CN117237684B - A pixel-level area matching method and device - Google Patents

Abstract

The invention provides a pixel-level region matching method and a pixel-level region matching device, wherein the method comprises the following steps: acquiring first characteristic parameter information of a target protection area and second characteristic parameter information of a target candidate area; constructing a first multi-channel feature map according to the first feature parameter information, and constructing a second multi-channel feature map according to the second feature parameter information; calculating target pixel points matched with each pixel in the first multi-channel feature map on the second multi-channel feature map, and obtaining a comparison feature map corresponding to the target candidate region according to each target pixel point; the first multi-channel feature map and the control feature map are saved as a control image group. According to the method, the problem that quantitative analysis cannot be performed due to excessive dependence on priori knowledge is avoided by pixelating the features in the region, the region matching at the pixel level is realized by constructing the multi-channel feature map, the control image group with high comparability and accuracy is obtained, and the evaluation accuracy of the target protection region is improved.

Description

A pixel-level area matching method and device

Technical field

The present invention relates to the field of area monitoring technology, and in particular to a pixel-level area matching method and device.

Background technique

The establishment of nature reserves has long been recognized as one of the most effective conservation measures for protecting biological diversity. There are many nature reserves around the world, covering a large area, but how to scientifically evaluate the effects of the establishment of nature reserves has been controversial. Evaluating the conservation effect of nature reserves depends largely on the selection of controls. Only by selecting a highly comparable control can reliable evaluation results be obtained.

The existing technology is based on a large amount of prior knowledge and parameter adjustment to obtain the comparison. The comparison obtained in this way will be affected by subjective factors, that is, the obtained comparison is not accurate, resulting in inaccurate evaluation results.

Therefore, the existing technology has defects and needs to be improved and developed.

Contents of the invention

This application provides a pixel-level area matching method and device to solve the technical problem of inaccurate comparison obtained in related technologies.

In order to achieve the above purpose, this application adopts the following technical solutions:

The first embodiment of the present application provides a pixel-level area matching method, which includes the following steps:

Obtain the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area;

Construct a first multi-channel feature map based on the first feature parameter information, and construct a second multi-channel feature map based on the second feature parameter information;

Calculate target pixel points on the second multi-channel feature map that match each pixel in the first multi-channel feature map, and obtain a comparison feature map corresponding to the target candidate area based on each target pixel point;

The first multi-channel feature map and the comparison feature map are saved as a comparison image group.

Optionally, the target candidate area is a circular area with the center point of the target protected area as the center, a first preset distance as the radius, and a circular area with the second preset distance as the radius. area as the second area, obtained by removing the first area from the second area; the second preset distance is greater than the first preset distance, and the area of the first area is greater than the target protection area of the district.

Optionally, the obtaining the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area includes:

Obtain first remote sensing image data corresponding to the target protected area, and obtain first characteristic parameter information of the target protected area based on the first remote sensing image data;

Obtain second remote sensing image data corresponding to the target candidate area, and obtain second characteristic parameter information of the target protected area based on the second remote sensing image data;

The characteristic parameters in the first characteristic parameter information and the second characteristic parameter information include: slope, altitude, vegetation type, deforestation status, first distance from the road, second distance from the water source, and distance from human activities The third distance of the area.

Optionally, constructing a first multi-channel feature map based on the first feature parameter information, and constructing a second multi-channel feature map based on the second feature parameter information includes:

Use each characteristic parameter in the first characteristic parameter information as a channel feature to construct a first multi-channel feature map;

Each feature parameter in the second feature parameter information is used as a channel feature to construct a second multi-channel feature map.

Optionally, calculate the target pixel points on the second multi-channel feature map that match each pixel in the first multi-channel feature map, and obtain a comparison feature map corresponding to the target candidate area based on each target pixel point. ,include:

Using the nearest neighbor algorithm to calculate the feature similarity between each pixel in the first multi-channel feature map and each pixel point in the second multi-channel feature map;

Use pixels whose feature similarity reaches a preset threshold as target pixels;

Each target pixel point is gathered to obtain a comparison feature map corresponding to the target candidate area.

