A remote sensing identification method and system for ginseng planting areas based on spectral index

Abstract

The present invention provides a ginseng planting area remote sensing identification method and system based on spectral index, which relates to the technical field of remote sensing image processing and agricultural resource monitoring. The present invention extracts and analyzes the spectral information of ginseng sheds and other objects by preprocessing remote sensing images, and divides the ginseng shed types into two categories: yellow sheds and blue sheds. Based on spectral characteristics and separability analysis, a ginseng shed identification index is constructed, and the spectral characteristic differences between yellow sheds and blue sheds and other objects are amplified through effective band combination and calculation, and background interference is suppressed. Through random sample generation and manual discrimination, precision iteration is performed to determine the optimal segmentation threshold, so as to realize fast and efficient automatic identification of different types of ginseng sheds. The present invention can effectively identify yellow and blue ginseng sheds at the same time, has good regional applicability, and effectively suppresses interference from objects such as bare soil and blue roofs, and has high recognition accuracy and stability.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing and agricultural resource monitoring, and in particular to a ginseng planting area remote sensing identification method and system based on spectral index. Background Art

[0002] Ginseng has an important position in the domestic and international markets due to its medicinal value and health benefits, and the importance of the ginseng industry in the local economy continues to increase. In order to scientifically and rationally plan the planting layout of ginseng and achieve sustainable development of the industry, it is crucial to timely grasp the distribution of ginseng planting areas. However, there are still many challenges in the rapid identification of ginseng planting areas on a large scale. Although traditional ground surveys and manual measurement methods can obtain accurate planting information, they have problems of low efficiency and high cost, making it difficult to promote and apply them in large-scale ginseng planting identification. With the development of remote sensing technology, remote sensing technology has gradually become a major means in large-scale agricultural monitoring due to its wide coverage and high acquisition efficiency.

[0003] However, existing remote sensing methods still have many technical difficulties in identifying ginseng planting areas, mainly because the types of greenhouse films used in ginseng planting areas are complex and diverse, including blue and yellow plastic films, different coatings and shade nets, etc. These diverse greenhouse film types complicate the differences in the spectral characteristics of ginseng greenhouses, thereby increasing the difficulty of accurate identification.

[0004] Remote sensing classifier methods usually rely on a large number of high-quality training samples and high-resolution image data, but in practical applications, the acquisition of these data is costly and difficult. In addition, when faced with complex backgrounds, classifier methods are easily disturbed by spectral features, resulting in large errors in recognition results.

[0005] The spectral index-based method has shown significant advantages in large-scale rapid extraction tasks, but a spectral index that can comprehensively identify various types of ginseng sheds has not yet been proposed, especially when the background of the ground objects is complex and the spectrum is mixed, the separation and applicability of the existing spectral index are insufficient. The above shortcomings make it difficult for existing technologies to achieve high-precision identification of ginseng planting areas within a large scale, thus limiting the efficiency and effectiveness of ginseng planting management and agricultural resource monitoring. Summary of the invention

[0006] In order to overcome the deficiencies of the prior art, the object of the present invention is to provide a ginseng planting area remote sensing identification method and system based on spectral index.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A remote sensing identification method for ginseng planting areas based on spectral index, comprising:

[0009] Collect a period of Sentinel-2 remote sensing images of the ginseng planting area during the greenhouse film erection period, and pre-process the Sentinel-2 remote sensing images to obtain surface reflectance images;

[0010] Obtain sample point data of ginseng planting areas and other landforms through field collection and high-resolution images, and use the sample point data to extract spectral information of ginseng sheds and other landforms in the surface reflectance image;

[0011] Analyzing the spectral information to classify different types of ginseng sheds into yellow shed types and blue shed types;

[0012] Based on the determined yellow shed and blue shed categories, the spectral differences between the yellow shed and blue shed categories and other ground objects are analyzed to determine the effective spectral bands with good separability;

[0013] Constructing a spectral identification index according to the effective spectral band, and randomly generating new sample points independent of spectral analysis;

[0014] Manually identify the new sample points to produce sample labels, and perform precision calculation based on the sample labels to obtain an optimal yellow shed segmentation threshold and an optimal blue shed segmentation threshold;

[0015] Based on the surface reflectivity image and the shed identification index, calculate and generate a shed identification index image;

[0016] The ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold are used to perform ginseng shed identification on the area to be identified, so as to realize remote sensing identification of ginseng planting areas.

[0017] Preferably, the effective spectral bands include a blue band, a green band, a red band and a short-wave infrared 2 band.

