CN109086661B - A kind of crops relative radiometric normalization method and device - Google Patents
A kind of crops relative radiometric normalization method and device Download PDFInfo
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Abstract
The invention discloses a kind of crops relative radiometric normalization method and devices, this method is by making full use of different times, the same area, Local Phase answers the SPECTRAL DIVERSITY feature of atural object of the same race under spatial distribution under different sensors, the NDVI codomain range of every scape image is classified according to the principle that natural breakpoint is classified, by calculate separately each rank spatial position Duplication determine mutual corresponding rank pair, and using the overlapping region of selected appropriate level centering as the sample data of the rank, equation of linear regression is established according to sample data, regression coefficient is solved using the principle of least square, and then realize image to be corrected and the relative radiometric normalization with reference to image, express that the Seasonal dynamics change information of the vegetation such as crops more accurately.
Description
Technical field
The present embodiments relate to remote sensing image preconditioning technique more particularly to a kind of crops relative radiometric normalization sides
Method and device.
Background technique
The difference of multi-source multi-temporal remote sensing image gray value is mainly from the Seasonal dynamics change of natural feature on a map, illumination and atmosphere etc.
Mutation and the decaying of sensor itself etc. caused by external condition, Yun Ying are blocked.In order to eliminate different times, different data sources
The radiance difference of atural object is not changed on image, common radiation normalization method is divided into absolute radiation correction and relative radiation
Correction.Wherein, image greyscale value is relatively accurately converted to the brightness of clutter reflections spoke or reflectivity by absolute radiation correction, such as
ARTC modelling, ISC-LSC modelling and FLAASH modelling etc., but the foundation for radiating transformation model needs accurate sensing
Device scaling parameter, solar zenith angle, atmospheric correction parameter and corresponding correcting algorithm etc..Relative detector calibration is by multidate
Atural object gray value replaces the brightness of clutter reflections spoke or reflectivity, by match cutting with the gray value of image to be corrected to reference
Weakly heterogeneous mainly includes the linearity correction such as image Return Law, pseudo- invariant features point method and dark collection-bright collection method etc. and non-linear school
Correction method such as histogram matching.Currently, these methods have wide application, and normalization effect is good under given conditions
It is good.
However, in radiation normalization method research now, for, variation big, impact factor complexity more with type etc.
The scrutiny of the specific atural object of feature such as crops is less.Even if when carrying out radiation normalization, being also according to atural object classification
Be roughly divided into several big typically species, or cannot sufficiently in binding region atural object detailed features, institute between image
The linear functional relation for having atural object same is not able to satisfy the radiant correction of degree of precision.The growth of crops is as influence image
One of an important factor for gray-value variation, radiates relative radiometric normalization and image of the elimination in general image of difference to it
It is extracted applied to crops and monitoring is of great significance.
The common counter that vegetation information is monitored in agricultural remote sensing is normalized differential vegetation index, is capable of the life of effective inverting vegetation
Object physical parameter, such as vegetation coverage, leaf area index and photosynthesis absorb radiation, mainly by Soil Background, phenology
Phase-farming goes through, atmospheric effect, solar elevation and orographic effect etc. influence, far from each other with image spectral signature difference source.
Summary of the invention
The embodiment of the present invention provides a kind of crops relative radiometric normalization method and device, realizes the height for being directed to crops
Precision relative radiometric normalization.
A kind of crops relative radiometric normalization method provided in an embodiment of the present invention, comprising:
It obtains and refers to image and image to be corrected；
Preliminary corrections are carried out to the reference image and the image to be corrected using pseudo- invariant features point methods；
According to natural breakpoint principle of classification respectively to after preliminary corrections it is described with reference to image and the image to be corrected into
Row vegetation index (Normalized Difference Vegetation Index, NDVI) classification；
Described after being classified according to NDVI is with reference to image and the image to be corrected determines each rank spatial location
Duplication, and determine that the mapping of the NDVI rank with reference to image between the image to be corrected is closed according to the Duplication
System；
The mapping of the NDVI rank with reference to image between the image to be corrected is determined according to the principle of least square
The equation of linear regression coefficient of each rank pair in relationship, and carry out the relative radiometric normalization of the image to be corrected.