Optionally, after saving the first multi-channel feature map and the comparison feature map as a comparison image group, the method further includes:

The target protected area and the target candidate area are monitored according to the comparison image group, and the protection effect evaluation result of the target protected area is obtained based on the monitoring results.

Optionally, monitor the target protected area and the target candidate area according to the comparison image group, and obtain the protection effect evaluation results of the target protected area based on the monitoring results, including:

When the predetermined monitoring time is reached, obtain the third remote sensing image data of the current target protected area and the fourth remote sensing image data of the target candidate area;

Obtain third characteristic parameter information of the target protected area according to the third remote sensing image data, and obtain fourth characteristic parameter information of the target candidate area according to the fourth remote sensing image data;

Construct a third multi-channel feature map based on the third feature parameter information, and construct a fourth multi-channel feature map based on the fourth feature parameter information;

Calculate target pixel points on the fourth multi-channel feature map that match each pixel in the third multi-channel feature map, and obtain a change feature map corresponding to the target candidate area based on each target pixel point;

Compare the fourth multi-channel feature map with the first multi-channel feature map in the comparison image group to obtain protection zone change information;

Compare the change feature map with the control feature map in the control image group to obtain candidate area change information;

The protection effect evaluation result of the target protected area is obtained based on the protected area change information and the candidate area change information.

The second embodiment of the present application provides a pixel-level area matching device, including:

An acquisition module, used to acquire the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area;

A building module configured to construct a first multi-channel feature map based on the first feature parameter information, and a second multi-channel feature map based on the second feature parameter information;

A calculation module configured to calculate target pixel points on the second multi-channel feature map that match each pixel in the first multi-channel feature map, and obtain comparison features corresponding to the target candidate area based on each target pixel point. picture;

A saving module, configured to save the first multi-channel feature map and the comparison feature map as a comparison image group.

A third embodiment of the present application provides a terminal. The terminal includes a memory, a processor, and a pixel-level area matching program stored in the memory and executable on the processor. The processor executes the When describing the pixel-level area matching procedure, the steps of the pixel-level area matching method as described above are implemented.

A fourth embodiment of the present application provides a computer-readable storage medium. A pixel-level area matching program is stored on the computer-readable storage medium. When the pixel-level area matching program is executed by a processor, the above steps are implemented. The steps of the pixel-level region matching method described above.

Beneficial effects of the present invention: The embodiment of the present invention obtains the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area; constructs a first multi-channel feature map according to the first characteristic parameter information, and according to The second feature parameter information constructs a second multi-channel feature map; calculates the target pixel points on the second multi-channel feature map that match each pixel in the first multi-channel feature map, and based on each target pixel point A comparison feature map corresponding to the target candidate area is obtained; and the first multi-channel feature map and the comparison feature map are saved as a comparison image group. This invention avoids the problem of being unable to perform quantitative analysis due to over-reliance on prior knowledge by pixelating the features in the region. By constructing a multi-channel feature map, it achieves pixel-level regional matching and obtains highly comparable and accurate comparisons. Image sets, thereby improving the accuracy of assessment of target protected areas.

Description of the drawings

The above and/or additional aspects and advantages of the present application will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

Figure 1 is a flow chart of a preferred embodiment of the pixel-level area matching method in the present invention.

Figure 2 is a schematic diagram of the relationship between the target protection area, the buffer area and the target candidate area in a preferred embodiment of the pixel-level area matching method of the present invention.

Figure 3 is a multi-channel feature map in a preferred embodiment of the pixel-level area matching method in the present invention.

FIG. 4 is a functional block diagram of a preferred embodiment of the pixel-level area matching device in the present invention.

Figure 5 is a functional functional block diagram of a preferred embodiment of the terminal in the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention clearer and clearer, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present invention and are not intended to limit the present invention.

The main disadvantages of existing techniques include: (1) they may not adequately capture the spatial heterogeneity and local variation of forest landscapes; (2) they are computationally expensive, especially in large-scale projects; (3) they require extensive prior knowledge and Parameter adjustment, which may cause the results to be affected by subjective factors; (4) The generally selected area is a continuous area.

