CN103744079B - Method and system for determining planting period of sugarcane - Google Patents
Method and system for determining planting period of sugarcane Download PDFInfo
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- CN103744079B CN103744079B CN201310683789.0A CN201310683789A CN103744079B CN 103744079 B CN103744079 B CN 103744079B CN 201310683789 A CN201310683789 A CN 201310683789A CN 103744079 B CN103744079 B CN 103744079B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9023—SAR image post-processing techniques combined with interferometric techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9027—Pattern recognition for feature extraction
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/904—SAR modes
- G01S13/9076—Polarimetric features in SAR
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/024—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using polarisation effects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/411—Identification of targets based on measurements of radar reflectivity
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Abstract
The invention is suitable for the technical field of agricultural remote sensing monitoring and provides a method for determining the planting period of a sugarcane. The method comprises obtaining sugarcane planting region multi-time phase full-polarized synthetic aperture radar data; performing registration on the obtained sugarcane planting region multi-time phase full-polarized synthetic aperture radar data; carrying out polarization interference correlation on time phase original single look complex data which is after registration; carrying out multi-look processing on the polarization interference correlation; carrying out decomposition on a polarization correlation matrix of the time phase; judging whether the plant is the sugarcane based on the data obtained from decomposition; if the plant is the sugarcane, setting the point to be a sugarcane sample point; and determining the sugarcane planting period of the sugarcane sample point based on optimum correlation coefficient and multi-time phase similarity. The method is capable of simply, rapidly, precisely and effectively distinguishing the sugarcane planting periods, and providing reliable basis for sugarcane yield estimation.
Description
Technical field
The invention belongs to agricultural remote sensing monitoring technical field, the determination method and system of more particularly, to a kind of Sugarcane Planting Stage.
Background technology
Crops spatial framework reflects human agriculture and produces the situation utilizing agricultural production resources in spatial dimension, is
Understand crop specie, structure, the important information of distribution characteristicss, be also by crop s structure adjustment and the foundation optimizing.Caulis Sacchari sinensis
It is important sugar crop and energy source raw material, the Caulis Sacchari sinensis output of sugar of China accounts for more than the 90% of the total product of sugar, therefore, in time, accurately
Ground monitoring Caulis Sacchari sinensis spatial framework and upgrowth situation, formulate sugar relevant policies, plan for the imports and exports to country, and sugar enterprise's peace
Row produces significant.
Satellite remote sensing can provide distributed intelligence on room and time for the crops, compared to traditional manual research side
Method, has not only saved substantial amounts of human and material resources, and has macroscopic view, dynamic, timely, accurate advantage.Due to different crops
Phenology feature between type has differences, and the remote sensing image using multidate is the main method that crops spatial framework monitors
One of.The Caulis Sacchari sinensis of China 90% are distributed in south China, southwestern provinces and regions, and this region atural object distribution is broken, crop mixes seriously, based on thing
The multidate classification waiting feature is to discriminate between Caulis Sacchari sinensis and the most effective method of other crops.
Compared with the crops such as Oryza sativa L., Semen Tritici aestivi, Semen Maydiss, Caulis Sacchari sinensis have implantation time span greatly, plant phase diversified feature,
Taking the Lezhou Peninsula as a example, plant sugarcane, 5 kinds of stubble cane including spring planting sugarcane, summer plantinge sugarcane, fall planting sugarcane, winter.And planted in Caulis Sacchari sinensis development
Cheng Zhong, people increasingly pay attention to early, middle and late ripe, and the different plant phase reasonably combined to realize high yield and high sugar, but this is simultaneously
Monitor more difficult to satellite remote sensing Caulis Sacchari sinensis spatial framework：Same phase, early, middle and late ripe, and the Caulis Sacchari sinensis remote sensing of different plant phase
There is some difference for feature so that the existing multidate of application is supervised（Decision tree）Classification is difficult to high-precision Caulis Sacchari sinensis space
Distribution drawing.
South China area cloud-prone and raining, active microwave remote sensing has not to be affected by sexual intercourse, round-the-clock, round-the-clock monitoring
Advantage, has been increasingly becoming the significant data source of southern crops monitoring.On the premise of image resolution ratio improves constantly, complete polarization
Become one of important process pattern of Spaceborne SAR System of new generation.Compared to list（Double）Polarization SAR, full-polarization SAR is to target
Architectural characteristic portray apparent, crops monitoring on, polarization characteristic to reaction crop growth state biophysicss ginseng
Number is more sensitive, and the crop type identification based on full-polarization SAR data and spatial framework's study on monitoring have become SAR crops prison
Survey new developing direction.
