CN107301660A - The polar plot processing method and system studied and judged for the condition of a disaster - Google Patents
The polar plot processing method and system studied and judged for the condition of a disaster Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
- G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention provides a kind of polar plot processing method and system studied and judged for the condition of a disaster, method is：Obtain national precipitation result map and reference map；Size to national precipitation result map is standardized, and obtains pending figure；According to pending figure and reference map, characteristic point is extracted, multipair characteristic point is obtained；According to multipair characteristic point, the transformation relation between pending figure and reference map is calculated, transformation matrix is obtained；According to transformation matrix, pending figure and reference map are subjected to registration, registering image is obtained.The present invention accurately shows national precipitation performance data in CGCS2000 through feature point extraction, image registration to national precipitation achievement picture picture corresponding with the CGCS2000 coordinate systems that national administrative area is divided, and contributes to studying and judging for the condition of a disaster.
Description
Technical field
The present invention relates to computer information technology field, polar plot processing method and be that more particularly, to the condition of a disaster is studied and judged
System.
Background technology
The national precipitation performance data of Chinese weather net issue is the picture of jpg forms, and national administrative area therein is divided
There is larger skew in the national administrative division under CGCS2000 (2000 national earth coordinates), if being shown under CGCS2000 complete
State's precipitation performance data, can cause that national precipitation achievement shows is inaccurate, and influence the condition of a disaster is studied and judged.
The content of the invention
For above-mentioned technical problem, the present invention provides a kind of polar plot processing method and system studied and judged for the condition of a disaster, right
National precipitation achievement picture picture corresponding with the CGCS2000 coordinate systems that national administrative area is divided is matched somebody with somebody through feature point extraction, image
Brigadier whole nation precipitation performance data is accurately shown in CGCS2000, contributes to studying and judging for the condition of a disaster.
In order to solve the above technical problems, the technical scheme that the present invention is provided is：
In a first aspect, the present invention provides a kind of polar plot processing method studied and judged for the condition of a disaster, including：
Step S1, obtains national precipitation result map and reference map；
Step S2, the size to the national precipitation result map is standardized, and obtains pending figure；
Step S3, according to the pending figure and the reference map, extracts characteristic point, obtains multipair characteristic point；
Step S4, according to the multipair characteristic point, calculates the transformation relation between the pending figure and the reference map,
Obtain transformation matrix；
Step S5, according to the transformation matrix, carries out registration by the pending figure and the reference map, obtains registering figure
Picture.
The polar plot processing method studied and judged provided by the present invention for the condition of a disaster, its technical scheme is：Obtain national precipitation into
Fruit figure and reference map；Size to the national precipitation result map is standardized, and obtains pending figure；Treated according to described
Processing figure and the reference map, extract characteristic point, obtain multipair characteristic point；According to the multipair characteristic point, wait to locate described in calculating
Transformation relation between reason figure and the reference map, obtains transformation matrix；According to the transformation matrix, by the pending figure and
The reference map carries out registration, obtains registering image.
The polar plot processing method studied and judged provided by the present invention for the condition of a disaster, it is administrative to national precipitation achievement picture and the whole nation
The corresponding picture of CGCS2000 coordinate systems of Division exists national precipitation performance data through feature point extraction, image registration
CGCS2000 is accurately shown, contributes to studying and judging for the condition of a disaster.
Further, in the step S3, characteristic point is extracted by Harris algorithms：
The directional derivative of the pending figure and the reference map is calculated, first direction inverse and second party guide is obtained
Number；
According to the pending figure and the pixel of the reference map, autocorrelation matrix is calculated；
According to the autocorrelation matrix, the pixel of the pending figure and the reference map is judged, angle is obtained
Point pixel, is used as the characteristic point of extraction.
Further, the step S4, be specially：
4 pairs of characteristic points are chosen in the multipair characteristic point；
4 pairs of characteristic points are inputted into projective transformation model, calculating obtains projective transformation matrix, is used as transformation matrix.
