CN111829957A - System and method for inverting moisture content of winter wheat plants based on multispectral remote sensing of unmanned aerial vehicle - Google Patents
System and method for inverting moisture content of winter wheat plants based on multispectral remote sensing of unmanned aerial vehicle Download PDFInfo
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Abstract
The invention belongs to the technical field of plant growth monitoring, and discloses a system and a method for inverting the moisture content of a winter wheat plant based on multispectral remote sensing of an unmanned aerial vehicle, wherein the system for inverting the moisture content of the winter wheat plant based on the multispectral remote sensing of the unmanned aerial vehicle comprises the following steps: the system comprises an area dividing module, a multispectral remote sensing image acquisition module, a remote sensing image preprocessing module, a spectral vegetation index extraction module, a central control module, an inversion model establishing module, an inversion model processing module, a winter wheat plant moisture content calculation module, a cloud storage module and a display module. According to the method, the unmanned aerial vehicle is used for carrying the multispectral remote sensing image acquisition device to obtain the five-waveband spectral remote sensing image of the winter wheat plant in the region to be detected, the nonlinear inversion model of the water content of the winter wheat plant in the region to be detected is constructed according to the spectral reflectivity and the spectral vegetation index, the calculation of the water content of the winter wheat plant is realized, a theoretical basis is provided for realizing accurate crop monitoring, and the applicability of the multispectral remote sensing monitoring of the unmanned aerial vehicle is enhanced.
Description
Technical Field
The invention belongs to the technical field of plant growth monitoring, and particularly relates to a system and a method for inverting the moisture content of winter wheat plants based on multispectral remote sensing of an unmanned aerial vehicle.
Background
At present, the plant water content is one of indexes reflecting the crop water condition, is also an important basis for developing crop water shortage diagnosis, timely and accurately obtains the plant water content information, and has important significance for accurate agricultural development and efficient utilization of agricultural water resources. The traditional method for estimating the water content of the plant mainly comprises destructive sampling and determination, is complex to operate, consumes a large amount of manpower and material resources, and is not enough to meet the requirement of implementing rapid monitoring. At present, research on plant water content based on satellite remote sensing or ground remote sensing at home and abroad has made a certain progress, and the unmanned aerial vehicle remote sensing technology plays an increasingly important role in the fields of agriculture, water quality monitoring, surveying and mapping and the like due to the advantages of strong mobility and good applicability of a platform, high image acquisition resolution and short operation period, and provides a new solution for agricultural condition monitoring research.
Through the above analysis, the problems and defects of the prior art are as follows: the traditional method for estimating the water content of the plant mainly comprises destructive sampling and determination, is complex to operate, consumes a large amount of manpower and material resources, and is not enough to meet the requirement of implementing rapid monitoring.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a system and a method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing.
The invention is realized in such a way that a method for inverting the moisture content of winter wheat plants based on multispectral remote sensing of an unmanned aerial vehicle comprises the following steps:
the method comprises the following steps of firstly, dividing a winter wheat planting area by an area dividing module through area dividing equipment to obtain a winter wheat area to be detected.
And step two, acquiring a five-waveband spectrum remote sensing image of the winter wheat plants in the region to be detected by using a multispectral remote sensing image acquisition device carried by the unmanned aerial vehicle through a multispectral remote sensing image acquisition module.
And thirdly, geometrically correcting the obtained five-waveband spectrum remote sensing image of the winter wheat plant by using an image preprocessing program through a remote sensing image preprocessing module to obtain the spectrum reflectivity of the winter wheat plant in the corrected to-be-detected area.
And step four, cutting the image of the spectral reflectivity of the winter wheat plant by utilizing ENVI software through a spectral vegetation index extraction module to obtain the spectral vegetation index.
And fifthly, controlling the normal operation of each module of the winter wheat plant moisture content inversion system based on the unmanned aerial vehicle multispectral remote sensing through a central control module by using a central processing unit.
And step six, establishing a nonlinear inversion model of the moisture content of the winter wheat plant corresponding to the area to be detected according to the spectral reflectivity of the winter wheat plant and the spectral vegetation index by using an inversion model establishing module and a model establishing program.
And seventhly, performing regression processing on the nonlinear inversion model by using a stepwise regression algorithm through an inversion model processing module.
And step eight, inverting the non-linear inversion model after the regression processing through a winter wheat plant water content calculation module to obtain the water content of the winter wheat plants in the area to be detected.
And step nine, storing the winter wheat area to be detected, the five-waveband spectrum remote sensing image of the winter wheat plant in the area to be detected, the spectrum reflectivity, the spectrum vegetation index, the nonlinear inversion model and the water content of the winter wheat plant in the area to be detected by using the cloud storage module and the cloud database server.
And step ten, displaying the five-waveband spectrum remote sensing image, the spectrum reflectivity, the spectrum vegetation index, the nonlinear inversion model and the real-time data of the moisture content of the winter wheat plant in the region to be detected by using the display through the display module.
