CN109031343A - A kind of SEVI regulatory factor algorithms of automatic optimization of window traversal - Google Patents
A kind of SEVI regulatory factor algorithms of automatic optimization of window traversal Download PDFInfo
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Abstract
The present invention relates to a kind of new SEVI regulatory factor algorithms of automatic optimization, comprising the following steps: window selection, vegetation index calculate, related coefficient calculates, single window optimization solution, window traversal, global (panorama) optimal solution.The present invention is without dem data auxiliary, classification of remote-sensing images and artificial specified calculating sample area, avoid the unstability in artificial selection sample area, the automatization level of SEVI calculating is improved, there is important scientific meaning and economic value in the accurate inverting of complicated landform mountain area vegetation information, the interference eliminated landform umbra and fall shadow to remote sensing.
Description
Technical field
The present invention relates to a kind of SEVI regulatory factor algorithms of automatic optimization of window traversal.
Background technique
Existing topographic shadowing, which eliminates vegetation index TAVI mesorelief regulatory factor f (△) optimization method, mainly 3 kinds: "
With optimizing method (national patent 200910111688X) ", " the method for optimizing extremums (national patent number 201010180895.3) " and
" correlation coefficient process (national patent number 2015108077580) ".
" matching optimizing " algorithm calculates step are as follows: and (1) image classification divides the Schattenseite and tailo of massif in remote sensing image,
And choose typical region；(2) target identification, by ground investigation data, on-the-spot investigation data, data of taking photo by plane or
High resolution image data of GoogleEarth etc. verify Schattenseite and tailo vegetation homogenieity, identify typical region Schattenseite and
The consistent or close part of tailo vegetation；(3) Optimized Matching enables f (△) since 0, incremented by successively, investigates TAVI in typical case
The vegetation index value of sample area Schattenseite and tailo vegetation uniform portion changes, and when the two is equal, that is, can determine the optimal knot of f (△)
Fruit.
" extremal optimization " algorithm calculates step are as follows: (1) image classification divides the Schattenseite and tailo of massif in remote sensing image；
(2) extreme value is calculated, the maximum value M of Schattenseite part TAVI is calculatedTAVI yinWith the maximum value M of tailo part TAVITAVI sun；(3) iteration is sought
It is excellent, enable f (△) since 0, it is incremented by successively, when meeting following formula condition, obtain f (△) optimal value.
|MTAVI yin-MTAVI sun|≤ε, ε → 0, f (△)=0~∞
" related coefficient " algorithm calculates step are as follows: (1) select sample area, in complicated landform mountain area the selection influence of topography it is obvious,
Massif tailo and the symmetrical typical region of Schattenseite；(2) sample area vegetation index TAVI, RVI and SVI are calculated；(3) phase is calculated
Relationship number, the coefficient R including TAVI and CVI1, the coefficient R of TAVI and SVI2；(4) optimization enables f (△) from 0
Start, it is incremented by successively, work as R1With R2When meeting following formula condition, determine that f (△) optimizes calculated result.
R1-R2≤ ε, ε → 0, f (△)=0~∞
Above-mentioned 3 kinds of optimization algorithms, support of the TAVI without data such as DEM can effectively cut down topographic shadowing and plant to mountain area
By the influence of information.But preceding 2 kinds of optimization algorithms are required to classification of remote-sensing images, wherein " searching of optimal matching " algorithm also needs ground
The support of data etc.；3rd kind of method needs artificial selection sample area, there are biggish unstable although being not necessarily to image classification
Property；In addition, these three methods are all easily trapped into locally optimal solution rather than globally optimal solution, this, which all limits topographic shadowing and eliminates, plants
The automatization level applied by index, is unfavorable for promoting and applying.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of SEVI regulatory factor algorithms of automatic optimization of window traversal.
The algorithm is not necessarily to dem data and classification of remote-sensing images, while independent of ground investigation data without selection sample area, to panorama
Image, which calculates SEVI and its application, has important scientific meaning and economic value.
