CN108596104A - Wheat powdery mildew remote sensing monitoring method with disease characteristic preprocessing function - Google Patents
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Abstract
The invention relates to a remote sensing monitoring method for wheat powdery mildew with a disease characteristic preprocessing function, which overcomes the defects of high redundancy and poor monitoring precision of wheat disease characteristics compared with the prior art. The invention comprises the following steps: acquiring and preprocessing remote sensing data; extracting characteristic variables; processing the characteristic variables; constructing and optimizing a powdery mildew monitoring model; and obtaining a remote sensing monitoring result of wheat powdery mildew. The invention combines two characteristic selection technologies of relief and mRMR with a support vector machine optimized by a genetic method to form effective remote sensing monitoring on powdery mildew of regional scale.
Description
Technical field
It is specifically a kind of with Disease Characters preprocessing function the present invention relates to technical field of remote sensing image processing
Wheat powdery mildew remote-sensing monitoring method.
Background technology
Wheat powdery mildew is one of the Major Diseases during Wheat Production, can all be occurred in the entire breeding time of wheat,
The serious underproduction and quality is caused to reduce, the aggrieved rear general underproduction reaches 20% or more between 5% to 10% when serious.And
When effectively detect the generation of wheat powdery mildew and be of great significance to the yield and quality for improving wheat.
Although traditional ground investigation method investigation result is preferable, need to expend a large amount of manpower and materials, be not suitable for big
The research in region.Many scholars are monitored prediction using meteorological data to crop disease and insect.Wang Hejun etc. utilizes temperature, illumination
The prediction model of wheat powdery mildew is established with rainfall.Stansbury etc. is using warm-wet index model to wheat India bunt
Disease is predicted.Dutta etc. establishes the prediction model of aphid using meteorologic parameters such as air themperature and humidity.But by
It can be influenced by landform, mankind's activity in meteorological data, can not accurately obtain continuous spatial information, and the length of wheat
Gesture information is also to react the important factor of Wheat Diseases And Insect Pests situation, so there are one based on meteorological crop disease and insect monitoring and forecasting
Fixed limitation.
Since remote sensing can obtain continuous spatial information, while it can also reflect the growing way situation of crop, so one
A little scholars have carried out a series of research using remote sensing to crop disease and insect.Have in the prior art and is combined using environmental satellite data
Wavelet analysis and supporting vector machine model establish the monitoring model of wheat powdery mildew；Wheat powdery mildew is supervised using PRI
It surveys；Monitoring model is established to wheat powdery mildew based on Adaboost models and mRMR algorithms；Using each of environment sing data inverting
Kind vegetation index passes through logistic and returns the Occurrence forecast for realizing wheat powdery mildew；Winter wheat is extracted using environment sing data
The growing way factor, factor of the habitat, the prediction model of wheat aphid is established in conjunction with Method Using Relevance Vector Machine.
And the remote sensing features index of current most of scholar's primary study image crop disease, the selection for characteristic variable
Be generally only in method by simple correlation analysis, T examine etc. primary election feature is screened, although the feature filtered out with
The correlation of wheat diseases is big, but the redundancy between feature can lead to the reduction of model accuracy.
Therefore, the remote sensing prison how developed a kind of feature higher based on disease correlation and minimum redundancy and realized
Survey method has become the technical issues of urgent need solves.
Invention content
It is high, monitoring accuracy difference scarce that the purpose of the present invention is to solve wheat diseases feature redundancies in the prior art
It falls into, provides and a kind of solve the above problems with the wheat powdery mildew remote-sensing monitoring method of Disease Characters preprocessing function.
To achieve the goals above, technical scheme is as follows：
A kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function, includes the following steps：
11) acquisition and pretreatment of remotely-sensed data, obtains the remotely-sensed data in research area, and is located in advance to remotely-sensed data
Reason, and pass through the cultivated area of Maximum likelihood classification extraction wheat；
12) extraction of characteristic variable, obtain wheat field survey data sample, using pretreated remote sensing image come into
The extraction of feature needed for the monitoring of row wheat powdery mildew；
13) processing of characteristic variable is calculated the weight of wheat powdery mildew feature using relief technologies, is sieved by threshold value
After choosing, using the mRMR choices of technology and target category correlation maximum and the character subset of mutual redundancy minimum is as white
The input variable of powder disease monitoring model；
14) structure of powdery mildew monitoring model and optimization build the powdery mildew monitoring model based on support vector machines,
And powdery mildew monitoring model is optimized；
15) acquisition of wheat powdery mildew remote sensing monitoring result is preserved from remote sensing image by the extraction characteristic variable of pixel
To the input variable as powdery mildew monitoring model in matrix A, the geographical coordinate for extracting each pixel is saved in matrix B, will
Matrix A is input in the powdery mildew monitoring model after optimization, obtains the matrix result C of research area's wheat powdery mildew monitoring situation,
Monitoring result drafting pattern is obtained the wheat powdery mildew monitoring knot of survey region by associate(d) matrix result C and geographical coordinate matrix B
Fruit spatial distribution map.