[0018] Preferably, the yellow shed type includes yellow plastic film and yellow plastic film coated with paint.

[0019] Preferably, the blue shed types include blue plastic film, blue plastic film coated with paint, blue plastic film covered with a shade net, blue plastic film coated with paint and covered with a shade net, and yellow plastic shed film covered with a shade net.

[0020] Preferably, the expression of the ginseng shed identification index is:

[0021]

[0022] in, is the shed identification index, represents the surface reflectance in the blue band, represents the surface reflectance of the green band, represents the surface reflectance of the red band, Represents the surface reflectance in the shortwave infrared 2 band.

[0023] Preferably, performing precision calculation according to the sample labels to obtain an optimal yellow shed segmentation threshold and an optimal blue shed segmentation threshold comprises:

[0024] Using the sample labels respectively, iteratively calculate the classification accuracy of the yellow shed class and the blue shed class under different index values;

[0025] The yellow shed threshold and the blue shed threshold when the accuracy is optimal are selected as the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold respectively.

[0026] Preferably, the ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold are used to identify the ginseng shed in the area to be identified, so as to realize the remote sensing identification of the ginseng planting area, including:

[0027] Traverse all pixel positions in the area to be identified. If the index value of the pixel position is less than the optimal yellow shed segmentation threshold or greater than the optimal blue shed segmentation threshold, the attribute of the pixel position is marked as a ginseng planting area, otherwise it is marked as other land features.

[0028] A ginseng planting area remote sensing identification system based on spectral index, characterized by comprising:

[0029] An image acquisition unit is used to collect a period of Sentinel-2 remote sensing images of the ginseng planting area during the greenhouse film erection period, and pre-process the Sentinel-2 remote sensing images to obtain a surface reflectance image;

[0030] A spectrum acquisition unit, used to acquire sample point data of ginseng planting areas and other ground objects through field collection and high-resolution images, and to extract spectrum information of ginseng sheds and other ground objects in the surface reflectance image using the sample point data;

[0031] A ginseng shed classification unit, used for analyzing the spectral information to classify different types of ginseng sheds into yellow shed categories and blue shed categories;

[0032] A band determination unit is used to analyze the spectral differences between the yellow shed class and the blue shed class and other ground objects based on the determined yellow shed class and blue shed class, so as to determine an effective spectral band with good separability;

[0033] An index construction unit, used to construct a reference shed identification index according to the effective spectral band, and randomly generate new sample points independent of spectral analysis;

[0034] An optimal segmentation threshold determination unit is used to manually identify the new sample points to produce sample labels, and perform precision calculation based on the sample labels to obtain an optimal yellow shed segmentation threshold and an optimal blue shed segmentation threshold;

[0035] An image generating unit, configured to calculate and generate a shed identification index image based on the surface reflectance image and the shed identification index;

[0036] The remote sensing identification unit is used to use the ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold to perform ginseng shed identification on the area to be identified, so as to realize remote sensing identification of ginseng planting areas.

[0037] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] The present invention provides a ginseng planting area remote sensing identification method and system based on spectral index, the method comprising: collecting a period of Sentinel-2 remote sensing images of the ginseng planting area in the greenhouse film erection period, and preprocessing the Sentinel-2 remote sensing images to obtain surface reflectance images; obtaining sample point data of the ginseng planting area and other ground objects through field collection and high-resolution images, and using the sample point data to extract spectral information of the ginseng greenhouses and other ground objects in the surface reflectance images; analyzing the spectral information to classify various types of ginseng greenhouses into yellow greenhouses and blue greenhouses; and analyzing the yellow greenhouses and blue greenhouses based on the determined yellow greenhouses and blue greenhouses. The spectral difference between sheds and other ground objects is used to determine the effective spectral band with good separability; the ginseng shed identification index is constructed according to the effective spectral band, and new sample points independent of spectral analysis are randomly generated; the new sample points are manually identified to produce sample labels, and the accuracy is calculated according to the sample labels to obtain the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold; based on the surface reflectance image and the ginseng shed identification index, a ginseng shed identification index image is calculated and generated; the ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold are used to identify the ginseng shed in the area to be identified, so as to realize the remote sensing identification of ginseng planting areas. The present invention can effectively identify yellow and blue ginseng sheds at the same time, has good regional applicability, and effectively suppresses interference from ground objects such as bare soil and blue roofs, and has high recognition accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0041] Figure 2 Schematic diagram of spectrum curves of various specific types of reference sheds provided in embodiments of the present invention;

[0042] Figure 3 Provided for the embodiment of the present invention are schematic diagrams of spectrum curves of two types of reference sheds and other ground objects;

