CN112597939A - Surface water body classification extraction method, system, equipment and computer storage medium - Google Patents
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Abstract
The invention provides a method, a system, equipment and a computer storage medium for classifying and extracting surface water bodies, wherein the method for classifying and extracting the surface water bodies comprises the following steps: classifying all pixels in the remote sensing image related to the surface water body into a feature space; the feature space is composed of a plurality of feature bands; selecting pixels positioned at the corner points from the characteristic space as surface feature samples, and forming a surface feature sample library; classifying the pixels to be classified according to the ground feature sample to obtain a ground surface classification result; and extracting the surface water body from the surface classification result. The method, the system, the equipment and the computer storage medium for classifying and extracting the surface water body can quickly and automatically extract large-area water bodies, have certain identification on the water bodies in different states, and can better inhibit the influence of background ground objects.
Description
Technical Field
The invention belongs to the technical field of remote sensing image classification, relates to an extraction method and system, and particularly relates to a surface water body classification extraction method, system, equipment and computer storage medium.
Background
The rapid and automatic extraction of large-area surface water bodies by using remote sensing images is a very important and challenging subject. Recently, the sentinel No. 2 free optical data released by the European Bureau provides a new opportunity for large-area water extraction. However, accurately extracting water bodies in different states and different scales from the sentinel No. 2 image and simultaneously inhibiting the influence of background ground objects also become an open problem to be solved urgently. Existing water extraction algorithms can be classified into the following three categories:
1. water body index. Water body index is a simple but widely used method for water body mapping. Generally speaking, the water body index is the fifth weakening of Beijing by using the spectral reflectivity difference of the water body in the green light wave band and the near infrared wave band to highlight the water body. Common water body indexes include normalized difference water body index (NDWI), improved NDWI (MNDWI), automatic water body extraction index (AWEI), and the like. The water body index is a simple, quick and effective algorithm for extracting the water body in the remote sensing image in a large area, but most of the indexes usually act on a specific waveband, for example, MNDWI needs participation of a short wave infrared waveband, AWEI is designed for Landsat 8OLI data, and large-area popularization of the water body index is limited.
2. And (4) unsupervised classification. The unsupervised classification algorithm classifies the image pixels into several classes defined in advance through a clustering algorithm. The unsupervised classification algorithm does not need a training sample, is easy to implement in practical application, but needs a user to determine the type of the ground objects to be classified of the image in advance, and has certain requirements on the specialty of the user.
3. And (5) supervising and classifying. The supervised classification algorithm requires training samples to adjust the parameters of the classifier, and then classifies the whole image. The support vector machine SVM and the random forest RF are water body supervision and classification algorithms which are widely applied.
In the field of water body classification and extraction, the supervision and classification algorithm has higher precision and robustness. However, the supervised classification algorithm still has the following two problems in the using process: (1) a large amount of manpower and material resources are consumed for selecting a proper sample, and the quality of the sample can directly influence the extraction result of the water body; (2) conventional supervised classification classifiers, such as SVM and RF, are directed to the absolute spectral reflectance of the terrain. However, it is difficult for conventional supervised classifiers like SVM, RF to simultaneously extract water in different states and suppress interference of background features.
Therefore, how to provide a method, a system, a device and a computer storage medium for classifying and extracting surface water bodies to solve the problem that it is difficult to extract water bodies in different states and simultaneously suppress interference of background ground objects in the prior art, has become a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, it is an object of the present invention to provide a surface water body classification extraction method, system, device and computer storage medium, which are used to solve the problem that it is difficult to extract water bodies in different states and simultaneously suppress the interference of background ground objects in the prior art.
To achieve the above and other related objects, an aspect of the present invention provides a method for classifying and extracting surface water, including: classifying all pixels in the remote sensing image related to the surface water body into a feature space; the feature space is composed of a plurality of feature bands; selecting pixels positioned at the corner points from the characteristic space as surface feature samples, and forming a surface feature sample library; classifying the pixels to be classified according to the ground feature sample to obtain a ground surface classification result; and extracting the surface water body from the surface classification result.