The acquisition and pretreatment of the remotely-sensed data include the following steps：
21) remotely-sensed data in wheat powdery mildew area is obtained, and carries out image radiation calibration and atmospheric correction processing, image
Radiation calibration formula is as follows：
L (λ)=GainDN+Bais,
Wherein, L (λ) is radiance value, and Gain is gain coefficient, and Bais is biasing coefficient, and DN is observation gray value；
22) the FLAASH atmospheric corrections module in ENVI5.1 remote sensing processing softwares is used to switch to the radiance of image
Reflectivity；
23) image is carried out cutting the image for obtaining wanted survey region；
24) according to the normalized differential vegetation index of survey region, near infrared reflectivity data, in conjunction with the maximum in ENVI5.1
Likelihood classification extracts the wheat planting area of survey region.
The extraction of the characteristic variable includes the following steps：
31) wheat field survey data sample is obtained comprising n sample point, each sample point remembered by manual tag
For healthy sample or disease sample；
32) the blue, green, red of remotely-sensed data, this four reflectivity datas of near-infrared and broadband vegetation index is chosen to make
For the primary election characterization factor of powdery mildew monitoring model, primary election characterization factor constitutes remote sensing image primary election feature set, wherein broadband
Vegetation index includes ratio vegetation index, triangle vegetation index, green wave band normalized differential vegetation index, enhancement mode meta file, returns
One changes vegetation index, optimization soil adjusts vegetation index, soil adjusts vegetation index, improved triangle vegetation index, improved
Simple ratio index, renormalization vegetation index.
The processing of the characteristic variable includes the following steps：
41) the primary election characterization factor for being directed to powdery mildew monitoring model calculates feature weight using relief methods；
From remote sensing image primary election feature set choose powdery mildew monitoring model primary election characterization factor, with this primary election feature because
Sub- σ carries out the calculating of feature weight, until all primary election characterization factors have carried out feature power in remote sensing image primary election feature set
The calculating of weight；
42) weight threshold is set, the average value of primary election characterization factor σ weights is compared with weight threshold；If more than
Primary election characterization factor σ is then included into screening characteristic set by weight threshold；If being less than weight threshold, primary election characterization factor σ is given up
It abandons；
43) dimension-reduction treatment is carried out to the feature for screening characteristic set by mRMR methods, is filtered out in relief methods
It screens in characteristic set using related between feature and disease classification, between feature and feature in mutual information measurement character subset
Degree, filters out n characteristic variable.
The structure of the powdery mildew monitoring model and optimization include the following steps：
51) powdery mildew monitoring model is built based on support vector machines, using n characteristic variable as sample set；
52) regression function minimum is handled, and support vector machines is by finding φ and b so that regression function is according to f (x)=ωT
The structural risk minimization of φ (x)+b；
Introduce non-negative slack variable ξiAnd penalty factor, problem representation to be optimized are：
Wherein, C is constant, ξiWithControl the upper bound and the lower bound of output constraint；
53) penalty factor and nuclear parameter are optimized using genetic method, to realize the optimization of powdery mildew monitoring model.
The calculating of the feature weight is as follows：
61) a sample point A is randomly selected in n actual sample point；
62) assume that sample point A is healthy sample, two nearest samples are found around sample point A, one is arest neighbors
Healthy sample H, one is arest neighbors disease sample M；
63) calculate sample point A and arest neighbors health sample H between primary election characterization factor difference sigmaAH；
64) calculate sample point A and arest neighbors disease sample M between primary election characterization factor difference sigmaAM；
If 65) σAH＜ σAM, then illustrate that primary election characterization factor σ is conducive to distinguish similar and inhomogeneous arest neighbors, increase just
Select the weight of characterization factor σ；
If 66) σAH＞ σAM, then illustrate that primary election characterization factor σ is unfavorable for distinguishing similar and inhomogeneous arest neighbors, reduce just
Select the weight of characterization factor σ；
67) 61) step m times is repeated, and calculates the average value of m primary election characterization factor σ weight.
