CN113724172A - Town boundary extraction method and system based on landscape function data - Google Patents
Town boundary extraction method and system based on landscape function data Download PDFInfo
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Abstract
The invention provides a town boundary extraction method and system based on landscape functional data, wherein the method comprises the following steps: and performing reclassification on different landscape function types based on the landscape function data to obtain reclassified images including town, country and urban and rural confusion areas, identifying the urban and rural confusion areas in the reclassified images as towns or villages, optimizing urban and rural boundaries, and extracting the urban and rural boundaries. The system which forms the same inventive concept with the method is also disclosed. The invention realizes the optimized extraction of the town boundary and improves the automation degree and the spatial resolution of the recognition of the town spatial range.
Description
Technical Field
The invention relates to the technical field of geographic and remote sensing image processing, in particular to a town boundary extraction method and system based on landscape functional data.
Background
The urban area provides production, living and ecological service space for urban residents, is heterogeneous and diverse in function types, and shows continuous hole-free morphological characteristics and the like in space. Town areas are spatially composed of heterogeneous functional areas, including not only areas where humans are dominant (such as commercial, residential and industrial areas), but also some relatively natural landscapes (such as lakes, forests and parks), and are therefore highly heterogeneous. In addition, some town landscapes have similar manifestations as country landscapes. For example, shed residential areas are confused with rural homesteads as a type of town residence because they all have a low, dense, chaotic building pattern and characteristics; in addition, both urban and rural areas have relatively natural landscapes, such as forests and lakes. Therefore, it is difficult to distinguish these town landscapes from country landscapes. In conclusion, the inside of the urban area has strong heterogeneity, and some landscapes are confused with the rural areas. The heterogeneity inside cities and towns and the confusion of cities and towns make the extraction of the boundaries of cities and towns very difficult.
However, the prior art methods roughly represent urban areas by densely populated, nighttime lighting, densely built and artificially impervious areas, completely ignoring both of the above problems. The mainstream method is to identify a unit with high population density belonging to an urban area based on the population density of an administrative unit (such as a district or a street), but real-time accurate population data is difficult to obtain. Thus, other methods utilize night-time satellite images (such as DMSP/OLS and LJ1-01) to measure population distributions and determine urban areas with high nighttime brightness. Similar still other methods calculate building and road densities to measure build-up indices and further extract town areas where built-up areas are considered to be places with dense building and road networks. The method has two problems in extracting town areas: first, it is difficult to select a common night light threshold or population and building density to define town areas, since different towns have different night light intensities, populations and building densities; second, these methods typically produce town spatial ranges with coarse spatial resolution. To address these problems, many current methods are based on machine learning techniques to extract water-impervious surfaces from satellite images to represent town areas. This method has a low dependency on the manually defined threshold and a relatively high resolution (typically better than 30 meters). Although these methods can effectively identify artificial construction areas, relatively natural landscapes (water bodies in towns, forest parks, etc.) in towns, which provide ecological services for towns, are important components of town areas, cannot be extracted.
Disclosure of Invention
The embodiment of the invention provides a town boundary extraction method and system based on landscape functional data, which are used for solving the problems of part or all of the problems of town area identification in the prior art in the town boundary extraction process.
In a first aspect, an embodiment of the present invention provides a town boundary extraction method based on landscape functional data, including:
reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban, rural and urban-rural confusion areas;
and identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary so as to extract the urban and rural boundary.
Preferably, the reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban, rural and urban-rural confusion areas comprises:
acquiring landscape function data covering towns and villages, wherein the landscape function data comprises different landscape function types of the towns and the villages;
and re-classifying the different landscape function types based on the re-classification rule to obtain re-classified images including urban, rural and urban-rural confusion areas.
Preferably, identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary is obtained by carrying out urban and rural division morphological operation on the basis of urban and rural boundary expansion operator and urban and rural boundary corrosion operator iteration; and the urban and rural boundary expansion operator and the urban and rural boundary corrosion operator correspond to different types of pixel convolution operation kernels.