Therefore, carry out the detection of Caulis Sacchari sinensis physical arrangement feature based on full-polarization SAR data, and apply on this basis simultaneously
Multi-temporal data carries out the determination of Sugarcane Planting Stage, is urgency problem to be studied.
Content of the invention
It is an object of the invention to provide one kind is simply, quickly, in high precision, and effectively distinguish Sugarcane Planting Stage, be that Caulis Sacchari sinensis are estimated
Produce the determination method and system of the Sugarcane Planting Stage that reliable basis are provided.
The present invention is achieved in that a kind of determination method of Sugarcane Planting Stage, the method comprising the steps of：
Obtain cane -growing region multidate fully polarization synthetic aperture radar data；
Registration is carried out to the described cane -growing region multidate fully polarization synthetic aperture radar data getting；
Original for phase after registration haplopia complex data is carried out polarization interference be concerned with；
Polarization interference is concerned with and carries out multiple look processing；
The polarization coherence matrix of phase is decomposed；
Judge whether it is Caulis Sacchari sinensis according to the data that decomposition obtains；
If it is judged that being Caulis Sacchari sinensis, then this point is set to Caulis Sacchari sinensis sampling point；
Judge the Sugarcane Planting Stage of described Caulis Sacchari sinensis sampling point based on optimized coherence coefficient and multidate similarity.
Another object of the present invention is to providing a kind of determination system of Sugarcane Planting Stage, described system includes：
Acquisition module, for obtaining cane -growing region multidate fully polarization synthetic aperture radar data；
Registration module, for carrying out to the described cane -growing region multidate fully polarization synthetic aperture radar data getting
Registration；
Polarization interference is concerned with module, is concerned with for original for the phase after registration haplopia complex data is carried out polarization interference；
Multiple look processing module, for polarization interference be concerned with carry out multiple look processing；
Decomposing module, for decomposing to the polarization coherence matrix of phase；
Judge module, for judging whether it is Caulis Sacchari sinensis according to the data that decomposition obtains；
Setup module, for if it is judged that being Caulis Sacchari sinensis, being then set to Caulis Sacchari sinensis sampling point by this point；
Sugarcane Planting Stage judge module, for judging described Caulis Sacchari sinensis sampling point based on optimized coherence coefficient and multidate similarity
Sugarcane Planting Stage.
In the present invention, Caulis Sacchari sinensis extraction and the estimation of plant phase are carried out based on multidate fully polarization synthetic aperture radar data, should
Scheme describes Caulis Sacchari sinensis exactly in tillering stage（April-May）The growth conditions of Caulis Sacchari sinensis and polarization characteristic, recurrence identification therein
Method can eliminate radar noise effectively to drawing problem produced by crops extraction, and Sugarcane Planting Stage evaluation method application two
The similarity feature of individual phase, the change of Caulis Sacchari sinensis before and after description tillering stage, and the foundation of plant phase differentiation is used as with this.The method
Can be used for identification, monitoring and the yield by estimation to Caulis Sacchari sinensis crops.
Brief description
Fig. 1 be the determination method of Sugarcane Planting Stage provided in an embodiment of the present invention realize schematic flow sheet.
Fig. 2 is the structural representation of the determination system of Sugarcane Planting Stage provided in an embodiment of the present invention.
Specific embodiment
In order that the purpose of the present invention, technical scheme and beneficial effect become more apparent, below in conjunction with accompanying drawing and enforcement
Example, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only in order to explain this
Bright, it is not intended to limit the present invention.
Refer to Fig. 1, be Sugarcane Planting Stage provided in an embodiment of the present invention determination method realize flow process, it includes following
Step：
In step S101, obtain cane -growing region multidate fully polarization synthetic aperture radar data；
In embodiments of the present invention, concrete phase be 4 months at the beginning of and by the end of May.
In step s 102, the described cane -growing region multidate fully polarization synthetic aperture radar data getting is carried out
Registration；
In embodiments of the present invention, at the beginning of 4 months and by the end of May when phase data carry out registration.