It is described that the feature progress dimension-reduction treatment for screening characteristic set is included the following steps by mRMR methods：
71) two stochastic variables x and y are given, the probability density function for corresponding to continuous variable is p (x), p (y), p
(x, y) p (x), p (y), p (x, y), then the mutual information expression between X and Y is as follows：
72) maximum correlation in mutual information calculating sifting characteristic set S between disease classification c, expression formula are utilized
It is as follows：
73) feature x in mutual information calculating sifting characteristic set S is utilizediAnd xjBetween redundancy, calculation expression is such as
Under：
74) mutual information difference criterion is utilized, the maximum feature of difference is selected, obtains a spy of minimal redundancy maximal correlation
This feature is included into the set of preferred features needed for modeling by sign；
75) 72) step n times are repeated in remaining sample set, finally obtain the preferred feature needed for n modeling.
Described being optimized to penalty factor and nuclear parameter using genetic method is included the following steps：
81) using the preferred feature needed for n modeling as sample data, training sample is formed；
82) average relative error of the output valve and desired value of training sample is calculated；
83) training sample selected, intersected, mutation operator；
84) judge whether the maximum genetic algebra for meeting initial setting up, obtained when meeting condition optimal penalty factor and
Nuclear parameter.
Advantageous effect
A kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function of the present invention, with the prior art
Compared to by being combined two kinds of Feature Selections of relief and mRMR with the support vector machines by genetic method optimization, formed
Effective remote sensing monitoring is carried out to the powdery mildew of regional scale.
The present invention is by conjunction with Feature Dimension Reduction is carried out, passing through and setting certain threshold value relief algorithms and mRMR algorithms
The weights of the good feature of discrimination are improved, obtain optimal character subset.It is good at solution global optimum using genetic algorithm to ask
The characteristics of topic, strong robustness, carrys out optimization monitoring model, to improve the monitoring accuracy of model.
Description of the drawings
Fig. 1 is the method precedence diagram of the present invention；
Fig. 2 a are weight shared by each feature of 14 features of primary election in the present invention after the calculating of relief methods
The weights figure of size；
Fig. 2 b be in the present invention weight with 500 be radix, number shared by each phase characteristic in 14 features of primary election
Weights figure；
Fig. 3 a are the wheat powdery mildew monitoring result spatial distribution map using relief algorithm combinations GSSVM；
Fig. 3 b are the monitoring result spatial distribution map of the feature and GASVM after being screened by relief algorithms；
Fig. 3 c are the monitoring result spatial distribution map of the feature and GSSVM after being screened by relief+mRMR algorithms；
Fig. 3 d are that the monitoring result of the feature and GASVM after the screening of the present invention by relief+mRMR algorithms is empty
Between distribution map；
Fig. 4 a are the monitoring result local distribution figure of the feature and GSSVM after being screened by relief+mRMR algorithms；
Fig. 4 b are the monitoring result of the feature and GASVM after the screening of the present invention by relief+mRMR algorithms
Local distribution figure.
Specific implementation mode
The effect of to make to structure feature of the invention and being reached, has a better understanding and awareness, to preferable
Embodiment and attached drawing cooperation detailed description, are described as follows：
As shown in Figure 1, a kind of wheat powdery mildew remote sensing monitoring with Disease Characters preprocessing function of the present invention
Method, it is high using relief methods operational efficiency, by calculating the weight of image feature, the feature strong to classification capacity assign compared with
High weight.In view of relief algorithms do not consider existing correlation between image feature, cannot remove between image feature
Redundancy, utilize the redundancy between mRMR methods removal image feature herein, obtain that there is between feature minimum redundancy
And the character subset with maximum correlation between feature and target.By two kinds of feature selection approach of relief combinations mRMR,
Filter out feature higher with disease correlation and minimum redundancy.It includes the following steps：
The first step, the acquisition and pretreatment of remotely-sensed data.The remotely-sensed data in research area is obtained, and remotely-sensed data is carried out pre-
The cultivated area of wheat is extracted in processing by Maximum likelihood classification.It is as follows：
(1) remotely-sensed data in wheat powdery mildew area is obtained, and carries out image radiation calibration and atmospheric correction processing, image
Radiation calibration formula is as follows：
L (λ)=GainDN+Bais,
Wherein, L (λ) is radiance value, and Gain is gain coefficient, and Bais is biasing coefficient, and DN is observation gray value.
(2) the FLAASH atmospheric corrections module in ENVI5.1 remote sensing processing softwares is used to switch to the radiance of image
Reflectivity.
(3) image is carried out cutting the image for obtaining the region to be studied.
(4) according to the normalized differential vegetation index of survey region, near infrared reflectivity data, in conjunction with the maximum in ENVI5.1
Likelihood classification extracts the wheat planting area of survey region.
Second step, the extraction of characteristic variable.Wheat field survey data sample is obtained, pretreated remote sensing image is utilized
To carry out the extraction of the feature needed for wheat powdery mildew monitoring.It is as follows：
(1) wheat field survey data sample is obtained comprising n sample point, manual tag has been each sample point
Healthy sample or disease sample.