Referring to Figure 1, the pixel-level area matching method according to the embodiment of the present invention includes the following steps:

Step S100: Obtain the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area.

In one embodiment, the target candidate area is a circular area with the center point of the target protected area as the center, a first preset distance as the radius as the first area, and a second preset distance as the radius. The circular area is used as the second area, which is obtained by removing the first area from the second area; the second preset distance is greater than the first preset distance, and the area of the first area is greater than the Describe the area of the target protected area.

Specifically, the protected area to be monitored is used as the target protected area, and a buffer zone with a fixed radius is selected around the target protected area. The buffer area is the area obtained after removing the target protected area from the first area, and the target candidate area is the area removed from the second area. The area obtained after the first area. For example, the first preset distance is set to 300km, and the second preset distance is set to 700km. As shown in Figure 2, the circular area in the middle represents the target protection zone, and the first annular area outside the circle is the buffer zone. The two annular areas are target candidate areas.

The embodiment of the present application selects the target candidate area corresponding to the target protected area through the above method, which can not only obtain the similar environment of the target protected area, but also avoid the problem that the comparison result is not obvious due to the protection area and the candidate area being too close, thereby achieving comparability. Strong contrast.

In one implementation, the step S100 specifically includes: obtaining the first remote sensing image data corresponding to the target protected area, and obtaining the first characteristic parameter information of the target protected area according to the first remote sensing image data; obtaining According to the second remote sensing image data corresponding to the target candidate area, the second characteristic parameter information of the target protected area is obtained according to the second remote sensing image data; among the first characteristic parameter information and the second characteristic parameter information The characteristic parameters include: slope, altitude, vegetation type, deforestation, first distance from roads, second distance from water sources, and third distance from human activity areas.

Specifically, the embodiment of this application collects multiple characteristic parameters of the target protected area and the target candidate area respectively. The characteristic parameters include slope, altitude, vegetation type, deforestation status, first distance from the road, second distance from the water source, and Third distance from human activity areas, etc. These characteristic parameters can be obtained from remote sensing image data. Remote Sensing Image (RS) refers to films or photos that record the size of electromagnetic waves of various ground objects. It is mainly divided into aerial photos and satellite photos. The embodiment of this application converts the geographical matching problem into the target pixel point matching problem in the image, and uses remote sensing image processing technology to obtain information about protected areas and candidate areas. Among them, the first distance to the road can be obtained by obtaining road network data, and then using an algorithm to obtain the distance of each location from the road.

Embodiments of this application may also use other remote sensing data sources, such as optical remote sensing, synthetic aperture radar remote sensing, etc., to further enrich feature information.

The embodiments of this application use traditional remote sensing processing methods, such as minimum distance classification, support vector machines, etc., to perform feature matching. By obtaining multiple feature parameters of the area, multi-channel feature maps can be constructed based on each feature parameter, thereby achieving pixel-level Region matching ultimately improves the accuracy of assessment.

As shown in Figure 1, the pixel-level area matching method also includes the following steps:

Step S200: Build a first multi-channel feature map based on the first feature parameter information, and build a second multi-channel feature map based on the second feature parameter information.

In one embodiment, the step S200 specifically includes: using each feature parameter in the first feature parameter information as a channel feature to construct a first multi-channel feature map; using the second feature parameter information Each feature parameter in is used as a channel feature to construct a second multi-channel feature map.

Specifically, this embodiment constructs the features of slope, altitude, vegetation type, deforestation, first distance from the road, second distance from the water source, and third distance from the human activity area as channel features of the image. As shown in Figure 3, each channel on the image represents a feature. Each target pixel is a 1 * n feature vector composed of n features. Among them, each pixel of the target protected area and target candidate area is a 1 * n feature vector, and the difference lies in the number of pixels.

The embodiment of this application implements a pixel-level candidate area matching method by constructing a multi-channel feature map, thereby obtaining a highly comparable comparison.