In step s 103, original for the phase after registration haplopia complex data is carried out polarization interference to be concerned with；
In embodiments of the present invention, described original for phase after registration haplopia complex data carried out polarization interference be concerned with, tool
Body is：
Wherein Si, i=1,2 represent 4 months respectively at the beginning of, original haplopia complex data by the end of May, ki, i=1,2 represents that its Pauli divides
Solution vector.
Two phase polarization interferences are concerned with, and are expressed as：
Wherein T11,T22Represent the polarization coherence matrix of two phases, Ω respectively12Represent the polarization interference of two phases, on
Mark H represents that transposition is conjugated.
In step S104, polarization interference is concerned with and carries out multiple look processing；
In embodiments of the present invention, described to polarization interference be concerned with carry out multiple look processing, specially：
Ω that polarization interference is concerned with carries out the multiple look processing of n*m, the value of n and m according to image orientation to distance to
Resolution is arranged, and after multiple look processing, distance is to consistent with azimuth resolution.Multiple look processing is as follows：
In step S105, the polarization coherence matrix of phase is decomposed；
In embodiments of the present invention, the characteristic vector based on coherence matrix is analyzed, and application Cloude decomposes to two phases
Polarization coherence matrix decomposed, extract respectively its scattering entropy H and Polarization scattering angle α, odd scattering component T11.
In step s 106, judge whether it is Caulis Sacchari sinensis according to the data that decomposition obtains；
In embodiments of the present invention, to image, the whole area of coverage carries out the judgement of pointwise, if the three-dimensional polarization of two phases
Feature（H、a、T11）It is satisfied by the criterion of following formula then it is assumed that this planted vegetation of point is Caulis Sacchari sinensis, this point is set to Caulis Sacchari sinensis sample
Point, otherwise this point be temporarily judged as non-Caulis Sacchari sinensis.
In step s 107, if it is judged that being Caulis Sacchari sinensis, then this point is set to Caulis Sacchari sinensis sampling point；
As one embodiment of the present invention, after step S107, also include：Entered based on the recurrence of Neighborhood Statistics feature
One step identifies whether this Caulis Sacchari sinensis sampling point is Caulis Sacchari sinensis.
Because radar data noise is many, scattering is easily subject to extraneous factor, still suffer from substantial amounts of reality in image overlay area sweet
Sugarcane growing area is still classified as non-Caulis Sacchari sinensis by mistake and carries it is therefore desirable to carry out further Caulis Sacchari sinensis based on the Caulis Sacchari sinensis sampling point extracting above
Take.
On the other hand, because Caulis Sacchari sinensis are subject to implantation time difference, its scattering signatures heterogeneity, if according to traditional supervision
Classification then can produce substantial amounts of mistake and divide.
The embodiment of the present invention is based on neighborhood n*n statistical nature, the resolution of image after the installation warrants multiple look processing of neighborhood n
Determine.
Step 1：Caulis Sacchari sinensis sampling point in statistics n*n neighborhood, is labeled as { ω1,ω2,Λ,ωj, j represents sampling point number.
Step 2：Caulis Sacchari sinensis sampling point is calculated two-by-two with its Wishart distance：
Total j*（j-1）Individual distance, obeys one-dimensional gaussian profile, collection of adjusting the distance carries out statistical analysiss, calculate its average m and
Variances sigma.
Step 3：To j Caulis Sacchari sinensis sampling point in n*n neighborhood, calculate its covariance matrix, as follows：
It is labeled as { ω }
Step 4：To the non-Caulis Sacchari sinensis point in n*n neighborhood, it is labeled asJudge its attribute one by one further,
Rule is as follows：
If { d (ηi, ω) and≤m+3 σ, then this point of labelling i is Caulis Sacchari sinensis, is still otherwise non-Caulis Sacchari sinensis.
Step 5：If non-Caulis Sacchari sinensis point attribute changes quantity in n*n neighborhoodThen return to step 1, enters
The next time non-Caulis Sacchari sinensis point attributive judgment of row, newly-increased Caulis Sacchari sinensis point is added in Caulis Sacchari sinensis sampling point, otherwise, terminates to judge.
The sugarcane planting of described Caulis Sacchari sinensis sampling point in step S108, is judged based on optimized coherence coefficient and multidate similarity
Phase.
In embodiments of the present invention, step S108 is specially：
Step 10：Point is sorted out to Caulis Sacchari sinensis, calculates optimized coherence coefficient and the similarity of two phases, as follows：
Optimized coherence coefficient
Optimized coherence coefficient is according to document（K.Papathanassiou and S.Cloude,“Three–stage
inversion process for polarimetric sar interferometry,”IEEE Proceedings-
Radar,Sonar and Navigation,vol.150,pp.125–134,June2003）Solve.