Optionally, described to be carried out tentatively with reference to image with the image to be corrected using pseudo- invariant features point methods to described
Correction, comprising:
It extracts with reference to the stable radiation point in the NDVI error image of image and image to be corrected as pseudo- invariant features point；
Preliminary relative radiometric normalization correction is carried out using pseudo- invariant features point.
Optionally, it is described according to natural breakpoint principle of classification respectively to after preliminary corrections it is described with reference to image and it is described to
Correcting image carries out NDVI classification, comprising:
According to the regularity of distribution of spectral value in NDVI image, by the ginseng centered on natural turning point, characteristic point, knick point
It examines pixel in image and the image to be corrected and is divided into the similar group of property；
According to group's internal variance and minimum, group's group difference maximization principle, to described with reference to image and described to be corrected
The NDVI figure of image is gradually classified.
Optionally, it is described be classified according to NDVI after described determined in each rank with reference to image and the image to be corrected
The Duplication of spatial position, and the NDVI grade with reference to image between the image to be corrected is determined according to the Duplication
Other mapping relations, comprising:
Calculate the pixel be overlapped with each NDVI rank of image to be corrected with reference to NDVI rank each in image
Number；
The largest number of appropriate levels of pixel will be overlapped as the rank pair mapped one by one, determined described with reference to image and institute
State the mapping relations of the NDVI rank between image to be corrected.
Optionally, described that the NDVI with reference to image between the image to be corrected is determined according to the principle of least square
The equation of linear regression coefficient of each rank pair in the mapping relations of rank, and the relative radiation for carrying out the image to be corrected is returned
One changes, comprising:
According to each rank pair in the mapping relations of the NDVI rank with reference to image between the image to be corrected,
Calculate separately equation of linear regression；
The regression fit coefficient of the equation of linear regression is sought using the principle of least square；
According to the NDVI codomain range of each rank of image to be corrected, the fitting coefficient of corresponding level is substituted into, is carried out
The relative radiometric normalization of the image to be corrected.
Correspondingly, the embodiment of the invention also provides a kind of crops relative radiometric normalization devices, comprising:
Acquiring unit refers to image and image to be corrected for obtaining；
Correction unit, for using pseudo- invariant features point methods to it is described with reference to image and the image to be corrected into
Row preliminary corrections；
Stage unit, for according to natural breakpoint principle of classification respectively to described with reference to image and described after preliminary corrections
Image to be corrected carries out NDVI classification；
Determination unit determines each rank with reference to image and the image to be corrected for described after being classified according to NDVI
The Duplication of spatial location, and the NDVI with reference to image between the image to be corrected is determined according to the Duplication
The mapping relations of rank；
Normalization unit, for according to the principle of least square determine it is described with reference to image between the image to be corrected
The equation of linear regression coefficient of each rank pair in the mapping relations of NDVI rank, and carry out the opposite spoke of the image to be corrected
Penetrate normalization.
Optionally, the correction unit is specifically used for:
It extracts with reference to the stable radiation point in the NDVI error image of image and image to be corrected as pseudo- invariant features point；
Preliminary relative radiometric normalization correction is carried out using pseudo- invariant features point.
Optionally, the stage unit is specifically used for:
According to the regularity of distribution of spectral value in NDVI image, by the ginseng centered on natural turning point, characteristic point, knick point
It examines pixel in image and the image to be corrected and is divided into the similar group of property；
According to group's internal variance and minimum, group's group difference maximization principle, to described with reference to image and described to be corrected
The NDVI figure of image is gradually classified.
