CN110399832A - TomoSAR vegetation pest and disease monitoring method and device based on coherence - Google Patents
TomoSAR vegetation pest and disease monitoring method and device based on coherence Download PDFInfo
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Abstract
The embodiment of the present application discloses the TomoSAR vegetation pest and disease monitoring method and device based on coherence.One specific embodiment of the monitoring method includes: that the diameter radar image data of the target vegetation based on acquisition obtain destination image data；Destination image data is handled using multi-signal sorting algorithm, obtains the three-dimensional structure data of target vegetation；The three-dimensional structure data of target vegetation and sample vegetation data are subjected to coherent analysis；The pest and disease damage situation of target vegetation is determined based on the analysis results.This embodiment can carry out round-the-clock, round-the-clock monitoring to vegetation, and can be realized high-acruracy survey of the height to vegetation structure, help to improve the accuracy of vegetation pest and disease monitoring result.
Description
Technical field
The invention relates to radar observation technical fields, more particularly to the TomoSAR vegetation disease pest based on coherence
Evil monitoring method and device.
Background technique
It is close that synthetic aperture radar, which chromatographs (Tomography Synthetic Aperture Radar, TomoSAR) technology,
A kind of emerging cutting edge technology of the three peacekeeping four-dimensional information of acquisition target with high precision to grow up for 10 years.After it is by changing imaging
Data processing algorithm, it can be achieved that height to distribution scatterer measurement.Combine with polarization information, mesh can also be obtained
Fine structure, physics and space distribution information are marked, so as to distinguish the multiple obstacles of different height, monitors scatterer
Spatial position change situation etc..The technology has been applied to forest structural variable estimation, city three-dimensional reconstruction and urban surface
The fields such as sedimentation, and have huge application potential in terms of the detection of geology, glaciology and land burial object.
Summary of the invention
The embodiment of the present application provides the TomoSAR vegetation pest and disease monitoring method and device based on coherence.
In a first aspect, the embodiment of the present application provides a kind of TomoSAR vegetation pest and disease monitoring method based on coherence,
It include: that the diameter radar image data of the target vegetation based on acquisition obtain destination image data；Using Multiple Signal Classification
Algorithm handles destination image data, obtains the three-dimensional structure data of target vegetation；By the three-dimensional structure number of target vegetation
Coherent analysis is carried out according to sample vegetation data；The pest and disease damage situation of target vegetation is determined based on the analysis results.
In some embodiments, the diameter radar image data of the target vegetation based on acquisition obtain target image number
According to, comprising: the diameter radar image data for obtaining the target vegetation under different monitoring height, to multiple picture numbers of acquisition
According to N Reference Alignment, phase compensation processing is carried out, destination image data is obtained.
In some embodiments, the diameter radar image data of the target vegetation under different monitoring height are obtained, it is right
Multiple image datas obtained carry out N Reference Alignment, phase compensation processing, comprising: using same synthetic aperture radar in different height
Target vegetation is monitored on degree face, obtains multiple image datas；Using an image data in multiple image datas as
Main image data carries out N Reference Alignment to remaining image data, phase compensation is handled.
In some embodiments, destination image data is handled using multi-signal sorting algorithm, obtains target vegetation
Three-dimensional structure data, comprising: construct the covariance matrix of destination image data, and feature carried out to the covariance matrix of building
It decomposes, obtains signal subspace matrix and noise subspace matrix；According to signal subspace matrix and noise subspace matrix structure
Build space spectral function, and carry out spectrum peak search, with obtain target vegetation height to structural information.
In some embodiments, the three-dimensional structure data of target vegetation and sample vegetation data are subjected to coherent analysis,
It include: to choose the pixel number evidence for being located at Vegetation canopy, and determine the pixel chosen in the three-dimensional structure data of target vegetation
The coherence factor of point data and the pixel number evidence for being located at sample vegetation same position.
In some embodiments, in the three-dimensional structure data of target vegetation, the pixel number for being located at Vegetation canopy is chosen
According to, comprising: in the three-dimensional structure data of target vegetation, certain pixel for being located at Vegetation canopy is chosen, with the pixel of selection
Centered on, the data for all pixels point being located in default size space are extracted, and generate the vector data of target vegetation.