Two phase similaritys：When two in this formula, phase matrix can not
Replace position.
Step 20：Sugarcane Planting Stage judges
If optimized coherence coefficient | γ | >=0.6 andThen it is labeled as winter plant sugarcane；
If optimized coherence coefficient | γ |≤0.3 andThen it is labeled as spring planting sugarcane；
If optimized coherence coefficient 0.6 >=| γ | >=0.3 andThen it is labeled as stubble cane.
Step 30：Based on the adjustment of Neighborhood Statistics feature Sugarcane Planting Stage
All Caulis Sacchari sinensis sampling points in statistics n*n neighborhood,
Winter plants sugarcane point and is labeled as { ν1i, i=1, Λ, k1,
Spring planting sugarcane point is labeled as { ν2i, i=1, Λ, k2,
Stubble cane point is labeled as { ν3i, i=1, Λ, k3.
Calculate its covariance matrix respectively：
Winter plants sugarcane, C1, spring planting sugarcane C2, stubble cane C3,
To Caulis Sacchari sinensis point unlabelled in neighborhood, pointwise judges its plant phase, and determination methods are as follows：
Assume Caulis Sacchari sinensis point { μ }, polarize coherence matrix T, calculates itself and C respectively1、C2、C3Wishart distance, be labeled as
d1、d2、d3.
If d1Minimum, then this point of labelling is winter plant sugarcane；
If d2Minimum, then this point of labelling is spring planting sugarcane；
If d3Minimum, then this point of labelling is stubble cane.
Refer to Fig. 2, be the structure of the determination system of Sugarcane Planting Stage provided in an embodiment of the present invention.For convenience of description,
Illustrate only the part related to the embodiment of the present invention.The determination system of described Sugarcane Planting Stage includes：.Described Sugarcane Planting Stage is really
Determine the unit that system can be software unit, hardware cell or software and hardware combining.
Acquisition module 101, for obtaining cane -growing region multidate fully polarization synthetic aperture radar data；
Registration module 102, for the described cane -growing region multidate fully polarization synthetic aperture radar data getting
Carry out registration；
Polarization interference is concerned with module 103, is concerned with for original for the phase after registration haplopia complex data is carried out polarization interference；
Multiple look processing module 104, for polarization interference be concerned with carry out multiple look processing；
Decomposing module 105, for decomposing to the polarization coherence matrix of phase；
Judge module 106, for judging whether it is Caulis Sacchari sinensis according to the data that decomposition obtains；
Setup module 107, for if it is judged that being Caulis Sacchari sinensis, being then set to Caulis Sacchari sinensis sampling point by this point；
Sugarcane Planting Stage judge module, for judging described Caulis Sacchari sinensis sampling point based on optimized coherence coefficient and multidate similarity
Sugarcane Planting Stage.
As one embodiment of the present invention, described system also includes：Identification module.
For the recurrence based on Neighborhood Statistics feature, identification module, identifies whether this Caulis Sacchari sinensis sampling point is Caulis Sacchari sinensis further.
As another preferred embodiment of the present invention, described system also includes：
Statistical module, for counting the Caulis Sacchari sinensis sampling point in n*n neighborhood, is labeled as { ω1,ω2,Λ,ωj, j represents sampling point
Number.
Distance calculation module, for calculating its Wishart distance two-by-two to Caulis Sacchari sinensis sampling point：
Covariance matrix computing module, for j Caulis Sacchari sinensis sampling point in n*n neighborhood, calculating its covariance matrix；
Attributive judgment module, for the non-Caulis Sacchari sinensis point in n*n neighborhood, being labeled asFurther one by one
Judge its attribute,
Control module, if for Caulis Sacchari sinensis point attribute changes quantity non-in n*n neighborhoodThen return system
In meter module, carry out non-Caulis Sacchari sinensis point attributive judgment next time, newly-increased Caulis Sacchari sinensis point is added in Caulis Sacchari sinensis sampling point, otherwise, terminate to judge.