Further, after the step S4, in addition to processing is fitted to the transformation matrix, obtains optimal mapping
Matrix：
According to the transformation matrices, to all characteristic points in the multipair characteristic point to carrying out Feature Points Matching to error
Calculating, obtain multiple error amounts；
The multiple error amount is compared with predetermined threshold value respectively, the error amount that will be greater than the predetermined threshold value is carried out
Iteration, recalculates and obtains new error amount；
According to the new error amount, calculating obtains optimal mapping matrix.
Further, after the step S1, in addition to color space is carried out to the pending figure and the reference map
Conversion：
R, G, the B component of the pending figure and the reference map are carried respectively；
According to described R, G, B component, YCbCr color space transformation models are inputted, the R, G, B component are turned in correspondence
Change the component in YCbCr color spaces into.
Second aspect, the present invention provides a kind of polar plot processing system studied and judged for the condition of a disaster, including：
Image collection module, for obtaining national precipitation result map and reference map；
Standardized module, is standardized for the size to the national precipitation result map, obtains pending figure；
Feature point extraction module, for according to the pending figure and the reference map, extracting characteristic point, obtaining multipair spy
Levy a little；
Transformation matrix generation module, for according to the multipair characteristic point, calculating the pending figure and the reference map
Between transformation relation, obtain transformation matrix；
Image registration module, for according to the transformation matrix, the pending figure and the reference map to be carried out into registration,
Obtain registering image.
The polar plot processing system studied and judged provided by the present invention for the condition of a disaster, its technical scheme is：Mould is obtained by image
Block, obtains national precipitation result map and reference map；By standardized module, enter for the size to the national precipitation result map
Row standardization, obtains pending figure；By feature point extraction module, according to the pending figure and the reference map, carry
Characteristic point is taken, multipair characteristic point is obtained；By transformation matrix generation module, according to the multipair characteristic point, wait to locate described in calculating
Transformation relation between reason figure and the reference map, obtains transformation matrix；By image registration module, according to the conversion square
Battle array, carries out registration by the pending figure and the reference map, obtains registering image.
Further, the feature point extraction module, specifically for extracting characteristic point by Harris algorithms：
The directional derivative of the pending figure and the reference map is calculated, first direction inverse and second party guide is obtained
Number；
According to the pending figure and the pixel of the reference map, autocorrelation matrix is calculated；
According to the autocorrelation matrix, the pixel of the pending figure and the reference map is judged, angle is obtained
Point pixel, is used as the characteristic point of extraction.
Further, the transformation matrix generation module, specifically for：
4 pairs of characteristic points are chosen in the multipair characteristic point；
4 pairs of characteristic points are inputted into projective transformation model, calculating obtains projective transformation matrix, is used as transformation matrix.
Further, after the transformation matrix generation module, in addition to transformation matrix optimization module, specifically for institute
State transformation matrix and be fitted processing, obtain optimal mapping matrix：
According to the transformation matrices, to all characteristic points in the multipair characteristic point to carrying out Feature Points Matching to error
Calculating, obtain multiple error amounts；
The multiple error amount is compared with predetermined threshold value respectively, the error amount that will be greater than the predetermined threshold value is carried out
Iteration, recalculates and obtains new error amount；
According to the new error amount, calculating obtains optimal mapping matrix.
Further, after described image acquisition module, in addition to color-space conversion module, specifically for being treated to described
Processing figure and the reference map carry out color space conversion：
R, G, the B component of the pending figure and the reference map are carried respectively；
According to described R, G, B component, YCbCr color space transformation models are inputted, the R, G, B component are turned in correspondence
Change the component in YCbCr color spaces into.
Brief description of the drawings
, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical scheme of the prior art
The accompanying drawing used required in embodiment or description of the prior art is briefly described.
