CN103198447B - A kind of wind arrow field real-time metrics method based on satellite cloud picture - Google Patents
A kind of wind arrow field real-time metrics method based on satellite cloud picture Download PDFInfo
- Publication number
- CN103198447B CN103198447B CN201310120689.7A CN201310120689A CN103198447B CN 103198447 B CN103198447 B CN 103198447B CN 201310120689 A CN201310120689 A CN 201310120689A CN 103198447 B CN103198447 B CN 103198447B
- Authority
- CN
- China
- Prior art keywords
- search
- sad
- data
- block
- satellite
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000007774 longterm Effects 0.000 claims abstract description 6
- 238000004364 calculation method Methods 0.000 claims abstract description 4
- 238000010792 warming Methods 0.000 claims abstract description 4
- 239000013598 vector Substances 0.000 claims description 22
- 239000011159 matrix material Substances 0.000 claims description 11
- 238000010845 search algorithm Methods 0.000 claims description 5
- 238000013519 translation Methods 0.000 claims description 3
- 230000000977 initiatory effect Effects 0.000 claims description 2
- 230000003068 static effect Effects 0.000 claims description 2
- 238000004422 calculation algorithm Methods 0.000 abstract description 9
- 238000012545 processing Methods 0.000 abstract description 3
- 238000012821 model calculation Methods 0.000 abstract description 2
- 238000006073 displacement reaction Methods 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000003044 adaptive effect Effects 0.000 description 4
- 230000003321 amplification Effects 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012913 prioritisation Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 230000033772 system development Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
Based on a wind arrow field real-time metrics method for satellite cloud picture, belong to image procossing and field of motion estimation.Solve existing model calculation of complex, real-time is not strong, the problems such as searching algorithm efficiency is not high.The global warming data detected are converted to gradation data, gradation data are converted to longitude and latitude data, then longitude and latitude data are converted to image coordinate, satellite cloud picture data show with image format; Contrast the satellite cloud picture of three continuous times, after Block-matching, select different searching methods according to real-time demand, determine wind arrow field longitude and latitude and direction; Integrated data pre-service and Search Results, obtain the gray scale of each wind arrow further, temperature and place isopressure surface thereof; Eventually through the observation position of wind arrow field on satellite cloud picture, size and Orientation and place isopressure surface thereof can observe general circulation and medium-term and long-term weather forecast.Calculation of complex when present invention, avoiding satellite cloud picture large-scale image processing, real-time, improve the execution efficiency of system.
Description
Technical field
The present invention relates to a kind of comparatively hard real-time efficient metric method of the wind arrow field based on satellite cloud picture, belong to image procossing and field of motion estimation.
Background technology
Satellite cloud picture grasp general circulation, in long-term weather forecast and diastrous weather research in play an important role.It converts gradation data again to by the temperature data in the infrared detecting set detection earth overhead on geostationary satellite and is made.Cloud-motion wind is to global weather and Typhoon Analysis and provide numerical value pre-initial wind field data to be all very important.Cloud-motion wind has become a kind of important Satellite Product at present.The updates that it can be measured as the conventional wind of land observational network, in ocean, plateau, the survey station such as desert is rare or without survey station area, it is main or unique wind information source.Cloud-motion wind data extensively model be applied to typhoon, heavy rain and the research of the synoptic meteorology such as flood, Mesoscale weather analysis, domestic scholars also explicitly points out, Cloud-motion wind can know the details vivo showing weather system development and change, in Numerical Weather analysis and forecast, have wide application prospect, shifting to the aspects such as forecast to Rainstorm Areas analysis and prediction, Typhoon Range and tropical cyclone has important indicative significance.
At present, motion estimation techniques to have become in digital image processing field a very important ingredient.Scientific workers have proposed a variety of Video Motion Estimation algorithm, such as bayes method, PRA, optical flow method and Block Matching Algorithm etc.Block Matching Algorithm is simply effective, and meet the requirement of real-time of image procossing, the calculated amount of needs is also relatively little.Wherein, self-adaptation Cross Search ARPS (adaptive rood pattern search) algorithm greatly can improve the efficiency of estimation relative to full-search algorithm, and on estimated quality, maintain certain precision.
Summary of the invention
The object of this invention is to provide a kind of wind arrow field real-time metrics method based on satellite cloud picture, in order to solve existing model calculation of complex, software and hardware realizes difficulty, and real-time is not strong, the problems such as searching algorithm efficiency is not high.