Second aspect, the embodiment of the present application provide a kind of TomoSAR vegetation pest and disease monitoring device based on coherence,
It include: generation unit, the diameter radar image data for being configured to the target vegetation based on acquisition obtain target image number
According to；Processing unit is configured to handle destination image data using multi-signal sorting algorithm, obtains the three of target vegetation
Tie up structured data；Analytical unit is configured to the three-dimensional structure data of target vegetation and sample vegetation data carrying out coherence
Analysis；Determination unit is configured to determine the pest and disease damage situation of target vegetation based on the analysis results.
In some embodiments, analytical unit is further configured in the three-dimensional structure data of target vegetation, is chosen
Positioned at the pixel number evidence of Vegetation canopy, and determine the pixel number chosen according to the pixel that is located at sample vegetation same position
The coherence factor of data.
The third aspect, it includes: processor that the embodiment of the present application, which provides a kind of electronic equipment,；Storage device stores thereon
There is computer program；When processor executes the computer program on storage device, so that electronic equipment realizes such as first aspect
TomoSAR vegetation pest and disease monitoring method based on coherence described in middle any embodiment.
Fourth aspect, the embodiment of the present application provide a kind of computer-readable medium, are stored thereon with computer program, meter
Realize that the TomoSAR as described in any embodiment in first aspect based on coherence plants when calculation machine program is executed by processor
By pest and disease monitoring method.
TomoSAR vegetation pest and disease monitoring method and device provided by the embodiments of the present application based on coherence, firstly, can
With the diameter radar image data of the target vegetation based on acquisition, to obtain destination image data.Then, using multi signal
Sorting algorithm handles destination image data, to obtain the three-dimensional structure data of target vegetation.Later, by target vegetation
Three-dimensional structure data and sample vegetation data carry out coherent analysis.Finally, can determine target vegetation based on the analysis results
Pest and disease damage situation.This method utilizes the diameter radar image data of target vegetation, and the round-the-clock, complete of vegetation may be implemented
Weather monitoring.And it is handled by multi-signal sorting algorithm, can be realized high-acruracy survey of the height to vegetation structure.Have in this way
Help improve the accuracy of vegetation pest and disease monitoring result.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the application's is other
Feature, objects and advantages will become more apparent upon:
Fig. 1 is that one embodiment of the application can be applied to exemplary system architecture figure therein；
Fig. 2 is one embodiment of the TomoSAR vegetation pest and disease monitoring method provided by the present application based on coherence
Flow chart；
Fig. 3 is one embodiment of the TomoSAR vegetation pest and disease monitoring device provided by the present application based on coherence
Structural schematic diagram.
Specific embodiment
The application is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining related invention, rather than the restriction to the invention.It also should be noted that in order to
Convenient for description, part relevant to related invention is illustrated only in attached drawing.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Fig. 1 shows the TomoSAR vegetation pest and disease monitoring method based on coherence that can apply the embodiment of the present application
Or the exemplary system architecture 100 of device.
As shown in Figure 1, system architecture 100 may include terminal 101, network 102, server 103 and synthetic aperture radar
104.Network 102 can be to provide the medium of communication link between terminal 101 and server 103.Network 102 may include
Various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal 101 and be interacted by network 102 with server 103, to receive or send message etc..
Such as user can send the Monitoring instruction etc. of vegetation by terminal 101 to server 103.It can be equipped in terminal 101 each
Kind client application, such as the application of vegetation disaster monitoring class, image player, browser and immediate communication tool etc..Here
Vegetation may include (but being not limited to) trees forest, bushes, grassland etc..Here disaster may include (but being not limited to) disease pest
Disaster, natural meteorological disaster (such as fire, freeze disaster), artificial felling disaster.
Here terminal 101 can be hardware, be also possible to software.When terminal 101 is hardware, can be has display
The various electronic equipments of screen, including but not limited to smart phone, tablet computer and desktop computer etc..When terminal 101 is soft
When part, it may be mounted in above-mentioned cited electronic equipment.Its may be implemented into multiple softwares or software module (such as
Distributed Services are provided), single software or software module also may be implemented into.It is not specifically limited herein.
Server 103 can be to provide the server of various services, such as can be the application installed to terminal 101 and mention
For the background server of support.Background server can pass through synthetic aperture when receiving the Monitoring instruction of the transmission of terminal 101
Radar 104 obtains the image data of target vegetation.And then these data can be analyzed and processed, and can will be at analysis
Reason result (the pest and disease damage situation of such as target vegetation) is sent to terminal 101.
Here server 103 equally can be hardware, be also possible to software.When server 103 is hardware, Ke Yishi
The distributed server cluster of ready-made multiple server compositions, also may be implemented into individual server.When server 103 is software
When, multiple softwares or software module (such as providing Distributed Services) may be implemented into, single software also may be implemented into
Or software module.It is not specifically limited herein.