In practical applications, field survey data can be investigated in Wheat in Grain Filling Stage and be obtained, and disease is carried out using 5 investigation methods
Investigation, i.e., take the sample prescription of 1m × 1m in each points for investigation, in investigation dot center global positioning system (global position
System, GPS) it is positioned, 5 symmetrical points are uniformly chosen in sample prescription, 20 plants of wheats is each clicked and is investigated, and record sample
Powdery mildew incidence in side.Severity uses improved wheat powdery mildew in agricultural industry criteria (NY/T613-2002)
" 0~9 grade of method records disease severity, and wheat is uniformly divided into 9 sections by when investigation from top to bottom, is marked according to classification
Standard is classified, and disease index (disesase index, DI) is then calculated.In the present invention, it is contemplated that grade excessively exists
The occurrence degree of disease is only divided into health and two types occurs by the difficulty in monitoring.
(2) the blue, green, red of remotely-sensed data, this four reflectivity datas of near-infrared and broadband vegetation index is chosen to make
For the primary election characterization factor of powdery mildew monitoring model, primary election characterization factor constitutes remote sensing image primary election feature set.Wherein, broadband
Vegetation index includes ratio vegetation index, triangle vegetation index, green wave band normalized differential vegetation index, enhancement mode meta file, returns
One changes vegetation index, optimization soil adjusts vegetation index, soil adjusts vegetation index, improved triangle vegetation index, improved
Simple ratio index, renormalization vegetation index.
Third walks, the processing of characteristic variable.Since primary election intrinsic dimensionality is larger, meter can be increased by being directly used in the structure of model
Calculation amount, and unscreened feature can there is a situation where it is small or even unrelated with the occurrence degree correlation of disease, between feature
There is also redundancies, will have a direct impact on the precision of model, therefore combine feature of the relief and mRMR technologies to wheat powdery mildew
Carry out dimensionality reduction.
The weight that wheat powdery mildew feature is calculated using relief technologies is set certain threshold value, is screened by threshold value
Afterwards, using the mRMR choices of technology and target category correlation maximum and the character subset of mutual redundancy minimum is as white powder
The input variable of sick monitoring model.It is as follows：
(1) the primary election characterization factor for being directed to powdery mildew monitoring model calculates feature weight using relief methods.
From remote sensing image primary election feature set choose powdery mildew monitoring model primary election characterization factor, with this primary election feature because
Sub- σ carries out the calculating of feature weight, until all primary election characterization factors have carried out feature power in remote sensing image primary election feature set
The calculating of weight.
Wherein, the calculating of feature weight is as follows：
A, a sample point A is randomly selected in n actual sample point；
B, assume that the sample point A randomly selected is healthy sample, two nearest samples of searching around sample point A, one
A is arest neighbors health sample H, and one is arest neighbors disease sample M.
Here, if the sample point A randomly selected is disease sample, distinguishes and carry out reversed distinguish i.e. by same thinking
It can.
For example, it is assumed that the sample point A randomly selected is disease sample, then an arest neighbors disease is found around sample point A
The difference of primary election characterization factor is σ between evil sample H, sample point A and arest neighbors disease sample HAH；It is found around sample point A
The difference of primary election characterization factor is σ between one arest neighbors health sample M, sample point A and arest neighbors health sample MAM。
C, calculate sample point A and arest neighbors health sample H between primary election characterization factor difference sigmaAH；
D, calculate sample point A and arest neighbors disease sample M between primary election characterization factor difference sigmaAM；
If E, σAH＜ σAM, then illustrate that primary election characterization factor σ is conducive to distinguish similar and inhomogeneous arest neighbors, increase just
Select the weight of characterization factor σ；
If F, σAH＞ σAM, then illustrate that primary election characterization factor σ is unfavorable for distinguishing similar and inhomogeneous arest neighbors, reduce just
Select the weight of characterization factor σ.
Comparison in this way, the size of correlation by evaluating characteristic to the separating capacity of short distance sample,
If the distance delta of sample A and sample H in this featureAHLess than the distance in this feature on sample A and sample M
σAM, then illustrate that this feature is beneficial to distinguishing similar and inhomogeneous arest neighbors, then increase the weight of this feature；Conversely, such as
Distance big σs of the fruit sample A and sample H in this featureAHIn the distance delta of sample A and sample M in this featureAM, illustrate this feature
Negative effect is played to distinguishing similar and inhomogeneous arest neighbors, then reduces the weight of this feature.The weight of this feature factor is bigger,
Indicate that the classification capacity of this feature is stronger, the weight of this feature factor is smaller, indicates that this feature classification capacity is weaker.