Optionally, the determination unit is specifically used for:
Calculate the pixel be overlapped with each NDVI rank of image to be corrected with reference to NDVI rank each in image
Number；
The largest number of appropriate levels of pixel will be overlapped as the rank pair mapped one by one, determined described with reference to image and institute
State the mapping relations of the NDVI rank between image to be corrected.
Optionally, the normalization unit is specifically used for:
According to each rank pair in the mapping relations of the NDVI rank with reference to image between the image to be corrected,
Calculate separately equation of linear regression；
The regression fit coefficient of the equation of linear regression is sought using the principle of least square；
According to the NDVI codomain range of each rank of image to be corrected, the fitting coefficient of corresponding level is substituted into, is carried out
The relative radiometric normalization of the image to be corrected.
Above-described embodiment shows by making full use of local additional space under different times, the same area, different sensors
The SPECTRAL DIVERSITY feature of atural object of the same race under distribution, the principle that the NDVI codomain range of every scape image is classified according to natural breakpoint into
Row classification, by calculate separately each rank spatial position Duplication determine mutual corresponding rank pair, and will be selected
Sample data of the overlapping region of appropriate level centering as the rank establishes equation of linear regression according to sample data, utilizes
The principle of least square solves regression coefficient, and then realizes image to be corrected and the relative radiometric normalization with reference to image, makes agriculture
The Seasonal dynamics change information of the vegetation such as crop is more accurately expressed.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly introduced, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill in field, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing.
Fig. 1 is a kind of flow diagram of crops relative radiometric normalization method provided in an embodiment of the present invention；
Fig. 2 is the schematic diagram provided in an embodiment of the present invention being classified with reference to image NDVI；
Fig. 3 is the schematic diagram of image NDVI classification to be corrected provided in an embodiment of the present invention；
Fig. 4 is the image contrast schematic diagram after correction provided in an embodiment of the present invention；
Fig. 5 is a kind of structural schematic diagram of crops relative radiometric normalization device provided in an embodiment of the present invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention make into
It is described in detail to one step, it is clear that described embodiments are only a part of the embodiments of the present invention, rather than whole implementation
Example.Based on the embodiments of the present invention, obtained by those of ordinary skill in the art without making creative efforts
All other embodiment, shall fall within the protection scope of the present invention.
Fig. 1 illustratively shows a kind of process of crops relative radiometric normalization provided in an embodiment of the present invention, should
Process can be executed by the device of crops relative radiometric normalization.
As shown in Figure 1, the process specifically includes:
Step 101, it obtains and refers to image and image to be corrected.
This can be remote sensing image, in embodiments of the present invention for ease of description, choosing with reference to image and image to be corrected
Taking Suzhou City of Jiangsu Province high and new technology industrial development zone is survey region, and high score No.1 16m resolution multi-spectral image is WFV2 sensing with reference to image
The device image on the 3rd of September in 2016, image to be corrected are WFV4 sensor August in 2016 image on the 14th.By geometric correction,
Ortho-rectification carries out being classified the relative radiation normalizing for being fitted to foundation with NDVI according to this programme after the pretreatment such as radiation calibration
Change.
Step 102, preliminary school is carried out to the reference image and the image to be corrected using pseudo- invariant features point methods
Just.
When carrying out preliminary corrections, it is specifically as follows: first extracts the NDVI error image for referring to image and image to be corrected
In stable radiation point as pseudo- invariant features point (pseudo-invariantfeatures, PIFs), then using pseudo- constant
Characteristic point carries out preliminary relative radiometric normalization correction.The stable radiation point can be non-change point in building.Δ NDVI=NDVIr-NDVIt, wherein RNirAnd RRedRespectively near infrared band and the reflection of red wave band
Rate value, NDVIrAnd NDVItIt is expressed as the NDVI image with reference to image and image to be corrected, Δ NDVI is expressed as differential chart.