In sum, the embodiment of the present invention carries out Caulis Sacchari sinensis extraction and plant based on multidate fully polarization synthetic aperture radar data
Phase is estimated, the program describes Caulis Sacchari sinensis exactly in tillering stage（April-May）The growth conditions of Caulis Sacchari sinensis and polarization characteristic, therein
Recurrence recognition methodss can eliminate radar noise effectively to drawing problem produced by crops extraction, and Sugarcane Planting Stage estimation side
Method apply two phases similarity feature, description tillering stage before and after Caulis Sacchari sinensis change, and with this be used as the plant phase differentiation according to
According to.The method can be used for identification, monitoring and the yield by estimation to Caulis Sacchari sinensis crops.
One of ordinary skill in the art will appreciate that it is permissible for realizing all or part of step in above-described embodiment method
Instruct related hardware to complete by program, described program can be stored in a computer read/write memory medium,
Described storage medium, such as ROM/RAM, disk, CD etc..
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.
Claims (6)
1. a kind of determination method of Sugarcane Planting Stage is it is characterised in that the method comprising the steps of：
Obtain cane -growing region multidate fully polarization synthetic aperture radar data；
Registration is carried out to the described cane -growing region multidate fully polarization synthetic aperture radar data getting；
Original for phase after registration haplopia complex data is carried out polarization interference be concerned with, described that original for phase after registration haplopia is multiple
Data carries out the relevant inclusion of polarization interference：
Wherein, Si, i=1,2 represent 4 months respectively at the beginning of, original haplopia complex data by the end of May, ki, i=1,2 represent its Pauli decompose to
Amount, two phase polarization interferences are concerned with, and are expressed as：
Wherein, T11,T22Represent the relevant square of polarization of two phases respectively
Battle array, Ω12Represent the polarization interference of two phases, subscript H represents that transposition is conjugated；
Polarization interference is concerned with and carries out multiple look processing；
The polarization coherence matrix of phase is decomposed；
According to decomposing the data that obtains judges whether it is Caulis Sacchari sinensis, described according to decomposing the data obtaining judges whether it is Caulis Sacchari sinensis
Including：The three-dimensional polarization characteristic (H, a, T11) of two phases is satisfied by the criterion of following formula then it is assumed that this planted vegetation of point is
Caulis Sacchari sinensis, otherwise this point be temporarily judged as non-Caulis Sacchari sinensis；
Described H, α and T11 are respectively the scattering entropy of polarization coherence matrix of described two phases, polarization dissipates
Firing angle and odd scattering component；
If it is judged that being Caulis Sacchari sinensis, then this point is set to Caulis Sacchari sinensis sampling point；
Judge the Sugarcane Planting Stage of described Caulis Sacchari sinensis sampling point based on optimized coherence coefficient and multidate similarity, described based on optimum phase
Responsibility number and multidate similarity include come the Sugarcane Planting Stage to judge described Caulis Sacchari sinensis sampling point：Point is sorted out to Caulis Sacchari sinensis, when calculating two
The optimized coherence coefficient of phase and similarity, as follows：
Optimized coherence coefficient
Two phase similaritys：When two in this formula, phase matrix can not replace position
Put, described T11,T22Represent the polarization coherence matrix of two phases, Ω respectively12Represent the polarization interference of two phases, n*n neighborhood
Interior Caulis Sacchari sinensis sampling point；
If optimized coherence coefficient | γ | >=0.6 andThen it is labeled as winter plant sugarcane；
If optimized coherence coefficient | γ |≤0.3 andThen it is labeled as spring planting sugarcane；
If optimized coherence coefficient 0.6 >=| γ | >=0.3 andThen it is labeled as stubble cane.
2. the method for claim 1 it is characterised in that described if it is judged that being Caulis Sacchari sinensis, be then set to sweet by this point
After the step of sugarcane sampling point, also include：
Recurrence based on Neighborhood Statistics feature identifies whether this Caulis Sacchari sinensis sampling point is Caulis Sacchari sinensis further.
3. method as claimed in claim 2 is it is characterised in that the described recurrence based on Neighborhood Statistics feature identifies this further
Whether Caulis Sacchari sinensis sampling point is the step of Caulis Sacchari sinensis, specially：
Caulis Sacchari sinensis sampling point in statistics n*n neighborhood, is labeled as { ω1,ω2,…,ωj, j represents sampling point number；
Caulis Sacchari sinensis sampling point is calculated two-by-two with its Wishart distance；
To j Caulis Sacchari sinensis sampling point in n*n neighborhood, calculate its covariance matrix；
To the non-Caulis Sacchari sinensis point in n*n neighborhood, it is labeled asJudge its attribute one by one further,
If non-Caulis Sacchari sinensis point attribute changes quantity in n*n neighborhoodThen return in statistical module, carry out next
Secondary non-Caulis Sacchari sinensis point attributive judgment, newly-increased Caulis Sacchari sinensis point is added in Caulis Sacchari sinensis sampling point, otherwise, terminates to judge.