It should be noted that the TomoSAR vegetation pest and disease monitoring side based on coherence provided by the embodiment of the present application
Method can generally be executed by server 103 (or terminal 101).Correspondingly, the TomoSAR vegetation pest and disease monitoring based on coherence
Device generally also can be set in server 103 (or terminal 101).
It should be understood that the number of terminal, network, server and synthetic aperture radar in Fig. 1 is only schematical.Root
It factually now needs, can have any number of terminal, network, server and synthetic aperture radar.
Fig. 2 is referred to, the TomoSAR vegetation pest and disease monitoring method based on coherence that it illustrates provided by the present application
The process 200 of one embodiment.This method may comprise steps of:
Step 201, the diameter radar image data of the target vegetation based on acquisition obtain destination image data.
In the present embodiment, executing subject (such as Fig. 1 of the TomoSAR vegetation pest and disease monitoring method based on coherence
Shown in server 103) synthetic aperture radar (SAR, the Synthetic of target vegetation can be obtained in several ways
Aperture Radar) image data.For example, executing subject can be received by wired connection mode or radio connection
The diameter radar image data for the target vegetation that user's using terminal (such as terminal 101 shown in Fig. 1) is sent.Example again
Such as, executing subject can obtain the diameter radar image number of target vegetation in resource (such as cloud) or database from network
According to.For another example executing subject can be by synthetic aperture radar (such as synthetic aperture radar 104 shown in Fig. 1) to target
Vegetation carries out actual observation, to obtain its image data.
It is understood that being observed using synthetic aperture radar, it will usually obtain the information in multiple polarization directions
Data.At this point it is possible to using Classification of Polarimetric SAR Image algorithm, to obtain the location drawing picture of target vegetation.
Specifically, all polarization information that each pixel of polarimetric SAR image includes, can generally be expressed as one 3 × 3
Polarization coherence matrix T:
In formula, TijFor each element of matrix T, subscript * is expressed as corresponding element and takes conjugation.
In order to reduce the influence of complicated atural object scatter echo stochastic volatility, orientation process is carried out to T matrix, is obtained new
Coherence matrix T ', be shown below:
T '=QTQH；
In formula,For angle of orientation spin matrix, subscript H is that conjugation is taken to turn
It setting, θ is that target rotates angle, in the range of (- p/2, p/2].
It is three components by the matrix decomposition after orientation process is gone, specific representation is as follows:
T '=PsTs+PdTd+PvTv；
In formula,Tij' for each element for removing matrix after orientation process, Ps、Pd、
PvCorresponding certain Pixel surface scattering, even scattering and the three-component performance number of volume scattering, Ts、Td、TvFor three kinds of basic scattering mechanisms
Corresponding polarization coherence matrix model.
In order to inhibit influence of the coherent speckle noise to experimental result, original polarization SAR data is filtered.
Utilize formula T '=PsTs+PdTd+PvTvOrientation three-component is carried out to coherence matrix T to decompose, and calculates each pixel
Performance number (the P of surface scattering, even scattering and three components of volume scatterings、Pd、Pv) and total power value (Span).
Span=Ps+Pd+Pv；
According to Ps、Pd、PvSize, determine the scattering mechanism that is dominant of each pixel, i.e. Pmax=max (Ps,Pd,Pv) corresponding
Scattering component.By the P of each pixels、Pd、PvConstitute a vector, i.e. P=[Ps,Pd,Pv]=[P1,P2,P3].Initial clustering
Divide such as following formula:
CP=1,2,3,4 respectively indicate surface scattering type, even scattering type, volume scattering type and mixing scattering class
Type.Th is experience percentage threshold, and numerical value is higher, and the accuracy rate that pixel corresponds to three kinds of scattering mechanisms is higher.
In forming first three scattering type, carried out respectively according to the corresponding pixel of size for the scattering mechanism performance number that is dominant
Sequence, and it is divided into 30 of substantially equal groups of number；Using Wishart distance measure the similarity degree between every two class into
Row categories combination, being incorporated into class number specified in advance, (N1, N2, N3 enable three be smaller than 30).Merge rule: if
Distance between certain two group of same scattering type is most short, then merges them, between class distance uses Wishart distance:
Dij=1/2 ln (| Vi|)+ln(|Vj|)+Tr(Vi -1Vj+Vj -1Vi)}；
In formula, Vi、VjIndicate that the average coherence matrix of the i-th class Yu jth class, Tr representing matrix seek mark.
