CN109471125A - A kind of SEVI regulatory factor method of global optimization - Google Patents
A kind of SEVI regulatory factor method of global optimization Download PDFInfo
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- CN109471125A CN109471125A CN201811205541.2A CN201811205541A CN109471125A CN 109471125 A CN109471125 A CN 109471125A CN 201811205541 A CN201811205541 A CN 201811205541A CN 109471125 A CN109471125 A CN 109471125A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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Abstract
The present invention relates to a kind of SEVI regulatory factor methods of global optimization, comprising the following steps: step S1: according to the selected research area of goal in research, obtaining corresponding remote sensing image；Step S2: research area's shadow removing vegetation index: step S3 is calculated using apparent reflectance data: calculating coefficient of variation CV；Step S4: carrying out loop iteration calculating, enablesIt is interval with 0.001 since 0, calculating SEVI and coefficient of variation CV incremented by successively, and compare CV size；Step S5: globally optimal solution is obtained when CV value minimum according to the calculated result of step S4.The present invention is not necessarily to dem data and classification of remote-sensing images, while independent of ground investigation data without selection sample area, and can calculate the globally optimal solution in entire research area.
Description
Technical field
The present invention relates to a kind of SEVI regulatory factor methods of global optimization.
Background technique
Existing topographic shadowing eliminates regulatory factor f (Δ) optimization in vegetation index SEVI and its early period achievement TAVI, TCVI
Method mainly has 3 kinds: " matching optimizing method (national patent 200910111688X) ", " the method for optimizing extremums (national patent number
" and " correlation coefficient process (national patent number 2015108077580) " 201010180895.3).
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, being not necessarily to DEM number the purpose of the present invention is to provide a kind of SEVI regulatory factor method of global optimization
According to and classification of remote-sensing images, while independent of ground investigation data without selection sample area.
To achieve the above object, the present invention adopts the following technical scheme:
A kind of method of the SEVI regulatory factor of global optimization, comprising the following steps:
Step S1: according to the selected research area of goal in research, corresponding remote sensing image is obtained；
Step S2: research area's shadow removing vegetation index is calculated using apparent reflectance data:
Step S3: coefficient of variation CV is calculated；
Step S4: carrying out loop iteration calculating, enable f (Δ) since 0, be interval with 0.001, calculating SEVI incremented by successively
With coefficient of variation CV, and compare CV size；
Step S5: globally optimal solution is obtained when CV value minimum according to the calculated result of step S4
CV '=min (CV), f (Δ) ∈ (0.000,1.000).
Further, the step S2 specifically:
Wherein: SEVI is shadow removing vegetation index, and f (Δ) is regulatory factor, BrIt is apparent for remote sensing image red spectral band
Reflectivity data, BnirFor remote sensing image near infrared band apparent reflectance data；
Further, the coefficient of variation CV calculation formula are as follows:
Wherein: M is SEVI average value, and S is SEVI standard variance, and CV is the SEVI coefficient of variation, and N is research area's pixel number,
XiTo study area SEVI pixel value.
Compared with the prior art, the invention has the following beneficial effects:
1, the present invention can calculate the globally optimal solution in entire research area；
2, present invention determine that globally optimal solution, guarantee SEVI can effectively eliminate all kinds of topographic shadowings to mountain area vegetation information
Interference；
3, the wave band data that the present invention only needs remote sensing image self-contained is not necessarily to ground investigation data or on-the-spot investigation number
According to equal support, data cost and time cost are realized and are minimized；
4, the method for the present invention process is simple, strong operability:
Detailed description of the invention
Fig. 1 is flow chart of the present invention；
Fig. 2 is f in one embodiment of the invention (Δ) optimization calculated curve and global optimum；
Fig. 3 is one embodiment of the invention mesorelief shadow correction figure.
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 method of the SEVI regulatory factor of global optimization, comprising the following steps:
Step S1: research area prepares, and selects Chinese Wuyishan Nature Reserve to study area, downloads Landsat data, obtain
Take corresponding remote sensing image；
Step S2: research area's shadow removing vegetation index is calculated using apparent reflectance data:
Wherein: SEVI is shadow removing vegetation index, and f (Δ) is regulatory factor, BrFor remote sensing image red spectral band data,
BnirFor remote sensing image near-infrared data；
Step S3: coefficient of variation CV is calculated；
Wherein: M is SEVI average value, and S is SEVI standard variance, and CV is the SEVI coefficient of variation, and N is research area's pixel number,
XiTo study area SEVI pixel value.