[0043] Figure 4 A schematic diagram of the accuracy result determined by iteratively using random samples provided in an embodiment of the present invention;

[0044] Figure 5 A schematic diagram of a true color image of a certain area and a result of spectral recognition provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The purpose of the present invention is to provide a ginseng planting area remote sensing identification method and system based on spectral index, which can effectively identify yellow and blue ginseng sheds at the same time, has good regional applicability, and effectively suppresses interference from ground objects such as bare soil and blue roofs, and has high recognition accuracy and stability.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a remote sensing identification method for ginseng planting areas based on spectral index, comprising:

[0049] Step 100: collect a Sentinel-2 remote sensing image of the ginseng planting area during the greenhouse film erection period, and pre-process the Sentinel-2 remote sensing image to obtain a surface reflectance image;

[0050] Step 200: Obtain sample point data of ginseng planting areas and other ground objects through field collection and high-resolution images, and use the sample point data to extract spectral information of ginseng sheds and other ground objects in the surface reflectance image;

[0051] Step 300: Analyze the spectrum information to classify different types of ginseng sheds into yellow sheds and blue sheds;

[0052] Step 400: Based on the determined yellow shed class and blue shed class, analyze the spectral differences between the yellow shed class and the blue shed class and other ground objects to determine an effective spectral band with good separability;

[0053] Step 500: constructing a spectral identification index according to the effective spectral band, and randomly generating new sample points independent of spectral analysis;

[0054] Step 600: manually identify the new sample points to create sample labels, and perform precision calculation based on the sample labels to obtain the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold;

[0055] Step 700: Calculate and generate a shed identification index image based on the surface reflectance image and the shed identification index;

[0056] Step 800: Use the ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold to perform ginseng shed identification on the area to be identified, so as to realize remote sensing identification of ginseng planting areas.

[0057] like Figures 2 to 4 As shown in the figure, a method for rapid identification of ginseng planting areas based on spectral index is proposed. This method uses satellite remote sensing technology to accurately and efficiently automatically identify a large range of ginseng planting areas. The specific steps are as follows:

[0058] S1, during the greenhouse film erection period (usually from May to September each year), collect a phase of Sentinel-2 remote sensing images of the ginseng planting area. After the image is collected, it is pre-processed by atmospheric correction and cloud removal to obtain a more accurate surface reflectance image. Atmospheric correction can reduce the impact of atmospheric components on image quality, and cloud removal can effectively remove cloud interference in the image to ensure the accuracy of subsequent analysis data. After pre-processing, the spectral information of the ground objects in the image is clearer, which can lay the foundation for subsequent ginseng greenhouse identification. Figure 2 As shown, these are the spectral curves of various types of ginseng. It can be seen that there are differences in the spectra of the seven different ginseng.

[0059] S2, through field collection and the assistance of high-resolution images, obtain sample point data of ginseng planting areas and other landforms. These sample points include various types of ginseng sheds and other typical landforms. Spectral information is extracted from the surface reflectance image obtained in step S1 using these sample points to obtain the spectral characteristics of various ginseng sheds and other landforms.

[0060] S3, analyzing the reference spectral samples obtained in step S2, and according to the similarity of the spectra, Figure 2The seven types of ginseng sheds in the study were summarized into two categories, namely yellow sheds and blue sheds. The yellow sheds include yellow plastic film and yellow plastic film with paint. The blue sheds include blue plastic film, blue plastic film with paint, blue plastic film covered with shade nets, and blue plastic film with both paint and shade nets. This simplifies the types of target objects and scientifically and rationally retains more comprehensive information about ginseng sheds, which is helpful for subsequent analysis.

[0061] S4, further analyzing the spectral feature differences between the two reference sheds (yellow shed and blue shed) determined in step S3 and other ground objects, such as Figure 3 As shown in the figure, the spectral curves of the yellow shed, blue shed and other objects are displayed. It can be seen that the reflectance characteristics of different types of objects in different spectral bands are different, which provides a basis for subsequent classification. Finally, the effective spectral bands with good separation effects were determined, including blue band, green band, red band and short-wave infrared 2 band.

[0062] S5, based on the effective spectral bands determined in step S4, construct the Ginseng Farming Index (GFI). This index can effectively highlight the spectral characteristics of the blue shed through the combination of blue, green, red and short-wave infrared bands, while reversely highlighting the yellow shed, and concentrating the spectra of other objects in the middle value, so as to distinguish the ginseng shed from other objects. Through the constructed ginseng shed identification index, the spectral characteristics of the target ginseng shed are significantly enhanced, providing an accurate spectral basis for subsequent classification.