Further, in the second step, the method for geometrically correcting the obtained five-waveband spectral remote sensing image of the winter wheat plant comprises the following steps:
firstly, performing rough correction on the remote sensing image by using an RPC method, then matching ground control point data in the corrected image, establishing a polynomial relationship between the control point and the roughly corrected image point to perform geometric correction on the roughly corrected image, and finishing geometric fine correction of the remote sensing image by combining DEM data.
Further, in the second step, after obtaining the five-waveband spectrum remote sensing image of the winter wheat plant in the region to be detected, firstly, filtering the image to be enhanced;
performing image quality enhancement processing on the filtered image;
performing region division on the image subjected to the image quality enhancement processing to obtain a plurality of image regions;
analyzing the plurality of image areas to obtain an over-enhanced image area and a weakly enhanced image area;
optimizing the over-enhanced image area and the weakly enhanced image area;
and splicing the image areas after the optimization processing to generate a complete image with enhanced image quality.
Further, in the third step, the reference image and the geometric distortion image in the geometric correction are respectively represented by f (x, y) and g (x ', y'), a corresponding relation is established according to the known control point pairs in the two images, and the geometric correction is realized through coordinate conversion;
the distortion relationship existing between the two image coordinate systems is as follows:
at h1(x, y) and h2And (x, y) under the condition that the coordinate of the corresponding pixel point of one pixel point in the other coordinate system is known, namely f (x, y) is equal to g (x ', y').
Further, in the third step, the method for obtaining the spectral reflectance of the winter wheat plant in the region to be detected comprises the following steps:
(1) synthesizing the five-waveband spectral images into a tif-format five-waveband spectral image;
(2) constructing an ROI to be detected by utilizing a mask method according to the synthesized five-waveband spectral image;
(3) respectively calculating the spectral reflectivity of the winter wheat plants of five wave bands in the region to be detected by using a band operation tool Bandmath;
(4) and calculating the average spectral reflectivity of the spectral reflectivities of the five wave bands in the ROI area to obtain the spectral reflectivity of the winter wheat in the area to be detected.
Further, in step four, the spectral vegetation index comprises: normalized spectral vegetation index NDVI, soil conditioning spectral vegetation index SAVI, enhanced spectral vegetation index EVI, ratio spectral vegetation index SR, greenness normalized spectral vegetation index GNDVI, and atmospheric resistance index VARI.
Further, in the sixth step, the method for establishing the non-linear inversion model of the moisture content of the winter wheat plant corresponding to the area to be detected through the inversion model establishing module comprises the following steps:
(I) analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral reflectance of the winter wheat by using a multivariate regression method of SPSS software;
(II) establishing a nonlinear inversion model containing the reflectivity of five wave bands by using a forced entry method;
and (III) sequentially introducing the moisture content of the winter wheat plants into a nonlinear inversion model one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of nonlinear inversion models.
Further, the nonlinear inversion model is:
wherein Z is the water content of winter wheat plant, m1N is a constant for adjusting the moisture content, m is a constant for ensuring that the logarithm is a positive value and a linear relationship0For constants used to compensate for zero meter moisture content, λi、λjAre respectively wavesSegment i and band j, RwIs the spectral reflectance of a winter wheat plant.
Further, in the seventh step, the method for performing regression processing on the nonlinear inversion model by using a stepwise regression algorithm includes:
1) selecting alternative factors of a stepwise regression algorithm from the nonlinear inversion model;
2) acquiring standardized input data;
3) acquiring a winter wheat plant moisture content influence factor according to the standardized input data, the significance test and the Gauss-Adam conversion;
4) and taking the nonlinear inversion model containing the influence factors of the water content of the winter wheat plants as a nonlinear inversion model after regression processing.
Another object of the present invention is to provide a system for inverting moisture content of winter wheat plants based on multispectral remote sensing of unmanned aerial vehicles, which applies the method for inverting moisture content of winter wheat plants based on multispectral remote sensing of unmanned aerial vehicles, the system comprising:
the system comprises an area dividing module, a multispectral remote sensing image acquisition module, a remote sensing image preprocessing module, a spectral vegetation index extraction module, a central control module, an inversion model establishing module, an inversion model processing module, a winter wheat plant moisture content calculation module, a cloud storage module and a display module.