The average coherence matrix for solving obtained each cluster, as class center, in four kinds of initial clusterings respectively
Wishart classifier is re-used to be iterated according to the distance at every pixel to all kinds of centers.Herein, in order to obtain stabilization
Convergence effect, can apply Wishart classifier iteration two to four times.
In addition, can indicate different using different colours according to the actual situation to more clearly from indicate all kinds of atural objects
Atural object, if blue can indicate surface scattering (such as ocean bare area), red can indicate that even scatters (such as city), and green can be with
It indicates volume scattering (such as forest cover).
In the present embodiment, target vegetation can be any vegetation for needing to be monitored, and such as need to carry out pest and disease damage shape
The forest of condition monitoring.Its geographic location, occupied area, vegetation type etc. are not intended to limit in this application.Herein, it executes
Main body can be based on the diameter radar image data of the target vegetation of acquisition, to obtain destination image data.For example, executing
Main body can pre-process the diameter radar image data of the target vegetation of acquisition, to obtain target image number
According to.Wherein, image data needed for destination image data can be subsequent processes.And it is to obtain that preprocessing process, which is usually,
Required destination image data and the relevant treatment carried out.Herein, preprocess method and destination image data can according to
The actual demand at family is configured.
As an example, destination image data can be certain specific region (such as trees canopy or tree branches of target vegetation
Region) image data.At this point, executing subject can sieve the diameter radar image data of the target vegetation of acquisition
Choosing, thus obtain include the specific region image diameter radar image data.Further, in order to improve subsequent place
Efficiency is managed, executing subject can also cut the image data filtered out, to remove unwanted in original digital image data
Image data obtains image data only comprising this feature area image.In application scenes, executing subject can also be right
The image data of the lack of resolution carries out cloud and mist processing etc., to reduce the influence of weather conditions.
It should be noted that in order to obtain target vegetation height (journey) to structural information, need to get not
Image data with the diameter radar image data of the target vegetation under monitoring angle, under especially different monitoring height.
I.e. synthetic aperture radar target vegetation is monitored under different height obtained from image data.At this point, executing subject can
It is screened with the diameter radar image data of the target vegetation to acquisition, to obtain multiple (i.e. different monitoring height
Under) image data.
It is understood that the acquisition modes of the image data under different monitoring height are in this application and unlimited here
System.For example, it may be being monitored using multiple synthetic aperture radar positioned at different height face to target vegetation.
In another example in order to simplify method, can be using same synthetic aperture radar respectively different height face (such as different height it is flat
Row track) on, obtained from being monitored to target vegetation.Or it can also be using the antenna for being equipped with multiple and different height
Synthetic aperture radar is monitored target vegetation.
In some optional implementations, executing subject can also to these difference monitoring height under image datas into
The processing such as row N Reference Alignment, phase compensation.It can be convenient for follow-up data processing in this way, improve treatment effeciency.As an example,
Executing subject can be according to the benchmark of artificial settings, to the image data under different monitoring height is corrected, phase deviation is mended
The processing such as repay.
Optionally, executing subject can also be using an image data in multiple above-mentioned image datas as master image number
According to i.e. reference image data, to carry out benchmark to remaining image data (image data i.e. other than removing main image data)
The processing such as correction, phase compensation, to obtain destination image data, the i.e. data as subsequent chromatography SAR imaging.It is specific as follows:
After polarization sensitive synthetic aperture radar system receives signal, two-dimentional back scattering complex image can be formed by imaging.
Herein, orientation is indicated with x；R indicate distance to；S indicate height to.Wherein, azimuth resolution ρx=(λ r)/(2 Δs
x)；Range resolution ρr=c/ (2BW).Wherein, λ is wavelength；Δ x is orientation blended space；C is the spread speed of wave；BW
For SAR system bandwidth.It is r ' for distance and is located at for the single pixel u (x ', r ') of zero doppler position x ', plural number
Signal indicates are as follows:
Wherein, γ (x, r, s) is the reflectivity equation of three-dimensional scenic；For ground target
To the direct range of sensor；F (x '-x, r '-r) indicates what the comprehensive function weighted in antenna directivity and imaging was formed
Point spread function has generally when not considering weighting
Single base station SAR imaging system carries out single regional (such as target vegetation) M times on the parallel orbit of different height
Observation, available M scape plural number SAR image.At this point it is possible to choose M/2 scape image as master image, it is other supplemented by image.