G, A is repeated) step (a sample point A is randomly selected in n actual sample point) m times, and calculate m primary election spy
The average value for levying factor sigma weight, to obtain the primary election characterization factor, (the blue, green, red of remotely-sensed data, near-infrared this four are anti-
Penetrate rate data and broadband vegetation index) average value.
(2) weight threshold is set, the average value of primary election characterization factor σ weights is compared with weight threshold；If more than
Primary election characterization factor σ is then included into screening characteristic set by weight threshold；If being less than weight threshold, primary election characterization factor σ is given up
It abandons.Screening characteristic set is the characteristic set after being screened to all primary election characterization factors, to select some influence powers
Greatly, the big feature of correlation.
(3) dimension-reduction treatment is carried out to the feature for screening characteristic set by mRMR methods, is filtered out in relief methods
It screens in characteristic set using related between feature and disease classification, between feature and feature in mutual information measurement character subset
Degree filters out n required characteristic variables.
MRMR methods are the characteristic feature dimension-reduction algorithms based on information theory, using mRMR methods from by relief technologies
It finds out in the feature of screening with disease classification with maximum correlation and the n feature with minimum redundancy between each other.Its
It is as follows：
A, give two stochastic variables x and y, correspond to continuous variable probability density function be p (x), p (y), p (x,
Y) p (x), p (y), p (x, y), then the mutual information expression between X and Y is as follows：
B, using the maximum correlation in mutual information calculating sifting characteristic set S between disease classification c, expression formula is such as
Under：
C, feature x in mutual information calculating sifting characteristic set S is utilizediAnd xjBetween redundancy, calculation expression is such as
Under：
D, using mutual information difference criterion, the maximum feature of difference is selected, obtains a feature of minimal redundancy maximal correlation,
This feature is included into the set of preferred features needed for modeling；
E, repeat B in remaining sample set) step (using in mutual information calculating sifting characteristic set S with disease
Maximum correlation between classification c) n times, finally obtain the preferred feature needed for n modeling.
Here, relief algorithms operational efficiency is high, by calculating the weight of feature, the feature strong to classification capacity assign compared with
High weight, but relief algorithms do not consider existing correlation between feature, so the redundancy between feature cannot be removed
Property.MRMR algorithms can obtain between feature with the feature with maximum correlation between minimum redundancy and feature and target
Subset, but weight size can not be calculated.Therefore by conjunction with Feature Dimension Reduction is carried out, leading to relief algorithms and mRMR algorithms
It crosses and sets certain threshold value to improve the weights of the good feature of discrimination, obtain optimal character subset.
As shown in Figure 2 a, in Fig. 2 a abscissa be primary election 14 features (the blue, green, red of remotely-sensed data, near-infrared this four
A reflectivity data, triangle vegetation index, green wave band normalized differential vegetation index, enhancement mode meta file, is returned at ratio vegetation index
One changes vegetation index, optimization soil adjusts vegetation index, soil adjusts vegetation index, improved triangle vegetation index, improved
Simple ratio index, renormalization vegetation index) each feature serial number, the weight size that ordinate is characterized.For another example Fig. 2 b
It is shown, indicate that by weight be radix with 500, number shared by each phase characteristic.
It can be seen that the size of weight shared by 14 features from Fig. 2 a, in conjunction with Fig. 2 b, when setting weight threshold as 2500,
Primary election feature of 8 features of the condition of satisfaction as mRMR algorithms can be filtered out, is then obtained by mRMR algorithms optimal
Three characteristic variables choose the maximum three characteristic variable conducts of weight that relief algorithms obtain as first group of characteristic variable
Second group of characteristic variable.
4th step, the structure of powdery mildew monitoring model and optimization.Build the powdery mildew monitoring based on support vector machines
Model, and powdery mildew monitoring model is optimized (the support vector machines GASVM for forming genetic algorithm optimization).
Wherein, the structure of powdery mildew monitoring model and optimization include the following steps：
(1) powdery mildew monitoring model is built based on support vector machines, using n characteristic variable as sample set.
(2) regression function minimum is handled, and support vector machines is by finding φ and b so that regression function is according to f (x)=ωT
The structural risk minimization of φ (x)+b；
Introduce non-negative slack variable ξiAnd penalty factor, problem representation to be optimized are：
Wherein：C is constant, ξiWithControl the upper bound and the lower bound of output constraint.
(3) penalty factor and nuclear parameter are optimized using genetic method, to realize the optimization of powdery mildew monitoring model.