The region dividing module is connected with the central control module and used for dividing the winter wheat planting region through region dividing equipment to obtain a winter wheat region to be detected;
the multispectral remote sensing image acquisition module is connected with the central control module and used for acquiring a five-waveband spectral remote sensing image of winter wheat plants in the region to be detected through a multispectral remote sensing image acquisition device carried by the unmanned aerial vehicle;
the remote sensing image preprocessing module is connected with the central control module and used for carrying out geometric correction on the obtained five-waveband spectrum remote sensing image of the winter wheat plant through an image preprocessing program to obtain the spectrum reflectivity of the winter wheat plant in the corrected region to be detected;
the spectral vegetation index extraction module is connected with the central control module and is used for cutting the image of the spectral reflectivity of the winter wheat plant by using ENVI software to obtain the spectral vegetation index;
the central control module is connected with the region dividing module, the multispectral remote sensing image acquisition module, the remote sensing image preprocessing module, the spectral vegetation index extraction module, the inversion model establishing module, the inversion model processing module, the winter wheat plant moisture content calculation module, the cloud storage module and the display module and is used for controlling the normal operation of each module of the unmanned aerial vehicle-based multispectral remote sensing inversion winter wheat plant moisture content system through the central processing unit;
the inversion model establishing module is connected with the central control module and used for establishing a nonlinear inversion model of the moisture content of the winter wheat plants corresponding to the area to be detected according to the spectral reflectivity of the winter wheat plants and the spectral vegetation index through a model establishing program;
the inversion model processing module is connected with the central control module and is used for carrying out regression processing on the nonlinear inversion model through a stepwise regression algorithm;
the winter wheat plant water content calculation module is connected with the central control module and is used for obtaining the water content of the winter wheat plant in the area to be detected through inversion according to the nonlinear inversion model after regression processing;
the cloud storage module is connected with the central control module and used for storing a winter wheat area to be detected, a five-waveband spectrum remote sensing image, a spectrum reflectivity, a spectrum vegetation index, a nonlinear inversion model and the water content of the winter wheat plant in the area to be detected through a cloud database server;
and the display module is connected with the central control module and is used for displaying the winter wheat area to be detected, the five-waveband spectrum remote sensing image of the winter wheat plant in the area to be detected, the spectrum reflectivity, the spectrum vegetation index, the nonlinear inversion model and the real-time data of the water content of the winter wheat plant in the area to be detected through the display.
Further, the inverse model processing module includes:
the selection unit is used for selecting alternative factors of the stepwise regression algorithm from the nonlinear inversion model;
a first acquisition unit for acquiring standardized input data;
the second acquisition unit is used for acquiring the influence factors of the moisture content of the winter wheat plants according to the standardized input data, the significance test and the Gauss-Adam conversion;
and the determining unit is used for taking the nonlinear inversion model containing the winter wheat plant water content influence factor as the nonlinear inversion model after regression processing.
Another object of the present invention is to provide a computer program product stored on a computer readable medium, comprising a computer readable program, which when executed on an electronic device, provides a user input interface to implement the method for inverting moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing.
Another object of the present invention is to provide a computer-readable storage medium storing instructions, which when executed on a computer, enable the computer to execute the method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing.
By combining all the technical schemes, the invention has the advantages and positive effects that: according to the method for inverting the moisture content of the winter wheat plants based on the multispectral remote sensing of the unmanned aerial vehicle, the multispectral remote sensing image acquisition device carried by the unmanned aerial vehicle is utilized to obtain the five-waveband spectral remote sensing image of the winter wheat plants in the region to be detected, further the spectral reflectivity and the spectral vegetation index are obtained, the nonlinear inversion model of the moisture content of the winter wheat plants corresponding to the region to be detected is constructed, the calculation of the moisture content of the winter wheat plants is achieved, a theoretical basis is provided for accurate crop monitoring, and the applicability of the multispectral remote sensing monitoring of the unmanned aerial vehicle is further enhanced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
FIG. 1 is a flow chart of a method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing provided by the embodiment of the invention.
FIG. 2 is a flowchart of a method for obtaining the spectral reflectance of a winter wheat plant in a region under test according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for establishing a non-linear inversion model of the moisture content of the winter wheat plant corresponding to the area to be detected by an inversion model establishing module according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for performing regression processing on the nonlinear inversion model through a stepwise regression algorithm according to an embodiment of the present invention.
FIG. 5 is a structural block diagram of a system for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing provided by the embodiment of the invention;
in the figure: 1. a region dividing module; 2. a multispectral remote sensing image acquisition module; 3. a remote sensing image preprocessing module; 4. a spectral vegetation index extraction module; 5. a central control module; 6. an inversion model building module; 7. an inversion model processing module; 8. a winter wheat plant water content calculation module; 9. a cloud storage module; 10. and a display module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a system and a method for inverting the moisture content of winter wheat plants based on multispectral remote sensing of an unmanned aerial vehicle, and the invention is described in detail below by combining the attached drawings.
As shown in fig. 1, the method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing provided by the embodiment of the invention comprises the following steps:
s101, dividing the winter wheat planting area by using an area dividing module through area dividing equipment to obtain the winter wheat area to be detected.
And S102, acquiring a five-waveband spectrum remote sensing image of the winter wheat plant in the region to be detected by using a multispectral remote sensing image acquisition device carried by the unmanned aerial vehicle through a multispectral remote sensing image acquisition module.
S103, carrying out geometric correction on the obtained five-waveband spectrum remote sensing image of the winter wheat plant by using an image preprocessing program through a remote sensing image preprocessing module to obtain the spectrum reflectivity of the winter wheat plant in the corrected to-be-detected area.