Preferably, identifying the urban and rural confusion area as a town or a country and optimizing the urban and rural boundary is obtained by performing urban and rural division morphological operation based on the iteration of an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator, and comprises the following steps:
identifying urban and rural confusion areas as towns or villages and optimizing the urban and rural boundaries based on the alternate corresponding execution of each urban and rural boundary expansion operator and each urban and rural boundary corrosion operator until all the urban and rural confusion areas are identified as towns or villages, wherein the operation execution process is as follows:
traversing each pixel in the reclassified image with the landscape function;
searching a matched pixel convolution operation kernel based on the current pixel type, the pixel type in the current pixel 3x3 field and the currently set operation type; the currently set operation types comprise an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator;
and modifying the category of the current pixel based on the output category of the convolution operation kernel so as to convert the type pixel of the urban and rural confusion area in the reclassified image into the type pixel of the town or the country.
In a second aspect, an embodiment of the present invention provides a town boundary extraction system based on landscape function data, including:
the type reclassification module is used for reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban areas, rural areas and mixed urban and rural areas;
and the identification and optimization module is used for identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary so as to extract the urban and rural boundary.
Preferably, the type reclassification module comprises a data acquisition module and a reclassification module;
the data acquisition module is used for acquiring landscape function data covering towns and villages, and the landscape function data comprises different landscape function types of the towns and the villages;
and the reclassification module is used for reclassifying different landscape function types based on reclassification rules to obtain reclassified images including town, country and urban and rural confusion areas.
Preferably, the identification and optimization module is configured to identify an urban and rural confusion area in the reclassified image as a town or a country and optimize an urban and rural boundary, and is obtained by performing urban and rural division morphological operation based on an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator iteration; and the urban and rural boundary expansion operator and the urban and rural boundary corrosion operator correspond to different types of pixel convolution operation kernels.
Preferably, the identifying and optimizing module is further specifically configured to identify the urban and rural confusion area as a town or a country and optimize the urban and rural boundary based on the alternate corresponding execution of each urban and rural boundary expansion operator and each urban and rural boundary erosion operator until all the urban and rural confusion areas are identified as a town or a country, and the operation execution process is as follows:
traversing each pixel in the reclassified image with the landscape function;
searching a matched pixel convolution operation kernel based on the current pixel type, the pixel type in the current pixel 3x3 field and the currently set operation type; the currently set operation types comprise an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator;
and modifying the category of the current pixel based on the output category of the convolution operation kernel so as to convert the type pixel of the urban and rural confusion area in the reclassified image into the type pixel of the town or the country.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps of any one of the methods for extracting town boundary based on landscape function data as provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method for extracting town boundary based on landscape function data as any one of the methods provided in the first aspect.
According to the town boundary extraction method and system based on the landscape function data, provided by the embodiment of the invention, the different landscape function types are reclassified based on the landscape function data to obtain reclassified images comprising town, country and urban and rural confusion areas, the urban and rural confusion areas in the reclassified images are identified as towns or villages, the urban and rural boundaries are optimized, and the town boundaries are extracted. The invention realizes the optimized extraction of the town boundary and improves the automation degree and the spatial resolution of the recognition of the town spatial range.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a town boundary extraction method based on landscape functional data provided by the invention;
FIG. 2 is a schematic view of the reclassification process of landscape functional data provided by the present invention;
FIG. 3 is a schematic structural diagram of an urban and rural boundary expansion operator and an urban and rural boundary erosion operator provided by the present invention;
FIG. 4 is a schematic structural diagram of a town boundary extraction system based on landscape function data according to the present invention;
FIG. 5 is a schematic diagram of a type reclassification module provided by the present invention;
fig. 6 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following describes a town boundary extraction method and system based on landscape function data provided by the present invention with reference to fig. 1-6.
The embodiment of the invention provides a town boundary extraction method based on landscape functional data. Fig. 1 is a schematic flow chart of a town boundary extraction method based on landscape functional data according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
110, reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban, rural and urban-rural confusion areas;
specifically, re-classification rules are defined, and different landscape function types in the landscape function data are re-classified into towns, villages and urban and rural confusion areas.
And 120, identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing an urban and rural boundary to extract the urban and rural boundary.
