CN112418075A - Corn lodging region detection method and system based on canopy height model - Google Patents
Corn lodging region detection method and system based on canopy height model Download PDFInfo
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Abstract
The invention discloses a method and a system for detecting a corn lodging region based on a canopy height model. Firstly, setting a threshold value by using a visible light wave band difference vegetation index to extract the distribution of soil points in a corn planting area, filtering the minimum value of a soil elevation scatter window, then performing spatial interpolation, and performing inversion by overlapping a plot digital surface model to obtain a corn canopy height model CHM. The method for directly classifying the canopy height models by using the OSTU threshold method is simple, convenient, feasible, rapid and high in precision, and therefore the corn lodging region can be accurately detected.
Description
Technical Field
The invention relates to the technical field of corn lodging region detection, in particular to a method and a system for detecting a corn lodging region based on a canopy height model.
Background
Crop lodging is a phenomenon that stems of crops are bent or roots of crops are displaced under the action of external force, and is often influenced by pathological changes caused by mechanical force or plant diseases and insect pests. Monitoring of lodging crops from satellite platforms, space platforms and ground stations has been studied. The traditional ground manual measuring method is troublesome and labor-consuming, and has low efficiency and large estimation error. On the satellite platform, monitoring of crop lodging from the visible near-infrared band based on HJ-1A/HJ-1B (ringer et al, 2015), from the microwave band based on RASARSAT-2(Yang, H., et al, 2015; Chauhan, S., et al, 2020), Sentiel-1A (Chauhan, S., et al, 2020) has been studied. Satellite remote sensing makes up for the deficiency of traditional ground monitoring to a certain extent, but is susceptible to the influence of a reentry cycle, severe weather and the like, and often cannot acquire lodging information quickly and accurately.
The unmanned aerial vehicle aerial survey has the advantages of mobility and flexibility, can acquire high-definition images of crops, obtain accurate spectrums and space information thereof, can invert various vegetation indexes, and is widely applied to crop type identification, crop growth and accurate disaster assessment. The crop lodging extraction by the unmanned aerial vehicle technology has achieved certain success. However, the spectrum and the texture characteristics of the unmanned aerial vehicle image are used for extracting the lodging range of the crop, and the unmanned aerial vehicle image is easily interfered by factors such as acquisition time, illumination conditions and shadows.
Disclosure of Invention
Based on the above, the invention aims to provide a method and a system for detecting a corn lodging region based on a canopy height model, which are used for accurately detecting the corn lodging region.
In order to achieve the purpose, the invention provides the following scheme:
a corn lodging region detection method based on a canopy height model comprises the following steps:
shooting a corn planting area through an unmanned aerial vehicle;
splicing a plurality of unmanned aerial vehicle shot pictures to obtain a digital ortho-image, and performing three-dimensional reconstruction on the plurality of unmanned aerial vehicle shot pictures to obtain a digital surface model;
determining soil points of a corn planting area according to the digital orthographic images;
determining a corn canopy height model from the digital surface model and the soil points;
and based on the corn canopy height model, adopting an OSTU algorithm to segment and extract a lodging region of the corn.
Optionally, the determining the soil point of the corn planting area according to the digital ortho-image specifically includes:
extracting a corn planting area by using a digital method based on the digital orthoimage;
and extracting soil points in the corn planting area by utilizing the visible light wave band difference vegetation index.
Optionally, the determining a model of the height of the corn canopy according to the digital surface model and the soil point specifically includes:
filtering the soil points by using the minimum value of 25 × 25 windows, and extracting the soil points with the lowest elevation values in the local windows to obtain soil elevation scattered points;
performing nearest neighbor interpolation on the soil elevation scattered points to obtain a non-covered standard elevation model;
and obtaining a 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 band difference vegetation index is as follows:
VDVI＝(2*ρG-ρB-ρR)/(2*ρG+ρB+ρR)
where ρ isG，ρBAnd ρRRespectively, a spectral value of a green band, a spectral value of a blue band, and a spectral value of a red band.
The invention also provides a corn lodging region 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 a plurality of unmanned aerial vehicle shot pictures to obtain a digital ortho-image and performing three-dimensional reconstruction on the plurality of unmanned aerial vehicle shot pictures to obtain a digital surface model;
the soil point determining module is used for determining soil points of the corn planting area according to the digital ortho-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 point;
and the lodging region detection module is used for segmenting and extracting a lodging region of the corn by adopting an OSTU algorithm based on the corn canopy height model.
Optionally, the soil point determination 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 ortho-image;
and the soil point extraction unit is used for extracting soil points in the corn planting area by utilizing the visible light waveband difference vegetation index.
Optionally, the corn canopy height model determining module specifically includes:
the soil elevation scattered point extraction unit is used for filtering the soil points by using the minimum value of 25 × 25 windows, extracting the soil points with the lowest elevation values in the local windows and obtaining the soil elevation scattered points;
the interpolation unit is used for carrying out nearest neighbor interpolation on the soil elevation scattered points to obtain a non-covered reference elevation model;
and 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.