And S104, cutting the image of the spectral reflectivity of the winter wheat plant by utilizing ENVI software through a spectral vegetation index extraction module to obtain the spectral vegetation index.
And S105, controlling the normal operation of each module of the winter wheat plant moisture content inversion system based on the unmanned aerial vehicle multispectral remote sensing through the central control module by using the central processing unit.
And S106, establishing a nonlinear inversion model of the moisture content of the winter wheat plant corresponding to the area to be detected according to the spectral reflectivity of the winter wheat plant and the spectral vegetation index by using an inversion model establishing module and a model establishing program.
And S107, performing regression processing on the nonlinear inversion model by using a stepwise regression algorithm through an inversion model processing module.
And S108, inverting the non-linear inversion model after the regression processing through a winter wheat plant water content calculation module to obtain the water content of the winter wheat plant in the area to be detected.
And S109, storing the winter wheat area to be detected, the five-waveband spectrum remote sensing image of the winter wheat plant in the area to be detected, the spectrum reflectivity, the spectrum vegetation index, the nonlinear inversion model and the water content of the winter wheat plant in the area to be detected by using the cloud storage module and the cloud database server.
S110, displaying the to-be-detected winter wheat area, the five-waveband spectrum remote sensing image of the winter wheat plant in the to-be-detected area, the spectrum reflectivity, the spectrum vegetation index, the nonlinear inversion model and the real-time data of the water content of the winter wheat plant in the to-be-detected area by using the display through the display module.
In step S102, the method for geometrically correcting the obtained five-waveband spectral remote sensing image of the winter wheat plant comprises the following steps:
firstly, performing rough correction on the remote sensing image by using an RPC method, then matching ground control point data in the corrected image, establishing a polynomial relationship between the control point and the roughly corrected image point to perform geometric correction on the roughly corrected image, and finishing geometric fine correction of the remote sensing image by combining DEM data.
In the step S102, after obtaining a five-waveband spectrum remote sensing image of a winter wheat plant in a region to be detected, firstly, filtering an image to be enhanced;
performing image quality enhancement processing on the filtered image;
performing region division on the image subjected to the image quality enhancement processing to obtain a plurality of image regions;
analyzing the plurality of image areas to obtain an over-enhanced image area and a weakly enhanced image area;
optimizing the over-enhanced image area and the weakly enhanced image area;
and splicing the image areas after the optimization processing to generate a complete image with enhanced image quality.
In step S103, the reference image and the geometric distortion image in the geometric correction are respectively represented by f (x, y) and g (x ', y'), a corresponding relationship is established according to the known control point pairs in the two images, and the geometric correction is realized through coordinate transformation;
the distortion relationship existing between the two image coordinate systems is as follows:
at h1(x, y) and h2Under the condition that (x, y) is known, calculating the coordinate of a pixel point corresponding to the pixel point in another coordinate system, namely f (x, y) ═ fg(x',y')。
As shown in fig. 2, in step S103, the method for obtaining the spectral reflectance of the winter wheat plant in the region to be detected includes:
s201, synthesizing the five-waveband spectral images into a tif-format five-waveband spectral image;
s202, constructing an ROI to be detected by using a mask method according to the synthesized five-waveband spectral image;
s203, respectively calculating the spectral reflectivity of the winter wheat plants of five wave bands in the ROI to be detected by using a band operation tool Bandmath;
and S204, calculating the average spectral reflectivity of the spectral reflectivities of the five wave bands in the ROI area to obtain the spectral reflectivity of the winter wheat in the area to be detected.
In step S104 provided in the embodiment of the present invention, the spectral vegetation index includes: normalized spectral vegetation index NDVI, soil conditioning spectral vegetation index SAVI, enhanced spectral vegetation index EVI, ratio spectral vegetation index SR, greenness normalized spectral vegetation index GNDVI, and atmospheric resistance index VARI.
As shown in fig. 3, in step S106 provided in the embodiment of the present invention, the method for establishing a non-linear inversion model of the moisture content of the winter wheat plant corresponding to the region to be detected by using an inversion model establishing module includes:
s301, analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral reflectance of the winter wheat by using a multivariate regression method of SPSS software;
s302, establishing a nonlinear inversion model containing the reflectivity of five wave bands by using a forced entry method;
and S303, sequentially introducing the moisture content of the winter wheat plants into nonlinear inversion models one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of nonlinear inversion models.
The nonlinear inversion model provided by the embodiment of the invention is as follows:
wherein Z is the water content of winter wheat plant, m1N is a constant for adjusting the moisture content, m is a constant for ensuring that the logarithm is a positive value and a linear relationship0For constants used to compensate for zero meter moisture content, λi、λjRespectively a wave band i and a wave band j, RwIs the spectral reflectance of a winter wheat plant.
