CN112418075B - Corn lodging area detection method and system based on canopy height model - Google Patents
Corn lodging area detection method and system based on canopy height model Download PDFInfo
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- 235000005822 corn Nutrition 0.000 title claims abstract description 90
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- 238000000034 method Methods 0.000 claims abstract description 18
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Abstract
The invention discloses a corn lodging area detection method and system based on a canopy height model. Firstly, extracting soil point distribution of a corn planting area by using a visible light wave band difference vegetation index set threshold value, carrying out spatial interpolation after filtering a minimum value of a soil height Cheng San point window, and obtaining a corn canopy height model CHM by overlapping a digital surface model inversion. The method for classifying the canopy height models directly by using the OSTU threshold method is simple, convenient, easy and high in accuracy, and therefore accurate detection can be carried out on the corn lodging areas.
Description
Technical Field
The invention relates to the technical field of corn lodging area detection, in particular to a corn lodging area detection method and system based on a canopy height model.
Background
Crop lodging is a phenomenon that crop is subjected to external force to cause stem bending or root displacement, and is often affected by mechanical force or disease and insect damage to generate lesions. There have been studies to monitor lodging crops from satellite platforms, aerospace platforms and ground stations. The traditional ground manual measurement method is labor-consuming, low in efficiency and large in estimation error. In satellite platforms, studies have been made to monitor crop lodging from the near infrared band of the visible light based on HJ-1A/HJ-1B (Ren Gongling et al, 2015), from the microwave band based on RASARSAT-2 (Yang, H., et al, 2015; chauhan, S., et al, 2020), sentinel-1A (Chauhan, S., et al, 2020). Satellite remote sensing makes up the defects of the traditional ground monitoring to a certain extent, is susceptible to the influence of a reentry cycle, bad weather and the like, and often cannot acquire lodging information quickly and accurately.
The unmanned aerial vehicle aerial survey has the advantages of maneuver and flexibility, can acquire high-definition images of crops, acquire accurate spectrums and spatial information thereof, can invert various vegetation indexes, and is widely applied to crop type identification, crop growth and accurate disaster assessment. Crop lodging extraction by using unmanned aerial vehicle technology has achieved a certain effect. But the spectrum and texture features of the unmanned aerial vehicle image are utilized to extract the lodging range of crops, and the unmanned aerial vehicle image is easy to be interfered by factors such as acquisition time, illumination conditions, shadows and the like.
Disclosure of Invention
Based on the detection method and the detection system, the invention aims to provide a corn lodging area detection method and system based on a canopy height model, which are used for accurately detecting a corn lodging area.
In order to achieve the above object, the present invention provides the following solutions:
a corn lodging area detection method based on a canopy height model comprises the following steps:
shooting a corn planting area through an unmanned aerial vehicle;
splicing the pictures shot by the unmanned aerial vehicles to obtain a digital orthographic image, and carrying out three-dimensional reconstruction on the pictures shot by the unmanned aerial vehicles to obtain a digital surface model;
determining soil points of a corn planting area according to the digital orthographic image;
determining a corn canopy height model according to the digital surface model and the soil points;
and based on the corn canopy height model, segmenting and extracting a lodging region of the corn by adopting an OSTU algorithm.
Optionally, the determining the soil point of the corn planting area according to the digital orthographic image specifically includes:
extracting a corn planting area by using a digitizing method based on the digital orthographic image;
and extracting soil points in the corn planting area by utilizing the visible light wave band difference vegetation index.
Optionally, the determining the corn canopy height model according to the digital surface model and the soil points specifically includes:
filtering the soil points by using a minimum value of a 25 x 25 window, and extracting the soil points with the lowest elevation values in the local window to obtain soil height Cheng San points;
performing nearest neighbor interpolation on the soil elevation scattered points to obtain a reference elevation model without vegetation coverage;
and obtaining the corn canopy height model by utilizing the difference between the digital surface model and the reference elevation model.
Optionally, the calculation formula of the visible light wave band difference vegetation index is as follows:
VDVI＝(2*ρ G -ρ B -ρ R )/(2*ρ G +ρ B +ρ R )
wherein ρ is G ，ρ B And ρ R The spectral values of the green band, the blue band and the red band, respectively.
