CN112016391A - Fishpond identification method, fishpond identification system and fishpond identification medium based on high-resolution satellite remote sensing image - Google Patents
Fishpond identification method, fishpond identification system and fishpond identification medium based on high-resolution satellite remote sensing image Download PDFInfo
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Abstract
The invention provides a fishpond identification method, a fishpond identification system and a fishpond identification medium based on a high-resolution satellite remote sensing image, wherein the fishpond identification method comprises the steps of preprocessing the high-resolution satellite remote sensing image to obtain reflectivity data including geometric positioning; based on the reflectivity data, processing by utilizing bilateral filtering, OTSU algorithm segmentation and open operation image processing methods to obtain an initial fishpond vector result; based on the reflectivity data, utilizing NDVI to extract an initial fishpond vector result and a vegetation coverage vector result of a vegetation coverage vector result, and performing vector erasure operation; and performing smooth surface operation on the candidate fishpond vector, and performing manual intervention to obtain a final fishpond extraction result. The invention adopts the high-resolution satellite remote sensing image, forms a flow method for automatically extracting the fishpond through a series of image processing methods, can reduce the workload of manually identifying and extracting the fishpond, reduces experience errors caused by manual intervention, realizes the automation of the identification method and improves the working efficiency.
Description
Technical Field
The invention belongs to the field of remote sensing image processing, and particularly relates to a fishpond identification method, a fishpond identification system and a fishpond identification medium based on a high-resolution satellite remote sensing image.
Background
Due to the rapid increase of the demand of residents, driven by two factors of policy and science and technology, the aquaculture industry in China has rapidly developed in a long time, and becomes the first aquaculture country in the world, and is also the only country in the world with the aquaculture yield exceeding the fishing yield, wherein the aquaculture yield of a fishpond accounts for 49% of the total aquaculture yield in China. However, it is worth noting that the aquaculture industry is rapidly developing, and the difficulty of aquaculture management is increasing. In different development stages of aquaculture industry, the total amount of water resources required by aquaculture shows a dynamic change process; and the farmers in China are vast, the water resources of aquaculture are widely distributed, and depending on the traditional field investigation method, the family base of the water resources of aquaculture in China is difficult to be scientifically and accurately found and the dynamic change of the water resources is difficult to be mastered.
With the continuous development and improvement of remote sensing technology, increasingly abundant remote sensing data provide opportunities for resource investigation. In order to enhance the monitoring of aquaculture resources, accurately grasp the current situation of spatial position and area of aquaculture in China, find out the bottom of aquaculture industry, reasonably plan aquaculture layout, further improve aquaculture development level and comprehensive effect, and the satellite image technology becomes an effective means for resource investigation and monitoring. Meanwhile, the satellite remote sensing data can dynamically and objectively record earth surface information in real time by virtue of the advantages of multiple time phases, short period, wide coverage range, abundant reflected ground information and the like, provides favorable conditions for tracking and observation, and becomes an important data source for dynamically monitoring aquaculture.
The fishpond information is extracted from the satellite remote sensing image, the distribution range and the culture area of the fishpond can be mastered, the aquaculture scale and the aquaculture yield of fishery are estimated, the reasonable layout of aquaculture industry is guided, and the regional economic development is promoted. The high-resolution satellite remote sensing data has the outstanding characteristic of high spatial resolution, has richer structural information and texture information, shows sufficient advantages in the refined extraction direction, has a better extraction effect on the small fishpond, and can effectively identify fishpond information.
However, the existing method for recognizing and extracting fishponds by using remote sensing images mainly carries out vectorization through manual drawing, is too dependent on manpower, has low automation degree and low efficiency, and seriously restricts the large-scale, accurate and efficient monitoring of the dynamic change of the aquaculture industry.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the fishpond identification method based on the high-resolution satellite remote sensing image can reduce the workload of manual identification and fishpond extraction, reduce experience errors caused by manual intervention, realize the automation of the identification method and improve the working efficiency.
The invention further provides a fishpond identification system based on the high-resolution satellite remote sensing image.
The invention also provides a medium for implementing the fishpond identification method based on the high-resolution satellite remote sensing image.