Step S4: carrying out loop iteration calculating, enable f (Δ) since 0, be interval with 0.001, calculating SEVI incremented by successively
With coefficient of variation CV, and compare CV size；
Step S5: according to the calculated result of step S4, when CV value minimum, globally optimal solution (Fig. 2, table 1) is obtained
CV '=min (CV), f (Δ) ∈ (0.000,1.000).
SEVI calculated result as shown in figure 3, by this method in Chinese Wuyishan Nature Reserve 2001 and 2016
The application and verifying of year two phase Landsat images, table 1 show SEVI that the present invention calculates landform umbra and fall shadow error it is exhausted
To value respectively lower than 2.89% and 4.25%, as known from Table 2 with the decision system of solar incident angle cosine value (cosi) correlation analysis
Number both less than 0.065, the coefficient of variation achieves good topographic shadowing calibration result less than 4.70%.
Table 1 verifies sample average, relative error analysis
Wherein, MselfFor umbra sample mean, McastTo fall shadow sample mean, MsunnyFor non-shadow sample mean
Value, EselfFor umbra error, EcastTo fall shadow error.
Table 2cosi-SEVI correlation analysis and the SEVI coefficient of variation are analyzed
Wherein, r2, k, d be cosi-SEVI Linear correlative analysis the coefficient of determination, regression straight line slope, intercept；Mean,
Standard deviation, CV are all sample means, standard variance and the coefficient of variation.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, is all covered by the present invention.
Claims (3)
1. a kind of method of the SEVI regulatory factor of global optimization, which comprises the following steps:
Step S1: according to the selected research area of goal in research, corresponding remote sensing image is obtained；
Step S2: research area's shadow removing vegetation index is calculated using apparent reflectance data:
Step S3: coefficient of variation CV is calculated；
Step S4: carrying out loop iteration calculating, enable f (Δ) since 0, be interval with 0.001, calculating SEVI incremented by successively and change
Different coefficient CV, and compare CV size；
Step S5: globally optimal solution is obtained when CV value minimum according to the calculated result of step S4
CV '=min (CV), f (Δ) ∈ (0.000,1.000).
2. a kind of SEVI regulatory factor method of global optimization according to claim 1, it is characterised in that: the step S2
Specifically:
Wherein: SEVI is shadow removing vegetation index, and f (Δ) is regulatory factor, BrFor remote sensing image red spectral band apparent reflectance
Data, BnirFor remote sensing image near infrared band apparent reflectance data.
3. a kind of SEVI regulatory factor method of global optimization according to claim 1, it is characterised in that: the variation lines
Number CV calculation formula are as follows:
Wherein: M is SEVI average value, and S is SEVI standard variance, and CV is the SEVI coefficient of variation, and N is research area's pixel number, XiTo grind
Study carefully area's SEVI pixel value.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111105402A (en) * | 2019-12-24 | 2020-05-05 | 福州大学 | SEVI (sequence independent variable) adjustment factor optimization method based on information entropy |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106324614A (en) * | 2016-08-10 | 2017-01-11 | 福州大学 | New TAVI combination algorithm |
| CN106600586A (en) * | 2016-12-09 | 2017-04-26 | 福州大学 | TAVI regulatory factor algorithm based on solar altitude |
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2018
- 2018-10-17 CN CN201811205541.2A patent/CN109471125B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106324614A (en) * | 2016-08-10 | 2017-01-11 | 福州大学 | New TAVI combination algorithm |
| CN106600586A (en) * | 2016-12-09 | 2017-04-26 | 福州大学 | TAVI regulatory factor algorithm based on solar altitude |
Non-Patent Citations (3)
| Title |
|---|
| JIN J ET AL.: ""Recent NDVI-based variation in growth of boreal intact forest landscapes and its correlation with climatic variables"", 《SUSTAINABILITY》 * |
| 江洪 等: ""地形调节植被指数构建及在植被覆盖度遥感监测中的应用"", 《福州大学学报（自然科学版）》 * |
| 江洪 等: ""基于波段比模型的地形调节植被指数组合算法构建与验证"", 《农业工程学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111105402A (en) * | 2019-12-24 | 2020-05-05 | 福州大学 | SEVI (sequence independent variable) adjustment factor optimization method based on information entropy |
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