For achieving the above object, the present invention takes following technical scheme:
Based on a wind arrow field real-time metrics method for satellite cloud picture, the specific implementation process of described wind arrow field real-time metrics method is:
The global warming data detected are converted to gradation data, then gradation data are converted to longitude and latitude data, then longitude and latitude data are converted to image coordinate, satellite cloud picture data are shown with image format by step one, satellite sounding data prediction;
Step 2, contrast three continuous times satellite cloud picture, after Block-matching (utilizing SAD matching criterior to mate), select different searching methods according to real-time demand, determine wind arrow field longitude and latitude and direction;
Step 3, integrated data pre-service and Search Results, obtain the gray scale of each wind arrow further, temperature and place isopressure surface thereof;
Step 4, eventually through observation the position of wind arrow field on satellite cloud picture, size and Orientation and place isopressure surface thereof can observe general circulation and medium-term and long-term weather forecast.
In step one, when gradation data being converted to longitude and latitude data, using satellite and the earth's core line as x-axis, with arctic direction for z-axis, according to right-hand screw rule, set up y-axis; If the earth is desirable ellipsoid, satellite sounding data file is the gray-scale value matrix of 2288 × 2288, on the corresponding earth of each element of matrix or an extraterrestrial sensing point (or claim sampled point); The substar of satellite is east longitude 86.5 degree, and north latitude 0 degree, matrix element corresponding to substar is positioned at the 1145th row and the 1145th row intersection of matrix.
In step 2, determine that the detailed process in wind arrow field longitude and latitude and direction is:
Step 2 (1), contrast three continuous time satellite cloud pictures:
Get continuous three static cloud atlas, solve intermediate time cloud atlas wind vector, with the previous moment for reference picture, a later moment is correcting image; The size of match block is 16 × 16, and region of search is 64 × 64, ensures that each pixel can both searchedly arrive, and does not repeat; Template translation 1 location of pixels is carried out summation absolute error with the field of search during scanning to mate at every turn;
Step 2 (2), block-based motion estimation, utilize SAD matching criterior to carry out:
Summation absolute error:
Wherein, u, v represent the prediction block in reference picture and the skew of current block in horizontal and vertical direction in present image ,-p≤u, v≤p; M, n represent the horizontal and vertical coordinate of certain pixel in current block; f
k(m, n) represents the gray-scale value of certain pixel of current block, f
k-1the gray-scale value of the respective pixel of (m+u, n+v) representative prediction block; P represents one direction maximum search distance, and M, N represent macroblock size; The relatively sad value of varying level and vertical shift, minimumly in all sad values is match block;
The search of step 2 (3), application self-adapting solves wind arrow field, and the key step of self-adaptation Cross Search algorithm is as follows:
1) if current macro is first macro block of present frame, then it can be used as search starting point, jump to the 5th) step;
2) if current macro is positioned at the far top row of frame, the motion vector alternatively search starting point of left side macro block is got; If be positioned at the left column of frame, the motion vector getting macro block is above candidate search starting point, jumps to the 4th) step;
3) otherwise, above getting and the mean value alternatively search starting point of left side macroblock motion vector;
4) sad value that to calculate with candidate search starting point and first macro block be respectively starting point, gets the initial value of smaller as minimum SAD, is designated as M
sAD, corresponding point is as actual search starting point;
5) in next circle, carry out spiral search, calculate the sad value of every bit, get 1 to first lap step-size in search, the step-size in search of other circle gets 2;
If be just greater than M in the calculation
sAD, exit calculating, under search a bit, otherwise calculate SAD completely;
If current SAD < M
sAD, then it is assigned to M
sAD, this circle of juxtaposition M
sADupgrading mark F is 1;
6) when the search of this circle terminates, if F=1, the 5th is forwarded to) step; Otherwise continue the 7th) step;
7) terminate spiral search, if current best match point is initiating searches point, continue the 8th) step; Otherwise 4 points (being 3 points when it is positioned at the second circle) do not searched for around it are searched for further, then continue the 8th) step;
8) by the 7th) carry out little search pattern search centered by the optimal match point that obtains of step, find the immediate macro block with match block, determine wind arrow field longitude and latitude and direction by this macro block.
In step 3 and four, carry out rough estimate by infrared cloud image cloud-top temperature according to atmospheric vertical temperature profile and calculate cloud-top height, can show that Cloud-motion wind vows the barometer altitude estimated value of representative, and utilize log-linear interpolation method, set up the relation between temperature T and pressure P:
T＝a+blnP (2)
Wherein
wherein, (t
1, p
1), (t
2, p
2) be two known temperature pressure points.