Fig. 1 shows a kind of flow for polar plot processing method studied and judged for the condition of a disaster that the embodiment of the present invention is provided
Figure；
Fig. 2 shows pending in a kind of polar plot processing method studied and judged for the condition of a disaster that the embodiment of the present invention is provided
The characteristic point distribution map of figure；
Fig. 3 shows reference map in a kind of polar plot processing method studied and judged for the condition of a disaster that the embodiment of the present invention is provided
Characteristic point distribution map；
Fig. 4 is shown at a kind of matching for polar plot processing method studied and judged for the condition of a disaster that the embodiment of the present invention is provided
Manage design sketch；
Fig. 5 shows a kind of signal for polar plot processing system studied and judged for the condition of a disaster that the embodiment of the present invention is provided
Figure.
Embodiment
The embodiment of technical solution of the present invention is described in detail below in conjunction with accompanying drawing.Following examples are only used for
Clearly illustrate technical scheme, therefore be intended only as example, and the protection of the present invention can not be limited with this
Scope.
Embodiment one
Fig. 1 shows a kind of flow for polar plot processing method studied and judged for the condition of a disaster that the embodiment of the present invention is provided
Figure；As shown in figure 1, a kind of polar plot processing method studied and judged for the condition of a disaster that embodiment one is provided, including：
Step S1, obtains national precipitation result map and reference map；
Step S2, the size to national precipitation result map is standardized, and obtains pending figure；
Step S3, according to pending figure and reference map, extracts characteristic point, obtains multipair characteristic point；
Step S4, according to multipair characteristic point, calculates the transformation relation between pending figure and reference map, obtains converting square
Battle array；
Step S5, according to transformation matrix, carries out registration by pending figure and reference map, obtains registering image.
The polar plot processing method studied and judged provided by the present invention for the condition of a disaster, its technical scheme is：Obtain national precipitation into
Fruit figure and reference map；Size to national precipitation result map is standardized, and obtains pending figure；According to pending figure and
Reference map, extracts characteristic point, obtains multipair characteristic point；According to multipair characteristic point, the change between pending figure and reference map is calculated
Relation is changed, transformation matrix is obtained；According to transformation matrix, pending figure and reference map are subjected to registration, registering image is obtained.
The polar plot processing method studied and judged provided by the present invention for the condition of a disaster, it is administrative to national precipitation achievement picture and the whole nation
The corresponding picture of CGCS2000 coordinate systems of Division exists national precipitation performance data through feature point extraction, image registration
CGCS2000 is accurately shown, contributes to studying and judging for the condition of a disaster.
Preferably, before feature point extraction, national precipitation achievement dimension of picture is standardized, Chinese weather
The national precipitation achievement dimension of picture of net issue has following three kinds：860px×697px、1043px×846px、2087px×
Pending image data is standardized as standard by 1693px, selection intermediate sizes 1043px × 846px as standard size
Size, specific C# codes are realized as follows：
Preferably, in step S3, characteristic point is extracted by Harris algorithms：
The directional derivative of pending figure and reference map is calculated, first direction inverse and second direction derivative is obtained；
According to pending figure and the pixel of reference map, autocorrelation matrix is calculated；
According to autocorrelation matrix, the pixel of pending figure and reference map is judged, corner pixels point is obtained, as
The characteristic point of extraction.
Wherein, Harris detects angle point by differentiating with autocorrelation matrix M, and differential operator can react pixel and exist
Grey scale change in any direction, angle is extracted as by the point that each pixel changes greatly.Specific algorithm is as follows：
1) directional derivative of image is calculated, I is designated asxAnd Iy.The local derviation of required image is generally calculated with Gaussian functions
Number.
2) local autocorrelation matrix M is calculated for each point, such as shown in (3.2)：
Wherein,For Gaussian filter, (u, v) is the flat of each pixel of image
Shifting amount.
3) λ tried to achieve1、λ2Value it is less than normal, pixel region is flat site；λ1、λ2Value vary, this
Pixel region is fringe region；If λ1、λ2It is all very big, this pixel is considered as to the angle point detected.