According to the embodiment of the first aspect of the invention, the fishpond identification method based on the high-resolution satellite remote sensing image comprises the following steps:
acquiring a high-resolution satellite remote sensing image, and preprocessing the image to obtain reflectivity data including geometric positioning; processing the reflectivity data based on bilateral filtering, an OTSU algorithm, an open operation morphology and NDVI to obtain an initial fishpond vector result and a vegetation coverage vector result; performing vector erasing operation on the initial fishpond vector result and the vegetation coverage vector result, and removing the pseudo fishpond vector of the vegetation area to obtain a candidate fishpond vector of the non-vegetation area; and processing the candidate fish pond vectors to obtain a final fish pond extraction result.
The fishpond identification method based on the high-resolution satellite remote sensing image, provided by the embodiment of the invention, has the following beneficial effects:
compared with the methods of field on-site investigation, fish pond distribution position and culture area statistics, digital fishpond vector drawing by means of manual drawing and the like, the method for identifying and extracting the fish pond has the advantages of high automation degree, less manual intervention, capability of quickly obtaining a fish pond vector result in a short time, improvement of fish pond identification and extraction efficiency, reduction of manual workload and reduction of interference errors of manual experience.
According to some embodiments of the invention, the pre-processing comprises radiometric calibration, atmospheric correction, and orthorectification.
According to some embodiments of the invention, the processing the reflectivity data comprises:
A. extracting the 4 th wave band of the reflectivity data, processing the wave band through bilateral filtering of a nonlinear filter, bringing the spatial distance relation and the color similarity of pixels into calculation, and reserving the target edge of the image and smoothing the internal details; B. b, performing threshold segmentation on the image processed in the step A based on a maximum inter-class variance method of an OTSU algorithm, automatically calculating to obtain an optimal segmentation threshold of the fish pond and non-fish pond area, and performing binarization processing according to the threshold; C. b, performing open operation morphological processing on the binary image obtained in the step B; D. vectorizing the image subjected to the opening operation processing in the step C to obtain vector data; E. and setting area thresholds D and D according to the statistical minimum value and the statistical maximum value of the area of the fishpond, and deleting vectors smaller than the threshold D and larger than the threshold D to obtain an initial fishpond vector result.
According to some embodiments of the invention, the processing the reflectivity data further comprises:
F. performing band operation on the reflectivity data based on NDVI, determining a vegetation and non-vegetation region segmentation threshold according to an actual operation result, and performing binarization processing according to the vegetation and non-vegetation region segmentation threshold; G. and F, vectorizing the image subjected to the binarization processing in the step F, calculating the minimum vegetation area according to the actual situation, and deleting the candidate vegetation vector with the area smaller than the minimum vegetation area to obtain a vegetation vector result.
According to some embodiments of the invention, the vector erase operation comprises:
setting the initial fishpond vector as an input element, and setting the vegetation vector as an erasing element; creating an element class by superimposing the input elements with polygons of the erased elements; and copying the part of the input element outside the external boundary of the erasing element to an output element class to obtain a non-vegetation area fishpond vector.
According to some embodiments of the invention, the processing the candidate fishpond vector comprises: and performing smooth surface treatment on the candidate fishpond vector, smoothing a vector boundary and removing burrs.
According to some embodiments of the invention, the processing the candidate fishpond vector comprises: and carrying out manual intervention on the candidate fishpond vector, and removing the wrong fishpond vector or supplementing the picture and omitting the fishpond vector.
According to a second aspect embodiment of the invention, the fishpond recognition system based on the high-resolution satellite remote sensing image comprises:
the preprocessing module is used for acquiring a high-resolution satellite remote sensing image, and preprocessing the image to obtain reflectivity data including geometric positioning; the first processing module is used for processing the reflectivity data based on bilateral filtering, an OTSU algorithm, an open operation morphology and NDVI to obtain an initial fishpond vector result and a vegetation coverage vector result; the second processing module is used for carrying out vector erasure operation on the initial fishpond vector result and the vegetation coverage vector result, and removing the pseudo fishpond vector of the vegetation area to obtain a candidate fishpond vector of the non-vegetation area; and the third processing module is used for processing the candidate fishpond vector to obtain a final fishpond extraction result.