Equation of linear regression y is established for each wave band in sample pointri=ki×xti+bi(i=1 ..., n), is asked using least square
It fetches and returns fitting coefficient kiAnd bi, wherein yriAnd xtiIt is expressed as i-th of wave band with reference to image and image to be corrected, n generation
Table wave band number.The preliminary relative radiometric normalization purport carried out in embodiments of the present invention using pseudo- invariant features point (PIFs) method
The radiation difference between image is substantially being reduced roughly, is keeping the radiation scope of two width images almost the same, is being established for NDVI classification correspondence
Fixed basis.
For example, preliminary relative radiometric normalization correction is carried out using pseudo- invariant features point (PIFs) method.It counts first
Calculate the NDVI image and its differential chart for referring to image and image to be corrected, and point based on building gray value in differential chart
Cloth establishes equation of linear regression according to selected PIFs according to its statistic means standard deviation as candidate stable radiation point
Such as table 1, the preliminary relative radiometric normalization of whole picture image is realized.
Table 1
| Wave band | yri=ki×xti+bi |
| B1 | Y=0.8849x+11.288 |
| B2 | Y=0.9202x-1.7181 |
| B3 | Y=0.9118x-5.794 |
| B4 | Y=0.975x-6.5937 |
Step 103, according to natural breakpoint principle of classification respectively to described with reference to image and described to school after preliminary corrections
Positive image carries out NDVI classification.
Natural breakpoint principle of classification fully demonstrates the thought of " things of a kind come together, people of a mind fall into the same group ", according to the distribution of spectral value in NDVI image
Pixel is divided into the similar group of property centered on natural turning point, characteristic point, knick point by rule, according to group's internal variance and
Minimum, group's group difference maximization principle, realization are gradually classified the NDVI figure of reference image and image to be corrected, due to
Classification is more careful can be on the basis of classification, and bond area plant growth feature judges crop type.
For example, NDVI codomain range is [- 1,1], in order to distinguish 0 value of background and 0 value in NDVI, first will
It is 2 that image background value, which carries out exposure mask,；Then will be joined according to the NDVI codomain range of image itself according to natural breakpoint principle of classification
It examines and is respectively divided into 11 grades with NDVI image to be corrected, wherein 1~10 grade of NDVI valid value range comprising image, 11 grades are background
Value.As shown in Figures 2 and 3, Fig. 2 is the NDVI classification figure with reference to image to classification figure, and Fig. 3 is that the NDVI of image to be corrected is classified
Figure.With reference to the effective codomain range of image NDVI be -0.38~0.62, the classification section of rank 1~11 be expressed as [- 0.38, -
0.18],[-0.18,-0.11],[-0.11,-0.05],[-0.05,0.01],[0.01,0.08],[0.08,0.15],[0.15,
0.23], [0.23,0.31], [0.31,0.40], [0.40,0.62], [0.62,2] }, the effective codomain model of image NDVI to be corrected
Enclosing is -0.42~0.65, the classification section of rank 1~11 be respectively [- 0.42, -0.3], [- 0.3, -0.14], [- 0.14, -
0.05],[-0.05,0.02],[0.02,0.09],[0.09,0.18],[0.18,0.27],[0.27,0.35],[0.35,
0.44],[0.44,0.65],[0.65,2]}.According to the high and new technology industrial development zone crop growth phenological period, it was exactly at August 14 days and September 3rd
The growth busy season of rice, in conjunction with land-use map and above-mentioned classification section, the threshold value of rice on the image of September 3 days (reference)
Section is [0.31,0.40] ∪ [0.40,0.62], on the image of August 14 days (to be corrected) threshold interval of rice be [0.35,
0.44] [0.44,0.65] ∪.
Step 104, the reference image and the image to be corrected after being classified according to NDVI determine that each rank is hollow
Between position Duplication, and the NDVI rank with reference to image between the image to be corrected is determined according to the Duplication
Mapping relations.
