WO2022095570A1

WO2022095570A1 - Urban vegetation type identification method and system, and device and medium

Info

Publication number: WO2022095570A1
Application number: PCT/CN2021/115177
Authority: WO
Inventors: 张�杰
Original assignee: 上海圣之尧智能科技有限公司
Priority date: 2020-11-09
Filing date: 2021-08-28
Publication date: 2022-05-12

Abstract

An urban vegetation type identification method and system, and a device and a medium. The method comprises: acquiring a plurality of detection images of a detection area, wherein the detection images correspond to part of the detection area; performing image stitching on the plurality of detection images so as to obtain a fused image of the detection area; performing feature extraction on the fused image so as to obtain fused color feature vectors and fused texture feature vectors of the fused image; on the basis of the fused color feature vectors, the fused texture feature vectors, a plurality of type color feature vectors corresponding to the fused color feature vectors, and a plurality of type texture feature vectors corresponding to the fused texture feature vectors, obtaining a plurality of type coefficients; and outputting a vegetation type corresponding to the minimum value in the plurality of type coefficients.

Description

A method, system, equipment and medium for identifying urban vegetation types

technical field

The invention relates to a technology in the field of vegetation identification, in particular to a method, system, equipment and medium for identifying urban vegetation types.

Background technique

In urban planning and management, it is necessary to identify the types of urban vegetation. Vegetation types are classified according to the external traits of organisms such as plant structure, shape, size, color, etc., which are jointly determined by genes and the environment. At present, vegetation types are mainly obtained by manual manual measurement or large-scale special imaging systems. Manual manual measurement methods have the disadvantages of low measurement efficiency, low controllability of data accuracy and high uncertainty of observation errors, while large-scale special imaging systems are expensive. Expensive, complex system and poor and insufficient portability.

SUMMARY OF THE INVENTION

Aiming at the above-mentioned shortcomings of the prior art, the present invention proposes a method, system, equipment and medium for identifying urban vegetation types, which can detect the multiple detection images of the detection area above the detection area by the drone. The images are spliced to form a complete fused image, and then feature extraction is performed on the fused image to obtain its color features and texture features, and then multiple type coefficients are obtained according to the color features and texture features. The minimum value of the multiple type coefficients corresponds to Vegetation type, the above method can quickly obtain the type of vegetation in the detection area, and the cost is low.

According to one aspect of the present invention, a method for identifying urban vegetation types, comprising:

collecting a plurality of detection images of a detection area, wherein the detection images correspond to parts of the detection area;

Perform image splicing on the plurality of detection images to obtain a fusion image of the detection area;

Perform feature extraction on the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image;

Based on the fused color feature vector, the fused texture feature vector, multiple types of color feature vectors corresponding to the fused color feature vector, and multiple types of texture feature vectors corresponding to the fused texture feature vector, a plurality of type factor;

The vegetation type corresponding to the minimum value among the plurality of type coefficients is output.

Preferably, performing image splicing on the plurality of detection images to obtain a fusion image of the detection area includes:

performing image preprocessing on each of the detected images;

performing image registration on the detection image after the image preprocessing to obtain an area image;

Perform image fusion on the region image to obtain the fusion image.

Preferably, performing image preprocessing on each of the detected images includes:

performing image distortion correction on each of the detected images;

performing image smoothing on the detected image;

Image enhancement is performed on the detected image after image smoothing.

Preferably, the calculation formula of the type coefficient is:

in:

d _ni is the type coefficient;

a _i is the type color feature vector;

b _i is the type texture feature vector;

c _n is the fusion color feature vector;

t _n is the fusion texture feature vector;

i is the vegetation type, and i is an integer.

Preferably, the type color feature vector and the fusion color feature vector are both 256-dimensional vectors;

The type texture feature vector and the fusion texture feature vector are both 3-dimensional vectors

According to an aspect of the present invention, a method for identifying urban vegetation types is provided, comprising:

Above a detection area, a plurality of detection images of the detection area are collected by a drone, wherein the detection images correspond to parts of the detection area;

The unmanned aerial vehicle performs image stitching on the plurality of detection images to obtain a fusion image of the detection area;

The UAV performs feature extraction on the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image;

The UAV is based on the fused color feature vector, the fused texture feature vector, multiple types of color feature vectors corresponding to the fused color feature vector, and multiple types of texture features corresponding to the fused texture feature vector. vector to obtain multiple type coefficients;

The UAV outputs the vegetation type corresponding to the minimum value among the plurality of type coefficients.