Then all data are registrated, the pretreatment such as phasing.The SAR complex image of the m times acquisition may be expressed as:
Herein, m=1 ..., M；
Wherein, b//mIndicate horizontal base line；b⊥mIndicate vertical parallax.
For convenience, it is assumed that point spread function is a two dimension Dirac function (i.e. Dirac delta function), for giving picture
For vegetarian refreshments (x ', r '), an available M dimensional vectorWherein each element can indicate are as follows:
Wherein, Δ s indicates the upward effective observation scope of height；Rm(s)=Rm(s, r '=r, x '=x).
Since the phase in above formula includes one and the relevant quadratic phase deviation of baselineTherefore it needs through docking by signal multiplied by a complex conjugate quadratic phase
FunctionTo which this quadratic phase deviation compensation be fallen.That is, needing to two-dimensional SAR image number
According to being gone tiltedly to handle, it may be assumed that
It is available after the past is tiltedly handled:
Phase term is merged into reflectivity equation γ (s), is obtained:
Wherein,For space (height) frequency.
It in practical applications, then can be by by reflectivity if necessary to consider the phase property of reflectivity equation γ (s)
Equation is multiplied by a complex conjugate QP functionRemove the phase deviation, with
The phase information of preservative reflex rate equation γ (s).
It should be noted that in additive noiseIn the presence of, the discrete expression of formula
Formula are as follows:
Or
Wherein, g=(g1,g2..., gM)TFor a column vector with M element；For the steering matrix of M × N,
Element is Rm×n=exp (- j2 π ξmsn)；For boot vector (steering matrixColumn vector):
γ is the reflection rate matrix of N-dimensional discretization, element γn=γ (sn), sn(n=1 ..., N) indicates discretization
Height and position.
Step 202, destination image data is handled using multi-signal sorting algorithm, obtains the three-dimensional knot of target vegetation
Structure data.
In the present embodiment, executing subject can use multi-signal sorting algorithm (MUSIC algorithm), in step 201
Obtained destination image data carries out tomography processing, to obtain the three-dimensional structure data of target vegetation.As an example, first
The covariance matrix of destination image data can be constructed by first carrying out main body, and carry out feature decomposition to the covariance matrix of building,
To obtain signal subspace matrix and noise subspace matrix；Later according to signal subspace matrix and noise subspace square
Battle array, space spectral function can be constructed, and carry out spectrum peak search, with obtain target vegetation height to structural information.Specifically such as
Under:
Firstly, can determine K rank auto-correlation function by means of following formula according to above-mentioned M observation sample value pointAnd construct sample covariance matrix
Wherein, n indicates variable, and value range is [1, M]；x*(n) conjugate complex number of x (n) is indicated；X (n) indicates to receive letter
Number,AjIndicate the scattering coefficient of point target；ωjIndicate each Scattering Targets point height
Corresponding frequency vector；V (n) indicates zero-mean.
It later, can be by above-mentioned sample covariance matrixCarry out feature decomposition.And minimum likelihood criterion estimation can be used
Signal source numberCharacteristic vector space at this time can be indicated with matrix S and G respectively.Wherein,It can be byInIt is a most
The corresponding feature vector (i.e. corresponding with signal) of big characteristic valueComposition, i.e. signal subspace；It can be with
ByIn the corresponding feature vector of minimal eigenvalueComposition, i.e. noise subspace.Thus, it can incite somebody to action
Space spectral function (i.e. MUSIC composes search function) is expressed as follows:
Wherein, β*(ω) indicates the conjugate complex number of β (ω)；β (ω)=[1, e-iω,…,e-i(M-1)ω]T。
In this way, passing through determination when β (ω) carries out global search in [0,2 π] rangeNumerical value energy
Enough realize determines the pseudo- power spectrum of signal.In space, spectral domain seeks spectral function maximum value, and the corresponding angle of spectral peak is to come
The estimated value of wave deflection.That is, position where spectral peak be height to value, exist so as to obtain target vegetation
Height to structural information.
It is understood that MUSIC algorithm exactly estimates sky using the orthogonal property between above-mentioned two complementary space
Between signal orientation.Institute's directed quantity of noise subspace can be used to construction spectrum, the peak position in all dimensional orientation spectrums
To the incoming wave orientation of induction signal.This algorithm can greatly improve direction finding resolution ratio, while be adapted to the day of arbitrary shape
Linear array.
Step 203, the three-dimensional structure data of target vegetation and sample vegetation data are subjected to coherent analysis.