Optionally, the calculation formula of the visible light band difference vegetation index is as follows:
VDVI＝(2*ρG-ρB-ρR)/(2*ρG+ρB+ρR)
where ρ isG，ρBAnd ρRRespectively, a spectral value of a green band, a spectral value of a blue band, and a spectral value of a red band.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method, firstly, visible light wave band difference vegetation indexes are used for extracting the distribution of the soil points of the corn plots, spatial interpolation is carried out after the minimum value of a soil elevation scatter point window is filtered, and a corn canopy height model CHM is obtained by superposing a plot digital surface model for inversion. The method for directly classifying the canopy height models by using the OSTU threshold method is simple, convenient and easy to implement and high in precision, and therefore the corn lodging region can be accurately detected.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for detecting a corn lodging region based on a canopy height model provided by the invention;
FIG. 2 is a schematic diagram of a canopy height model-based corn lodging region detection method provided by the invention;
fig. 3 is a block diagram of a corn lodging region detection system structure based on a canopy height model provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for detecting a corn lodging region based on a canopy height model, which are used for accurately detecting the corn lodging region.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1-2, a method for detecting a corn lodging region based on a canopy height model comprises the following steps:
step 101: shoot the maize planting area through unmanned aerial vehicle.
Step 102: the method comprises the steps of splicing a plurality of pictures shot by the unmanned aerial vehicle to obtain a Digital ortho-image (DOM), and carrying out three-dimensional reconstruction on the plurality of pictures shot by the unmanned aerial vehicle to obtain a Digital Surface Model (Digital Surface Model).
Step 103: and determining soil points of the corn planting area according to the digital orthographic images.
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, adopting an OSTU algorithm to segment and extract a lodging region of the corn.
Wherein, step 103 specifically comprises:
step 1031: and extracting a corn planting area by using a digital method based on the digital orthoimage.
Step 1032: and setting a threshold value to be less than 0 by using a Visible-Band Difference Vegetation Index (Visible-Band Difference Vegetation Index), and extracting soil points in the corn planting area.
The visible light band vegetation index is calculated to obtain the soil point distribution, and commonly used visible light vegetation indexes are ExG (m.woebbeck, d., et al.,1995), NGRDI (Hunt, e.r., et al.,2005) and VDVI (wankian, 2015), etc., which can represent vegetation features on an optical image. The invention compares the capacity of distinguishing the soil background and the corn canopy in the corn land by three vegetation indexes, and finally adopts VDVI to extract the soil background, which is defined as follows:
VDVI＝(2*ρG-ρB-ρR)/(2*ρG+ρB+ρR)
where ρ isG，ρBAnd ρRSpectral values in the green, blue and red bands, respectively.
Step 104 specifically includes:
step 1041: and filtering the soil points by using the minimum value of 25 × 25 windows, and extracting the soil points with the lowest elevation values in the local windows to obtain the soil elevation scatter points.
Step 1042: and performing nearest neighbor interpolation on the soil Elevation scattered points to obtain a non-covered reference Elevation Model (DEM).
Step 1043: and performing difference between the digital surface Model DSM and the reference elevation Model DEM to obtain a Canopy Height Model (CHM) of the corn.
And (3) superposing soil points on the digital surface model DSM, and performing spatial interpolation on the extracted soil elevation scattered points to obtain a reference elevation model (DEM) of the corn plot. Because unmanned aerial vehicle positioning accuracy and image concatenation in-process have the deviation, receive the influence of topography fluctuation simultaneously, the maize plot elevation information that DSM directly reflects has certain error. In order to reduce the influence of the error on interpolation, the method adopts the soil elevation scattered points at the minimum value filtering position of a 25 × 25 window, screens out the soil elevation points with error interference, and then performs spatial interpolation to obtain the DEM.
Inversion of vegetation canopy height. The vegetation Canopy Height Model (CHM) can reflect the height information of the vertical direction of crops, and can be obtained by calculation through DSM obtained by splicing images of the unmanned aerial vehicle and a reference elevation DEM obtained by interpolation, and the calculation formula is as follows: DSM-DEM.
Wherein step 105 specifically comprises:
the OSTU algorithm is proposed by the japanese scholars in the profession, also known as the maximum inter-class variance method (n.otsu, 1979). The idea is to search a threshold value t which maximizes the inter-class variance to realize the binarization processing of the image and extract the target ground object. The lodging region extraction researched by the invention is actually a binary problem, and the Ostu threshold algorithm is used for classifying the CHM, so that the distribution range of the lodging region can be objectively expressed.
The OSTU implementation method comprises the following steps of dividing an image by assuming an initial threshold value as t, and enabling the proportion omega of the pixel elements of the category A and the category B to beAAnd ωB＝1-ωAThe mean value of the pixel gray levels is muAAnd muBWherein the image ensemble mean μ ═ ωA*μA+ωB*μBSo that:
g＝ω0(μ0-μ)2+ω1(μ1-μ)2
and measuring the maximum inter-class variance by using an objective function g, wherein when the value of g is maximum, the corresponding threshold t is the optimal threshold for two classes.