Preferably, performing feature extraction on the fused image to obtain the fused color feature vector and fused texture feature vector of the fused image further includes:

converting the fused image from the RGB color space to the HSV color space;

Perform feature extraction on the fused image in the HSV color space to obtain the feature extraction to obtain the fused color feature vector and the fused texture feature vector.

According to one aspect of the present invention, a system for identifying urban vegetation types is provided, comprising:

a collection module, collecting a plurality of detection images of a detection area, wherein the detection images correspond to a part of the detection area;

a fusion module, performing image splicing on the plurality of detection images to obtain a fusion image of the detection area;

an extraction module, which performs feature extraction on the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image;

A coefficient acquisition module, based on the fusion color feature vector, the fusion texture feature vector, a plurality of types of color feature vectors corresponding to the fusion color feature vector, and a plurality of types of texture feature vectors corresponding to the fusion texture feature vector , to obtain multiple type coefficients;

an output module, outputting the vegetation type corresponding to the minimum value of the plurality of type coefficients

According to one aspect of the present invention, a device for identifying urban vegetation types is provided, comprising:

processor;

a memory in which executable instructions for the processor are stored;

Wherein, the processor is configured to execute the steps of the above-mentioned urban vegetation type identification method by executing the executable instructions.

According to one aspect of the present invention, a computer-readable storage medium is provided for storing a program, which implements the steps of the above-mentioned method for identifying urban vegetation types when the program is executed.

The beneficial effects of the above technical solutions are:

The copper foil defect detection system in the present invention can extract the detection image of the copper foil to be tested, and compare the detection image with the standard copper foil image. The comparison speed is improved, and the detection of the copper foils on both sides can be completed in a short time.

Other features and advantages of the present invention, as well as the structure and operation of various embodiments of the present invention, will be described in detail below with reference to the accompanying drawings. It should be noted that the present invention is not limited to the specific embodiments described herein. These examples are presented herein for illustrative purposes only.

Description of drawings

Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings.

1 is a schematic diagram of an implementation scenario of the present invention;

Figure 2 is a schematic diagram of a method for identifying urban vegetation types;

3 is a schematic diagram of a fusion image acquisition process;

4 is a schematic diagram of an image preprocessing acquisition process;

5 is a schematic diagram of a feature extraction process;

Fig. 6 is another kind of urban vegetation type identification method of the present invention;

Figure 7 is a block diagram of the results of an urban vegetation type identification system;

8 is a schematic diagram of an urban vegetation type identification device of the present invention;

FIG. 9 is a schematic structural diagram of a computer-readable storage medium of the present invention.

The features and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. Throughout the drawings, like drawings identify corresponding elements. In the drawings, the same reference numbers generally refer to identical, functionally similar and/or structurally similar elements.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

As used in this application, "first", "second" and similar words do not denote any order, quantity, or importance, but are merely used to distinguish the various components. "Comprises" or "comprising" and similar words mean that the elements or things appearing before the word encompass the elements or things recited after the word and their equivalents, but do not exclude other elements or things. Words like "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "Down", "Left", "Right", etc. are only used to represent the relative positional relationship, and when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

It should be noted that the embodiments of the present invention and the features of the embodiments may be combined with each other under the condition of no conflict.

The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but it is not intended to limit the present invention.

According to an aspect of the present invention, a method for identifying urban vegetation types is provided.

FIG. 1 is a schematic diagram of an implementation scenario of the present invention. FIG. 1 shows an implementation scenario 100 of a method for identifying urban vegetation types. A drone 102 is arranged above the detection area 101 shown in FIG. 1 , and the drone 102 flies above the detection area 101 . Contains buildings and vegetation, such as numbers and flowers. The drone 102 hovering over the detection area 101 is provided with an image acquisition device 103 . The drone 101 uses the image acquisition device 103 to collect detection images of the detection area 101 , and each detection image only includes a part of the detection area 101 , because the image acquisition device 103 of the drone 101 cannot capture the detection area 101 at one time. full image.