In an embodiment of the present invention, the plurality of characteristic bands include tan (blue band spectral value-green band spectral value), tan (blue band spectral value-red band spectral value), tan (blue band spectral value-near infrared band spectral value), tan (green band spectral value-red band spectral value), tan (green band spectral value-near infrared band spectral value), and tan (red band spectral value-near infrared band spectral value).
In an embodiment of the invention, the pixels of different corner points belong to different surface feature categories.
In an embodiment of the present invention, the step of classifying the pixels to be classified according to the surface feature sample to obtain the surface classification result includes: calculating the spectral similarity between the pixels to be classified and the surface feature samples; the spectrum similarity between the pixel to be classified and the surface feature sample is characterized by a spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample.
In an embodiment of the present invention, a calculation formula of a spectrum angle between a spectrum of a pixel to be classified and a spectrum of a surface feature sample is as follows:
wherein, XiSpectrum of the ith pixel is represented, i is 1, 2, 3, …, N and N represent the total number of pixels to be classified in the remote sensing image, and Y represents the total number of pixels to be classified in the remote sensing imagejSpectrum representing the j-th type ground object sample, M represents the total number of ground object types in the ground object sample library, and alpha represents the spectrum X of the pixel to be classifiediSpectrum Y of ground object samplejThe spectral angle therebetween.
In an embodiment of the present invention, the step of classifying the pixels to be classified according to the surface feature sample to obtain the surface classification result further includes: calculating a correlation coefficient between the spectrum of the pixel to be classified and the spectrum of the surface feature sample, and if the correlation coefficient is greater than 0, calculating a cosine value of a spectrum angle by using the spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample as a classification basis for classifying the pixel to be classified; if the correlation coefficient is less than or equal to 0, amplifying the spectrum angle, and calculating a cosine value of the amplified spectrum angle; when the cosine value of the spectrum angle or the cosine value of the amplified spectrum angle is smaller than a preset threshold value, the similarity between the pixel to be classified and the ground object sample is high; and when the cosine value of the spectrum angle amplified by the cosine value of the spectrum angle is greater than or equal to a preset threshold value, the similarity between the pixel to be classified and the ground object sample is low.
In an embodiment of the present invention, the step of extracting the surface water from the surface classification result includes: and carrying out binarization processing on the earth surface classification result, and extracting an earth surface water body from the earth surface result after binarization processing.
In another aspect, the present invention provides a surface water body classification and extraction system, including: the classification module is used for classifying all pixels in the remote sensing image related to the surface water body into a feature space; the feature space is composed of a plurality of feature bands; the sample selection module is used for selecting pixels positioned at the corner points from the characteristic space as surface feature samples and forming a surface feature sample library; the classification module is used for classifying the pixels to be classified according to the surface feature sample so as to obtain a surface classification result; and the extraction module is used for extracting the surface water body from the surface classification result.
Yet another aspect of the present invention provides a computer storage medium having a computer program stored thereon, which when executed by a processor, implements the surface water body classification extraction method.
In a final aspect, the present invention provides a surface water body classification and extraction apparatus, including: a processor and a memory;
the memory is used for storing computer programs, and the processor is used for executing the computer programs stored by the memory so as to enable the surface water body classification extraction equipment to execute the surface water body classification extraction method.
As described above, the surface water body classification extraction method, system, device and computer storage medium according to the present invention have the following advantages:
the method, the system, the equipment and the computer storage medium for classifying and extracting the surface water body can quickly and automatically extract large-area water bodies, have certain identification on the water bodies in different states, and can better inhibit the influence of background ground objects.
Drawings
Fig. 1 is a schematic flow chart of a surface water body classification and extraction method according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of S13 in the surface water body classification extraction method of the present invention.
Fig. 3 is a schematic structural diagram of a surface water body classification and extraction system according to an embodiment of the present invention.