To achieve the above object, the present invention adopts the following technical scheme: a kind of SEVI regulatory factor of window traversal is automatic
Optimization algorithm, it is characterised in that the following steps are included:
Step S1: the massif distribution on one scape remote sensing image of observation judges massif length of grade by Schattenseite, tailo, and selection is most
Big length of grade determines calculation window parameter K；Assuming that the size of whole scape remote sensing image is M row N column；
Step S2: vegetation index SEVI is eliminated with the apparent reflectance data computational shadowgraph of whole scape remote sensing image, ratio is planted
By index RVI and shade vegetation index SVI；
Step S3: calculating related coefficient, specific as follows:
Wherein: R1For the related coefficient of SEVI and RVI, R2For the related coefficient of SEVI and SVI, x, y1、y2Respectively remote sensing
The pixel number of the image data of image SEVI, RVI and SVI calculated result, n SEVI, RVI and SVI；
Step S4: enabling f (△) since 0, is spaced a, calculating SEVI incremented by successively, while it is related to RVI's to investigate SEVI
Coefficients R1And the coefficient R of SEVI and SVI2, work as R1With R2When meeting the following conditions, interior circulation is exited, obtains single window optimization
Solve FL:
R1-R2≤ ε, ε → 0, f (△)=0~∞；
Step S5: from first, the remote sensing image upper left corner, pixel (1,1) starts to calculate the f of first window (1:K, 1:K)
(△) optimal value；Then Row Column f (△) optimal value incremented by successively for calculating other windows；Finally obtaining a ranks is
The f (△) of (M-K, N-K) optimizes value matrix；
Step S6: the cut off value F of m% quantity before calculating f (△) optimal value from high to lower, to obtain the f of full-view image
(△) globally optimal solution FG。
In an embodiment of the present invention, window parameter K is 50,100,150 or 200.
In an embodiment of the present invention, 0.001 a.
In an embodiment of the present invention, 3 m.
In an embodiment of the present invention, shadow removing vegetation index SEVI, ratio vegetation index RVI and shade vegetation index
The calculation formula of SVI are as follows:
Wherein: f (△) is regulatory factor；BrFor remote sensing image red spectral band data, BnirFor remote sensing image near infrared band
Data.
Compared with the prior art, the invention has the following beneficial effects:
1, can be used for whole scape image to calculate: the f (△) that algorithms of automatic optimization of the invention can calculate whole scape image is optimal
Solution, rather than the local optimum solution in sample area, practical application and engineering for SEVI, which are promoted, has important scientific value and warp
Ji benefit.
2, topographical correction effect is obvious: present invention determine that global (panorama) optimal solution of f (△), guarantee that SEVI can be effective
It eliminates landform umbra and falls interference of the shadow to mountain area vegetation information.
3, data requirements is few, at low cost: the wave band data that the present invention only needs remote sensing image self-contained, is not necessarily to ground tune
The support of data or on-the-spot investigation data etc. is looked into, data cost and time cost, which are realized, to be minimized.
4, process is simple, strong operability: the present invention is mainly by " window selection, vegetation index calculating, related coefficient meter
Calculation, approximation computation, window traversal, global optimization value determine " and etc. just can determine global (panorama) optimal solution of f (△), process
Simply, operation is easy, and without links such as classification of remote-sensing images, the selections of sample area, greatly improves SEVI application automatization level.
Detailed description of the invention
Fig. 1 is the technology of the present invention flow diagram.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
Fig. 1 is please referred to, the present invention provides a kind of SEVI regulatory factor algorithms of automatic optimization of window traversal comprising following
Step:
Step S1: window selection: the massif distribution on one scape remote sensing image of observation judges massif slope by Schattenseite, tailo
It is long, select Maximal slope length to determine calculation window parameter K.In conjunction with practicability and computational efficiency, referring to 30 meters of skies of Landsat image
Between resolution ratio, window parameter K can choose 50,100,150,200 etc. to characterize window size on image.
Step S2: vegetation index calculates: with the elimination of whole scape remote sensing image (M row N column) apparent reflectance data computational shadowgraph
Vegetation index, ratio vegetation index and shade vegetation index；
Wherein: SEVI is shadow removing vegetation index；RVI is ratio vegetation index；SVI is shade vegetation index；f(△)
For regulatory factor；BrFor remote sensing image red spectral band data, BnirFor remote sensing image near-infrared data.
Step S3: related coefficient calculates, specific as follows:
Wherein: R1For the related coefficient of SEVI and CVI, R2For the related coefficient of SEVI and SVI, x, y1, y2 it is respectively distant
The pixel number of the image data of sense image SEVI, CVI and SVI calculated result, n SEVI, CVI and SVI.
Step S4: optimization enables f (△) since 0, is interval with a (a can be for 0.001), incremented by successively to be planted
By the calculating of index, while investigating the coefficient R of SEVI and CVI1And the coefficient R of SEVI and SVI2, work as R1With R2Meet
When the following conditions, interior circulation is exited, obtains single window optimization solution FL:
R1-R2≤ ε, ε → 0, f (△)=0~∞.