Harris provide one be used for differentiate the point whether be angle point formula：As shown by the following formula：
R (x, y)=det (M)-k (Trace (M))2
The determinant of wherein det (M) representing matrix；The mark of Trace (M) representing matrix；K is pre-set threshold
Value, general k=0.04~0.06.Therefore, R (x, y)=λ1λ2-k(λ1+λ2)2.For λ as more than1And λ2Judgement, to enter one
Step judges R (x, y) size, similarly, as R (x, y) ＜ 0, judges this point as detected angle point.
Harris algorithms are a kind of methods for doing the preferable speed of feature point detection effect at present, are had to illumination good
Robustness, effective angle point is extracted based on figure to be matched, characteristic point is used as.
In addition, can also in the picture need to meet invariant features by the artificial selected characteristic point of arcmap softwares, characteristic point,
Such as：Enclosed region, edge, profile, angle point etc..Feature Points Extraction needs to meet three conditions：
(a) conspicuousness, the feature extracted should be obvious, feature that is widely distributed, being easy to extraction；
(b) noise immunity, it is with stronger noise inhibiting ability and insensitive to the change of image-forming condition；
(c) uniformity, the common characteristic of two images can be detected exactly；
According to above principle, what is selected in the present embodiment is located at border, flex point, 5 pairs of characteristic points of point of interface, wherein waiting to match somebody with somebody
Each point coordinates is in quasi- image：
(747.941,-123.995)、(108.929,-448.864)、(902.079,-723.186)、(665.485,-
765.494), (756.042, -523.778), position is as shown in Figure 2；
Each point coordinates is in base vector figure (reference map)：
(851032.206,5451041.134)、(-2395936.274,3795372.296)
(1633824.001,2403974.219)、(433359.253,2187983.277)
(892502.539,3412941.740), position such as Fig. 3.
Extract after characteristic point, carry out the registration of two images, image registration is given two images, to wherein piece image
(image subject to registration) is converted so that the content of image after conversion and another image (benchmark image) is corresponding in topology
And geometrically align, i.e. the broad match of image.Implementation process i.e. according to a number of registration control points determine two width or
Coordinate pair between the pixel of multiple image answers relational implementation to match.It is abnormal according to the geometry between image subject to registration and reference picture
The geometric transformation model changed between the situation of change, selection energy best fit two images, global map model or local mapping
Model.Wherein, global map model carries out global parameter estimation using all control point information；Local mapping model utilizes image
Local feature carries out local parameter estimation respectively.
The relevant interface provided using arcgis development kit can quickly realize that combining characteristic matching, geometry becomes
Change, the registration process of image scaling, specific interface is：
(1)ESRI.ArcGIS.DataSourcesRaster.
IRasterGeometryProc Warp (IPointCollection sourceControlPoints,
IPointCollection-targetControlPoints,esriGeoTransTypeEnum-transform Type,
IRaster ipRaster) method, the sample point coordinate set in incoming picture subject to registration, and target point coordinate set,
Carry out registration.
Parameter declaration such as following table：
(2) Rectify of ESRI.ArcGIS.DataSourcesRaster.IrasterGeometryProc interfaces
(string saveas_name, string Format, IRaster ipRaster) method, set export path and
Form, exports as the image of " TIFF " type.
Interface parameters such as following table：
| Parameter | Type | Explanation |
| saveas_nam | string | Result data preserves title |
| Format | string | Result images type |
| ipRaster | IRaster | Input image |
C# code is realized as follows：
Handling obtained precipitation image by program can coincide with base vector figure, and effect such as Fig. 4 thus it is seen that achievement
The national contour line in the land of data and base vector shape matching.
Preferably, step S4, be specially：
4 pairs of characteristic points are chosen in multipair characteristic point；
4 pairs of characteristic points are inputted into projective transformation model, calculating obtains projective transformation matrix, is used as transformation matrix.