4. a kind of determination system of Sugarcane Planting Stage is it is characterised in that described system includes：
Acquisition module, for obtaining cane -growing region multidate fully polarization synthetic aperture radar data；
Registration module, for joining to the described cane -growing region multidate fully polarization synthetic aperture radar data getting
Accurate；
Polarization interference is concerned with module, is concerned with for original for the phase after registration haplopia complex data is carried out polarization interference, described will
Phase original haplopia complex data after registration carries out the relevant inclusion of polarization interference：
Wherein, Si, i=1,2 represent 4 months respectively at the beginning of, original haplopia complex data by the end of May, ki, i=1,2 represent its Pauli decompose to
Amount, two phase polarization interferences are concerned with, and are expressed as：
Wherein, T11,T22Represent the relevant square of polarization of two phases respectively
Battle array, Ω12Represent the polarization interference of two phases, subscript H represents that transposition is conjugated；
Multiple look processing module, carries out multiple look processing for being concerned with to polarization interference；
Decomposing module, for decomposing to the polarization coherence matrix of phase；
Judge module, for according to decomposing the data that obtains judges whether it is Caulis Sacchari sinensis, described according to decompose the data obtaining Lai
Judge whether it is that Caulis Sacchari sinensis include：The three-dimensional polarization characteristic (H, a, T11) of two phases be satisfied by following formula criterion then it is assumed that
This planted vegetation of point is Caulis Sacchari sinensis, and otherwise this point is temporarily judged as non-Caulis Sacchari sinensis；
Described H, α and T11 are respectively the scattering entropy of polarization coherence matrix of described two phases, polarization
Angle of scattering and odd scattering component；
Setup module, for if it is judged that being Caulis Sacchari sinensis, being then set to Caulis Sacchari sinensis sampling point by this point；
Sugarcane Planting Stage judge module, for judging the sweet of described Caulis Sacchari sinensis sampling point based on optimized coherence coefficient and multidate similarity
Sugarcane plants the phase, described is included based on the Sugarcane Planting Stage that optimized coherence coefficient and multidate similarity judge described Caulis Sacchari sinensis sampling point：Right
Caulis Sacchari sinensis sort out point, calculate optimized coherence coefficient and the similarity of two phases, as follows：
Optimized coherence coefficient
Two phase similaritys：When two in this formula, phase matrix can not replace position
Put, described T11,T22Represent the polarization coherence matrix of two phases, Ω respectively12Represent the polarization interference of two phases, n*n neighborhood
Interior Caulis Sacchari sinensis sampling point；
If optimized coherence coefficient | γ | >=0.6 andThen it is labeled as winter plant sugarcane；
If optimized coherence coefficient | γ |≤0.3 andThen it is labeled as spring planting sugarcane；
If optimized coherence coefficient 0.6 >=| γ | >=0.3 andThen it is labeled as stubble cane.
5. system as claimed in claim 4 is it is characterised in that described system also includes：
For the recurrence based on Neighborhood Statistics feature, identification module, identifies whether this Caulis Sacchari sinensis sampling point is Caulis Sacchari sinensis further.
6. system as claimed in claim 5 is it is characterised in that described system also includes：
Statistical module, for counting the Caulis Sacchari sinensis sampling point in n*n neighborhood, is labeled as { ω1,ω2,…,ωj, j represents sampling point number；
Distance calculation module, for calculating its Wishart distance two-by-two to Caulis Sacchari sinensis sampling point：
Covariance matrix computing module, for j Caulis Sacchari sinensis sampling point in n*n neighborhood, calculating its covariance matrix；
Attributive judgment module, for the non-Caulis Sacchari sinensis point in n*n neighborhood, being labeled asJudge one by one further
Its attribute；
Control module, if for Caulis Sacchari sinensis point attribute changes quantity non-in n*n neighborhoodThen return statistics mould
In block, carry out non-Caulis Sacchari sinensis point attributive judgment next time, newly-increased Caulis Sacchari sinensis point is added in Caulis Sacchari sinensis sampling point, otherwise, terminate to judge.
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