In the present embodiment, executing subject can be by the three-dimensional structure data of target vegetation obtained in step 202, with sample
This vegetation data carry out coherent analysis.Wherein, the vegetation of sample vegetation typically normal (not suffering a calamity).Example
Such as, sample vegetation vegetation typically same or similar with the vegetation type of target vegetation, and/or the ground with target vegetation
Manage vegetation similar in position.And sample vegetation data can be configured according to the actual situation.As sample vegetation data can be
The image data of vegetation entirety, or the image data of vegetation specific region.For another example sample vegetation data can also be
The image data of the target vegetation of a certain specific period (such as mid-April, and do not have pest and disease damage situation).Herein, coherence point
The concrete mode of analysis is not intended to limit.
It should be noted that being generally required to realize to the monitoring of disaster (such as pest and disease damage) situation of target vegetation
The branches and leaves region of vegetation is monitored again.Therefore in some embodiments, in order to improve the accuracy of monitoring efficiency and monitoring result, hold
Row main body can choose the pixel number evidence for being located at Vegetation canopy in the three-dimensional structure data of target vegetation.And it can be true
Surely the pixel number chosen is according to the coherence factor with the pixel number evidence for being located at sample vegetation same position.Here same position
It can refer to Vegetation canopy, can also refer to position of the pixel in Vegetation canopy of selection.
Herein, the selection mode of the pixel number evidence of Vegetation canopy is not intended to limit in this application, such as can be taking human as choosing
It takes, can also be chosen by image recognition.As an example, can be selected in the three-dimensional structure data of target vegetation first
Fetch bit is in certain pixel of Vegetation canopy；It can extract centered on the pixel of selection later and be located at default size space (such as
3 × 3 × 3) data of all pixels point in；The vector that finally target vegetation can be generated according to the pixel number of extraction evidence
Data.Such as 27 pixel numbers of said extracted can be arranged according to according to a certain sequence to form vector data X.Using same
The vector data Y of the available sample vegetation of the method for sample.Coherence's detection is carried out to vector data X and Y, obtains coherence factor
ρ:
Wherein, Y*Represent the conjugate complex number of Y；Mathematic expectaion is sought in E representative.
Step 204, the pest and disease damage situation of target vegetation is determined based on the analysis results.
In the present embodiment, executing subject can be according to the analysis in step 203 as a result, the disease pest to determine target vegetation
Evil situation.As an example, executing subject can be according to the coherence factor of above-mentioned determination and the relationship of default value range, to determine
The pest and disease damage situation of target vegetation.For example, if coherence factor ρ [a1,1) between, it can be said that the branches and leaves of improving eyesight mark vegetation have
Slight obscission, branches and leaves have 0~30% to fall off.If coherence factor ρ [a2, a1) between, it can be said that improving eyesight mark vegetation
Branches and leaves have more serious obscission, lose leaf rate and are about 30%~50%.If coherence factor ρ [a3, a2) between, then can be with
Illustrate the branches and leaves severe detachment of target vegetation, loses leaf rate and be about 50%~80%, it is serious by pest disaster.If coherence factor ρ
[0, a3) between, it can be said that the branches and leaves severe detachment of improving eyesight mark vegetation, vegetation is extremely serious by pest disaster, loses leaf rate and connects
Nearly 80%~100%.For fallen leaves forest vegetation, it is generally the case that a1=0.98, a2=0.95, a3=0.85.
It is understood that in order to improve the accuracy of monitoring result, it usually needs choose multiple groups pixel number according to progress
Analysis.It is located at this point, above-mentioned coherence factor can be according in the average value of each group pixel number evidence of selection and sample vegetation
What the mean value calculation of each group pixel number evidence of same position obtained；It is also possible to each group pixel number evidence point according to selection
It is not calculated with corresponding group in sample vegetation of pixel number evidence.
Optionally, executing subject can also be according to the ratio for each coherence factor being located in different value ranges, to determine
The pest and disease damage situation of target vegetation.For example, if be located at [a1,1) between the quantity of coherence factor account for coherence factor total quantity
Ratio reaches 30%, and/or be located at [a3, a2) and [0, a3) between the quantity of coherence factor account for the ratio of coherence factor total quantity
Example reaches 50%, it can be said that the branches and leaves of improving eyesight mark vegetation have serious obscission, there are serious pest disasters.For falling
For leaf forest vegetation, it is generally the case that a1=0.98, a2=0.95, a3=0.85.