Penalty factor and nuclear parameter are optimized using conventional method using genetic method, specifically include following steps：
A, using n modeling needed for preferred feature be used as sample data, formation training sample, can also this distinguish verification
Sample, to verify the reliability of model；
B, the average relative error of the output valve and desired value of training sample is calculated；
C, training sample selected, intersected, mutation operator；
D, judge whether the maximum genetic algebra for meeting initial setting up, optimal penalty factor and core are obtained when meeting condition
Parameter.
5th step, the acquisition of wheat powdery mildew remote sensing monitoring result, by the extraction characteristic variable of pixel from remote sensing image
It preserves into matrix A the input variable as powdery mildew monitoring model, the geographical coordinate for extracting each pixel and is saved in matrix B
In, matrix A is input in the powdery mildew monitoring model after optimization, the matrix knot of research area's wheat powdery mildew monitoring situation is obtained
Monitoring result drafting pattern is obtained the wheat powdery mildew prison of survey region by fruit C, associate(d) matrix result C and geographical coordinate matrix B
Survey result space distribution map.
In practical applications, realistic model can preferably be embodied by being verified to model using independent sample data
Precision.6 kinds of models of field survey data pair of present invention combination Hebei Shijiazhuang City research area's on May 24th, 2014 carry out
Assessment.
2 kinds of feature selecting algorithms (relief+mRMR algorithms) are listed in table 1, and in conjunction with 3 kinds of modeling methods, (SVM, grid are searched
Rope method optimization support vector machines GSSVM, genetic algorithm optimization support vector machines GASVM) establish powdery mildew monitoring model
User's precision, cartographic accuracy, overall accuracy and Kappa coefficients.
1 SVM, GSSVM, GASVM modeling method verification result contrast table of table
From table 1 it follows that the overall accuracy and Kappa coefficients of SVM monitoring models are monitored less than GSSVM and GASVM
Model, the overall accuracy and Kappa coefficients of relief+mRMR-SVM monitoring models are carried compared with relief-SVM monitoring models
Height, but only 64% and 0.286；In 2 GSSVM monitoring models, monitoring model that relief+mRMR algorithms are established it is total
Body precision and Kappa coefficients are 78.5% and 0.571, the monitoring model established higher than relief algorithms；It is supervised in 2 GASVM
It surveys in model, the monitoring model precision and Kappa coefficients that relief+mRMR algorithms are established are 85.7% and 0.714, are higher than
The monitoring model that the feature that relief algorithms filter out is established, can deduce and filter out the feature with disease correlation maximum
While removal feature between redundancy can effectively improve the precision of model.
Overall precision, user's precision and the cartographic accuracy of the above 3 kinds of model method institute established models of comparison can be seen that
Relief algorithm+mRMR algorithms carry out the precision of Feature Selection and the GASVM monitoring models (relief+mRMR-GASVM) built
The overall accuracy of highest, overall accuracy ratio GSSVM and SVM institutes established model is higher by 21.7% and 7.2%, and user's precision and drawing
Precision reaches 85.7%.It these results suggest that relief+mRMR algorithms institute established model is better than the mould that relief algorithms are established
Type, the monitoring model that the SVM (GASVM) based on genetic algorithm optimization is established is better than not optimized SVM and is based on grid search
The SVM (GSSVM) of method optimization, the powdery mildew monitoring model that relief+mRMR algorithm combinations GASVM is established are (of the present invention
Relief+mRMR-GASVM the precision of model can) be improved.
Equally, by taking Hebei Shijiazhuang City studies the remote sensing image of area's on May 26th, 2014 as an example.As shown in Figure 3a, it is
The wheat powdery mildew monitoring result spatial distribution map of relief algorithm combinations GSSVM；As shown in Figure 3b, it is to pass through relief
The monitoring result of feature and GASVM after algorithm screening；Fig. 3 c be by relief+mRMR algorithms screen after feature with
The monitoring result of GSSVM；Fig. 3 d are the monitoring result of the feature and GASVM after being screened by relief+mRMR algorithms.Fig. 3 a-
In Fig. 3 d, white area part is non-wheat planting district, and black region part is region of disease.
Onset area accounts in the percentage of total area, and Fig. 3 a are 44.5%, Fig. 3 b are 62.1%, Fig. 3 c are 62.4%,
Fig. 3 d are 60.4%.Fig. 3 a onset areas compared with its excess-three width figure are less, seriously occur with the wheat powdery mildew of on-site inspection
Deviation is larger.Compare from Fig. 3 a, Fig. 3 b, Fig. 3 c, Fig. 3 d it can be found that the result powdery mildew region of disease that Fig. 3 a and Fig. 3 b are monitored
Scattered distribution, and wheat powdery mildew is caused by cloth powdery mildew, its feature is that reproduction speed is fast, and propagation area is wide, one
As scattered will not occur, so the characteristics of monitoring result and wheat powdery mildew of Fig. 3 a and Fig. 3 b, runs counter to, and Fig. 3 c and Fig. 3 d
Monitoring result more meets wheat powdery mildew occurrence characteristic, confidence level higher.Fig. 3 c and Fig. 3 d are generally performing out consistency, but
It has differences in detail.