The fishpond recognition system based on the high-resolution satellite remote sensing image, provided by the embodiment of the invention, has the following beneficial effects:
compared with the field on-site investigation, the fishpond distribution position and culture area statistics, the fishpond vector digitalization method based on manual drawing and the like, the fishpond identification system based on the high-resolution satellite remote sensing image has the advantages that the fishpond identification system based on the high-resolution satellite remote sensing image is high in automation degree and less in manual intervention, fishpond vector results can be obtained quickly in a short time, fishpond identification and extraction efficiency is improved, manual workload is reduced, and interference errors of manual experience are reduced.
According to some embodiments of the present invention, the first processing module is configured to process the reflectivity data by bilateral filtering, OTSU algorithm segmentation, and an on-operation image processing method based on the reflectivity data obtained by the preprocessing module, so as to obtain an initial fishpond vector result.
A computer readable storage medium according to an embodiment of the third aspect of the present invention has stored thereon program instructions which, when executed by a processor, implement the method according to any one of the embodiments of the first aspect of the present invention.
All the advantages of the first aspect of the present invention are achieved because the computer-readable storage medium of the embodiment of the present invention stores computer-executable instructions for executing the fishpond identification method based on high-resolution satellite remote sensing images according to any one of the first aspect of the present invention.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of pond extraction according to an embodiment of the present invention;
FIG. 2 is a satellite remote sensing image map of an embodiment of the invention;
FIG. 3 is a diagram of bilateral filtering effects according to an embodiment of the present invention;
FIG. 4 is a diagram of the OTSU threshold segmentation effect of the embodiment of the present invention;
FIG. 5 is a fishpond initial vector diagram of an embodiment of the invention;
FIG. 6 is a final extraction plot for a fish pond according to an embodiment of the present invention;
FIG. 7 is a diagram of the amplification effect of fishpond extraction according to an embodiment of the invention;
fig. 8 is a system block diagram of an embodiment of the invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a fishpond identification method based on a high-resolution satellite remote sensing image, including the following steps:
step 1, acquiring a high-resolution satellite remote sensing image (refer to fig. 2), and preprocessing the image to obtain reflectivity data with accurate geometric positioning;
in some embodiments, domestic GF2 remote sensing data is used as an input image, the data is a 0.8 m high-resolution multi-spectral image, radiometric calibration is performed by using a calibration coefficient provided by a resource satellite center, an atmosphere correction processing is performed on the image by using a flash atmosphere correction model, then an orthometric correction is performed on the image by selecting 10 m-resolution DEM data, and the original data is processed into reflectivity data with accurate geometric positioning through the preprocessing steps.
Step 2, processing by using bilateral filtering, OTSU segmentation and open operation image processing methods to obtain an initial fishpond vector result;
in some embodiments, the preprocessed 4 th band image is selected as data to be processed, an image processing method is used for enhancing edges, smoothing internal textures, improving segmentation effects, and extracting an initial fishpond vector, and the specific implementation manner is as follows:
step 2.1, selecting bilateral filtering of a nonlinear filter to process the preprocessed 4 th wave band image, incorporating the spatial distance relation and color similarity of pixels into calculation, and effectively reserving the fishpond edge and smoothing internal details;
due to the diversity and complexity of high-resolution images, the images cannot avoid the phenomena of noise interference, region error segmentation and the like, and in order to improve the accuracy of fishpond edge extraction, the images are processed by bilateral filtering, edge protection and denoising are performed, edge information is enhanced, and textures in the fishpond are smoothed.
Bilateral filtering is a nonlinear filter that can effectively preserve the edges of objects and smooth the internal details of objects. It uses a weighted average method, and uses the weighted average of the peripheral pixel brightness value to represent the intensity of a certain pixel, and the used weighted average is based on Gaussian distribution. The weight of the bilateral filtering in calculating the central pixel not only considers the Euclidean distance of the pixel, but also considers the radiation difference in the pixel range domain (such as the similarity degree, color intensity, depth distance and the like between the pixel and the central pixel in a convolution kernel), and the weight simultaneously takes the spatial distance relation and the color similarity of the pixel into calculation, so that when the image is filtered, the non-edge detail part of the target in the image can be smoothed, and meanwhile, the edge information of the target can be kept.