[0063] S6, in order to achieve accurate distinction of the types of sheds, artificial discrimination is performed based on randomly generated new sample points independent of spectral analysis to produce sample labels. These new sample points are used to iteratively calculate the classification accuracy of yellow sheds and blue sheds under different index values, and finally the yellow shed threshold α and blue shed threshold β with the best classification accuracy are selected as the classification thresholds. This method ensures that the selected threshold has the best classification effect through continuous iteration, such as Figure 4 As shown in the figure, through multiple iterative analyses, the optimal classification thresholds were determined. The yellow shed classification threshold α was -0.335, and the blue shed classification threshold β was 0.005, ensuring the accuracy and robustness of the classification.

[0064] S7, based on the surface reflectance image obtained in step S1 and the ginseng shed identification index constructed in step S5, an index calculation is performed to generate a ginseng shed identification index image for the entire study area. The index image effectively amplifies the spectral difference between the ginseng shed and other ground objects, which helps to achieve high-precision regional identification.

[0065] S8, using the ginseng shed identification index image generated in step S7, identify the ginseng shed in the area to be identified. If the cultivation index value of the pixel position in the area to be identified is less than the yellow shed segmentation threshold α or greater than the blue shed segmentation threshold β, the pixel position is marked as a ginseng planting area, otherwise it is marked as other landforms. This classification method can quickly realize the accurate identification of ginseng planting areas.

[0066] S9, by looping through steps S7 and S8, until all pixel positions in the area to be identified are traversed, the remote sensing identification of the ginseng planting area is completed. Finally, the identification result of the ginseng shed in the entire area is generated. Figure 5 As shown, true color images of a certain area and ginseng shed identification results are displayed. The results show that the method of the present invention can accurately identify ginseng planting areas and significantly reduce the occurrence of misclassification and missed classification.

[0067] In another embodiment, the ability of the method of the present invention to identify ginseng plantings in other regions has been further verified. By fine-tuning the classification threshold, the method can be extended to other ginseng planting areas to achieve efficient monitoring and identification of large-scale areas. This flexibility greatly increases the practicality and universality of the method.

[0068] Corresponding to the above method, this embodiment further provides a ginseng planting area remote sensing identification system based on spectral index, which is characterized by comprising:

[0069] An image acquisition unit is used to collect a period of Sentinel-2 remote sensing images of the ginseng planting area during the greenhouse film erection period, and pre-process the Sentinel-2 remote sensing images to obtain a surface reflectance image;

[0070] A spectrum acquisition unit, used to acquire sample point data of ginseng planting areas and other ground objects through field collection and high-resolution images, and to extract spectrum information of ginseng sheds and other ground objects in the surface reflectance image using the sample point data;

[0071] A ginseng shed classification unit, used for analyzing the spectral information to classify different types of ginseng sheds into yellow shed categories and blue shed categories;

[0072] A band determination unit is used to analyze the spectral differences between the yellow shed class and the blue shed class and other ground objects based on the determined yellow shed class and blue shed class, so as to determine an effective spectral band with good separability;

[0073] An index construction unit, used to construct a reference shed identification index according to the effective spectral band, and randomly generate new sample points independent of spectral analysis;

[0074] An optimal segmentation threshold determination unit is used to manually identify the new sample points to produce sample labels, and perform precision calculation based on the sample labels to obtain an optimal yellow shed segmentation threshold and an optimal blue shed segmentation threshold;

[0075] An image generating unit, configured to calculate and generate a shed identification index image based on the surface reflectance image and the shed identification index;

[0076] The remote sensing identification unit is used to use the ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed threshold to perform ginseng shed identification on the area to be identified, so as to realize remote sensing identification of ginseng planting areas.

[0077] The beneficial effects of the present invention are as follows:

[0078] The present invention constructs a specific ginseng shed identification index, effectively enhances the spectral characteristics of different types of ginseng sheds, reduces background interference, and makes the spectral differences between yellow sheds and blue sheds and other objects more significant, thereby achieving efficient and accurate identification of ginseng planting areas. Experiments show that the method of the present invention has good applicability in different regions, and the threshold can be promoted and applied by fine-tuning, which significantly improves the recognition efficiency and classification accuracy, and provides an efficient and reliable means for agricultural management.

