is a first shade-removed vegetation index, NSR-NDVI is a second shade-removed vegetation index, R 760nm and R 680nm are respectively a reflectivity value corresponding to a wavelength of 760nm and a reflectivity value corresponding to 680nm, doLP is a linear polarization degree, and a and b are respectively a preset first factor and a preset second factor.
10. A vegetation shade removal system, comprising:
the image acquisition module is used for acquiring vegetation spectrum data images when the central wave band is near infrared wavelength and red light wavelength and acquiring polarization data images of vegetation under different preset polarization angles;
The data processing module is used for calculating the linear polarization degree of the vegetation detection optical signals according to the polarization data image, and calculating a first reflectivity value corresponding to the near infrared wavelength and a second reflectivity value corresponding to the red light wavelength according to the spectrum data image;
Substituting the linear polarization degree, the first reflectivity value and the second reflectivity value into a pre-constructed shadow removal vegetation index to realize vegetation shadow removal; wherein the shade-removed vegetation index is constructed based on the construction method of the shade-removed vegetation index as claimed in any one of claims 1 to 6.
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