Wherein, projective transformation is based on affine transformation, and it is still straight line that can keep after the mapping of image cathetus, but can not guarantee straight
Line is still parallel.It is projective transformation (Projective Transform) by this transform definition.It has stronger adaptability.Its
Specific expression-form such as following nonsingular 3*3 matrixes, i.e., shown in equation below：
Then a pixel (x, y) in two-dimensional coordinate system is mapped to the pixel obtained in another coordinate system through above formula
(x',y').Shown in (x', the y') equation below solved：
Their transformation parameter mi(i=0,1 ..., 8) it is exactly that unknown parameter is obtained required by us.Projective transformation has 8 certainly
By variable.We can calculate the transfer square between two images according to obtained feature point set in the projective transformation model
Battle array.Projective transformation has stronger adaptability, is applicable to the description of relation between diverse image, and algorithm is easily understood.
Therefore, in order to obtain accurate transfer matrix stitching image, the present invention is used as the model for solving transformation matrix from this model.
Preferably, after step S4, in addition to processing is fitted to transformation matrix, obtains optimal mapping matrix：
According to transformation matrices, to all characteristic points in multipair characteristic point to carrying out calculating of the Feature Points Matching to error,
Obtain multiple error amounts；
Multiple error amounts are compared with predetermined threshold value respectively, the error amount that will be greater than predetermined threshold value is iterated, weight
New calculate obtains new error amount；
According to new error amount, calculating obtains optimal mapping matrix.
Specific algorithm flow is as follows：
1) characteristic point for extracting pending figure and reference map is as sample characteristics point set, and the quantity of characteristic point will be more than
Certain amount, the projective transformation model that (be greater than total characteristic points here 10%) is set up just is set up.
2) the transfer matrix initial value between image is determined first, i.e., the characteristic point in feature point set, which is calculated, obtains one
Transformation matrix；
3) Feature Points Matching is calculated to error d, and formula is as follows；
D=I'(x', y')-I (x, y)
Wherein, if (x, y) is a bit in image I, (x', y') is a bit matched in image I'.When d is less than
During defined threshold value, the point is judged as intra-office point, is otherwise point not in the know, is cast out.
4) iteration refining transformation matrix；(based on Levenberg-Marquardt non-linear minimisations iterative algorithm (referred to as
LM algorithms)) judge d size, if d reduces but is not less than threshold value yet, model is estimated again, new d is calculated, until d is small
When the threshold value parameter provided, stop iteration, obtain the transformation matrix H between final image, as optimal mapping matrix.
The core concept of above-mentioned algorithm be by iteration determine between all characteristic points pair apart from sum, andWherein select minimum value.When distance and E are less than defined threshold, stop
Iteration, obtains the transformation matrix between final image.
Preferably, after step S1, in addition to color space conversion is carried out to pending figure and reference map：
R, G, the B component of pending figure and reference map are carried respectively；
According to R, G, B component, input YCbCr color space transformation models, by R, G, B component corresponding conversion into
Component in YCbCr color spaces.
Wherein, YCbCr color spaces are the color spaces commonly used in image, and the color space is also by YUV color spaces
What conversion was obtained.In a model, Y is used to indicate that the brightness of image, and Cb and Cr represent colourity respectively.To picture in the present invention
Processing in YCbCr color spaces, image is transformed into Cb by RGB color space, and Cr color spaces are simple, quickly.
In a computer, obtain image to store in an rgb format, so need RGB component to change into YCbCr components,
Conversion formula is as follows：
YCbCr components are changed into RGB formula as follows：
RGB color component is expressed in YCbCr color spaces, is so easy to the calculating and preservation of color component,
Colouring information is intact, the color of pending figure is able to complete preservation through processing, data display effect is more preferable.
Second aspect, Fig. 2 shows a kind of polar plot processing system studied and judged for the condition of a disaster that the embodiment of the present invention is provided
The schematic diagram of system, referring to Fig. 2, the present invention provides a kind of polar plot processing system 10 studied and judged for the condition of a disaster, including：
Image collection module 101, for obtaining national precipitation result map and reference map；
Standardized module 102, is standardized for the size to national precipitation result map, obtains pending figure；
Feature point extraction module 103, for according to pending figure and reference map, extracting characteristic point, obtaining multipair characteristic point；
Transformation matrix generation module 104, for according to multipair characteristic point, calculating the conversion between pending figure and reference map
Relation, obtains transformation matrix；
Image registration module 105, for according to transformation matrix, pending figure and reference map being carried out into registration, registration is obtained
Image.