The invention also provides a corn lodging area detection system based on the canopy height model, which comprises:
the shooting module is used for shooting the corn planting area through the unmanned aerial vehicle;
the splicing and reconstructing module is used for splicing the pictures shot by the unmanned aerial vehicles to obtain digital orthographic images, and carrying out three-dimensional reconstruction on the pictures shot by the unmanned aerial vehicles to obtain a digital surface model;
the soil point determining module is used for determining the soil point of the corn planting area according to the digital orthographic image;
the corn canopy height model determining module is used for determining a corn canopy height model according to the digital surface model and the soil points;
and the lodging area detection module is used for segmenting and extracting lodging areas of the corn by adopting an OSTU algorithm based on the corn canopy height model.
Optionally, the soil point determining module specifically includes:
the corn planting area extracting unit is used for extracting a corn planting area by a digital method based on the digital orthographic image;
and the soil point extraction unit is used for extracting the soil points in the corn planting area by utilizing the visible light wave band difference vegetation indexes.
Optionally, the corn canopy height model determining module specifically includes:
the soil height Cheng San point extraction unit is used for filtering the soil points by using the minimum value of the 25 x 25 window, extracting the soil points with the lowest elevation values in the local window, and obtaining the soil height Cheng San points;
the interpolation unit is used for carrying out nearest neighbor interpolation on the soil elevation scattered points to obtain a reference elevation model without vegetation coverage;
and the difference making unit is used for making a difference between the digital surface model and the reference elevation model to obtain the corn canopy height model.
Optionally, the calculation formula of the visible light wave band difference vegetation index is as follows:
VDVI＝(2*ρ G -ρ B -ρ R )/(2*ρ G +ρ B +ρ R )
wherein ρ is G ，ρ B And ρ R The spectral values of the green band, the blue band and the red band, respectively.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method, firstly, the visible light wave band difference vegetation index is used for extracting the soil point distribution of the corn land, spatial interpolation is carried out after the minimum value of a window with the soil height of Cheng San points is filtered, and the corn canopy height model CHM is obtained through inversion of a superimposed land digital surface model. The method for classifying the canopy height models directly by using the OSTU threshold method is simple, convenient and feasible, and has high precision, so that the corn lodging region can be accurately detected.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a corn lodging area detection method based on a canopy height model provided by the invention;
FIG. 2 is a schematic diagram of a corn lodging area detection method based on a canopy height model provided by the invention;
fig. 3 is a structural block diagram of a corn lodging area detection system based on a canopy height model provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a corn lodging area detection method and system based on a canopy height model, which are used for accurately detecting a corn lodging area.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1-2, a corn lodging area detection method based on a canopy height model comprises the following steps:
step 101: shooting the corn planting area through the unmanned aerial vehicle.
Step 102: and splicing the pictures shot by the unmanned aerial vehicles to obtain a digital orthophoto (Digital Orthophoto Map, DOM), and carrying out three-dimensional reconstruction on the pictures shot by the unmanned aerial vehicles to obtain a digital surface model (Digital Surface Model).
Step 103: and determining the soil point of the corn planting area according to the digital orthographic image.
Step 104: and determining a corn canopy height model according to the digital surface model and the soil points.
Step 105: and based on the corn canopy height model, segmenting and extracting a lodging region of the corn by adopting an OSTU algorithm.
Step 103 specifically includes:
step 1031: and extracting the corn planting area by using a digitizing method based on the digital orthographic image.
Step 1032: and setting a threshold value to be smaller than 0 by using a Visible light wave band difference vegetation index (Visible-Band Difference Vegetation Index), and extracting soil points in the corn planting area.
The soil point distribution is obtained by calculating the vegetation index in the visible light wave band, and the common visible light vegetation index includes ExG (M.Woebbecke, D.et al, 1995), NGRDI (Hunt, E.R., et al, 2005), VDVI (Wang Xiaoqin, 2015) and the like, and can represent the vegetation characteristics on the optical image. The invention compares the capability of three vegetation indexes to distinguish soil background and corn canopy in corn plots, and finally adopts VDVI to extract soil background, and the definition is as follows:
VDVI＝(2*ρ G -ρ B -ρ R )/(2*ρ G +ρ B +ρ R )
wherein ρ is G ，ρ B And ρ R The spectral values of the green, blue and red bands, respectively.