As shown in fig. 4, in step S107 provided in the embodiment of the present invention, the method for performing regression processing on the nonlinear inversion model by using a stepwise regression algorithm includes:
s401, selecting alternative factors of a stepwise regression algorithm from a nonlinear inversion model;
s402, acquiring standardized input data;
s403, acquiring a winter wheat plant water content influence factor according to the standardized input data, the significance test and the Gauss-Adam conversion;
s404, taking the nonlinear inversion model containing the winter wheat plant water content influence factor as a nonlinear inversion model after regression processing.
As shown in fig. 5, the system for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing provided by the embodiment of the invention comprises: the system comprises an area dividing module 1, a multispectral remote sensing image acquisition module 2, a remote sensing image preprocessing module 3, a spectral vegetation index extraction module 4, a central control module 5, an inversion model establishing module 6, an inversion model processing module 7, a winter wheat plant water content calculation module 8, a cloud storage module 9 and a display module 10.
The region dividing module 1 is connected with the central control module 5 and used for dividing the winter wheat planting region through region dividing equipment to obtain a winter wheat region to be detected;
the multispectral remote sensing image acquisition module 2 is connected with the central control module 5 and used for acquiring a five-waveband spectral remote sensing image of winter wheat plants in the region to be detected through a multispectral remote sensing image acquisition device carried by the unmanned aerial vehicle;
the remote sensing image preprocessing module 3 is connected with the central control module 5 and is used for carrying out geometric correction on the obtained five-waveband spectrum remote sensing image of the winter wheat plant through an image preprocessing program to obtain the spectrum reflectivity of the winter wheat plant in the corrected region to be detected;
the spectral vegetation index extraction module 4 is connected with the central control module 5 and is used for cutting the image of the spectral reflectivity of the winter wheat plant by using ENVI software to obtain the spectral vegetation index;
the central control module 5 is connected with the region dividing module 1, the multispectral remote sensing image acquisition module 2, the remote sensing image preprocessing module 3, the spectral vegetation index extraction module 4, the inversion model establishing module 6, the inversion model processing module 7, the winter wheat plant moisture content calculation module 8, the cloud storage module 9 and the display module 10, and is used for controlling the normal operation of each module of the unmanned aerial vehicle-based multispectral remote sensing inversion winter wheat plant moisture content system through a central processing unit;
the inversion model establishing module 6 is connected with the central control module 5 and is used for establishing a nonlinear inversion model of the moisture content of the winter wheat plants corresponding to the area to be detected according to the spectral reflectivity of the winter wheat plants and the spectral vegetation index through a model establishing program;
the inversion model processing module 7 is connected with the central control module 5 and is used for carrying out regression processing on the nonlinear inversion model through a stepwise regression algorithm;
the winter wheat plant water content calculation module 8 is connected with the central control module 5 and is used for obtaining the water content of the winter wheat plant in the region to be detected through inversion according to the nonlinear inversion model after regression processing;
the cloud storage module 9 is connected with the central control module 5 and used for storing a winter wheat area to be detected, a five-waveband spectrum remote sensing image of a winter wheat plant in the area to be detected, a spectrum reflectivity, a spectrum vegetation index, a nonlinear inversion model and a water content of the winter wheat plant in the area to be detected through a cloud database server;
and the display module 10 is connected with the central control module 5 and is used for displaying the winter wheat area to be detected, the five-waveband spectrum remote sensing image of the winter wheat plant in the area to be detected, the spectrum reflectivity, the spectrum vegetation index, the nonlinear inversion model and the real-time data of the water content of the winter wheat plant in the area to be detected through a display.
