CN114694048A - Sparse shrub species identification method and system based on unmanned aerial vehicle remote sensing technology - Google Patents
Sparse shrub species identification method and system based on unmanned aerial vehicle remote sensing technology Download PDFInfo
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Abstract
The invention discloses a sparse shrub species identification method and system based on an unmanned aerial vehicle remote sensing technology; acquiring sparse shrub data by using an unmanned aerial vehicle remote sensing technology, and constructing a Digital Surface Model (DSM) and a multispectral image according to the sparse shrub data; constructing an image segmentation model, and segmenting the digital surface model DSM and the multispectral image by using the image segmentation model to obtain segmented sparse shrub data; the identification among all sparse shrub species is completed by utilizing the segmented sparse shrub data, and the data of the identified sparse shrub species are counted by utilizing spatial statistical analysis ARCGIS; based on multi-temporal multispectral wave band information and a Digital Surface Model (DSM) of the unmanned aerial vehicle, desert plants can be extracted and distinguished by using relative height and wave band values on the basis of object segmentation, images with high spatial resolution and time resolution are obtained at low cost, identification of sparse shrub species is completed, and a foundation is laid for species mapping and related research and management.
Description
Technical Field
The invention relates to the technical field of shrub species identification, in particular to a sparse shrub species identification method and system based on an unmanned aerial vehicle remote sensing technology.
Background
The haloxylon ammodendron is widely distributed in Asia, Afghanistan, Iran and western China, and plays the roles of stabilizing sand surface, reducing wind speed and providing ecological services such as food for wild animals. However, due to the influence of underground water level reduction and climate change caused by human over-development, the area of the haloxylon plants is continuously reduced, the population is difficult to update, and the survival is seriously threatened. The area of the haloxylon ammodendron in Xinjiang accounts for 73.1 percent of the total area of the haloxylon ammodendron in China, wherein the national secondary protective species haloxylon ammodendron is only distributed in Guerbantong Gute desert in northern Xinjiang and partial protection areas and Yili areas of the haloxylon ammodendron forest. Researches show that the water using strategies of the haloxylon ammodendron and the haloxylon persicum are different, the haloxylon persicum mainly depends on underground water, and the haloxylon persicum mainly depends on deep soil water, so that the influence of human activities and climate change on vegetation is very critical to distinguishing, and the control of the distribution characteristics of the haloxylon ammodendron and the haloxylon persicum is particularly important. However, the coverage of the haloxylon plants growing in arid regions is extremely low, leaves of the plants degenerate into assimilation branches, and the remote sensing extraction of growth indexes such as the coverage of the haloxylon plants is difficult.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a sparse shrub species identification method and system based on an unmanned aerial vehicle remote sensing technology.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
on one hand, the sparse shrub species identification method based on the unmanned aerial vehicle remote sensing technology comprises the following steps:
s1, acquiring sparse shrub data by using an unmanned aerial vehicle remote sensing technology, and constructing a Digital Surface Model (DSM) and a multispectral image according to the sparse shrub data;
s2, constructing an image segmentation model, and performing multi-scale threshold segmentation on the digital surface model DSM and the multispectral image by using the image segmentation model to obtain segmented sparse shrub data;
s3, identifying each sparse shrub species according to the segmented sparse shrub data;
and S4, carrying out statistical analysis on the data of the sparse shrub species after the ARCGIS is subjected to statistical identification by using spatial statistical analysis.
Preferably, step S2 is specifically:
classifying data in the multispectral image by image segmentation to obtain segmented characteristic image patches; meanwhile, carrying out element set segmentation on data in the digital surface model DSM by using a multi-scale threshold segmentation algorithm to obtain a segmented element set; the segmented feature image patch and the segmented element set jointly form segmented sparse shrub data.
Preferably, the parameters of the multi-scale threshold segmentation algorithm are respectively set as:
color weight set to 0.9, shape weight set to 0.1, firmness weight set to 0.1, smoothness weight set to 0.1; setting multiple scale thresholds as follows: 2.5 and 10.
Preferably, step S3 is specifically:
screening the segmented element set by using a preset minimum DSM (design language) to obtain sparse shrub species meeting the conditions; and distinguishing the segmented characteristic image patches according to the difference values of the NDVI values in autumn and winter, the near infrared band and the blue band preset in the multispectral image to obtain sparse shrub species based on the same genus, and obtaining the segmented sparse shrub species.