Step S5: window traversal, from first, the remote sensing image upper left corner pixel (1,1) start to calculate first window (1:
K, 1:K) f (△) optimal value；Then Row Column f (△) optimal value incremented by successively for calculating other windows.Finally obtain one
A ranks are that the f (△) of (M-K, N-K) optimizes value matrix.
Step S6: global optimum determines that m% (m can take 3) quantity divides before calculating f (△) optimal value from high to lower
Dividing value F, to obtain f (△) the global optimum F of full-view imageG。
Main flow schematic diagram is referring to Fig. 1.
Further, the vegetation index is shadow removing vegetation index SEVI, ratio vegetation index RVI and shade vegetation
Index SVI, corresponding calculation formula are as follows:
Wherein: BrFor remote sensing image red spectral band data, BnirFor remote sensing image near-infrared data.
By in the verifying of Landsat8 OLI full-view image, showing the SEVI of the invention calculated in landform sheet this method
Shadow and the error for falling shadow are below 5%, hence it is evident that better than the effect of C topographical correction and 6S+C atmosphere and landform complex correction such as table 1
It is shown.
Table 1
Note: TOA is apparent reflectance data；C is C terrain correction data；6S+C is 6S atmospheric correction and C topographical correction number
According to.The foregoing is merely presently preferred embodiments of the present invention, all according to equivalent changes and modifications within the scope of the patent application of the present invention,
It is all covered by the present invention.
Claims (5)
1. a kind of SEVI regulatory factor algorithms of automatic optimization of window traversal, which comprises the following steps:
Step S1: the massif distribution on one scape remote sensing image of observation judges massif length of grade by Schattenseite, tailo, selects maximum slope
It is long to determine calculation window parameter K；
Step S2: vegetation index SEVI is eliminated with the apparent reflectance data computational shadowgraph of whole scape remote sensing image, ratio vegetation refers to
Number RVI and shade vegetation index SVI；Assuming that the size of whole scape remote sensing image is M row N column；
Step S3: calculating related coefficient, specific as follows:
Wherein: R1For the related coefficient of SEVI and RVI, R2For the related coefficient of SEVI and SVI, x, y1、y2Respectively remote sensing image
The pixel number of the image data of SEVI, RVI and SVI calculated result, n SEVI, RVI and SVI；
Step S4: enabling f (△) since 0, is spaced a, calculating SEVI incremented by successively, while investigating the coefficient R of SEVI and RVI1
And the coefficient R of SEVI and SVI2, work as R1With R2When meeting the following conditions, interior circulation is exited, obtains single window optimization solution FL:
R1-R2≤ ε, ε → 0, f (△)=0~∞；
Step S5: from first, the remote sensing image upper left corner, pixel (1,1) starts to calculate the f (△) of first window (1:K, 1:K)
Optimal value；Then Row Column f (△) optimal value incremented by successively for calculating other windows；Finally obtain a ranks be (M-K,
N-K f (△)) optimizes value matrix；
Step S6: the cut off value F of m% quantity before calculating f (△) optimal value from high to lower, to obtain the f (△) of full-view image
Globally optimal solution FG。
2. the SEVI regulatory factor algorithms of automatic optimization of window traversal according to claim 1, it is characterised in that: window ginseng
Number K is 50,100,150 or 200.
3. the SEVI regulatory factor algorithms of automatic optimization of window according to claim 1 traversal, it is characterised in that: a is
0.001。
4. the SEVI regulatory factor algorithms of automatic optimization of window traversal according to claim 1, it is characterised in that: m 3.
5. the SEVI regulatory factor algorithms of automatic optimization of window traversal according to claim 1, it is characterised in that: shade disappears
Except the calculation formula of vegetation index SEVI, ratio vegetation index RVI and shade vegetation index SVI are as follows:
Wherein: f (△) is regulatory factor；BrFor remote sensing image red spectral band data, BnirFor remote sensing image near-infrared data.
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| CN111753792A (en) * | 2020-06-30 | 2020-10-09 | 福州大学 | Calculated efficient SEVI (sequence independent variable) adjustment factor optimization method |
| CN112964643A (en) * | 2021-02-03 | 2021-06-15 | 福州大学 | Method for correcting landform falling shadow of visible light wave band of remote sensing image |
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| CN111753792B (en) * | 2020-06-30 | 2022-05-13 | 福州大学 | Calculated efficient SEVI (sequence independent variable) adjustment factor optimization method |
| CN112964643A (en) * | 2021-02-03 | 2021-06-15 | 福州大学 | Method for correcting landform falling shadow of visible light wave band of remote sensing image |
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