The principle formula of bilateral filtering is as follows:
h(x)＝k-1∫∫f(ξ)c(ξ，x)s(f(ξ)，f(x))dξ
k(x)＝∫∫c(ξ，x)s(f(ξ)，f(x))dξ
wherein c (ξ, x) and s (f (ξ), f (x)) are calculated as follows:
for discretization images, the principle formula of bilateral filtering is rewritten as follows:
in some embodiments, the diameter of the bilateral filtering window is set to be 25 pixels, the variance of the pixel value domain is 10, the variance of the spatial domain is 10, and the effect after the bilateral filtering processing of the image is as shown in fig. 3, so that the information of the fishpond edge is enhanced, the internal details are smoothed, and the subsequent extraction of the fishpond information is facilitated.
Step 2.2, performing threshold segmentation by using an OTSU maximum inter-class variance method, automatically calculating to obtain an optimal segmentation threshold of a fishpond and a non-fishpond area, and performing binarization according to the threshold;
the OTSU method was first proposed by Otsu of Japan, also called Otsu method or the maximum inter-class variance method. According to a one-dimensional histogram of an image, the method takes the maximum inter-class variance of a target and a background as a threshold selection criterion. The method is simple, has high processing speed, and is especially suitable for fish pond extraction.
The basic idea of the OTSU method is as follows: firstly, a normalized histogram of the image is calculated, and the gray level of the image is assumed to be L, the number of pixels with the gray value of i is assumed to be niWhere i ∈ [0, L ]]Is positive, then the total pixels of the image are
Probability of gray value i is Pi＝niand/N. Then calculating cumulative probability and cumulative mean, assuming that the searched segmentation threshold is T, then the segments smaller than T are classified into one class, and the segments larger than T are classified into another class, and according to the probability and mean calculation formula, we can obtain cumulative probability P (T) and mean in classμ (T), the specific calculation formula is as follows:
wherein, P0(T) cumulative probability of being foreground, μ0(T) is the mean value within class, P, of the foreground1(T) is the cumulative probability of the background, μ1(T) is the intra-class mean of the background.
Then, according to the intra-class mean and the cumulative probability calculated by the above formula, the inter-class variance σ (T) is calculated, and the specific calculation formula is as follows:
σ(T)＝P0(T)×P1(T)×(μ0(T)-μ1(T))2
and specifies a threshold value that maximizes σ (T) as an optimal threshold value.
In some embodiments, an optimal segmentation threshold T is calculated for the bilaterally filtered image by using the OTSU method, the calculated result threshold T is 9, the image is binarized according to the threshold, pixels larger than T are assigned with 0, pixels smaller than T are assigned with 1, and the segmentation effect is shown in fig. 4.
Step 2.3, performing open operation morphological processing on the binary image obtained in the previous step, namely corroding and expanding, eliminating small objects, separating the objects at slender points, smoothing the boundary of a larger object and not obviously changing the area of the larger object;
after the image is segmented by the OTSU method, the obtained target edge may have the conditions of burr protrusion, adjacent target adhesion and the like, so that the subsequent precise fishpond boundary extraction is influenced, the accuracy is reduced, and therefore after the image binarization obtains a primary result, the subsequent extraction effect is improved by adopting a mathematical morphology method for processing.
The basic idea of mathematical morphology, also called image algebra, is to use structural elements with certain morphology to measure and extract corresponding shapes in an image, so as to achieve the purpose of analyzing and identifying the image. The basic operations of mathematical morphology are four: dilation, erosion, open and close operations.
In some embodiments, the binarized image is morphologically processed by performing an operation of erosion and then expansion on the initial result, separating the adjacent fishponds which are adhered to each other, smoothing the boundaries of the fishponds, and removing part of the interference of non-fishpond pixel points, thereby improving the integrity and accuracy of fishpond extraction.
Step 2.4, vectorizing the image subjected to open operation processing in the step 2.3 to obtain vector data;
and 2.5, counting the minimum area and the maximum area of the fishpond, setting an area threshold value d, and deleting the vectors smaller than the threshold value d to obtain an initial fishpond vector result.