Specifically, knowledge-driven urban and rural boundary expansion and erosion operators are defined, and urban and rural confusion zones are identified as towns or villages and urban and rural boundaries are optimized based on the defined urban and rural boundary expansion and erosion operators.
Compared with the prior art, the method provided by the embodiment of the invention reclassifies the landscape function data based on the defined landscape function type reclassification rule to obtain a reclassified image comprising a town, a country and a town-country confusion area, identifies the town-country confusion area in the reclassified image as the town or the country based on the knowledge-driven town-country boundary expansion and corrosion operator, realizes the automatic identification and optimized extraction of the town boundary, and applies the result to the township monitoring, planning and management. The invention realizes the optimized extraction of the town boundary and improves the automation degree and the spatial resolution of the recognition of the town spatial range.
Based on any of the above embodiments, as shown in fig. 2, the reclassifying different landscape function types based on the landscape function data to obtain reclassified images including town, country and urban and rural confusion areas includes:
specifically, the landscape function data generally includes the types of the landscape functions of cities and towns such as industrial areas, commercial areas, first-class residential areas, second-class residential areas, third-class residential areas, science and education institutions, unused areas, public transportation areas, public open spaces, forest lawns, water areas, farmlands, and the like.
And step 220, reclassifying the different landscape function types based on reclassification rules to obtain reclassified images including urban, rural and urban-rural confusion areas.
Specifically, reclassification rules are defined, farmland is reclassified as village, forest grassland, water area and three types of residential areas are reclassified as urban and rural confusion area, and other functional categories are reclassified as town. Therefore, different landscape function types in the landscape function data are reclassified into three categories of cities, towns, villages, urban and rural confusion areas and the like by defining reclassification rules.
Based on any embodiment, identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary is obtained by carrying out urban and rural division morphological operation on the basis of the iteration of an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator; the urban and rural boundary expansion operator and the urban and rural boundary corrosion operator correspond to different types of pixel convolution operation kernels.
Specifically, defining an urban and rural boundary expansion operator, and identifying an urban and rural confusion area as a town or a country according to urban and rural morphological characteristics; defining an urban and rural boundary corrosion operator, and optimizing the reliability of the urban and rural boundary; iterative operation of an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator to realize urban and rural boundary identification and optimized extraction
Defining an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator:
Fig. 3 is a schematic structural diagram of an urban and rural boundary dilation operator and an urban and rural boundary erosion operator provided in the embodiment of the present invention. As shown in fig. 3, the urban and rural boundary dilation operator is composed of 7 operators, and its main purpose is to identify the urban and rural confusion area as a town or a country according to the morphological characteristics of the town and the country. In addition, the urban and rural boundary corrosion operator is also composed of 7 operators, and the main purpose of the urban and rural boundary corrosion operator is to optimize the reliability of the urban and rural boundary according to the morphological characteristics of cities, towns and villages.
Based on any embodiment, the identifying the urban and rural confusion area as a town or a country and optimizing the urban and rural boundary is obtained by performing urban and rural division morphological operation based on the iteration of an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator, and comprises the following steps:
identifying urban and rural confusion areas as towns or villages and optimizing the urban and rural boundaries based on the alternate corresponding execution of each urban and rural boundary expansion operator and each urban and rural boundary corrosion operator until all the urban and rural confusion areas are identified as towns or villages, namely, the urban and rural boundary corrosion operators and the urban and rural boundary expansion operators are alternately executed in sequence; the iteration termination condition is that the urban and rural confusion areas are all identified as towns or villages. That is, the urban and rural boundary erosion operator and the urban and rural boundary dilation operator are alternately executed until the urban and rural confusion zones are all identified as towns or villages. The operation execution process is as follows:
traversing each pixel in the reclassified image with the landscape function;
searching a matched pixel convolution operation kernel based on the current pixel type, the pixel type in the current pixel 3x3 field and the currently set operation type; the currently set operation types comprise an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator;
and modifying the category of the current pixel based on the output type of the convolution operation kernel so as to convert the type pixel of the urban and rural confusion area in the reclassified image into the type pixel of the town or the country.