It is classified the Duplication that figure calculates each rank spatial location based on NDVI, NDVI between image is judged according to Duplication
The mapping relations of rank.Calculate the pixel number Chong Die in each rank of image to be corrected with reference to each rank in image, weight
The largest number of appropriate levels of fold-over member obtain the overlapping region conduct of corresponding level pair as the rank pair mapped one by one
The fitting sample in the region, while NDVI rank can be used as the threshold range foundation for dividing crops classification.
For example, (since 11 grades are background, the Duplication of this rank need not be calculated) as shown in table 2, selection weight
The largest number of ranks of fold-over member relatively obtain green area as corresponding mapping relations, by Vertical Numerical lateral in table 2
For the one-to-one relationship that each rank is selected, wherein rank 1 is less for fringe region pixel number on image, and overlay region is compared
Effect is unobvious, but influences on Integral correction result smaller.
2 NDVI of table spatial distribution Duplication at different levels
Step 105, the NDVI grade with reference to image between the image to be corrected is determined according to the principle of least square
The equation of linear regression coefficient of each rank pair in other mapping relations, and carry out the relative radiation normalizing of the image to be corrected
Change.
The equation of linear regression coefficient of each rank pair is calculated according to the principle of least square, and then is realized based on NDVI rank
The relative radiometric normalization of image.Linear regression side is calculated separately according to the corresponding level fitting sample point obtained in step 104
Journey establishes equation of linear regression y to each wave band for each rankrij=kij×xtij+bij(i=1 ..., n, j=
1 ..., m), regression fit coefficient k is sought using least squareijAnd bij, wherein yrijAnd xtijBe expressed as with reference to image and
I-th of wave band, j-th of rank of image to be corrected, n represent wave band number, and m represents classification number.According to each grade of image to be corrected
The fitting coefficient of corresponding level is substituted into and realizes more fine relative radiometric normalization by other NDVI codomain range.
The equation of linear regression coefficient such as table 3 of 1-10 ranks pair is calculated according to the principle of least square, and then substitutes into formulaWherein n=4, m=10 are realized and are realized shadow based on NDVI rank
The relative radiometric normalization of picture.
3 NDVI hierarchy mapping equation of linear regression of table
Context of methods has been described in detail according to wave band each in table 4 and average spoke brightness and normalizes forward and backward image spoke brightness change
Change situation, can by NDVI be classified fitting correction after image and with reference to image radiance more close to.In order to further
The quality for measuring relative detector calibration result is described using root-mean-square error (RMSE) with reference between image and image to be corrected
Difference, as shown in table 5,Wherein N is pixel sum, yi, xiRespectively refer to shadow
The brightness value of i-th of pixel of image after picture and correction, RMSE is smaller to show that image is closer, and calibration result is better.Correction of a final proof
Image can be as shown in Figure 4 with the comparison for correcting preceding image afterwards.
4 NDVI of table is classified the average spoke brightness of each wave band of image before and after radiation normalization
The RMSE of 5 radiation normalization result of table
Above-described embodiment shows by making full use of local additional space under different times, the same area, different sensors
The SPECTRAL DIVERSITY feature of atural object of the same race under distribution, the principle that the NDVI codomain range of every scape image is classified according to natural breakpoint into
Row classification, by calculate separately each rank spatial position Duplication determine mutual corresponding rank pair, and will be selected
Sample data of the overlapping region of appropriate level centering as the rank establishes equation of linear regression according to sample data, utilizes
The principle of least square solves regression coefficient, and then realizes image to be corrected and the relative radiometric normalization with reference to image, makes agriculture
The Seasonal dynamics change information of the vegetation such as crop is more accurately expressed.
In embodiments of the present invention, it mainly for image itself gray value, spectral signature, while having fully considered between image
Spatial distribution characteristic, when avoiding regular correction, type of ground objects divide, sample point choose etc. more manual intervention, have compared with
Strong objectivity, accuracy.