The lodging extraction of the canopy height model by using the OSTU is a piecewise function, and the threshold value t is a boundary for distinguishing whether lodging exists or not.
As shown in fig. 3, the present invention further provides a corn lodging area detection system based on canopy height model, comprising:
And the splicing and rebuilding module 302 is used for splicing the pictures shot by the unmanned aerial vehicles to obtain digital orthoimages, and performing three-dimensional rebuilding on the pictures shot by the unmanned aerial vehicles to obtain a digital surface model.
And the soil point determining module 303 is used for determining the soil points of the corn planting area according to the digital ortho-image.
The soil point determination 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 ortho-image;
and the soil point extraction unit is used for extracting soil points in the corn planting area by utilizing the visible light waveband difference vegetation index.
A corn canopy height model determining module 304 for determining a corn canopy height model from the digital surface model and the soil points.
The corn canopy height model determining module 304 specifically includes:
the soil elevation scattered point extraction unit is used for filtering the soil points by using the minimum value of 25 × 25 windows, extracting the soil points with the lowest elevation values in the local windows and obtaining the soil elevation scattered points;
the interpolation unit is used for carrying out nearest neighbor interpolation on the soil elevation scattered points to obtain a non-covered reference elevation model;
and 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 region detection module 305 is configured to extract lodging regions of the corn by using an OSTU algorithm based on the corn canopy height model.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principle and the implementation manner of the present invention are explained by applying specific examples, the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof, the described embodiments are only a part of the embodiments of the present invention, not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.
Claims (8)
1. A corn lodging region detection method based on a canopy height model is characterized by comprising the following steps:
shooting a corn planting area through an unmanned aerial vehicle;
splicing a plurality of unmanned aerial vehicle shot pictures to obtain a digital ortho-image, and performing three-dimensional reconstruction on the plurality of unmanned aerial vehicle shot pictures to obtain a digital surface model;
determining soil points of a corn planting area according to the digital orthographic images;
determining a corn canopy height model from the digital surface model and the soil points;
and based on the corn canopy height model, adopting an OSTU algorithm to segment and extract a lodging region of the corn.
2. The method for detecting the corn lodging regions based on the canopy height model as claimed in claim 1, wherein the determining the soil points of the corn planting region according to the digital ortho-image specifically comprises:
extracting a corn planting area by using a digital method based on the digital orthoimage;
and extracting soil points in the corn planting area by utilizing the visible light wave band difference vegetation index.
3. The method for detecting a corn lodging region based on the canopy height model as claimed in claim 1, wherein the determining the corn canopy height model according to the digital surface model and the soil point specifically comprises:
filtering the soil points by using the minimum value of 25 × 25 windows, and extracting the soil points with the lowest elevation values in the local windows to obtain soil elevation scattered points;
performing nearest neighbor interpolation on the soil elevation scattered points to obtain a non-covered standard elevation model;
and obtaining a corn canopy height model by utilizing the difference between the digital surface model and the reference elevation model.
4. The method for detecting the corn lodging regions based on the canopy height model of claim 2, wherein the visible light band difference vegetation index is calculated according to the following formula:
VDVI＝(2*ρG-ρB-ρR)/(2*ρG+ρB+ρR)
where ρ isG，ρBAnd ρRRespectively, a spectral value of a green band, a spectral value of a blue band, and a spectral value of a red band.
5. A canopy height model-based corn lodging area detection system, 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 a plurality of unmanned aerial vehicle shot pictures to obtain a digital ortho-image and performing three-dimensional reconstruction on the plurality of unmanned aerial vehicle shot pictures to obtain a digital surface model;
the soil point determining module is used for determining soil points of the corn planting area according to the digital ortho-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 point;
and the lodging region detection module is used for segmenting and extracting a lodging region of the corn by adopting an OSTU algorithm based on the corn canopy height model.
6. The canopy height model-based corn lodging area detection system as claimed in claim 5, wherein the soil point determination module specifically comprises:
the corn planting area extracting unit is used for extracting a corn planting area by a digital method based on the digital ortho-image;
and the soil point extraction unit is used for extracting soil points in the corn planting area by utilizing the visible light waveband difference vegetation index.
7. The canopy height model-based corn lodging area detection system as claimed in claim 5, wherein the corn canopy height model determination module specifically comprises:
the soil elevation scattered point extraction unit is used for filtering the soil points by using the minimum value of 25 × 25 windows, extracting the soil points with the lowest elevation values in the local windows and obtaining the soil elevation scattered points;
the interpolation unit is used for carrying out nearest neighbor interpolation on the soil elevation scattered points to obtain a non-covered reference elevation model;
and 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.
8. The canopy height model-based corn lodging area detection system of claim 6, wherein the visible band differential vegetation index is calculated as follows:
VDVI＝(2*ρG-ρB-ρR)/(2*ρG+ρB+ρR)
where ρ isG，ρBAnd ρRRespectively, a spectral value of a green band, a spectral value of a blue band, and a spectral value of a red band.
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