The inversion model processing module 7 provided by the embodiment of the invention comprises:
the selection unit 7-1 is used for selecting alternative factors of a stepwise regression algorithm from the nonlinear inversion model;
a first acquisition unit 7-2 for acquiring standardized input data;
a second obtaining unit 7-3, configured to obtain a winter wheat plant moisture content influence factor according to the standardized input data, the significance test, and the gaussian-adam transformation;
and the determining unit 7-4 is used for taking the nonlinear inversion model containing the winter wheat plant water content influence factor as the nonlinear inversion model after regression processing.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The method for inverting the moisture content of the winter wheat plant based on the multispectral remote sensing of the unmanned aerial vehicle is characterized by comprising the following steps of:
dividing a winter wheat planting area by using an area dividing module and area dividing equipment to obtain a winter wheat area to be detected;
acquiring a five-waveband spectrum remote sensing image of the winter wheat plant in the region to be detected by using a multispectral remote sensing image acquisition device carried by an unmanned aerial vehicle through a multispectral remote sensing image acquisition module;
thirdly, geometrically correcting the obtained five-waveband spectrum remote sensing image of the winter wheat plant by using an image preprocessing program through a remote sensing image preprocessing module to obtain the spectrum reflectivity of the winter wheat plant in the corrected region to be detected;
the method for geometrically correcting the obtained five-waveband spectral remote sensing image of the winter wheat plant comprises the following steps:
firstly, roughly correcting a remote sensing image by adopting an RPC method, then matching ground control point data in the corrected image, establishing a polynomial relationship between a control point and the roughly corrected image point to geometrically correct the roughly corrected image, and finishing geometric fine correction of the remote sensing image by combining DEM data;
cutting the image of the spectral reflectivity of the winter wheat plant by utilizing ENVI software through a spectral vegetation index extraction module to obtain the spectral vegetation index;
controlling the normal operation of each module of the winter wheat plant moisture content inversion system based on the unmanned aerial vehicle multispectral remote sensing through a central control module by using a central processing unit;
step six, establishing a nonlinear inversion model of the moisture content of the winter wheat plant corresponding to the area to be detected according to the spectral reflectivity of the winter wheat plant and the spectral vegetation index by using an inversion model establishing module and a model establishing program;
step seven, performing regression processing on the nonlinear inversion model by using a stepwise regression algorithm through an inversion model processing module;
step eight, inverting the non-linear inversion model after the regression processing through a winter wheat plant water content calculation module to obtain the water content of the winter wheat plants in the area to be detected;
step nine, storing a winter wheat area to be detected, a five-waveband spectrum remote sensing image of a winter wheat plant in the area to be detected, a spectrum reflectivity, a spectrum vegetation index, a nonlinear inversion model and a water content of the winter wheat plant in the area to be detected by using a cloud database server through a cloud storage module;
and step ten, displaying the five-waveband spectrum remote sensing image, the spectrum reflectivity, the spectrum vegetation index, the nonlinear inversion model and the real-time data of the moisture content of the winter wheat plant in the region to be detected by using the display through the display module.
2. The method for inverting the moisture content of the winter wheat plants based on the multispectral remote sensing of the unmanned aerial vehicle according to claim 1, wherein in the second step, after a five-waveband spectrum remote sensing image of the winter wheat plants in the region to be detected is obtained, filtering processing is firstly carried out on an image to be enhanced;
performing image quality enhancement processing on the filtered image;
performing region division on the image subjected to the image quality enhancement processing to obtain a plurality of image regions;
analyzing the plurality of image areas to obtain an over-enhanced image area and a weakly enhanced image area;
optimizing the over-enhanced image area and the weakly enhanced image area;
and splicing the image areas after the optimization processing to generate a complete image with enhanced image quality.
3. The method for inverting the moisture content of the winter wheat plant based on the multispectral remote sensing of the unmanned aerial vehicle as claimed in claim 1, wherein in the third step, the reference image and the geometric distortion image in the geometric correction are respectively represented by f (x, y) and g (x ', y'), a corresponding relation is established according to known control point pairs in the two images, and the geometric correction is realized through coordinate conversion;
the distortion relationship existing between the two image coordinate systems is as follows:
at h1(x, y) and h2And (x, y) under the condition that the coordinate of the corresponding pixel point of one pixel point in the other coordinate system is known, namely f (x, y) is equal to g (x ', y').
4. The method for inverting the moisture content of the winter wheat plant based on the multispectral remote sensing of the unmanned aerial vehicle as claimed in claim 1, wherein in the third step, the method for obtaining the spectral reflectivity of the winter wheat plant in the region to be detected comprises the following steps:
(1) synthesizing the five-waveband spectral images into a tif-format five-waveband spectral image;
(2) constructing an ROI to be detected by utilizing a mask method according to the synthesized five-waveband spectral image;
(3) respectively calculating the spectral reflectivity of the winter wheat plants of five wave bands in the region to be detected by using a band operation tool Bandmath;
(4) and calculating the average spectral reflectivity of the spectral reflectivities of the five wave bands in the ROI area to obtain the spectral reflectivity of the winter wheat in the area to be detected.
5. The method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing as claimed in claim 1, wherein in step four, the spectral vegetation index comprises: normalized spectral vegetation index NDVI, soil conditioning spectral vegetation index SAVI, enhanced spectral vegetation index EVI, ratio spectral vegetation index SR, greenness normalized spectral vegetation index GNDVI, and atmospheric resistance index VARI.
6. The method for inverting the moisture content of the winter wheat plant based on the multispectral remote sensing of the unmanned aerial vehicle as claimed in claim 1, wherein in the sixth step, the method for establishing the nonlinear inversion model of the moisture content of the winter wheat plant corresponding to the area to be measured through the inversion model establishing module comprises the following steps:
(I) analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral reflectance of the winter wheat by using a multivariate regression method of SPSS software;
(II) establishing a nonlinear inversion model containing the reflectivity of five wave bands by using a forced entry method;
and (III) sequentially introducing the moisture content of the winter wheat plants into a nonlinear inversion model one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of nonlinear inversion models.