Description of the element reference numerals
3 categorised extraction system of surface water
31 categorizing module
32 sample selection module
33 Classification Module
34 extraction module
S11-S14
S131 to S134 steps
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Example one
The embodiment provides a method for classifying and extracting surface water, which comprises the following steps:
classifying all pixels in the remote sensing image related to the surface water body into a feature space; the feature space is composed of a plurality of feature bands;
selecting pixels positioned at the corner points from the characteristic space as surface feature samples, and forming a surface feature sample library;
classifying the pixels to be classified according to the ground feature sample to obtain a ground surface classification result;
and extracting the surface water body from the surface classification result.
The surface water body classification and extraction method provided by the present embodiment will be described in detail below with reference to the drawings. Please refer to fig. 1, which is a flowchart illustrating a surface water classification and extraction method according to an embodiment. As shown in fig. 1, the surface water body classification and extraction method specifically includes the following steps:
and S11, classifying all pixels in the remote sensing image related to the surface water body into a feature space. In the embodiment, the remote sensing image related to the surface water body comprises optical data of a 4-wave band sentinel with a resolution of 2 # 10 m.
The eigenspace is composed of a plurality of eigenbands.
Specifically, the plurality of characteristic bands include TAN (blue band spectral value-green band spectral value), TAN (blue band spectral value-red band spectral value), TAN (blue band spectral value-near infrared band spectral value), TAN (green band spectral value-red band spectral value), TAN (green band spectral value-near infrared band spectral value), and TAN (red band spectral value-near infrared band spectral value), that is, the characteristic space is a tangent space (TAN space) formed by tangent values between every two of 6 bands. The tangent function can perform nonlinear stretching on the basis of the original pixel value, and can reduce the difference between the same land species and enlarge the difference between different land species.
And S12, selecting the pixels positioned at the corner points in the feature space as the surface feature samples, and forming a surface feature sample library.
The detailed steps of sample extraction are as follows: and aiming at the convex polygon formed by all pixel scattered points in the new characteristic space, manually judging the pixels at the corner points belonging to the convex polygon, wherein the pixels at different corner points belong to different ground object categories. And for the pixels of different classes manually selected for the first time, corresponding the pixels to the original image according to the positions of the pixels in the image, and then performing screening again. The principle of screening is as follows: the selected sample pixels at different corner points should belong to different categories, and the sample pixels should be relatively uniform, pure and free of mixed pixels in the category. And screening the pixels selected for the first time according to the principle, and forming a final sample library after eliminating the pixels which do not accord with the principle.
In this embodiment, after pixel points to be classified in an image are expanded in the feature space, it can be found that pixel scatter points present a very regular convex polygon feature, which explains the effectiveness of feature space construction. Based on the feature space and a convex geometry theory of sample selection, the image elements of the feature space at the corner points are selected as surface samples, and the image elements of different corner points belong to different surface feature categories.
And S13, classifying the pixels to be classified according to the surface feature sample to obtain a surface classification result.
Please refer to fig. 2, which shows a flowchart of S13. As shown in fig. 2, the S13 includes:
s131, calculating the spectral similarity between the pixels to be classified and the surface feature samples; the spectrum similarity between the pixel to be classified and the surface feature sample is characterized by a spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample.
In this embodiment, the calculation formula of the spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample is as follows:
wherein, XiSpectrum of the ith pixel is represented, i is 1, 2, 3, …, N and N represent the total number of pixels to be classified in the remote sensing image, and Y represents the total number of pixels to be classified in the remote sensing imagejSpectrum representing the j-th type ground object sample, M represents the total number of ground object types in the ground object sample library, and alpha represents the spectrum X of the pixel to be classifiediSpectrum Y of ground object samplejThe spectral angle therebetween.
S132, calculating a correlation coefficient between the spectrum of the pixel to be classified and the spectrum of the surface feature sample, and judging whether the correlation coefficient is greater than 0; if yes, go to S133; if not, go to S134. Typically the angle is calculated between 0 deg. -90 deg., which means that the spectral angle algorithm described above cannot identify spectral negative correlations. However, such a drawback is very limited in the surface water extraction process, and in a complex urban environment, some artificial ground objects have the exact opposite spectral characteristics of the surface water body, so that the traditional spectral angle classification can classify the artificial ground objects into the water body. Therefore, the judgment of the pearson correlation coefficient between the spectrum of the pixel to be classified and the spectrum of the surface feature sample is introduced on the basis of the spectrum angle classification, and the pearson correlation coefficient between the image pixel and the spectrum of the sample is calculated, so that the negative correlation relationship between the pixel to be classified and the sample can be identified, and the occurrence of the misclassification phenomenon is avoided to the greatest extent.