Step 104 specifically includes:
step 1041: and filtering the soil points by using a minimum value of a 25 x 25 window, and extracting the soil points with the lowest elevation values in the local window to obtain the soil height Cheng San points.
Step 1042: and carrying out nearest neighbor interpolation on the soil elevation scattered points to obtain a reference elevation model (Digital Elevation Model, DEM) without vegetation coverage.
Step 1043: the digital surface model DSM is used to make a difference with the reference elevation model DEM to obtain a maize canopy height model (Canopy Height Model, CHM).
And (3) superposing soil points on the digital surface model DSM, and performing spatial interpolation on the extracted soil height Cheng San points to obtain a reference elevation model (DEM) of the corn field. Because unmanned aerial vehicle positioning accuracy and image splice in-process have the deviation, receive the influence of topography fluctuation simultaneously, corn field elevation information that DSM directly reflected has certain error. In order to reduce the influence of the error on interpolation, the method adopts a soil height Cheng San point at a 25 x 25 window minimum value filtering position, and performs spatial interpolation after removing the soil elevation point of error interference to obtain the DEM.
Inverting vegetation canopy height. The vegetation Canopy Height Model (CHM) can reflect the height information of crops in the vertical direction, and the DSM obtained by unmanned aerial vehicle image stitching and the reference elevation DEM obtained by interpolation can be obtained by calculation, and the calculation formula is as follows: chm=dsm-DEM.
The step 105 specifically includes:
the OSTU algorithm was proposed by Japanese scholars in Otsu, also known as the maximum inter-class variance method (N.Otsu, 1979). The idea is to find a threshold t which maximizes the inter-class variance to realize the binarization processing of the image and extract the target ground object. The lodging area extraction studied by the invention is actually a classification problem, and the distribution range of the lodging area can be objectively expressed by classifying CHM by using an Ostu threshold algorithm.
The OSTU implementation method is as follows, and the initial threshold is assumed to be t, so that the image is divided, and the proportion omega of the pixels of the category A and the category B is calculated A And omega B ＝1-ω A The average value of the gray scale of the pixels is mu A Sum mu B Wherein the overall mean of the image μ=ω A *μ A +ω B *μ B Such that:
g＝ω 0 (μ 0 -μ) 2 +ω 1 (μ 1 -μ) 2
and measuring the maximum inter-class variance by using an objective function g, wherein when the g value is maximum, the corresponding threshold t is the optimal threshold of the classification.
The lodging extraction of the canopy height model by using OSTU is a piecewise function, and the threshold t is a boundary for distinguishing whether lodging exists.
As shown in fig. 3, the present invention further provides a corn lodging area detection system based on a canopy height model, including:
the shooting module 301 is configured to shoot a corn planting area through an unmanned aerial vehicle.
The splicing and reconstructing module 302 is configured to splice the pictures shot by the multiple unmanned aerial vehicles to obtain a digital orthographic image, and reconstruct the pictures shot by the multiple unmanned aerial vehicles in three dimensions to obtain a digital surface model.
A soil point determination module 303 for determining a soil point of a corn planting area based on the digital orthographic image.
The soil point determining module 303 specifically includes:
the corn planting area extracting unit is used for extracting a corn planting area by a digital method based on the digital orthographic image;
and the soil point extraction unit is used for extracting the soil points in the corn planting area by utilizing the visible light wave band difference vegetation indexes.
The corn canopy height model determining module 304 is configured to determine a corn canopy height model according to the digital surface model and the soil points.
The corn canopy height model determining module 304 specifically includes:
the soil height Cheng San point extraction unit is used for filtering the soil points by using the minimum value of the 25 x 25 window, extracting the soil points with the lowest elevation values in the local window, and obtaining the soil height Cheng San points;
the interpolation unit is used for carrying out nearest neighbor interpolation on the soil elevation scattered points to obtain a reference elevation model without vegetation coverage;
and the difference making unit is used for making a difference between the digital surface model and the reference elevation model to obtain the corn canopy height model.