Figure 2 is a schematic diagram of a method for identifying urban vegetation types. The urban vegetation type identification method shown in FIG. 2 includes steps S101, S102, S103, S104, and S105. Step S101 , collecting a plurality of detection images of a detection area, wherein the detection images correspond to parts of the detection area. Step S102, performing image splicing on a plurality of detection images to obtain a fusion image of a detection area. Step S103, perform feature extraction on the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image. Step S104: Obtain multiple type coefficients based on the fused color feature vector, the fused texture feature vector, multiple types of color feature vectors corresponding to the fused color feature vector, and multiple types of texture feature vectors corresponding to the fused texture feature vector. Step S105, output the vegetation type corresponding to the minimum value among the plurality of type coefficients.

FIG. 3 is a schematic diagram of a fusion image acquisition process. Referring to FIG. 3 , step S102 specifically includes the following steps: step S201 , step S202 , and step S203 . In step S201, image preprocessing is performed on each detected image. In step S202, image registration is performed on the detection image after image preprocessing to obtain a region image. Image registration is a key step in the process of image stitching, and the accuracy of registration determines the quality of regional images obtained by stitching. Image registration refers to aligning two or more images in space, and it achieves registration by calculating the best match between the two images. In step S203, image fusion is performed on the region images to obtain a fusion image. Image fusion is to eliminate the obvious splicing traces of the image, and realize pixel fusion and interpolation on the overlapping part of the image, so as to obtain a smooth and seamless image, that is, a fusion image.

FIG. 4 is a schematic diagram of an image preprocessing acquisition process. Referring to FIG. 4, step S201 specifically includes step S301, step S302, and step S303. In step S301, image distortion correction is performed on each detected image. Since the image acquisition device used for UAV aerial photography is an ordinary digital camera, there is optical distortion at the edge of the image, and the distortion will deviate the actual image point position in the image, causing displacement of the image point coordinates and changing the ground of the actual object. The position will ultimately affect the matching accuracy and the generation of digital orthoprojection products. Therefore, the distortion difference must be corrected before post-processing of the image. For the lens distortion correction of the UAV image, that is, the image distortion correction, the self-checking method in the aerial triangulation operation of the area network is mainly used to correct the possible systematic errors, including the actual measurement focal length f of the camera, and the image principal point offset value Δx , Δy, various distortion parameters of the objective lens, etc., among which:

Δx, Δy are the distortion correction parameters of the digital camera;

(x,y) is the coordinate of the image point in the image plane coordinate system;

(x ₀ , y ₀ ) is the image principal point coordinate;

(k ₁ , k ₂ ) is the radial distortion coefficient;

(p ₁ ,p ₂ ) is the bias distortion coefficient;

a is the non-square scale factor of CCD;

b is the non-orthogonality distortion coefficient.

In step S302, image smoothing is performed on the detected image. During the process of formation, transmission, reception and processing, the detection images collected by UAVs will contain more or less noise due to external or internal interference, which will degrade the quality of the images. The purpose of image smoothing is to reduce noise and improve image quality. In step S302, image enhancement is performed on the detected image after image smoothing. Its purpose is to improve the visual effect of the image, so that the processed image can meet the needs of some special analysis more than the original image. After the detection image of the UAV is enhanced, it can be more suitable for human observation and computer analysis and processing. It lays a good foundation for subsequent analysis and processing, and it does not increase the relevant information in the image data and change the basic characteristics of the image.

FIG. 5 is a schematic diagram of a feature extraction process. Referring to Fig. 5, step S103 further includes step S401 and step S402. In step S401, the fused image is converted from the RGB color space to the HSV color space. For the fusion image collected by the drone, to extract the available color features, it must be extracted in a specific color space. Common color spaces include RGB color space and HSV color space. Since the RGB color space has the disadvantages of not intuitively representing the cognitive attributes of colors and the color space is not uniform, the present invention will use the HSV color space to represent the color features. The HSV color space is a color model for visual perception. The human eye's perception of color mainly includes three elements: one is the hue of the color, the other is the saturation of the color, and the third is the brightness of the color. Among them, Hue (Hue) refers to the color of the light in the color image; Saturation (Saturation) refers to the depth of the color in the color image; Brightness (Value) refers to the human eye perceives the light from the color image. The degree of light and shade, the degree of light and shade of light is proportional to the reflectivity of the object. Since the fused image is initially displayed in the RGB color model, it is necessary to convert the color value from the RGB expression to the HSV color space expression. Converts to a set of (H, S, V) values for a given set of (R, G, B) values in the RGB color space. Obtained by the following formula:

In step S402, feature extraction is performed on the fused image in the HSV color space to obtain a fused color feature vector and a fused texture feature vector.