Preferably, step S4 is specifically:
and (3) performing statistical analysis on the plant tree, the height and the crown area of the data of the identified sparse shrub species by using the spatial statistical analysis ARCGIS, and calculating the density and the coverage of each sparse shrub species according to the statistical data.
On the other hand, a sparse shrub species identification system based on unmanned aerial vehicle remote sensing technology includes:
the data processing module is used for acquiring sparse shrub data by using an unmanned aerial vehicle remote sensing technology and constructing a Digital Surface Model (DSM) and a multispectral image according to the sparse shrub data;
the data segmentation module is used for constructing an image segmentation model and performing multi-scale threshold segmentation on the digital surface model DSM and the multispectral image by using the image segmentation model to obtain segmented sparse shrub data;
the data identification module is used for identifying various sparse shrub species according to the segmented sparse shrub data;
and the data statistical module is used for statistically analyzing the data of the sparse shrub species after the ARCGIS is subjected to identification by using spatial statistical analysis.
The invention has the following beneficial effects:
acquiring sparse shrub data by using an unmanned aerial vehicle remote sensing technology, and constructing a Digital Surface Model (DSM) and a multispectral image according to the sparse shrub data; constructing an image segmentation model, and segmenting the digital surface model DSM and the multispectral image by using the image segmentation model to obtain segmented sparse shrub data; recognizing various sparse shrub species by using the segmented sparse shrub data, and counting the recognized sparse shrub species by using spatial statistical analysis ARCGIS; based on multi-temporal multispectral wave band information and a Digital Surface Model (DSM) of the unmanned aerial vehicle, desert plants can be extracted and distinguished by using relative height and wave band values on the basis of object segmentation, images with high spatial resolution and time resolution are obtained at low cost, identification of sparse shrub species is completed, and a foundation is laid for species mapping and related research and management.
Drawings
FIG. 1 is a flow chart of steps of a sparse shrub species identification method based on an unmanned aerial vehicle remote sensing technology, provided by the invention;
FIG. 2 is a diagram showing the result of shuttle identification according to the embodiment of the present invention;
fig. 3 is a diagram illustrating the recognition result of the shuttle with white shuttle in the embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, an embodiment of the invention provides a sparse shrub species identification method based on an unmanned aerial vehicle remote sensing technology, which comprises the following steps:
s1, acquiring sparse shrub data by using an unmanned aerial vehicle remote sensing technology, and constructing a Digital Surface Model (DSM) and a multispectral image according to the sparse shrub data;
in the embodiment of the invention, based on multi-temporal multispectral wave band information of an unmanned aerial vehicle and a Digital Surface Model (DSM), the desert plant shuttle and the white shuttle are extracted and distinguished by using relative height and wave band values on the basis of object segmentation;
firstly, a flight mission is newly built, a flight path is planned, the height and the speed are set, the ground resolution is ensured to be 2.5cm-5cm, the DSM resolution is 5cm-10cm, the relative height of the shuttle with a small area is reduced due to the fact that the DSM resolution is too low, and the translation is ignored. The aviation flying overlapping rate is 75-80%, and the photo splicing quality is ensured. The flight mission is at least twice in autumn and winter and spring and summer, and the yellow period of the shuttle leaves in autumn and winter distinguishes the shuttle from the white shuttle. Picking Fusarium in spring and summer; sequentially carrying out point cloud calculation, multispectral ortho-image generation, splicing and radiation correction on the collected sparse shrub data to obtain a digital surface model DSM; the calculation can be completed by adopting the Xinjiang intelligent graph in the prior art; and by adopting a principle of partitioned image mosaic, firstly, a general global UTM projection is used for partitioning a large area to form a 6-degree partitioned strip, ARCGIS10.2 software is used for carrying out projection conversion on data in the partition, and WGS84 ellipsoid and UTM projection are selected as conversion parameters to complete construction of a multispectral image.
S2, constructing an image segmentation model, and segmenting the digital surface model DSM and the multispectral image by using the image segmentation model to obtain segmented sparse shrub data;
preferably, step S2 is specifically:
classifying data in the multispectral image by image segmentation to obtain a segmented characteristic image patch; meanwhile, carrying out element set segmentation on data in the digital surface model DSM by using a multi-scale threshold segmentation algorithm to obtain a segmented element set; the segmented feature image patch and the segmented element set jointly form segmented sparse shrub data.