And the lodging area detection module 305 is used for segmenting and extracting lodging areas of the corn by adopting an OSTU algorithm based on the corn canopy height model.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, which are intended to be only illustrative of the methods and concepts underlying the invention, and not all examples are intended to be within the scope of the invention as defined by the appended claims.
Claims (6)
1. The corn lodging area detection method based on the canopy height model is characterized by comprising the following steps of:
shooting a corn planting area through an unmanned aerial vehicle;
splicing the pictures shot by the unmanned aerial vehicles to obtain a digital orthographic image, and carrying out three-dimensional reconstruction on the pictures shot by the unmanned aerial vehicles to obtain a digital surface model;
determining soil points of a corn planting area according to the digital orthographic image;
determining a corn canopy height model according to the digital surface model and the soil points; the method specifically comprises the following steps: filtering the soil points by using a minimum value of a 25 x 25 window, and extracting the soil points with the lowest elevation values in the local window to obtain soil height Cheng San points; performing nearest neighbor interpolation on the soil elevation scattered points to obtain a reference elevation model without vegetation coverage; obtaining a corn canopy height model by utilizing the difference between the digital surface model and the reference elevation model;
and based on the corn canopy height model, segmenting and extracting a lodging region of the corn by adopting an OSTU algorithm.
2. The canopy height model-based corn lodging zone detection method of claim 1, wherein the determining soil points of the corn planting zone from the digital orthographic image comprises:
extracting a corn planting area by using a digitizing method based on the digital orthographic image;
and extracting soil points in the corn planting area by utilizing the visible light wave band difference vegetation index.
3. The canopy height model-based corn lodging area detection method of claim 2, wherein the calculation formula of the visible light band difference vegetation index is as follows:
VDVI＝(2*ρ G -ρ B -ρ R )/(2*ρ G +ρ B +ρ R )
wherein ρ is G ，ρ B And ρ R The spectral values of the green band, the blue band and the red band, respectively.
4. Corn lodging area detecting system based on canopy height model, characterized by comprising:
the shooting module is used for shooting the corn planting area through the unmanned aerial vehicle;
the splicing and reconstructing module is used for splicing the pictures shot by the unmanned aerial vehicles to obtain digital orthographic images, and carrying out three-dimensional reconstruction on the pictures shot by the unmanned aerial vehicles to obtain a digital surface model;
the soil point determining module is used for determining the soil point of the corn planting area according to the digital orthographic image;
the corn canopy height model determining module is used for determining a corn canopy height model according to the digital surface model and the soil points; the method specifically comprises the following steps: the soil height Cheng San point extraction unit is used for filtering the soil points by using the minimum value of the 25 x 25 window, extracting the soil points with the lowest elevation values in the local window, and obtaining the soil height Cheng San points; the interpolation unit is used for carrying out nearest neighbor interpolation on the soil elevation scattered points to obtain a reference elevation model without vegetation coverage; the difference making unit is used for making a difference between the digital surface model and the reference elevation model to obtain a corn canopy height model;
and the lodging area detection module is used for segmenting and extracting lodging areas of the corn by adopting an OSTU algorithm based on the corn canopy height model.
5. The canopy height model-based corn lodging zone detection system of claim 4, wherein the soil point determination module comprises:
the corn planting area extracting unit is used for extracting a corn planting area by a digital method based on the digital orthographic image;
and the soil point extraction unit is used for extracting the soil points in the corn planting area by utilizing the visible light wave band difference vegetation indexes.
6. The canopy height model-based corn lodging zone detection system of claim 5, wherein the visible band differential vegetation index is calculated as follows:
VDVI＝(2*ρ G -ρ B -ρ R )/(2*ρ G +ρ B +ρ R )
wherein ρ is G ，ρ B And ρ R The spectral values of the green band, the blue band and the red band, respectively.
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| CN108007438A (en) * | 2017-12-01 | 2018-05-08 | 云南大学 | The estimating and measuring method of unmanned plane aeroplane photography remote sensing wetland plant biomass |
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| CN108007438A (en) * | 2017-12-01 | 2018-05-08 | 云南大学 | The estimating and measuring method of unmanned plane aeroplane photography remote sensing wetland plant biomass |
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