In some of the present embodiments, where the minimum area of the fishpond is set to 400 square meters and the maximum area is set to 20000 square meters, vectors with areas less than 400 and greater than 20000 square meters are deleted, and the initial fishpond vector results are shown in fig. 5, it can be seen that both small debris and larger area non-fishpond vectors are deleted.
Step 3, based on the reflectivity data obtained in the step 1, utilizing NDVI to normalize the vegetation index, and extracting a vegetation coverage vector result;
in some embodiments, the normalized vegetation index NDVI is used for performing band operation on the reflectivity data in step 1, the vegetation and non-vegetation region segmentation threshold d is determined to be 0.3 according to the actual operation result, pixels larger than the threshold 0.3 are assigned to be 1, and the rest pixels are assigned to be 0, so that binarization is realized; and then vectorizing the binarized image, calculating the minimum vegetation area to be 100 square meters according to the actual situation, and deleting the candidate vegetation vector with the area smaller than 100 square meters to obtain a vegetation vector result.
And 4, carrying out vector erasure operation by using the initial fishpond vector obtained in the step 2 and the vegetation vector extracted in the step 3. Vector erasure creates an element class by superimposing an input element with a polygon of an erased element, and copies only a portion of the input element outside the outer boundary of the erased element to an output element class. The method comprises the steps that an initial fishpond vector is an input element, a vegetation vector is an erasing element, and a pseudo fishpond vector of a vegetation region is removed through erasing operation to obtain a fishpond vector of a non-vegetation region;
and 5, performing smooth surface treatment on the candidate fishpond vectors in the step 4, smoothing vector boundaries, removing burrs, performing manual intervention, deleting wrong-extraction fishpond vectors or supplementing pictures and omitting extraction fishpond to obtain a final fishpond extraction vector result, wherein the final extraction result is shown in fig. 6, and the amplification effect graph of the extracted fishpond is shown in fig. 7. The above content introduces the fishpond identification method based on the high-resolution satellite remote sensing image provided by the invention in detail.
Corresponding to the foregoing embodiments, the present invention also provides system embodiments. For the system embodiment, since it basically corresponds to the method embodiment, the relevant points may be referred to the partial description of the method embodiment. The above-described embodiments of the apparatus are merely illustrative, and 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 can be selected according to actual needs to achieve the purpose of the disclosed solution. One of ordinary skill in the art can understand and implement it without inventive effort.
As shown in fig. 8, fig. 8 is a diagram illustrating a fishpond recognition system based on high-resolution satellite remote sensing images according to an exemplary embodiment of the invention, which includes:
the preprocessing module is used for acquiring a high-resolution satellite remote sensing image, and preprocessing the image to obtain reflectivity data including geometric positioning;
the first processing module is used for processing the reflectivity data to obtain an initial fishpond vector result and a vegetation coverage vector result;
the second processing module is used for carrying out vector erasure operation on the initial fishpond vector result and the vegetation coverage vector result, and removing the pseudo fishpond vector of the vegetation area to obtain a non-vegetation area candidate fishpond vector;
and the third processing module is used for processing the candidate fishpond vector to obtain a final extraction result of the fishpond.
In some embodiments, the first processing module is configured to process the reflectivity data by using bilateral filtering, OTSU algorithm segmentation, and an open-operation image processing method based on the reflectivity data obtained by the preprocessing module, so as to obtain an initial fishpond vector result.
It should be recognized that the method steps in embodiments of the present invention may be embodied or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The method may use standard programming techniques. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of android computing platform that is operatively connected to a suitable. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated onto a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, which when read by the computer can be used to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention may also include the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (10)
1. A fishpond identification method based on high-resolution satellite remote sensing images is characterized by comprising the following steps:
acquiring a high-resolution satellite remote sensing image, and preprocessing the image to obtain reflectivity data including geometric positioning;
processing the reflectivity data based on bilateral filtering, an OTSU algorithm, an open operation morphology and NDVI to obtain an initial fishpond vector result and a vegetation coverage vector result;
carrying out vector erasure operation on the initial fishpond vector result and the vegetation coverage vector result, and removing the pseudo fishpond vector of the vegetation area to obtain a candidate fishpond vector of the non-vegetation area;
and processing the candidate fishpond vector to obtain a final fishpond extraction result.