As shown in Figure 1, the pixel-level area matching method also includes the following steps:

Step S300: Calculate target pixel points on the second multi-channel feature map that match each pixel in the first multi-channel feature map, and obtain a comparison feature map corresponding to the target candidate area based on each target pixel point.

In one embodiment, the step S300 specifically includes: using the nearest neighbor algorithm to calculate the feature similarity between each pixel in the first multi-channel feature map and each pixel point in the second multi-channel feature map. degree; use pixels whose feature similarity reaches a preset threshold as target pixels; gather each of the target pixels to obtain a comparison feature map corresponding to the target candidate area.

Specifically, the best pixel point matched to the target protected area pixel i using KNN (nearest neighbor algorithm) from the target candidate area is used as the target pixel point. Each target pixel point on the target protected area corresponds to one of the target candidate areas. target pixel. Finally, all matched points are combined into a complete control feature map for comparative analysis.

This embodiment can fully capture spatial heterogeneity and local changes. By pixelating the geographical location and channelizing the image of influencing factors, it can avoid the problem of being unable to perform quantitative analysis due to excessive reliance on prior knowledge, and can achieve point-to-point comparative analysis. , instead of using the continuous area to continuous area comparison method.

As shown in Figure 1, the pixel-level area matching method also includes the following steps:

Step S400: Save the first multi-channel feature map and the comparison feature map as a comparison image group.

The embodiments of the present application improve the accuracy of comparative analysis of protected areas and reduce the influence of subjective factors; improve the efficiency of comparative analysis through automated processing; and the matching effect based on the KNN algorithm is better than the traditional distance matching method; pixel-level Matching can avoid mismatching or missing matching problems in the region matching process.

In one embodiment, the step S400 further includes: step S500, monitoring the target protected area and the target candidate area according to the comparison image group, and obtaining the target protected area according to the monitoring results. Protection effect evaluation results.

Specifically, in the embodiment of the present application, on the premise of obtaining a highly comparable control image pair, all subsequent monitoring data are compared with the control image pair, which improves the accuracy of the comparative analysis of the protected area and reduces the risk of subjective factors. Influence.

In one implementation, step S500 specifically includes:

Step S510: When the predetermined monitoring time is reached, obtain the third remote sensing image data of the current target protected area and the fourth remote sensing image data of the target candidate area;

Step S520: Obtain third characteristic parameter information of the target protected area according to the third remote sensing image data, and obtain fourth characteristic parameter information of the target candidate area according to the fourth remote sensing image data;

Step S530: Construct a third multi-channel feature map based on the third feature parameter information, and construct a fourth multi-channel feature map based on the fourth feature parameter information;

Step S540: Calculate the target pixel points on the fourth multi-channel feature map that match each pixel in the third multi-channel feature map, and obtain the change feature map corresponding to the target candidate area according to each target pixel point;

Step S550: Compare the fourth multi-channel feature map with the first multi-channel feature map in the comparison image group to obtain protection zone change information;

Step S560: Compare the change feature map with the control feature map in the control image group to obtain candidate area change information;

Step S570: Obtain the protection effect evaluation result of the target protected area based on the protected area change information and the candidate area change information.

Specifically, the embodiments of the present application improve the efficiency of comparative analysis through automated processing. The embodiments of this application can also introduce time series data to implement dynamic comparative analysis to evaluate the protection effect of the protected area in different time periods. The application scenarios of this application can include wetland reserves, marine reserves, etc., with wide versatility.

In one embodiment, as shown in Figure 4, based on the above pixel level area matching method, the present invention also provides a pixel level area matching device, including:

The acquisition module 100 is used to acquire the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area;

Building module 200, configured to construct a first multi-channel feature map based on the first feature parameter information, and build a second multi-channel feature map based on the second feature parameter information;

The calculation module 300 is used to calculate the target pixel points on the second multi-channel feature map that match each pixel in the first multi-channel feature map, and obtain the comparison corresponding to the target candidate area based on each target pixel point. feature map;

The saving module 400 is configured to save the first multi-channel feature map and the comparison feature map as a comparison image group.