Specifically, as shown in FIG. 2, the urban and rural boundary dilation operator and the urban and rural boundary erosion operator are essentially two groups of 14-class 3x3 pixel convolution kernels (Conv)1,Conv2,Conv14) And the processing object is a landscape function reclassification image. The specific operation method comprises the following steps: traversing each pixel in landscape function reclassification imageiFinding a matched convolution operation kernel Conv according to the pixel class, the pixel class of the 3x3 field and the current specified operation type (including urban and rural boundary expansion operation or corrosion operation)j(1. ltoreq. j. ltoreq.14) according to ConvjThe output result of the convolution operation kernel modifies the pixel of the current pixeliA category. And converting all urban and rural confusion area type pixels in the landscape function reclassification image into town or rural type pixels according to the operation result, so as to realize urban and rural boundary identification and extraction.
The town boundary extraction system based on landscape functional data provided by the invention is described below, and the town boundary extraction method based on landscape functional data described below and described above can be referred to correspondingly.
Fig. 4 is a schematic structural diagram of a town boundary extraction system based on landscape functional data according to an embodiment of the present invention, and as shown in fig. 4, the system includes a type reclassification module 410 and an identification and optimization module 420;
the type reclassification module 410 is used for reclassifying different landscape function types based on the landscape function data to obtain reclassified images including town, country and urban and rural confusion areas;
the identifying and optimizing module 420 is configured to identify the urban and rural confusion area in the reclassified image as a town or a country and optimize an urban and rural boundary to extract the urban and rural boundary.
Compared with the prior art, the system provided by the embodiment of the invention reclassifies the landscape function data based on the defined landscape function type reclassification rule to obtain reclassified images including towns, villages and urban and rural confusion areas, identifies the urban and rural confusion areas in the reclassified images as towns or villages based on the knowledge-driven urban and rural boundary expansion and corrosion operators, realizes the automatic identification and optimized extraction of the urban and rural boundaries, and applies the result to the township monitoring, planning and management. The invention realizes the optimized extraction of the town boundary and improves the automation degree and the spatial resolution of the recognition of the town spatial range.
Based on any of the above embodiments, as shown in fig. 5, the type reclassification module 500 includes a data acquisition module 510 and a reclassification module 520;
the data acquisition module 510 is configured to acquire landscape function data covering towns and villages, where the landscape function data includes different landscape function types of the towns and the villages;
the reclassification module 520 is configured to reclassify the different landscape function types based on a reclassification rule to obtain a reclassified image including a town, a country and a mixed urban and rural area.
Based on any one of the above embodiments, the identification and optimization module is configured to identify the urban and rural confusion area in the reclassified image as a town or a country and optimize the urban and rural boundary, and is obtained by performing an urban and rural division morphological operation based on an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator iteration; and the urban and rural boundary expansion operator and the urban and rural boundary corrosion operator correspond to different types of pixel convolution operation kernels.
Based on any of the above embodiments, the identifying and optimizing module is further specifically configured to identify the urban and rural confusion area as a town or a country and optimize the urban and rural boundary based on the alternate corresponding execution of each urban and rural boundary expansion operator and each urban and rural boundary corrosion operator until all the urban and rural confusion areas are identified as a town or a country, and the operation execution process is as follows:
traversing each pixel in the reclassified image with the landscape function;
searching a matched pixel convolution operation kernel based on the current pixel type, the pixel type in the current pixel 3x3 field and the currently set operation type; the currently set operation types comprise an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator;
and modifying the category of the current pixel based on the output type of the convolution operation kernel so as to convert the type pixel of the urban and rural confusion area in the reclassified image into the type pixel of the town or the country.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, the electronic device may include: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. Processor 610 may invoke logic instructions in memory 630 to perform a town boundary extraction method based on landscape functional data, the method comprising: reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban, rural and urban-rural confusion areas; and identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary so as to extract the urban and rural boundary.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is capable of executing the town boundary extraction method based on landscape function data provided by the above methods, and the method includes: reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban, rural and urban-rural confusion areas; and identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary so as to extract the urban and rural boundary.
In still another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the provided town boundary extraction method based on landscape function data, where the method includes: reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban, rural and urban-rural confusion areas; and identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary so as to extract the urban and rural boundary.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A town boundary extraction method based on landscape functional data is characterized by comprising the following steps:
reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban, rural and urban-rural confusion areas;
and identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary so as to extract the urban and rural boundary.