In step S104, the calculation formula of the type coefficient is:

in:

d _ni is the type coefficient;

a _i is the type color feature vector;

b _i is the type texture feature vector;

c _n is the fusion color feature vector;

t _n is the fusion texture feature vector;

i is the vegetation type, and i is an integer.

The type color feature vector and the fusion color feature vector are both 256-dimensional vectors; the type texture feature vector and the fusion texture feature vector are both 3-dimensional vectors.

According to one aspect of the present invention, another method for identifying urban vegetation types is provided.

FIG. 6 is another method for identifying urban vegetation types according to the present invention. The method shown in FIG. 6 includes: step S501, above a detection area, collecting a plurality of detection images of the detection area by a drone, wherein the detection images correspond to parts of the detection area; step S502, the drone will Image splicing is performed on the detection images to obtain a fusion image of a detection area; step S503, the UAV performs feature extraction on the fusion image to obtain a fusion color feature vector and a fusion texture feature vector of the fusion image; step S504, the drone is based on the fusion color Feature vectors, fused texture feature vectors, multiple types of color feature vectors corresponding to the fused color feature vectors, and multiple types of texture feature vectors corresponding to the fused texture feature vectors, to obtain multiple type coefficients; step S505, the UAV outputs multiple types of coefficients; The vegetation type corresponding to the minimum value of the type coefficients.

According to one aspect of the present invention, an urban vegetation type identification system is provided.

Figure 7 is a block diagram of the results of an urban vegetation type identification system. The system 200 shown in FIG. 7 includes:

The collection module 201 collects a plurality of detection images of a detection area, wherein the detection images correspond to parts of the detection area;

The fusion module 202 performs image splicing on a plurality of detection images to obtain a fusion image of a detection area;

Extraction module 203, performing feature extraction on the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image;

The coefficient acquisition module 204 obtains multiple types of coefficients based on the fusion color feature vector, the fusion texture feature vector, the multiple types of color feature vectors corresponding to the fusion color feature vector, and the multiple types of texture feature vectors corresponding to the fusion texture feature vector;

The output module 205 outputs the vegetation type corresponding to the minimum value among the plurality of type coefficients.

According to one aspect of the present invention, there is provided a device for identifying urban vegetation types, comprising: a processor; a memory, in which executable instructions of the processor are stored; wherein, when the executable instructions are executed, the processor executes a method for identifying urban vegetation types A step of.

FIG. 8 is a schematic diagram of the apparatus for identifying urban vegetation types according to the present invention. The electronic device 600 according to this embodiment of the present invention is described below with reference to FIG. 8 . The electronic device 600 shown in FIG. 8 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present invention.

As shown in FIG. 8, electronic device 600 takes the form of a general-purpose computing device. Components of the electronic device 600 may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.

The storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the above steps in this specification. For example, the processing unit 610 may perform the steps shown in FIG. 2 .

The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and/or a cache storage unit 6202 , and may further include a read only storage unit (ROM) 6203 .

The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, An implementation of a network environment may be included in each or some combination of these examples.

The bus 630 may be representative of one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures bus.

The electronic device 600 may also communicate with one or more external devices 700 (eg, keyboards, pointing devices, Bluetooth devices, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with Any device (eg, router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 650 . Also, the electronic device 600 may communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through a network adapter 660 . Network adapter 660 may communicate with other modules of electronic device 600 through bus 630 . It should be understood that, although not shown, other hardware and/or software modules may be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage platform, etc.

According to one aspect of the present invention, a computer-readable storage medium is provided for storing a program, which implements the steps of the above method when the program is executed.

FIG. 9 is a schematic structural diagram of a computer-readable storage medium of the present invention. Referring to FIG. 9, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read only memory (CD-ROM) and include program codes, and can be used in a terminal device, For example running on a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (non-exhaustive list) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

A computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable storage medium can also be any readable medium other than a readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural programming Language - such as the "C" language or similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).