It should be noted that the foregoing explanation of the pixel-level area matching method embodiment also applies to the pixel-level area matching device of this embodiment, and will not be described again here.

The invention discloses a pixel-level area matching method and device. The method includes: obtaining the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area; constructing the third characteristic parameter information according to the first characteristic parameter information. A multi-channel feature map, and constructing a second multi-channel feature map based on the second feature parameter information; calculating targets on the second multi-channel feature map that match each pixel in the first multi-channel feature map According to each target pixel point, a comparison feature map corresponding to the target candidate area is obtained; and the first multi-channel feature map and the comparison feature map are saved as a comparison image group. This invention avoids the problem of being unable to conduct quantitative analysis due to over-reliance on prior knowledge by pixelating the features in the region. By constructing a multi-channel feature map, it achieves pixel-level regional matching and obtains highly comparable and accurate comparisons. Image sets, thereby improving the accuracy of assessment of target protected areas.

Figure 5 is a schematic structural diagram of a terminal provided by an embodiment of the present application. The terminal can include:

Memory 501, processor 502, and a computer program stored on memory 501 and executable on processor 502.

When the processor 502 executes the program, it implements the pixel-level area matching method provided in the above embodiment.

Furthermore, the terminal also includes:

Communication interface 503 is used for communication between the memory 501 and the processor 502.

Memory 501 is used to store computer programs that can be run on processor 502.

The memory 501 may include high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and complete communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of presentation, only one line is used in the figure, but it does not mean that there is only one bus or one type of bus.

Optionally, in terms of specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on one chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through the internal interface.

The processor 502 may be a Central Processing Unit (CPU for short), an Application Specific Integrated Circuit (ASIC for short), or one or more processors configured to implement the embodiments of the present application. integrated circuit.

This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above pixel-level region matching method is implemented.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "an example," "specific examples," or "some examples" or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.

In addition, the terms “first” and “second” are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically limited.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent modules, segments, or portions of code that include one or N executable instructions for implementing customized logical functions or steps of the process, And the scope of the preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functionality involved, which should be considered herein. It is understood by those skilled in the art to which the embodiments of the application belong.

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered a sequenced list of executable instructions for implementing the logical functions, and may be embodied in any computer-readable medium, For use with or in conjunction with an instruction execution system, device or device (such as a computer-based system, a system including a processor or other system that can read instructions from an instruction execution system, device or device and execute the instructions), device or equipment. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections with one or N wires (electronic device), portable computer disk cartridge (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium on which the program is printed, as the paper or other medium may be optically scanned and subsequently edited, interpreted, or otherwise processed as necessary. to obtain a program electronically and then store it in computer memory.

It should be understood that various parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: discrete logic circuits having logic gate circuits for implementing logical functions on data signals , application-specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

Those of ordinary skill in the art can understand that all or part of the steps involved in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, , including one of the steps of the method embodiment or a combination thereof.

In addition, each functional unit in various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software function modules. Integrated modules can also be stored in a computer-readable storage medium if they are implemented in the form of software function modules and sold or used as independent products.

The storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are illustrative and cannot be understood as limitations of the present application. Those of ordinary skill in the art can make modifications to the above-mentioned embodiments within the scope of the present application. The embodiments are subject to changes, modifications, substitutions and variations.

Claims (10)