As shown in figures 4 a and 4b, Fig. 4 a are the monitoring knot of the feature and GSSVM after being screened by relief+mRMR algorithms
Fruit local distribution figure, Fig. 4 b are the local distribution of the monitoring result of the feature and GASVM after being screened by relief+mRMR algorithms
Figure.
Comparing Fig. 4 a and Fig. 4 b it can be found that same, in Fig. 4 a and Fig. 4 b, white area part is non-wheat planting district,
Black region part is region of disease.The region for being divided into health in Fig. 4 d is divided into morbidity in fig.4.In Fig. 4 b wheat health with
It is distributed relatively uniform between morbidity plot, and the region that Fig. 4 a then show monoblock mostly is all diseased region, only a small number ofly
Block shows to be uniformly distributed, compare two width local distribution figures can be found that by relief+mRMR algorithms screen after feature with
The monitoring model goodness of fit of GSSVM is not high, although monitoring overall trend be actually consistent, in detail section separating capacity
The monitoring model of feature and GASVM after being screened compared to relief+mRMR algorithms is weaker, and relief+mRMR-GASVM models exist
It is still applicable in the monitoring of part.
In conclusion relief+mRMR-GASVM models of the present invention are thin in overall trend, characteristics of incidence, part
It is all more consistent with actual conditions on section, the onset state of wheat powdery mildew can be really reflected, daily life is can be used for
It generates in the demand monitored in real time to wheat powdery mildew in living, by accurately obtaining the onset state of powdery mildew, spatial distribution is special
Sign carrys out planned offer evidence for prevention and cure, improves the yield of wheat.
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and what is described in the above embodiment and the description is only the present invention
Principle, various changes and improvements may be made to the invention without departing from the spirit and scope of the present invention, these variation and
Improvement is both fallen in the range of claimed invention.The present invention claims protection domain by appended claims and its
Equivalent defines.
Claims (8)
1. a kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function, which is characterized in that including following
Step：
11) acquisition and pretreatment of remotely-sensed data, obtains the remotely-sensed data in research area, and is pre-processed to remotely-sensed data, and
The cultivated area of wheat is extracted by Maximum likelihood classification；
12) extraction of characteristic variable is obtained wheat field survey data sample, is carried out using pretreated remote sensing image small
The extraction of feature needed for wheat powdery mildew monitoring；
13) processing of characteristic variable is calculated the weight of wheat powdery mildew feature using relief technologies, is screened by threshold value
Afterwards, using the mRMR choices of technology and target category correlation maximum and the character subset of mutual redundancy minimum is as white powder
The input variable of sick monitoring model；
14) structure of powdery mildew monitoring model and optimization build the powdery mildew monitoring model based on support vector machines, and right
Powdery mildew monitoring model optimizes；
15) acquisition of wheat powdery mildew remote sensing monitoring result, the extraction characteristic variable by pixel from remote sensing image are preserved to square
Input variable in battle array A as powdery mildew monitoring model, the geographical coordinate for extracting each pixel is saved in matrix B, by matrix A
It is input in the powdery mildew monitoring model after optimization, the matrix result C of research area's wheat powdery mildew monitoring situation is obtained, in conjunction with square
Monitoring result drafting pattern is obtained the wheat powdery mildew monitoring result space of survey region by battle array result C and geographical coordinate matrix B
Distribution map.
2. a kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function according to claim 1,
It is characterized in that, the acquisition and pretreatment of the remotely-sensed data include the following steps：
21) remotely-sensed data in wheat powdery mildew area is obtained, and carries out image radiation calibration and atmospheric correction processing, image radiation
It is as follows to calibrate formula：
L (λ)=GainDN+Bais,
Wherein, L (λ) is radiance value, and Gain is gain coefficient, and Bais is biasing coefficient, and DN is observation gray value；
22) the FLAASH atmospheric corrections module in ENVI5.1 remote sensing processing softwares is used to switch to reflect by the radiance of image
Rate；
23) image is carried out cutting the image for obtaining the region to be studied；
24) according to the normalized differential vegetation index of survey region, near infrared reflectivity data, in conjunction with the maximum likelihood in ENVI5.1
Classification extracts the wheat planting area of survey region.