TomoSAR vegetation pest and disease monitoring method provided in this embodiment based on coherence, it is possible, firstly, to based on obtaining
Target vegetation diameter radar image data, to obtain destination image data.Then, using multi-signal sorting algorithm pair
Destination image data is handled, to obtain the three-dimensional structure data of the target vegetation.Later, by the three of the target vegetation
It ties up structured data and sample vegetation data carries out coherent analysis.Finally, can determine the disease of target vegetation based on the analysis results
Insect pest situation.This method utilizes the diameter radar image data of target vegetation, and round-the-clock, the whole day of vegetation may be implemented
Wait monitoring.And it is handled by multi-signal sorting algorithm, can be realized high-acruracy survey of the height to vegetation structure.It helps in this way
In the accuracy for improving vegetation pest and disease monitoring result.
With further reference to Fig. 3, as the realization to method shown in the various embodiments described above, present invention also provides one kind to be based on
One embodiment of the TomoSAR vegetation pest and disease monitoring device of coherence.Shown in the Installation practice and the various embodiments described above
Embodiment of the method it is corresponding.The device specifically can be applied in various electronic equipments.
As shown in figure 3, the monitoring device 300 of the present embodiment may include: generation unit 301, it is configured to based on acquisition
The diameter radar image data of target vegetation obtain destination image data；Processing unit 302 is configured to using more letters
Number sorting algorithm handles destination image data, obtains the three-dimensional structure data of the target vegetation；Analytical unit 303,
It is configured to the three-dimensional structure data of the target vegetation and sample vegetation data carrying out coherent analysis；Determination unit 304,
It is configured to determine the pest and disease damage situation of the target vegetation based on the analysis results.
In some embodiments, generation unit 301 can be further configured to obtain the target under different monitoring height
The diameter radar image data of vegetation carry out N Reference Alignment, phase compensation processing to multiple image datas of acquisition, obtain
Destination image data.
Optionally, generation unit 301 can be further configured to using same synthetic aperture radar in different height face
On target vegetation is monitored, obtain multiple image datas；Using an image data in multiple image datas as master map
As data, N Reference Alignment is carried out to remaining image data, phase compensation is handled.
In some embodiments, processing unit 302 can be further configured to the covariance of building destination image data
Matrix, and feature decomposition is carried out to the covariance matrix of building, obtain signal subspace matrix and noise subspace matrix；According to
Signal subspace matrix and noise subspace matrix construct space spectral function, and carry out spectrum peak search, are planted with obtaining the target
By height to structural information.
Optionally, analytical unit 303 can be further configured in the three-dimensional structure data of the target vegetation, choosing
Fetch bit in the pixel number evidence of Vegetation canopy, and determine the pixel number chosen according to the pixel that is located at sample vegetation same position
The coherence factor of point data.
Further, analytical unit 303 can be further configured in the three-dimensional structure data of the target vegetation,
Certain pixel for being located at Vegetation canopy is chosen, centered on the pixel of selection, is extracted all in default size space
The data of pixel, and generate the vector data of the target vegetation.
It is understood that all units recorded in the device 300 and each step phase in the method with reference to Fig. 2 description
It is corresponding.As a result, above with respect to the operation of method description, the beneficial effect of feature and generation be equally applicable to the device 300 and
Unit wherein included, details are not described herein.
It should be noted that flow chart and block diagram in attached drawing, illustrate the system according to the various embodiments of the application, side
The architecture, function and operation in the cards of method and computer program product.In this regard, every in flowchart or block diagram
A box can represent a part of a module, program segment or code, and a part of the module, program segment or code includes
One or more executable instructions for implementing the specified logical function.It should also be noted that in some realizations as replacement
In, function marked in the box can also occur in a different order than that indicated in the drawings.For example, two succeedingly indicate
Box can actually be basically executed in parallel, they can also be executed in the opposite order sometimes, this is according to related function
Depending on energy.It is also noted that each box in block diagram and or flow chart and the box in block diagram and or flow chart
Combination, can the dedicated hardware based systems of the functions or operations as defined in executing realize, or can with it is dedicated firmly
The combination of part and computer instruction is realized.
Being described in unit involved in the embodiment of the present application can be realized by way of software, can also be by hard
The mode of part is realized.Described unit also can be set in the processor.Wherein, the title of these units is in certain situation
Under do not constitute restriction to the unit itself.For example, generation unit is also described as " the target vegetation based on acquisition
Diameter radar image data obtain the unit of destination image data ".