NDVI is classified the subdivision for facilitating vegetation type, growth period, reduces since Crop Group, vegetation coverage cause
Radiation difference, in addition, correction result to the collaborations such as the extraction, monitoring and analysis of crops in multidate image using provide
Helpful reference.
Based on the same technical idea, Fig. 5 also shows a kind of crops relative radiation provided in an embodiment of the present invention and returns
The structure that one makeup is set, the device can execute the process of crops relative radiometric normalization method.
As shown in figure 5, the apparatus may include:
Acquiring unit 501 refers to image and image to be corrected for obtaining；
Correction unit 502, for referring to image and the shadow to be corrected to described using pseudo- invariant features point methods
As carrying out preliminary corrections；
Stage unit 503, for according to natural breakpoint principle of classification respectively to described after preliminary corrections with reference to image and
The image to be corrected carries out NDVI classification；
Determination unit 504 is determined for described after being classified according to NDVI with reference to image and the image to be corrected each
The Duplication of rank spatial location, and according to the Duplication determine it is described with reference to image between the image to be corrected
The mapping relations of NDVI rank；
Normalization unit 505, for according to the principle of least square determine it is described with reference to image and the image to be corrected it
Between NDVI rank mapping relations in each rank pair equation of linear regression coefficient, and carry out the phase of the image to be corrected
To radiation normalization.
Optionally, the correction unit 502 is specifically used for:
It extracts with reference to the stable radiation point in the NDVI error image of image and image to be corrected as pseudo- invariant features point；
Preliminary relative radiometric normalization correction is carried out using pseudo- invariant features point.
Optionally, the stage unit 503 is specifically used for:
According to the regularity of distribution of spectral value in NDVI image, by the ginseng centered on natural turning point, characteristic point, knick point
It examines pixel in image and the image to be corrected and is divided into the similar group of property；
According to group's internal variance and minimum, group's group difference maximization principle, to described with reference to image and described to be corrected
The NDVI figure of image is gradually classified.
Optionally, the determination unit 504 is specifically used for:
Calculate the pixel be overlapped with each NDVI rank of image to be corrected with reference to NDVI rank each in image
Number；
The largest number of appropriate levels of pixel will be overlapped as the rank pair mapped one by one, determined described with reference to image and institute
State the mapping relations of the NDVI rank between image to be corrected.
Optionally, the normalization unit 505 is specifically used for:
According to each rank pair in the mapping relations of the NDVI rank with reference to image between the image to be corrected,
Calculate separately equation of linear regression；
The regression fit coefficient of the equation of linear regression is sought using the principle of least square；
According to the NDVI codomain range of each rank of image to be corrected, the fitting coefficient of corresponding level is substituted into, is carried out
The relative radiometric normalization of the image to be corrected.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (6)
1. a kind of crops relative radiometric normalization method characterized by comprising
It obtains and refers to image and image to be corrected；
Preliminary corrections are carried out to the reference image and the image to be corrected using pseudo- invariant features point methods；
Described after preliminary corrections is returned with reference to image and the image to be corrected respectively according to natural breakpoint principle of classification
One changes difference vegetation index NDVI classification；
Described after being classified according to NDVI is with reference to image and the image to be corrected determines the overlapping of each rank spatial location
Rate, and determine according to the Duplication mapping relations of the NDVI rank with reference to image between the image to be corrected；
It is described be classified according to NDVI after it is described with reference to image and the image to be corrected determines the overlapping of each rank spatial location
Rate, and determine according to the Duplication mapping relations of the NDVI rank with reference to image between the image to be corrected,
Include:
Calculate the pixel number be overlapped with each NDVI rank of image to be corrected with reference to NDVI rank each in image；
The largest number of appropriate levels of pixel will be overlapped as the rank pair mapped one by one, while obtaining the overlapping of corresponding level pair
Fitting sample of the region as the region determines the mapping of the NDVI rank with reference to image between the image to be corrected
Relationship；
The mapping relations of the NDVI rank with reference to image between the image to be corrected are determined according to the principle of least square
In each rank pair equation of linear regression coefficient, and carry out the relative radiometric normalization of the image to be corrected, comprising:
According to each rank pair in the mapping relations of the NDVI rank with reference to image between the image to be corrected, respectively
Calculate equation of linear regression；
The regression fit coefficient of the equation of linear regression is sought using the principle of least square；
According to the NDVI codomain range of each rank of image to be corrected, the fitting coefficient of corresponding level is substituted into, described in progress
The relative radiometric normalization of image to be corrected.