In described geocentric coordinate system, the earth can be considered desirable ellipsoid, satellite sounding to the earth near gray-scale value matrix be known, known substar is east longitude 86.5 degree, north latitude 0 degree, matrix element corresponding to substar is positioned at the 1145th row and the 1145th row intersection of matrix.
Two described wind arrows have four indexs: the latitude of starting point, longitude, the direction of wind arrow, size.Use block matching algorithm to solve block motion vector, and then can wind vectors be tried to achieve.Meanwhile, in 40 degree, south latitude to north latitude 40 degree, in east longitude 46 degree to 126 degree scopes, calculate the number of whole non-zero wind arrow.
The invention has the beneficial effects as follows:
The inventive method realizes using the self-adaptation Cross Search algorithm high-level efficiency of block matching algorithm to solve motion vector, and then tries to achieve wind vectors, for global weather and Typhoon Analysis and provide numerical forecasting to be all very important.The inventive method has comparatively hard real-time, the advantage such as efficient.
The satellite cloud picture that the inventive method detects for geo-synchronous orbit satellite, proposing a kind of method determining satellite cloud picture wind arrow field compared with hard real time ground efficiently, is the prerequisite work grasping general circulation and medium-term and long-term weather forecast.Calculation of complex when present invention, avoiding satellite cloud picture large-scale image processing, the problems such as real-time is not strong, improve the execution efficiency of system, have good treatment effect.The present invention is based in the wind vector Block-matching process of satellite cloud picture, and application self-adapting Cross Search algorithm carries out Efficient Solution, detecting coupling whether success, obtaining the object of the wind vector of satellite cloud picture when reaching efficient real by arranging threshold value.
Window size and hunting zone can be determined adaptively in Block-matching process.In order to take into account real-time and accuracy, the motion vector of current macro is predicted with the motion vector of known macro block, with large search pattern center, region of search and around eight some places carry out matching primitives, find cost function smallest point, thus realize adaptive search pattern.
Herein by the prediction of search starting point, make the initial motion vectors of current block likely close to its final motion vector, then simply and effectively image classified according to image local feature and select suitable search pattern, adaptive search can be carried out according to the type of motion, finally employing search stop criterion ensures to have enough precision at the end of Search Results is near this starting point predicted, thus realizes quick, that even, precision is high motion-vector search.
Concrete advantage main manifestations is the following aspects:
1. pass through the method for Block-matching to the satellite cloud picture modeling of Cloud motion wind, and utilize efficient self-adaptation cross search to deal with problems;
2. Block Matching Algorithm is simply effective, and meet the requirement of real-time of image procossing, the calculated amount of needs is also relatively little.
3. self-adaptation Cross Search can improve the efficiency of estimation greatly, and on estimated quality, maintain certain precision.
4. in Numerical Weather analysis and forecast, have wide application prospect, shifting to the aspects such as forecast to Rainstorm Areas analysis and prediction, Typhoon Range and tropical cyclone has important indicative significance.
Accompanying drawing explanation
Fig. 1 is the workflow schematic diagram of the inventive method; Fig. 2 is the satellite cloud picture of earth surface; Fig. 3 is Cloud motion wind Block-matching schematic diagram; Fig. 4 is self-adaptation cross search process flow diagram; Fig. 5 is temperature and air pressure graph of a relation; Fig. 6-1 is for being Cloud motion wind figure before correction; Fig. 6-2 is for correcting rear Cloud motion wind figure; Fig. 7 is Cloud motion wind upper left schematic diagram before amplification post-equalization; Fig. 8 is Cloud motion wind upper left schematic diagram after amplification post-equalization.
Embodiment
For technical scheme advantage of the present invention being described clearly, be described in further detail the specific embodiment of the present invention below in conjunction with accompanying drawing, obviously described embodiment is section Example of the present invention, instead of whole embodiments.Embodiments of the invention can be expanded on this basis, when overall architecture is consistent, be obtained more prioritization schemes.According to embodiments of the invention, the ordinary skill high-ranking official of this area, without the basis of creative work realizing every other embodiment of the present invention, belongs to protection scope of the present invention.
Fig. 1 is the schematic diagram of the specific embodiment of the invention, and as shown in Figure 1, this flow process comprises the following steps:
The global warming data detected are converted to gradation data, then gradation data are converted to longitude and latitude data, then longitude and latitude data are converted to image coordinate, satellite cloud picture data are shown with image format by step 1: satellite sounding data prediction.