7. The method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing as claimed in claim 6, wherein the nonlinear inversion model is as follows:
wherein Z is the water content of winter wheat plant, m1N is a constant for adjusting the moisture content, m is a constant for ensuring that the logarithm is a positive value and a linear relationship0For constants used to compensate for zero meter moisture content, λi、λjRespectively a wave band i and a wave band j, RwIs the spectral reflectance of a winter wheat plant.
8. The method for inverting the moisture content of the winter wheat plant based on the unmanned aerial vehicle multispectral remote sensing as recited in claim 1, wherein in the seventh step, the method for performing regression processing on the nonlinear inversion model through a stepwise regression algorithm comprises the following steps:
1) selecting alternative factors of a stepwise regression algorithm from the nonlinear inversion model;
2) acquiring standardized input data;
3) acquiring a winter wheat plant moisture content influence factor according to the standardized input data, the significance test and the Gauss-Adam conversion;
4) and taking the nonlinear inversion model containing the influence factors of the water content of the winter wheat plants as a nonlinear inversion model after regression processing.
9. The system for inverting the moisture content of the winter wheat plant based on the multispectral remote sensing of the unmanned aerial vehicle based on the method for inverting the moisture content of the winter wheat plant based on the multispectral remote sensing of the unmanned aerial vehicle according to any one of claims 1 to 8 is characterized by comprising the following steps:
the system comprises an area dividing module, a multispectral remote sensing image acquisition module, a remote sensing image preprocessing module, a spectral vegetation index extraction module, a central control module, an inversion model establishing module, an inversion model processing module, a winter wheat plant moisture content calculation module, a cloud storage module and a display module;
the region dividing module is connected with the central control module and used for dividing the winter wheat planting region through region dividing equipment to obtain a winter wheat region to be detected;
the multispectral remote sensing image acquisition module is connected with the central control module and used for acquiring a five-waveband spectral remote sensing image of winter wheat plants in the region to be detected through a multispectral remote sensing image acquisition device carried by the unmanned aerial vehicle;
the remote sensing image preprocessing module is connected with the central control module and used for carrying out geometric correction on the obtained five-waveband spectrum remote sensing image of the winter wheat plant through an image preprocessing program to obtain the spectrum reflectivity of the winter wheat plant in the corrected region to be detected;
the spectral vegetation index extraction module is connected with the central control module and is used for cutting the image of the spectral reflectivity of the winter wheat plant by using ENVI software to obtain the spectral vegetation index;
the central control module is connected with the region dividing module, the multispectral remote sensing image acquisition module, the remote sensing image preprocessing module, the spectral vegetation index extraction module, the inversion model establishing module, the inversion model processing module, the winter wheat plant moisture content calculation module, the cloud storage module and the display module and is used for controlling the normal operation of each module of the unmanned aerial vehicle-based multispectral remote sensing inversion winter wheat plant moisture content system through the central processing unit;
the inversion model establishing module is connected with the central control module and used for establishing a nonlinear inversion model of the moisture content of the winter wheat plants corresponding to the area to be detected according to the spectral reflectivity of the winter wheat plants and the spectral vegetation index through a model establishing program;
the inversion model processing module is connected with the central control module and is used for carrying out regression processing on the nonlinear inversion model through a stepwise regression algorithm;
the winter wheat plant water content calculation module is connected with the central control module and is used for obtaining the water content of the winter wheat plant in the area to be detected through inversion according to the nonlinear inversion model after regression processing;
the cloud storage module is connected with the central control module and used for storing a winter wheat area to be detected, a five-waveband spectrum remote sensing image, a spectrum reflectivity, a spectrum vegetation index, a nonlinear inversion model and the water content of the winter wheat plant in the area to be detected through a cloud database server;
and the display module is connected with the central control module and is used for displaying the winter wheat area to be detected, the five-waveband spectrum remote sensing image of the winter wheat plant in the area to be detected, the spectrum reflectivity, the spectrum vegetation index, the nonlinear inversion model and the real-time data of the water content of the winter wheat plant in the area to be detected through the display.