In the embodiment of the invention, the graph segmentation model is expressed as follows:
S＝W*Hdsm+(1-W)*Hshape
Hshape＝Wsmo*Hsmo+(1-Wsmo)*Hcom
Hsmo＝L/b
wherein S is a heterogeneity scale, W is a digital surface elevation weight, and the following conditions are satisfied: 0<W<1；HdsmDigital surface elevation heterogeneity, digital elevation standard deviation within the subject; hshapeIs a shape heterogeneity; w is a group ofsmoFor smoothness weights, satisfy: 0<Wsmo<1；HsmoIs a smoothness index; hcomIs a compactness index; l is the number of pixels contained in the object boundary; n is the number of pixels included in the object; b is the boundary length of the minimum bounding rectangle of the object S.
Preferably, the parameters of the multi-scale threshold segmentation algorithm are respectively set as:
color weight set to 0.9, shape weight set to 0.1, firmness weight set to 0.1, smoothness weight set to 0.1; setting multiple scale thresholds as follows: 2.5 and 10.
In the embodiment of the invention, the digital surface model and the multispectral image are segmented and classified in the software eCoginization development 8.7 (Munich, Germany), and the segmentation takes the shape, the color and the compactness as reference to generate the object. The image segmentation refers to a technical method for cutting an image into characteristic similar patches according to a certain rule by using a spectrum (wave band or image layer), a shape and a texture factor of the image, and the patches formed by segmentation have the characteristics of uniform spectrum reflection and uniform structure texture; threshold segmentation is carried out on the basis of homogeneity or heterogeneity criteria, the regions represented by pixels or pixel clusters are aggregated, element set segmentation is realized by using an object-oriented software eCoginization and a multi-scale segmentation algorithm, the color weight is set to be 0.9, the shape weight is set to be 0.1, the compactness weight is set to be 0.1, and the smoothness weight is set to be 0.1; various scale thresholds 2, 5, 10 are set.
S3, identifying various sparse shrub species according to the segmented sparse shrub data;
preferably, step S3 is specifically:
screening the segmented element set by using a preset minimum DSM (design language) to obtain sparse shrub species meeting the conditions; and distinguishing the segmented characteristic image patches according to the difference values of the NDVI values in autumn and winter, the near infrared band and the blue band preset in the multispectral image to obtain sparse shrub species based on the same genus, and obtaining the segmented sparse shrub species.
In the embodiment of the invention, a preset minimum DSM at the periphery is used for screening the segmented element set to obtain sparse shrub species meeting the conditions, and the specific judgment process is as follows:
Rheight＝DSM-Min(Ndsm)
wherein R isheightFor relative height, DSM is an object DSM, NdsmFor adjacent objects DSM, Min (N)dsm) The DSM for the minimum periphery of the object is assumed to be soil DSM, when Rheight>When the length is 0.5 m, some object is the shrub of the genus Clostridia.
In the embodiment of the invention, shrubs are extracted from the segmented object by using DSM-peripheral minimum DSM, and as the white haloxylon ammodendron leaves earlier and the white haloxylon ammodendron leaves later, the white haloxylon persicum and the white haloxylon ammodendron can be distinguished by adopting NDVI values in autumn and winter and near infrared and blue wave band difference values in multispectral.
And S4, carrying out statistical analysis on the data of the sparse shrub species after the ARCGIS is subjected to statistical identification by using spatial statistical analysis.
Preferably, step S4 is specifically:
and (3) performing statistical analysis on the plant tree, the height and the crown area of the data of the identified sparse shrub species by using the spatial statistical analysis ARCGIS, and calculating the density and the coverage of each sparse shrub species according to the statistical data.
In the embodiment of the invention, the space statistical analysis ARCGIS is utilized to convert the haloxylon into points, the points are linked to the haloxylon through a spatial join to tool, the haloxylon in the haloxylon is determined, the white haloxylon is screened out again, the plant trees, the heights and the crown width areas of the haloxylon and the white haloxylon are respectively counted, and the density and the coverage of each sparse shrub species are calculated according to statistical data.
On the other hand, a sparse shrub species identification system based on unmanned aerial vehicle remote sensing technology includes:
the data processing module is used for acquiring sparse shrub data by using an unmanned aerial vehicle remote sensing technology and constructing a Digital Surface Model (DSM) and a multispectral image according to the sparse shrub data;
the data segmentation module is used for constructing an image segmentation model and performing multi-scale threshold segmentation on the digital surface model DSM and the multispectral image by using the image segmentation model to obtain segmented sparse shrub data;
the data identification module is used for identifying various sparse shrub species according to the segmented sparse shrub data;
and the data statistical module is used for statistically analyzing the data of the sparse shrub species after the ARCGIS is subjected to identification by using spatial statistical analysis.