The polar plot processing system 10 studied and judged provided by the present invention for the condition of a disaster, its technical scheme is：Obtained by image
Module 101, obtains national precipitation result map and reference map；By standardized module 102, for the chi to national precipitation result map
It is very little to be standardized, obtain pending figure；By feature point extraction module 103, according to pending figure and reference map, extract
Characteristic point, obtains multipair characteristic point；By transformation matrix generation module 104, according to multipair characteristic point, pending figure and base are calculated
Transformation relation between quasi- figure, obtains transformation matrix；By image registration module 105, according to transformation matrix, by pending figure and
Reference map carries out registration, obtains registering image.
Preferably, feature point extraction module 103, specifically for extracting characteristic point by Harris algorithms：
The directional derivative of pending figure and reference map is calculated, first direction inverse and second direction derivative is obtained；
According to pending figure and the pixel of reference map, autocorrelation matrix is calculated；
According to autocorrelation matrix, the pixel of pending figure and reference map is judged, corner pixels point is obtained, as
The characteristic point of extraction.
Preferably, transformation matrix generation module 104, specifically for：
4 pairs of characteristic points are chosen in multipair characteristic point；
4 pairs of characteristic points are inputted into projective transformation model, calculating obtains projective transformation matrix, is used as transformation matrix.
Preferably, after transformation matrix generation module 104, in addition to transformation matrix optimization module, specifically for conversion
Matrix is fitted processing, obtains optimal mapping matrix：
According to transformation matrices, to all characteristic points in multipair characteristic point to carrying out calculating of the Feature Points Matching to error,
Obtain multiple error amounts；
Multiple error amounts are compared with predetermined threshold value respectively, the error amount that will be greater than predetermined threshold value is iterated, weight
New calculate obtains new error amount；
According to new error amount, calculating obtains optimal mapping matrix.
Preferably, after image collection module 101, in addition to color-space conversion module, specifically for pending figure
Color space conversion is carried out with reference map：
R, G, the B component of pending figure and reference map are carried respectively；
According to R, G, B component, input YCbCr color space transformation models, by R, G, B component corresponding conversion into
Component in YCbCr color spaces.
Finally it should be noted that：Various embodiments above is merely illustrative of the technical solution of the present invention, rather than its limitations；To the greatest extent
The present invention is described in detail with reference to foregoing embodiments for pipe, it will be understood by those within the art that：Its according to
The technical scheme described in foregoing embodiments can so be modified, or which part or all technical characteristic are entered
Row equivalent substitution；And these modifications or replacement, the essence of appropriate technical solution is departed from various embodiments of the present invention technology
The scope of scheme, it all should cover among the claim of the present invention and the scope of specification.
Claims (10)
1. a kind of polar plot processing method studied and judged for the condition of a disaster, it is characterised in that including：
Step S1, obtains national precipitation result map and reference map；
Step S2, the size to the national precipitation result map is standardized, and obtains pending figure；
Step S3, according to the pending figure and the reference map, extracts characteristic point, obtains multipair characteristic point；
Step S4, according to the multipair characteristic point, calculates the transformation relation between the pending figure and the reference map, obtains
Transformation matrix；
Step S5, according to the transformation matrix, carries out registration by the pending figure and the reference map, obtains registering image.
2. the polar plot processing method according to claim 1 studied and judged for the condition of a disaster, it is characterised in that
In the step S3, characteristic point is extracted by Harris algorithms：
The directional derivative of the pending figure and the reference map is calculated, first direction inverse and second direction derivative is obtained；
According to the pending figure and the pixel of the reference map, autocorrelation matrix is calculated；
According to the autocorrelation matrix, the pixel of the pending figure and the reference map is judged, angle point picture is obtained
Vegetarian refreshments, is used as the characteristic point of extraction.