1. A pixel-level area matching method, characterized by: Obtain the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area; Construct a first multi-channel feature map based on the first feature parameter information, and construct a second multi-channel feature map based on the second feature parameter information; Calculate target pixel points on the second multi-channel feature map that match each pixel in the first multi-channel feature map, and obtain a comparison feature map corresponding to the target candidate area based on each target pixel point; The first multi-channel feature map and the comparison feature map are saved as a comparison image group. 2. The pixel-level area matching method according to claim 1, wherein the target candidate area is a circular area with the center point of the target protection zone as the center and the first preset distance as the radius. As the first area, a circular area with the second preset distance as the radius is used as the second area, which is obtained by removing the first area from the second area; the second preset distance is greater than the first The preset distance is such that the area of the first area is larger than the area of the target protection zone. 3. The pixel-level area matching method according to claim 1, wherein said obtaining the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area includes: Obtain first remote sensing image data corresponding to the target protected area, and obtain first characteristic parameter information of the target protected area based on the first remote sensing image data; Obtain second remote sensing image data corresponding to the target candidate area, and obtain second characteristic parameter information of the target protected area based on the second remote sensing image data; The characteristic parameters in the first characteristic parameter information and the second characteristic parameter information include: slope, altitude, vegetation type, deforestation status, first distance from the road, second distance from the water source, and distance from human activities The third distance of the area. 4. The pixel-level region matching method according to claim 3, characterized in that a first multi-channel feature map is constructed according to the first characteristic parameter information, and a second multi-channel feature map is constructed according to the second characteristic parameter information. Feature maps, including: Use each characteristic parameter in the first characteristic parameter information as a channel feature to construct a first multi-channel feature map; Each feature parameter in the second feature parameter information is used as a channel feature to construct a second multi-channel feature map. 5. The pixel-level region matching method according to claim 3, characterized in that, calculating the target pixel point on the second multi-channel feature map that matches each pixel in the first multi-channel feature map, Obtain the comparison feature map corresponding to the target candidate area according to each target pixel point, including: Using the nearest neighbor algorithm to calculate the feature similarity between each pixel in the first multi-channel feature map and each pixel point in the second multi-channel feature map; Use pixels whose feature similarity reaches a preset threshold as target pixels; Each target pixel point is gathered to obtain a comparison feature map corresponding to the target candidate area. 6. The pixel-level area matching method according to claim 1, characterized in that, after saving the first multi-channel feature map and the comparison feature map as a comparison image group, it further includes: The target protected area and the target candidate area are monitored according to the comparison image group, and the protection effect evaluation result of the target protected area is obtained based on the monitoring results. 7. The pixel-level area matching method according to claim 6, characterized in that the target protected area and the target candidate area are monitored according to the comparison image group, and the target protection is obtained according to the monitoring results. The results of the area’s protection effect assessment include: When the predetermined monitoring time is reached, obtain the third remote sensing image data of the current target protected area and the fourth remote sensing image data of the target candidate area; Obtain third characteristic parameter information of the target protected area according to the third remote sensing image data, and obtain fourth characteristic parameter information of the target candidate area according to the fourth remote sensing image data; Construct a third multi-channel feature map based on the third feature parameter information, and construct a fourth multi-channel feature map based on the fourth feature parameter information; Calculate target pixel points on the fourth multi-channel feature map that match each pixel in the third multi-channel feature map, and obtain a change feature map corresponding to the target candidate area based on each target pixel point; Compare the fourth multi-channel feature map with the first multi-channel feature map in the comparison image group to obtain protection zone change information; Compare the change feature map with the control feature map in the control image group to obtain candidate area change information; The protection effect evaluation result of the target protected area is obtained based on the protected area change information and the candidate area change information. 8. A pixel-level area matching device, characterized by including: An acquisition module, used to acquire the first characteristic parameter information of the target protected area and the second characteristic parameter information of the target candidate area; A building module configured to construct a first multi-channel feature map based on the first feature parameter information, and a second multi-channel feature map based on the second feature parameter information; A calculation module configured to calculate target pixel points on the second multi-channel feature map that match each pixel in the first multi-channel feature map, and obtain comparison features corresponding to the target candidate area based on each target pixel point. picture; A saving module, configured to save the first multi-channel feature map and the comparison feature map as a comparison image group. 9. A terminal, characterized in that the terminal includes a memory, a processor, and a pixel-level area matching program stored in the memory and operable on the processor, and the processor executes the pixel When performing a level area matching procedure, the steps of implementing the pixel level area matching method as described in any one of claims 1 to 7 are implemented. 10. A computer-readable storage medium, characterized in that a pixel-level area matching program is stored on the computer-readable storage medium. When the pixel-level area matching program is executed by a processor, the implementation as claimed in claim 1 -The steps of the pixel-level area matching method described in any of 7.