To sum up, the method, system, device and medium for identifying urban vegetation types in the present invention can splicing the multiple detection images to form a complete detection image through the multiple detection images of the detection area above the detection area by the drone. Fuse the images, and then perform feature extraction on the fused image to obtain its color features and texture features, and then calculate and obtain multiple type coefficients according to the color features and texture features, and the minimum value of the multiple type coefficients corresponds to the vegetation type. Quickly obtain the type of vegetation in the detection area with low cost.

The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be considered that the specific implementation of the present invention is limited to these descriptions. For those of ordinary skill in the technical field of the present invention, without departing from the concept of the present invention, some simple deductions or substitutions can be made, which should be regarded as belonging to the protection scope of the present invention.

Claims

A method for identifying urban vegetation types, comprising:

collecting a plurality of detection images of a detection area, wherein the detection images correspond to parts of the detection area;

Perform image splicing on the plurality of detection images to obtain a fusion image of the detection area;

Perform feature extraction on the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image;

Based on the fused color feature vector, the fused texture feature vector, multiple types of color feature vectors corresponding to the fused color feature vector, and multiple types of texture feature vectors corresponding to the fused texture feature vector, a plurality of type factor;

The vegetation type corresponding to the minimum value among the plurality of type coefficients is output.
The method for identifying urban vegetation types according to claim 1, wherein the performing image splicing on the plurality of detection images to obtain a fusion image of the detection area comprises:

performing image preprocessing on each of the detected images;

performing image registration on the detection image after the image preprocessing to obtain an area image;

Perform image fusion on the region image to obtain the fusion image.
The method for identifying urban vegetation types according to claim 2, wherein the performing image preprocessing on each of the detected images comprises:

performing image distortion correction on each of the detected images;

performing image smoothing on the detected image;

Image enhancement is performed on the detected image after image smoothing.
The method for identifying urban vegetation types according to claim 1, wherein the calculation formula of the type coefficient is:

in:

d ni is the type coefficient;

a i is the type color feature vector;

b i is the type texture feature vector;

c n is the fusion color feature vector;

t n is the fusion texture feature vector;

i is the vegetation type, and i is an integer.
The method for identifying urban vegetation types according to claim 1, wherein,

The type color feature vector and the fusion color feature vector are both 256-dimensional vectors;

The type texture feature vector and the fusion texture feature vector are both 3-dimensional vectors.
The method for identifying urban vegetation types according to claim 1, wherein the feature extraction of the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image further comprises:

converting the fused image from the RGB color space to the HSV color space;

Perform feature extraction on the fused image in the HSV color space to obtain the fused color feature vector and the fused texture feature vector.
A method for identifying urban vegetation types, comprising:

Above a detection area, a plurality of detection images of the detection area are collected by a drone, wherein the detection images correspond to parts of the detection area;

The UAV performs image splicing on the plurality of detection images to obtain a fusion image of the detection area;

The UAV performs feature extraction on the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image;

The UAV is based on the fused color feature vector, the fused texture feature vector, multiple types of color feature vectors corresponding to the fused color feature vector, and multiple types of texture features corresponding to the fused texture feature vector. vector to obtain multiple type coefficients;

The UAV outputs the vegetation type corresponding to the minimum value among the plurality of type coefficients.
An urban vegetation type identification system, characterized in that it includes:

a collection module, collecting a plurality of detection images of a detection area, wherein the detection images correspond to a part of the detection area;

a fusion module, performing image splicing on the plurality of detection images to obtain a fusion image of the detection area;

an extraction module, which performs feature extraction on the fused image to obtain a fused color feature vector and a fused texture feature vector of the fused image;

A coefficient acquisition module, based on the fused color feature vector, the fused texture feature vector, multiple types of color feature vectors corresponding to the fused color feature vector, and multiple types of texture feature vectors corresponding to the fused texture feature vector , to obtain multiple type coefficients;

The output module outputs the vegetation type corresponding to the minimum value among the plurality of type coefficients.
An urban vegetation type identification device, characterized in that it includes:

processor;

a memory in which executable instructions for the processor are stored;

Wherein, the processor is configured to execute the steps of the urban vegetation type identification method according to any one of claims 1-8 by executing the executable instructions.
A computer-readable storage medium for storing a program, characterized in that, when the program is executed, the steps of the method for identifying urban vegetation types according to any one of claims 1-8 are implemented.