S133, if the correlation coefficient is larger than 0, the spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample is used as a classification basis for classifying the pixel to be classified, and the cosine value of the spectrum angle is calculated.
And S134, if the correlation coefficient is less than or equal to 0, amplifying the spectrum angle, and calculating a cosine value of the amplified spectrum angle. In practical applications, the spectral angle can be enlarged by 100 times, i.e., α × 100.
When the cosine value of the spectrum angle or the cosine value of the amplified spectrum angle is smaller than a preset threshold value, the similarity between the pixel to be classified and the ground object sample is high, and the pixel to be classified is classified under the category of the ground object sample;
and when the cosine value of the spectrum angle amplified by the cosine value of the spectrum angle is greater than or equal to a preset threshold value, the similarity between the pixel to be classified and the ground object sample is low.
And S14, extracting the surface water body from the surface classification result.
Specifically, the earth surface classification result is subjected to binarization processing, and earth surface water is extracted from the earth surface result after binarization processing.
The surface water body classification extraction method can be used for rapidly and automatically extracting large-area water bodies, has certain identification on the water bodies in different states, and can well inhibit the influence of background ground objects.
The present embodiment also provides a computer storage medium (also referred to as a computer-readable storage medium) having a computer program stored thereon, where the computer program is executed by a processor to implement the above-mentioned surface water body classification extraction method.
One of ordinary skill in the art will appreciate that the computer-readable storage medium is: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Example two
The embodiment provides a categorised extraction system of surface water body, includes:
the classification module is used for classifying all pixels in the remote sensing image related to the surface water body into a feature space; the feature space is composed of a plurality of feature bands;
the sample selection module is used for selecting pixels positioned at the corner points from the characteristic space as surface feature samples and forming a surface feature sample library;
the classification module is used for classifying the pixels to be classified according to the surface feature sample so as to obtain a surface classification result;
and the extraction module is used for extracting the surface water body from the surface classification result.
The surface water body classification and extraction system provided by the present embodiment will be described in detail with reference to the drawings. Fig. 3 is a schematic structural diagram of a surface water body classification and extraction system in an embodiment. As shown in fig. 3, the surface water classification and extraction system 3 includes a classification module 31, a sample selection module 32, a classification module 33, and an extraction module 34.
The classifying module 31 is configured to classify all pixels in the remote sensing image related to the surface water body into a feature space. In the embodiment, the remote sensing image related to the surface water body comprises optical data of a 4-wave band sentinel with a resolution of 2 # 10 m.
The eigenspace is composed of a plurality of eigenbands.
Specifically, the plurality of characteristic bands include TAN (blue band spectral value-green band spectral value), TAN (blue band spectral value-red band spectral value), TAN (blue band spectral value-near infrared band spectral value), TAN (green band spectral value-red band spectral value), TAN (green band spectral value-near infrared band spectral value), and TAN (red band spectral value-near infrared band spectral value), that is, the characteristic space is a tangent space (TAN space) formed by tangent values between every two of 6 bands. The tangent function can perform nonlinear stretching on the basis of the original pixel value, and can reduce the difference between the same land species and enlarge the difference between different land species.
The sample selecting module 32 is configured to select a pixel located at an angular point in the feature space as a surface feature sample, and form a surface feature sample library.
Specifically, the method comprises the following steps: the sample selecting module 32 artificially judges the pixels at the corner points belonging to the convex polygon aiming at the convex polygon formed by all the pixel scattered points in the new feature space, and the pixels at different corner points belong to different ground object categories. And for the pixels of different classes manually selected for the first time, corresponding the pixels to the original image according to the positions of the pixels in the image, and then performing screening again. The principle of screening is as follows: the selected sample pixels at different corner points should belong to different categories, and the sample pixels should be relatively uniform, pure and free of mixed pixels in the category. And screening the pixels selected for the first time according to the principle, and forming a final sample library after eliminating the pixels which do not accord with the principle.