As on the other hand, present invention also provides a kind of computer-readable mediums.Here computer-readable medium can
To be computer-readable signal media or computer readable storage medium either the two any combination.The computer
Readable medium can be included in electronic equipment described in the various embodiments described above；It is also possible to individualism, and without
It is incorporated in the electronic equipment.Above-mentioned computer-readable medium carries computer program, when computer program is by the electronic equipment
When execution, so that the TomoSAR vegetation as described in above-mentioned any embodiment based on coherence may be implemented in the electronic equipment
Pest and disease monitoring method.
Above description is only the preferred embodiment of the application and the explanation to institute's application technology principle.Those skilled in the art
Member is it should be appreciated that invention scope involved in the application, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic
Scheme, while should also cover in the case where not departing from foregoing invention design, it is carried out by above-mentioned technical characteristic or its equivalent feature
Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed herein
Can technical characteristic replaced mutually and the technical solution that is formed.
Claims (10)
1. a kind of TomoSAR vegetation pest and disease monitoring method based on coherence, comprising:
The diameter radar image data of target vegetation based on acquisition obtain destination image data；
Destination image data is handled using multi-signal sorting algorithm, obtains the three-dimensional structure data of the target vegetation；
The three-dimensional structure data of the target vegetation and sample vegetation data are subjected to coherent analysis；
The pest and disease damage situation of the target vegetation is determined based on the analysis results.
2. according to the method described in claim 1, the diameter radar image data of the target vegetation based on acquisition obtain
To destination image data, comprising:
The diameter radar image data for obtaining the target vegetation under different monitoring height, to multiple image datas of acquisition into
Row N Reference Alignment, phase compensation processing, obtain destination image data.
3. according to the method described in claim 2, the synthetic aperture radar for obtaining the target vegetation under different monitoring height
Image data carries out N Reference Alignment to multiple image datas of acquisition, phase compensation is handled, comprising:
Target vegetation is monitored on different height face using same synthetic aperture radar, obtains multiple image datas；It will
An image data in multiple image datas carries out N Reference Alignment to remaining image data, phase is mended as main image data
Repay processing.
4. being obtained according to the method described in claim 1, described handled destination image data using multi-signal sorting algorithm
To the three-dimensional structure data of the target vegetation, comprising:
The covariance matrix of destination image data is constructed, and feature decomposition is carried out to the covariance matrix of building, obtains signal subspace
Space matrix and noise subspace matrix；
Space spectral function is constructed according to signal subspace matrix and noise subspace matrix, and carries out spectrum peak search, to obtain
State target vegetation height to structural information.
5. method described in one of -4 according to claim 1, the three-dimensional structure data by the target vegetation and sample are planted
Coherent analysis is carried out by data, comprising:
In the three-dimensional structure data of the target vegetation, the pixel number evidence for being located at Vegetation canopy is chosen, and determine selection
Pixel number is according to the coherence factor with the pixel number evidence for being located at sample vegetation same position.
6. selection is located at vegetation according to the method described in claim 5, described in the three-dimensional structure data of the target vegetation
The pixel number evidence of canopy, comprising:
In the three-dimensional structure data of the target vegetation, certain pixel for being located at Vegetation canopy is chosen, with the pixel of selection
Centered on, the data for all pixels point being located in default size space are extracted, and generate the vector data of the target vegetation.
7. a kind of TomoSAR vegetation pest and disease monitoring device based on coherence, comprising:
Generation unit, the diameter radar image data for being configured to the target vegetation based on acquisition obtain target image number
According to；
Processing unit is configured to handle destination image data using multi-signal sorting algorithm, obtains the target and plants
The three-dimensional structure data of quilt；
Analytical unit is configured to the three-dimensional structure data of the target vegetation and sample vegetation data carrying out coherence point
Analysis；
Determination unit is configured to determine the pest and disease damage situation of the target vegetation based on the analysis results.
8. device according to claim 7, the analytical unit is further configured to the three-dimensional in the target vegetation
In structured data, the pixel number evidence for being located at Vegetation canopy is chosen, and determines the pixel number evidence chosen and is located at sample vegetation
The coherence factor of the pixel number evidence of same position.
9. a kind of electronic equipment, comprising:
Processor；
Storage device is stored thereon with computer program；
When the processor executes the computer program on the storage device, so that electronic equipment realizes such as claim 1-
TomoSAR vegetation pest and disease monitoring method described in one of 6 based on coherence.
10. a kind of computer-readable medium is stored thereon with computer program, real when the computer program is executed by processor
The now TomoSAR vegetation pest and disease monitoring method based on coherence as described in one of claim 1-6.
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