3. a kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function according to claim 1,
It is characterized in that, the extraction of the characteristic variable includes the following steps：
31) obtain wheat field survey data sample comprising n sample point, each sample point manual tag be denoted as it is strong
Health sample or disease sample；
32) the blue, green, red of remotely-sensed data, this four reflectivity datas of near-infrared and broadband vegetation index are chosen as white
The primary election characterization factor of powder disease monitoring model, primary election characterization factor constitute remote sensing image primary election feature set, wherein broadband vegetation
Index includes ratio vegetation index, triangle vegetation index, green wave band normalized differential vegetation index, enhancement mode meta file, normalization
Vegetation index, optimization soil adjust vegetation index, soil adjusts vegetation index, improved triangle vegetation index, improved simple
Ratio index, renormalization vegetation index.
4. a kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function according to claim 1,
It is characterized in that, the processing of the characteristic variable includes the following steps：
41) the primary election characterization factor for being directed to powdery mildew monitoring model calculates feature weight using relief methods；
The primary election characterization factor that powdery mildew monitoring model is chosen from remote sensing image primary election feature set, with this primary election characterization factor σ
The calculating of feature weight is carried out, until all primary election characterization factors have carried out feature weight in remote sensing image primary election feature set
It calculates；
42) weight threshold is set, the average value of primary election characterization factor σ weights is compared with weight threshold；If more than weight
Primary election characterization factor σ is then included into screening characteristic set by threshold value；If being less than weight threshold, primary election characterization factor σ is given up；
43) dimension-reduction treatment is carried out to the feature for screening characteristic set by mRMR methods, in the screening that relief methods filter out
The degree of correlation in character subset between feature and disease classification, between feature and feature is weighed using mutual information in characteristic set,
Filter out n characteristic variable.
5. a kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function according to claim 1,
It is characterized in that, the structure of the powdery mildew monitoring model and optimization include the following steps：
51) powdery mildew monitoring model is built based on support vector machines, using n characteristic variable as sample set；
52) regression function minimum is handled, and support vector machines is by finding φ and b so that regression function is according to f (x)=ωTφ(x)
The structural risk minimization of+b；
Introduce non-negative slack variable ξiAnd penalty factor, problem representation to be optimized are：
Wherein, C is constant, ξiWithControl the upper bound and the lower bound of output constraint；
53) penalty factor and nuclear parameter are optimized using genetic method, to realize the optimization of powdery mildew monitoring model.
6. a kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function according to claim 4,
It is characterized in that, the calculating of the feature weight is as follows：
61) a sample point A is randomly selected in n actual sample point；
62) assume that sample point A is healthy sample, two nearest samples are found around sample point A, one is arest neighbors health
Sample H, one is arest neighbors disease sample M；
63) calculate sample point A and arest neighbors health sample H between primary election characterization factor difference sigmaAH；
64) calculate sample point A and arest neighbors disease sample M between primary election characterization factor difference sigmaAM；
If 65) σAH＜ σAM, then illustrate that primary election characterization factor σ is conducive to distinguish similar and inhomogeneous arest neighbors, it is special to increase primary election
Levy the weight of factor sigma；
If 66) σAH＞ σAM, then illustrate that primary election characterization factor σ is unfavorable for distinguishing similar and inhomogeneous arest neighbors, it is special to reduce primary election
Levy the weight of factor sigma；
67) 61) step m times is repeated, and calculates the average value of m primary election characterization factor σ weight.
7. a kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function according to claim 4,
It is characterized in that, described include the following steps the feature progress dimension-reduction treatment for screening characteristic set by mRMR methods：
71) two stochastic variables x and y are given, the probability density function for corresponding to continuous variable is p (x), p (y), p (x, y) p
(x), p (y), p (x, y), then the mutual information expression between X and Y is as follows：
72) utilize the maximum correlation in mutual information calculating sifting characteristic set S between disease classification c, expression formula as follows：
73) feature x in mutual information calculating sifting characteristic set S is utilizediAnd xjBetween redundancy, calculation expression is as follows：
74) mutual information difference criterion is utilized, the maximum feature of difference is selected, obtains a feature of minimal redundancy maximal correlation, it will
This feature is included into the set of preferred features needed for modeling；
75) 72) step n times are repeated in remaining sample set, finally obtain the preferred feature needed for n modeling.
8. a kind of wheat powdery mildew remote-sensing monitoring method with Disease Characters preprocessing function according to claim 5,
It is characterized in that, described being optimized to penalty factor and nuclear parameter using genetic method is included the following steps：
81) using the preferred feature needed for n modeling as sample data, training sample is formed；
82) average relative error of the output valve and desired value of training sample is calculated；
83) training sample selected, intersected, mutation operator；
84) judge whether the maximum genetic algebra for meeting initial setting up, optimal penalty factor and core ginseng are obtained when meeting condition
Number.
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