The sparse shrub species identification system based on the unmanned aerial vehicle remote sensing technology provided by the embodiment of the invention has all the beneficial effects of the sparse shrub species identification system based on the unmanned aerial vehicle remote sensing technology.
As shown in fig. 2 and 3, the identification result diagrams of the shuttle and the white shuttle in the embodiment of the invention are respectively, the shuttle and the white shuttle are firstly distinguished and extracted from the unmanned aerial vehicle by utilizing multi-temporal multispectral data and the phenological information, and the user precision of the white shuttle is 82%, the user precision of the shuttle is 93% and the user precision of the shuttle community is 83% after verification of 200 ground real-time measuring points, so that the effect is good, and the fact that the unmanned aerial vehicle can identify desert shrub species is proved to be feasible.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (6)
1. A sparse shrub species identification method based on an unmanned aerial vehicle remote sensing technology is characterized by comprising the following steps:
s1, acquiring sparse shrub data by using an unmanned aerial vehicle remote sensing technology, and constructing a Digital Surface Model (DSM) and a multispectral image according to the sparse shrub data;
s2, constructing an image segmentation model, and performing multi-scale threshold segmentation on the digital surface model DSM and the multispectral image by using the image segmentation model to obtain segmented sparse shrub data;
s3, identifying various sparse shrub species according to the segmented sparse shrub data;
and S4, carrying out statistical analysis on the data of the sparse shrub species after the ARCGIS is subjected to statistical identification by using spatial statistical analysis.
2. The sparse shrub species identification method based on the unmanned aerial vehicle remote sensing technology as claimed in claim 1, wherein step S2 specifically comprises:
classifying data in the multispectral image by image segmentation to obtain a segmented characteristic image patch; meanwhile, carrying out element set segmentation on data in the digital surface model DSM by using a multi-scale threshold segmentation algorithm to obtain a segmented element set; the segmented feature image patch and the segmented element set jointly form segmented sparse shrub data.
3. The sparse shrub species identification method based on unmanned aerial vehicle remote sensing technology as claimed in claim 2, wherein parameters of the multi-scale threshold segmentation algorithm are respectively set as:
color weight set to 0.9, shape weight set to 0.1, firmness weight set to 0.1, smoothness weight set to 0.1; setting multiple scale thresholds as follows: 2.5 and 10.
4. The sparse shrub species identification method based on the unmanned aerial vehicle remote sensing technology as claimed in claim 1, wherein step S3 specifically comprises:
screening the segmented element set by using a preset minimum DSM (design language) to obtain sparse shrub species meeting the conditions; and distinguishing the segmented characteristic image patches according to the difference values of the NDVI values in autumn and winter, the near infrared band and the blue band preset in the multispectral image to obtain sparse shrub species based on the same genus, and obtaining the segmented sparse shrub species.
5. The sparse shrub species identification method based on the unmanned aerial vehicle remote sensing technology as claimed in claim 1, wherein step S4 specifically comprises:
and (3) performing statistical analysis on the plant tree, the height and the canopy area of the data of the sparse shrub species after the ARCGIS statistics identification, and calculating the density and the coverage of each sparse shrub species according to the statistical data.
6. The utility model provides a sparse shrub species identification system based on unmanned aerial vehicle remote sensing technology which characterized in that includes:
the data processing module is used for acquiring sparse shrub data by using an unmanned aerial vehicle remote sensing technology and constructing a Digital Surface Model (DSM) and a multispectral image according to the sparse shrub data;
the data segmentation module is used for constructing an image segmentation model and performing multi-scale threshold segmentation on the digital surface model DSM and the multispectral image by using the image segmentation model to obtain segmented sparse shrub data;
the data identification module is used for identifying various sparse shrub species according to the segmented sparse shrub data;
and the data statistical module is used for statistically analyzing the data of the sparse shrub species after the ARCGIS is subjected to identification by using spatial statistical analysis.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116168290A (en) * | 2022-12-28 | 2023-05-26 | 二十一世纪空间技术应用股份有限公司 | Method and device for classifying arbor and shrub in remote sensing image |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116168290A (en) * | 2022-12-28 | 2023-05-26 | 二十一世纪空间技术应用股份有限公司 | Method and device for classifying arbor and shrub in remote sensing image |
| CN116168290B (en) * | 2022-12-28 | 2023-08-08 | 二十一世纪空间技术应用股份有限公司 | Arbor-shrub grass classification method based on high-resolution remote sensing image and three-dimensional data |
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