3. the polar plot processing method according to claim 1 studied and judged for the condition of a disaster, it is characterised in that
The step S4, be specially：
4 pairs of characteristic points are chosen in the multipair characteristic point；
4 pairs of characteristic points are inputted into projective transformation model, calculating obtains projective transformation matrix, is used as transformation matrix.
4. the polar plot processing method according to claim 1 studied and judged for the condition of a disaster, it is characterised in that
After the step S4, in addition to processing is fitted to the transformation matrix, obtains optimal mapping matrix：
According to the transformation matrices, to all characteristic points in the multipair characteristic point to carrying out meter of the Feature Points Matching to error
Calculate, obtain multiple error amounts；
The multiple error amount is compared with predetermined threshold value respectively, the error amount that will be greater than the predetermined threshold value is changed
In generation, recalculate and obtain new error amount；
According to the new error amount, calculating obtains optimal mapping matrix.
5. the polar plot processing method according to claim 1 studied and judged for the condition of a disaster, it is characterised in that
After the step S1, in addition to color space conversion is carried out to the pending figure and the reference map：
R, G, the B component of the pending figure and the reference map are carried respectively；
According to described R, G, B component, input YCbCr color space transformation models, by the R, G, B component corresponding conversion into
Component in YCbCr color spaces.
6. a kind of polar plot processing system studied and judged for the condition of a disaster, it is characterised in that including：
Image collection module, for obtaining national precipitation result map and reference map；
Standardized module, is standardized for the size to the national precipitation result map, obtains pending figure；
Feature point extraction module, for according to the pending figure and the reference map, extracting characteristic point, obtaining multipair feature
Point；
Transformation matrix generation module, for according to the multipair characteristic point, calculating between the pending figure and the reference map
Transformation relation, obtain transformation matrix；
Image registration module, for according to the transformation matrix, the pending figure and the reference map being carried out into registration, obtained
Registering image.
7. the polar plot processing system according to claim 6 studied and judged for the condition of a disaster, it is characterised in that
The feature point extraction module, specifically for extracting characteristic point by Harris algorithms：
The directional derivative of the pending figure and the reference map is calculated, first direction inverse and second direction derivative is obtained；
According to the pending figure and the pixel of the reference map, autocorrelation matrix is calculated；
According to the autocorrelation matrix, the pixel of the pending figure and the reference map is judged, angle point picture is obtained
Vegetarian refreshments, is used as the characteristic point of extraction.
8. the polar plot processing system according to claim 6 studied and judged for the condition of a disaster, it is characterised in that
The transformation matrix generation module, specifically for：
4 pairs of characteristic points are chosen in the multipair characteristic point；
4 pairs of characteristic points are inputted into projective transformation model, calculating obtains projective transformation matrix, is used as transformation matrix.
9. the polar plot processing system according to claim 6 studied and judged for the condition of a disaster, it is characterised in that
After the transformation matrix generation module, in addition to transformation matrix optimization module, specifically for entering to the transformation matrix
Row process of fitting treatment, obtains optimal mapping matrix：
According to the transformation matrices, to all characteristic points in the multipair characteristic point to carrying out meter of the Feature Points Matching to error
Calculate, obtain multiple error amounts；
The multiple error amount is compared with predetermined threshold value respectively, the error amount that will be greater than the predetermined threshold value is changed
In generation, recalculate and obtain new error amount；
According to the new error amount, calculating obtains optimal mapping matrix.
10. the polar plot processing system according to claim 6 studied and judged for the condition of a disaster, it is characterised in that
After described image acquisition module, in addition to color-space conversion module, specifically for the pending figure and described
Reference map carries out color space conversion：
R, G, the B component of the pending figure and the reference map are carried respectively；
According to described R, G, B component, input YCbCr color space transformation models, by the R, G, B component corresponding conversion into
Component in YCbCr color spaces.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710400434.4A CN107301660A (en) | 2017-05-31 | 2017-05-31 | The polar plot processing method and system studied and judged for the condition of a disaster |
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