2. the method as described in claim 1, which is characterized in that described to refer to image to described using pseudo- invariant features point methods
Preliminary corrections are carried out with the image to be corrected, comprising:
It extracts with reference to the stable radiation point in the NDVI error image of image and image to be corrected as pseudo- invariant features point；
Preliminary relative radiometric normalization correction is carried out using pseudo- invariant features point.
3. the method as described in claim 1, which is characterized in that it is described according to natural breakpoint principle of classification respectively to preliminary corrections
The reference image and the image to be corrected afterwards carries out NDVI classification, comprising:
According to the regularity of distribution of spectral value in NDVI image, shadow is referred to by described centered on natural turning point, characteristic point, knick point
Pixel is divided into the similar group of property in picture and the image to be corrected；
According to group's internal variance and minimum, group's group difference maximization principle, image and the image to be corrected are referred to described
NDVI figure be gradually classified.
4. a kind of crops relative radiometric normalization device characterized by comprising
Acquiring unit refers to image and image to be corrected for obtaining；
Correction unit, for being carried out just with reference to image with the image to be corrected using pseudo- invariant features point methods to described
Step correction；
Stage unit, for according to natural breakpoint principle of classification respectively to described with reference to image and described to school after preliminary corrections
Difference vegetation index NDVI classification is normalized in positive image；
Determination unit determines that each rank is hollow with reference to image and the image to be corrected for described after being classified according to NDVI
Between position Duplication, and the NDVI rank with reference to image between the image to be corrected is determined according to the Duplication
Mapping relations；The determination unit is specifically used for: calculating described with reference to each NDVI rank in image and the image to be corrected
The pixel number being overlapped in each NDVI rank；The largest number of appropriate levels of pixel will be overlapped as the rank pair mapped one by one,
Fitting sample of the overlapping region as the region for obtaining corresponding level pair simultaneously determines described with reference to image and described to be corrected
The mapping relations of NDVI rank between image；
Normalization unit, for determining the NDVI with reference to image between the image to be corrected according to the principle of least square
The equation of linear regression coefficient of each rank pair in the mapping relations of rank, and the relative radiation for carrying out the image to be corrected is returned
One changes；The normalization unit is specifically used for:
According to each rank pair in the mapping relations of the NDVI rank with reference to image between the image to be corrected, respectively
Calculate equation of linear regression；The regression fit coefficient of the equation of linear regression is sought using the principle of least square；According to school
The NDVI codomain range of each rank of positive image, the fitting coefficient of corresponding level is substituted into, and carries out the image to be corrected
Relative radiometric normalization.
5. device as claimed in claim 4, which is characterized in that the correction unit is specifically used for:
It extracts with reference to the stable radiation point in the NDVI error image of image and image to be corrected as pseudo- invariant features point；
Preliminary relative radiometric normalization correction is carried out using pseudo- invariant features point.
6. device as claimed in claim 4, which is characterized in that the stage unit is specifically used for:
According to the regularity of distribution of spectral value in NDVI image, shadow is referred to by described centered on natural turning point, characteristic point, knick point
Pixel is divided into the similar group of property in picture and the image to be corrected；
According to group's internal variance and minimum, group's group difference maximization principle, image and the image to be corrected are referred to described
NDVI figure be gradually classified.
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