[0079] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0080] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A remote sensing identification method for ginseng planting areas based on spectral index, characterized in that: include: Collect a period of Sentinel-2 remote sensing images of the ginseng planting area during the greenhouse film erection period, and pre-process the Sentinel-2 remote sensing images to obtain surface reflectance images; Obtain sample point data of ginseng planting areas and other landforms through field collection and high-resolution images, and use the sample point data to extract spectral information of ginseng sheds and other landforms in the surface reflectance image; Analyzing the spectral information to classify different types of ginseng sheds into yellow shed types and blue shed types; Based on the determined yellow shed and blue shed categories, the spectral differences between the yellow shed and blue shed categories and other ground objects are analyzed to determine the effective spectral bands with good separability; Constructing a spectral identification index according to the effective spectral band, and randomly generating new sample points independent of spectral analysis; Manually identify the new sample points to produce sample labels, and perform precision calculation based on the sample labels to obtain an optimal yellow shed segmentation threshold and an optimal blue shed segmentation threshold; Based on the surface reflectivity image and the shed identification index, calculate and generate a shed identification index image; Using the ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold to identify the ginseng shed in the area to be identified, so as to realize remote sensing identification of ginseng planting areas; The effective spectral bands include blue band, green band, red band and short-wave infrared 2 band; The expression of the ginseng shed identification index is: Among them, GFI is the reference shed identification index, ρ Blue Represents the surface reflectance in the blue band, ρ Green represents the surface reflectance of the green band, ρ Red represents the surface reflectance of the red band, ρ SWIR2 Represents the surface reflectance in the shortwave infrared 2 band.

2. The ginseng planting area remote sensing identification method based on spectral index according to claim 1 is characterized in that: The yellow shed category includes yellow plastic film and yellow plastic film coated with paint.

3. The remote sensing identification method of ginseng planting areas based on spectral index according to claim 1 is characterized in that: The blue shed category includes blue plastic film, blue plastic film coated with paint, blue plastic film covered with a shade net, blue plastic film coated with paint and covered with a shade net, and yellow plastic shed film covered with a shade net.

4. The ginseng planting area remote sensing identification method based on spectral index according to claim 1 is characterized in that: The accuracy is calculated according to the sample labels to obtain the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold, including: Using the sample labels respectively, iteratively calculate the classification accuracy of the yellow shed class and the blue shed class under different index values; The yellow shed threshold and the blue shed threshold when the accuracy is optimal are selected as the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold respectively.

5. The remote sensing identification method of ginseng planting areas based on spectral index according to claim 1 is characterized in that: Using the ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold to identify the ginseng shed in the area to be identified, so as to realize remote sensing identification of ginseng planting areas, including: Traverse all pixel positions in the area to be identified. If the index value of the pixel position is less than the optimal yellow shed segmentation threshold or greater than the optimal blue shed segmentation threshold, the attribute of the pixel position is marked as a ginseng planting area, otherwise it is marked as other land features.

6. A ginseng planting area remote sensing identification system based on spectral index, characterized in that: include: An image acquisition unit is used to collect a period of Sentinel-2 remote sensing images of the ginseng planting area during the greenhouse film erection period, and pre-process the Sentinel-2 remote sensing images to obtain a surface reflectance image; A spectrum acquisition unit, used to acquire sample point data of ginseng planting areas and other ground objects through field collection and high-resolution images, and to extract spectrum information of ginseng sheds and other ground objects in the surface reflectance image using the sample point data; A ginseng shed classification unit, used for analyzing the spectral information to classify different types of ginseng sheds into yellow shed categories and blue shed categories; A band determination unit is used to analyze the spectral differences between the yellow shed class and the blue shed class and other ground objects based on the determined yellow shed class and blue shed class, so as to determine an effective spectral band with good separability; An index construction unit, used to construct a reference shed identification index according to the effective spectral band, and randomly generate new sample points independent of spectral analysis; An optimal segmentation threshold determination unit is used to manually identify the new sample points to produce sample labels, and perform precision calculation based on the sample labels to obtain an optimal yellow shed segmentation threshold and an optimal blue shed segmentation threshold; An image generating unit, configured to calculate and generate a shed identification index image based on the surface reflectance image and the shed identification index; A remote sensing identification unit, used for performing ginseng shed identification on the area to be identified by using the ginseng shed identification index image, the optimal yellow shed segmentation threshold and the optimal blue shed segmentation threshold, so as to realize remote sensing identification of the ginseng planting area; The effective spectral bands include blue band, green band, red band and short-wave infrared 2 band; The expression of the ginseng shed identification index is: Among them, GFI is the reference shed identification index, ρ Blue Represents the surface reflectance in the blue band, ρ Green represents the surface reflectance of the green band, ρ Red represents the surface reflectance of the red band, ρ SWIR2 Represents the surface reflectance in the shortwave infrared 2 band.

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