10. The system for inversion of moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing as claimed in claim 9, wherein the inversion model processing module comprises:
the selection unit is used for selecting alternative factors of the stepwise regression algorithm from the nonlinear inversion model;
a first acquisition unit for acquiring standardized input data;
the second acquisition unit is used for acquiring the influence factors of the moisture content of the winter wheat plants according to the standardized input data, the significance test and the Gauss-Adam conversion;
and the determining unit is used for taking the nonlinear inversion model containing the winter wheat plant water content influence factor as the nonlinear inversion model after regression processing.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113063740A (en) * | 2021-02-25 | 2021-07-02 | 北京麦飞科技有限公司 | Wheat canopy nitrogen content monitoring method based on multi-source remote sensing data |
| CN113358581A (en) * | 2021-06-04 | 2021-09-07 | 长光禹辰信息技术与装备(青岛)有限公司 | Unmanned aerial vehicle multispectral remote sensing data acquisition and processing integrated system and method |
| CN113655003A (en) * | 2021-09-02 | 2021-11-16 | 中科禾信遥感科技（苏州）有限公司 | Method for estimating soil moisture content of winter wheat at green-turning stage by using unmanned aerial vehicle photo |
| CN116124709A (en) * | 2022-09-20 | 2023-05-16 | 中国水利水电科学研究院 | Winter wheat drought unmanned aerial vehicle rapid monitoring and distinguishing method based on chlorophyll relative content |
| CN117288693A (en) * | 2023-11-27 | 2023-12-26 | 慧诺云谱(海南)科技有限公司 | Method and airborne device for acquiring vegetation index map in real time |
| CN117592604A (en) * | 2023-11-22 | 2024-02-23 | 河北省农林科学院旱作农业研究所 | Unmanned aerial vehicle-mounted remote sensing identification method for water-efficient wheat varieties |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105865424A (en) * | 2016-04-13 | 2016-08-17 | 中测新图(北京)遥感技术有限责任公司 | Nonlinear model-based multispectral remote sensing water depth inversion method and apparatus thereof |
| CN106780079A (en) * | 2016-11-28 | 2017-05-31 | 山东省农业可持续发展研究所 | Winter wheat different growing inversion method of leaf area index based on remotely-sensed data |
| US20190251678A1 (en) * | 2017-09-30 | 2019-08-15 | Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences | Automatic cross-platform geometric correction method for moon-based earth observation image |
| CN110222475A (en) * | 2019-07-03 | 2019-09-10 | 中国水利水电科学研究院 | A method of based on unmanned plane multispectral remote sensing inverting winter wheat plant moisture content |
-
2020
- 2020-07-07 CN CN202010648575.XA patent/CN111829957A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105865424A (en) * | 2016-04-13 | 2016-08-17 | 中测新图(北京)遥感技术有限责任公司 | Nonlinear model-based multispectral remote sensing water depth inversion method and apparatus thereof |
| CN106780079A (en) * | 2016-11-28 | 2017-05-31 | 山东省农业可持续发展研究所 | Winter wheat different growing inversion method of leaf area index based on remotely-sensed data |
| US20190251678A1 (en) * | 2017-09-30 | 2019-08-15 | Institute Of Remote Sensing And Digital Earth, Chinese Academy Of Sciences | Automatic cross-platform geometric correction method for moon-based earth observation image |
| CN110222475A (en) * | 2019-07-03 | 2019-09-10 | 中国水利水电科学研究院 | A method of based on unmanned plane multispectral remote sensing inverting winter wheat plant moisture content |
Non-Patent Citations (7)
| Title |
|---|
| 刘吉平: "《遥感原理及遥感信息分析基础》", 武汉大学出版社, pages: 345 - 347 * |
| 姜海玲等: "扬花期冬小麦叶面积指数高光谱遥感反演研究", 《吉林师范大学学报(自然科学版)》, no. 01, 4 February 2020 (2020-02-04) * |
| 张周威等: "无人机遥感数据处理流程及产品分级体系研究", 《武汉理工大学学报》 * |
| 张周威等: "无人机遥感数据处理流程及产品分级体系研究", 《武汉理工大学学报》, no. 05, 31 May 2013 (2013-05-31) * |
| 戴丽君等: "基于ARCINFO及ERDAS的地形景观图的制作――以西安市?哄鄙?态区为例", 《山东师范大学学报(自然科学版)》 * |
| 戴丽君等: "基于ARCINFO及ERDAS的地形景观图的制作――以西安市?哄鄙?态区为例", 《山东师范大学学报(自然科学版)》, no. 02, 15 July 2006 (2006-07-15) * |
| 杨可明: "《遥感原理与应用》", 30 September 2016, 中国矿业大学出版社, pages: 150 - 153 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN113655003B (en) * | 2021-09-02 | 2024-01-12 | 中科禾信遥感科技（苏州）有限公司 | Method for estimating soil moisture content of winter wheat in green-turning period by using unmanned aerial vehicle photo |
| CN116124709A (en) * | 2022-09-20 | 2023-05-16 | 中国水利水电科学研究院 | Winter wheat drought unmanned aerial vehicle rapid monitoring and distinguishing method based on chlorophyll relative content |
| CN116124709B (en) * | 2022-09-20 | 2023-09-12 | 中国水利水电科学研究院 | Winter wheat drought unmanned aerial vehicle monitoring and distinguishing method based on chlorophyll relative content |
| CN117592604A (en) * | 2023-11-22 | 2024-02-23 | 河北省农林科学院旱作农业研究所 | Unmanned aerial vehicle-mounted remote sensing identification method for water-efficient wheat varieties |
| CN117288693A (en) * | 2023-11-27 | 2023-12-26 | 慧诺云谱(海南)科技有限公司 | Method and airborne device for acquiring vegetation index map in real time |
| CN117288693B (en) * | 2023-11-27 | 2024-03-26 | 慧诺云谱(海南)科技有限公司 | Method and airborne device for acquiring vegetation index map in real time |
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