In this embodiment, after pixel points to be classified in an image are expanded in the feature space, it can be found that pixel scatter points present a very regular convex polygon feature, which explains the effectiveness of feature space construction. Based on the feature space and a convex geometry theory of sample selection, the image elements of the feature space at the corner points are selected as surface samples, and the image elements of different corner points belong to different surface feature categories.
The classification module 33 is configured to classify the pixels to be classified according to the surface feature sample to obtain a surface classification result.
Specifically, the classification module 33 calculates the spectral similarity between the pixels to be classified and the surface feature samples; calculating a correlation coefficient between the spectrum of the pixel to be classified and the spectrum of the surface feature sample, and judging whether the correlation coefficient is greater than 0; if so, calculating a cosine value of a spectrum angle by taking the spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample as a classification basis for classifying the pixel to be classified; if not, amplifying the spectrum angle, and calculating the cosine value of the amplified spectrum angle. When the cosine value of the spectrum angle or the cosine value of the amplified spectrum angle is smaller than a preset threshold value, the similarity between the pixel to be classified and the ground object sample is high, and the pixel to be classified is classified under the category of the ground object sample; and when the cosine value of the spectrum angle amplified by the cosine value of the spectrum angle is greater than or equal to a preset threshold value, the similarity between the pixel to be classified and the ground object sample is low.
The spectrum similarity between the pixel to be classified and the surface feature sample is characterized by a spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample.
In this embodiment, the calculation formula of the spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample is as follows:
wherein, XiSpectrum of the ith pixel is represented, i is 1, 2, 3, …, N and N represent the total number of pixels to be classified in the remote sensing image, and Y represents the total number of pixels to be classified in the remote sensing imagejSpectrum representing the j-th type ground object sample, M represents the total number of ground object types in the ground object sample library, and alpha represents the spectrum X of the pixel to be classifiediSpectrum Y of ground object samplejThe spectral angle therebetween.
In practical applications, the classification module 33 may magnify the spectrum angle by 100 times, i.e. α × 100.
The extraction module 34 is configured to extract the surface water from the surface classification result.
Specifically, the extraction module 34 performs binarization processing on the earth surface classification result, and extracts an earth surface water body from the earth surface result after binarization processing.
It should be noted that the division of the modules of the above system is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And the modules can be realized in a form that all software is called by the processing element, or in a form that all the modules are realized in a form that all the modules are called by the processing element, or in a form that part of the modules are called by the hardware. For example: the x module can be a separately established processing element, and can also be integrated in a certain chip of the system. In addition, the x-module may be stored in the memory of the system in the form of program codes, and may be called by one of the processing elements of the system to execute the functions of the x-module. Other modules are implemented similarly. All or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software. These above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors (DSPs), one or more Field Programmable Gate Arrays (FPGAs), and the like. When a module is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. These modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).
EXAMPLE III
This embodiment a categorised extraction equipment of surface water body includes: a processor, memory, transceiver, communication interface, or/and system bus; the storage and the communication interface are connected with the processor and the transceiver through a system bus and are used for completing mutual communication, the storage is used for storing the computer program, the communication interface is used for communicating with other equipment, and the processor and the transceiver are used for operating the computer program to enable the surface water body classification and extraction equipment to execute the steps of the surface water body classification and extraction method.
The above-mentioned system bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
The protection scope of the surface water body classification and extraction method of the present invention is not limited to the execution sequence of the steps listed in this embodiment, and all the schemes of adding, subtracting, and replacing steps in the prior art according to the principle of the present invention are included in the protection scope of the present invention.
The invention also provides a surface water body classification and extraction system, which can realize the surface water body classification and extraction method, but the implementation device of the surface water body classification and extraction method provided by the invention comprises but is not limited to the structure of the surface water body classification and extraction system listed in the embodiment, and all structural deformation and replacement in the prior art according to the principle of the invention are included in the protection scope of the invention.