2. The method for extracting town boundary based on landscape function data as claimed in claim 1, wherein the reclassifying different landscape function types based on landscape function data to obtain reclassified images including town, country and urban-rural confusion zones comprises:
acquiring landscape function data covering towns and villages, wherein the landscape function data comprises different landscape function types of the towns and the villages;
and re-classifying the different landscape function types based on the re-classification rule to obtain re-classified images including urban, rural and urban-rural confusion areas.
3. The method for extracting town boundary based on landscape functional data as claimed in claim 1, wherein identifying the town or country confusion zone in the reclassified image and optimizing the town or country boundary is obtained by performing an urban and rural division morphological operation based on the iteration of an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator; and the urban and rural boundary expansion operator and the urban and rural boundary corrosion operator correspond to different types of pixel convolution operation kernels.
4. The method for extracting town boundary based on landscape functional data as claimed in claim 3, wherein identifying the town confusion area as town or country and optimizing the town-country boundary is obtained by performing an iteration of a town-country division morphological operation based on a town-country boundary expansion operator and a town-country boundary corrosion operator, and comprises:
identifying urban and rural confusion areas as towns or villages and optimizing the urban and rural boundaries based on the alternate corresponding execution of each urban and rural boundary expansion operator and each urban and rural boundary corrosion operator until all the urban and rural confusion areas are identified as towns or villages, wherein the operation execution process is as follows:
traversing each pixel in the reclassified image with the landscape function;
searching a matched pixel convolution operation kernel based on the current pixel type, the pixel type in the current pixel 3x3 field and the currently set operation type; the currently set operation types comprise an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator;
and modifying the category of the current pixel based on the output category of the convolution operation kernel so as to convert the type pixel of the urban and rural confusion area in the reclassified image into the type pixel of the town or the country.
5. A town boundary extraction system based on landscape functional data is characterized by comprising:
the type reclassification module is used for reclassifying different landscape function types based on the landscape function data to obtain reclassified images including urban areas, rural areas and mixed urban and rural areas;
and the identification and optimization module is used for identifying the urban and rural confusion area in the reclassified image as a town or a country and optimizing the urban and rural boundary so as to extract the urban and rural boundary.
6. The system for extracting town boundary based on landscape functional data according to claim 5, wherein the type reclassification module comprises a data acquisition module and a reclassification module;
the data acquisition module is used for acquiring landscape function data covering towns and villages, and the landscape function data comprises different landscape function types of the towns and the villages;
and the reclassification module is used for reclassifying different landscape function types based on reclassification rules to obtain reclassified images including town, country and urban and rural confusion areas.
7. The system of claim 5, wherein the recognition and optimization module is configured to recognize the urban and rural confusion area in the reclassified image as a town or a country and optimize the urban and rural boundaries based on an urban and rural boundary expansion operator and an urban and rural boundary erosion operator for performing an urban and rural division morphological operation; and the urban and rural boundary expansion operator and the urban and rural boundary corrosion operator correspond to different types of pixel convolution operation kernels.
8. The system of claim 7, wherein the identifying and optimizing module is further specifically configured to identify the urban and rural confusion area as a town or a country and optimize the urban and rural boundary based on the alternate correspondence between each urban and rural boundary dilation operator and each urban and rural boundary erosion operator until all the urban and rural confusion areas are identified as a town or a country, and the operation is performed as follows:
traversing each pixel in the reclassified image with the landscape function;
searching a matched pixel convolution operation kernel based on the current pixel type, the pixel type in the current pixel 3x3 field and the currently set operation type; the currently set operation types comprise an urban and rural boundary expansion operator and an urban and rural boundary corrosion operator;
and modifying the category of the current pixel based on the output category of the convolution operation kernel so as to convert the type pixel of the urban and rural confusion area in the reclassified image into the type pixel of the town or the country.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, carries out the steps of the method for town boundary extraction based on landscape functional data according to any of claims 1 to 4.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the town boundary extraction method based on landscape functional data according to any one of claims 1 to 4.
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