2. The fishpond identification method based on the high-resolution satellite remote sensing image as claimed in claim 1, wherein the preprocessing comprises radiometric calibration, atmospheric correction and orthometric correction.
3. The fishpond recognition method based on the high-resolution satellite remote sensing image as claimed in claim 1, wherein the processing the reflectivity data comprises:
A. extracting the 4 th wave band of the reflectivity data, processing the wave band through bilateral filtering of a nonlinear filter, bringing the spatial distance relation and the color similarity of pixels into calculation, and reserving the target edge of the image and smoothing the internal details;
B. b, performing threshold segmentation on the image processed in the step A based on a maximum inter-class variance method of an OTSU algorithm, automatically calculating to obtain an optimal segmentation threshold of a fishpond and a non-fishpond area, and performing binarization processing according to the threshold;
C. b, performing opening operation morphological processing on the binary image obtained in the step B;
D. vectorizing the image subjected to the opening operation processing in the step C to obtain vector data;
E. and setting area thresholds D and D according to the statistical minimum value and the statistical maximum value of the area of the fishpond, and deleting vectors smaller than the threshold D and larger than the threshold D to obtain an initial fishpond vector result.
4. The fishpond recognition method based on the high-resolution satellite remote sensing image as claimed in claim 1 or 3, wherein the processing of the reflectivity data further comprises:
F. performing band operation on the reflectivity data based on NDVI, determining a vegetation and non-vegetation region segmentation threshold according to an actual operation result, and performing binarization processing according to the vegetation and non-vegetation region segmentation threshold;
G. and F, vectorizing the image subjected to the binarization processing in the step F, calculating the minimum vegetation area according to the actual situation, and deleting the candidate vegetation vector with the area smaller than the minimum vegetation area to obtain a vegetation vector result.
5. The fishpond recognition method based on the high-resolution satellite remote sensing image as claimed in claim 1, wherein the vector erasure operation comprises:
setting the initial fishpond vector as an input element, and setting the vegetation vector as an erasing element;
creating an element class by superimposing the input elements with polygons of the erased elements;
and copying the part of the input element outside the external boundary of the erasing element to an output element class to obtain a non-vegetation area fishpond vector.
6. The fishpond recognition method based on the high-resolution satellite remote sensing image as claimed in claim 1, wherein the processing of the candidate fishpond vector comprises:
and performing smooth surface treatment on the candidate fishpond vector, smoothing vector boundaries and removing burrs.
7. The fishpond recognition method based on the high-resolution satellite remote sensing image as claimed in claim 1, wherein the processing of the candidate fishpond vector comprises:
and performing manual intervention on the candidate fishpond vector, and removing the wrong fishpond vector or supplementing the picture and omitting the fishpond.
8. A fishpond recognition system based on high-resolution satellite remote sensing images is characterized by comprising:
the preprocessing module is used for acquiring a high-resolution satellite remote sensing image, and preprocessing the image to obtain reflectivity data including geometric positioning;
the first processing module is used for processing the reflectivity data based on bilateral filtering, an OTSU algorithm, an open operation morphology and NDVI to obtain an initial fishpond vector result and a vegetation coverage vector result;
the second processing module is used for carrying out vector erasure operation on the initial fishpond vector result and the vegetation coverage vector result, and removing the pseudo fishpond vector of the vegetation area to obtain a candidate fishpond vector of the non-vegetation area;
and the third processing module is used for processing the candidate fishpond vector to obtain a final fishpond extraction result.
9. The fishpond recognition system based on the high-resolution satellite remote-sensing image as claimed in claim 8, wherein the first processing module is configured to process the reflectivity data based on the reflectivity data obtained by the preprocessing module through bilateral filtering, OTSU algorithm segmentation and open operation image processing methods to obtain an initial fishpond vector result.
10. A computer readable storage medium having stored thereon program instructions which, when executed by a processor, implement the method of any one of claims 1 to 7.
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