In summary, the method, the system, the equipment and the computer storage medium for surface water classification extraction can rapidly and automatically extract large-area water, have certain identification on water in different states, and can better inhibit the influence of background ground objects. The invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A surface water body classification extraction method is characterized by comprising the following steps:
classifying all pixels in the remote sensing image related to the surface water body into a feature space; the feature space is composed of a plurality of feature bands;
selecting pixels positioned at the corner points from the characteristic space as surface feature samples, and forming a surface feature sample library;
classifying the pixels to be classified according to the ground feature sample to obtain a ground surface classification result;
and extracting the surface water body from the surface classification result.
2. The surface water body classification extraction method according to claim 1, wherein the plurality of characteristic bands include tan (blue band spectral value-green band spectral value), tan (blue band spectral value-red band spectral value), tan (blue band spectral value-near infrared band spectral value), tan (green band spectral value-red band spectral value), tan (green band spectral value-near infrared band spectral value), and tan (red band spectral value-near infrared band spectral value).
3. The surface water body classification extraction method according to claim 1, characterized in that the pixels of different corner points belong to different surface feature classes.
4. The method for classifying and extracting the surface water body according to claim 1, wherein the step of classifying the pixels to be classified according to the surface feature sample to obtain the surface classification result comprises the following steps:
calculating the spectral similarity between the pixels to be classified and the surface feature samples; the spectrum similarity between the pixel to be classified and the surface feature sample is characterized by a spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample.
5. The surface water body classification extraction method according to claim 4, characterized in that the calculation formula of the spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample is as follows:
wherein, XiSpectrum of the ith pixel is represented, i is 1, 2, 3, …, N and N represent the total number of pixels to be classified in the remote sensing image, and Y represents the total number of pixels to be classified in the remote sensing imagejSpectrum representing the j-th type ground object sample, M represents the total number of ground object types in the ground object sample library, and alpha represents the spectrum X of the pixel to be classifiediSpectrum Y of ground object samplejThe spectral angle therebetween.
6. The surface water body classification and extraction method according to claim 5, wherein the step of classifying the pixels to be classified according to the surface feature sample to obtain the surface classification result further comprises:
calculating a correlation coefficient between the spectrum of the pixel to be classified and the spectrum of the surface feature sample, and if the correlation coefficient is greater than 0, calculating a cosine value of a spectrum angle by using the spectrum angle between the spectrum of the pixel to be classified and the spectrum of the surface feature sample as a classification basis for classifying the pixel to be classified; if the correlation coefficient is less than or equal to 0, amplifying the spectrum angle, and calculating a cosine value of the amplified spectrum angle;
when the cosine value of the spectrum angle or the cosine value of the amplified spectrum angle is smaller than a preset threshold value, the similarity between the pixel to be classified and the ground object sample is high;
and when the cosine value of the spectrum angle amplified by the cosine value of the spectrum angle is greater than or equal to a preset threshold value, the similarity between the pixel to be classified and the ground object sample is low.
7. The surface water body classification extraction method according to claim 6, wherein the step of extracting the surface water body from the surface classification result comprises:
and carrying out binarization processing on the earth surface classification result, and extracting an earth surface water body from the earth surface result after binarization processing.
8. A surface water body classification extraction system is characterized by comprising:
the classification module is used for classifying all pixels in the remote sensing image related to the surface water body into a feature space; the feature space is composed of a plurality of feature bands;
the sample selection module is used for selecting pixels positioned at the corner points from the characteristic space as surface feature samples and forming a surface feature sample library;
the classification module is used for classifying the pixels to be classified according to the surface feature sample so as to obtain a surface classification result;
and the extraction module is used for extracting the surface water body from the surface classification result.
9. A computer storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the surface water body classification extraction method of any one of claims 1 to 7.
10. A surface water body classification extraction device is characterized by comprising: a processor and a memory;
the memory is used for storing computer programs, and the processor is used for executing the computer programs stored by the memory to cause the surface water body classification extraction equipment to execute the surface water body classification extraction method according to any one of claims 1 to 7.
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