Step 2: as shown in Figure 3, supposes that cloud mass only translation motion occurs, and A is cloud mass t
1the position at moment cloud atlas place, C is t
2moment cloud atlas position, for x direction, modules A is at Δ t=t
2-t
1the displacement in the time interval can be described as X=x
0+ x ', wherein x
0represent integral multiple pixel displacement air quantity, x ' is Displacement component, namely | x ' | > 1 pixel dimension.
Utilize correlation method in the region of search of the second width cloud atlas, find the matching module of object module A, obtain module B, according to both position difference computes integer times pixel displacement x
0.If A module is mated completely with B module, then not think to there is Displacement, the point-to-point speed of cloud mass in x direction is V=x
0/ Δ t, otherwise, think that the difference of two modules is produced by the Displacement of cloud mass, Fourier phase analytic approach can be utilized further to carry out spectrum analysis, according to phase difference calculating Displacement, by x to modules A and B
0and obtain point-to-point speed V ' after x ' merging
x=(x
0+ x ')/Δ t.In like manner, displacement and the speed that can calculate y direction are respectively Y=y
0+ y ', V '
y=(y
0+ y ')/Δ t.
Step 3: summation absolute error coupling, summation absolute error:
Wherein, u, v represent the prediction block in reference picture and the skew of current block in horizontal and vertical direction in present image ,-p≤u, v≤p; M, n represent the horizontal and vertical coordinate of certain pixel in current block; f
k(m, n) represents the gray-scale value of certain pixel of current block, f
k-1the gray-scale value of the respective pixel of (m+u, n+v) representative prediction block.P represents one direction maximum search distance, and M, N represent macroblock size.The relatively sad value of varying level and vertical shift, minimumly in all sad values is match block.
Step 4: by the prediction of search starting point, make the initial motion vectors of current block likely close to its final motion vector, then simply and effectively image classified according to image local feature and select suitable search pattern, adaptive search can be carried out according to the type of motion, search stop criterion is finally adopted to ensure to have enough precision at the end of Search Results is near this starting point predicted, thus realize quick, that even, precision is high motion-vector search, as shown in Fig. 4, Fig. 6-1 and 6-2.
Step 5: illustrate the relation between temperature and air pressure as shown in Figure 5.Log-linear interpolation formula is made to be by this figure
T＝a+blnP(2)
Respectively by (t
1, lnp
1) and (t
2, lnp
2) bring formula (2) into and can obtain
t
1＝a+blnp
1，t
2＝a+blnp
2(3)
Separate these two equations can obtain
Can be obtained by formula (2)
The isopressure surface at non-zero wind arrow place can be found by this kind of method.
Step 6: eventually through the observation position of wind arrow field on satellite cloud picture, size and Orientation and place isopressure surface thereof can observe general circulation and medium-term and long-term weather forecast.
Claims (2)
1., based on a wind arrow field real-time metrics method for satellite cloud picture, the specific implementation process of described wind arrow field real-time metrics method is:
The global warming data detected are converted to gradation data, then gradation data are converted to longitude and latitude data, then longitude and latitude data are converted to image coordinate, satellite cloud picture data are shown with image format by step one, satellite sounding data prediction;
Step 2, contrast three continuous times satellite cloud picture, after Block-matching, select different searching methods according to real-time demand, determine wind arrow field longitude and latitude and direction;
Step 3, integrated data pre-service and Search Results, obtain the gray scale of each wind arrow further, temperature and place isopressure surface thereof;
Step 4, eventually through observation the position of wind arrow field on satellite cloud picture, size and Orientation and place isopressure surface thereof can observe general circulation and medium-term and long-term weather forecast;
In step one, when gradation data being converted to longitude and latitude data, using satellite and the earth's core line as x-axis, with arctic direction for z-axis, according to right-hand screw rule, set up y-axis; If the earth is desirable ellipsoid, satellite sounding data file is the gray-scale value matrix of 2288 × 2288, on the corresponding earth of each element of matrix or an extraterrestrial sensing point (or claim sampled point); The substar of satellite east longitude 86.5 degree, north latitude 0 degree, matrix element corresponding to substar is positioned at the 1145th row and the 1145th row intersection of matrix;
It is characterized in that: in step 2, determine that the detailed process in wind arrow field longitude and latitude and direction is:
Step 2 (1), contrast three continuous time satellite cloud pictures:
Get continuous three static cloud atlas, solve intermediate time cloud atlas wind vector, with the previous moment for reference picture, a later moment is correcting image; The size of match block is 16 × 16, and region of search is 64 × 64, ensures that each pixel can both searchedly arrive, and does not repeat; Template translation 1 location of pixels is carried out summation absolute error with the field of search during scanning to mate at every turn;
Step 2 (2), block-based motion estimation, utilize SAD matching criterior to carry out:
Summation absolute error:
Wherein, u, v represent the prediction block in reference picture and the skew of current block in horizontal and vertical direction in present image ,-p≤u, v≤p; M, n represent the horizontal and vertical coordinate of certain pixel in current block; f
k(m, n) represents the gray-scale value of certain pixel of current block, f
k-1the gray-scale value of the respective pixel of (m+u, n+v) representative prediction block; P represents one direction maximum search distance, and M, N represent macroblock size; The relatively sad value of varying level and vertical shift, minimumly in all sad values is match block;
The search of step 2 (3), application self-adapting solves wind arrow field, and the key step of self-adaptation Cross Search algorithm is as follows:
1) if current macro is first macro block of present frame, then it can be used as search starting point, jump to the 5th) step;
2) if current macro is positioned at the far top row of frame, the motion vector alternatively search starting point of left side macro block is got; If be positioned at the left column of frame, the motion vector getting macro block is above candidate search starting point, jumps to the 4th) step;
3) otherwise, above getting and the mean value alternatively search starting point of left side macroblock motion vector;
4) sad value that to calculate with candidate search starting point and first macro block be respectively starting point, gets the initial value of smaller as minimum SAD, is designated as M
sAD, corresponding point is as actual search starting point;
5) in next circle, carry out spiral search, calculate the sad value of every bit, get 1 to first lap step-size in search, the step-size in search of other circle gets 2;
If be just greater than M in the calculation
sAD, exit calculating, under search a bit, otherwise calculate SAD completely;
If current SAD<M
sAD, then it is assigned to M
sAD, this circle of juxtaposition M
sADupgrading mark F is 1;
6) when the search of this circle terminates, if F=1, the 5th is forwarded to) step; Otherwise continue the 7th) step;
7) terminate spiral search, if current best match point is initiating searches point, continue the 8th) step; Otherwise, do search further, then continue the 8th for 4 that do not search for around it) and step;
8) by the 7th) carry out little search pattern search centered by the optimal match point that obtains of step, find the immediate macro block with match block, determine wind arrow field longitude and latitude and direction by this macro block.
2. the wind arrow field real-time metrics method based on satellite cloud picture according to claim 1, is characterized in that:
In step 3 and four, carry out rough estimate by infrared cloud image cloud-top temperature according to atmospheric vertical temperature profile and calculate cloud-top height, can show that Cloud-motion wind vows the barometer altitude estimated value of representative, and utilize log-linear interpolation method, set up the relation between temperature T and pressure P:
T＝a+blnP (2)
Wherein
wherein, (t
1, p
1), (t
2, p
2) be two temperature pressure points.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310120689.7A CN103198447B (en) | 2013-04-09 | 2013-04-09 | A kind of wind arrow field real-time metrics method based on satellite cloud picture |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310120689.7A CN103198447B (en) | 2013-04-09 | 2013-04-09 | A kind of wind arrow field real-time metrics method based on satellite cloud picture |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103198447A CN103198447A (en) | 2013-07-10 |
| CN103198447B true CN103198447B (en) | 2015-09-09 |
Family
ID=48720960
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310120689.7A Expired - Fee Related CN103198447B (en) | 2013-04-09 | 2013-04-09 | A kind of wind arrow field real-time metrics method based on satellite cloud picture |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103198447B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679650B (en) * | 2013-11-26 | 2016-05-25 | 四川大学 | Rock core 3-D view restorative procedure |
| CN104036462B (en) * | 2014-06-06 | 2016-11-23 | 南京大学 | A kind of method utilizing GMS Image estimation tropical cyclone of northwestern Pacific Ocean intensity |
| CN105046056B (en) * | 2015-06-24 | 2018-01-16 | 北京航天宏图信息技术股份有限公司 | The method of inspection and device of Cloud motion wind data |
| CN106443830B (en) * | 2016-06-16 | 2019-08-16 | 杭州师范大学 | A method of Typhoon Monitoring and evaluation monitoring accuracy based on multi-source satellite data |
| CN106204686A (en) * | 2016-07-19 | 2016-12-07 | 华东师范大学 | A kind of accurate Drawing method in coastline on satellite cloud picture |
| CN107798712B (en) * | 2017-11-16 | 2021-01-12 | 中国科学院海洋研究所 | IDL polar region vector field visualization oriented algorithm |
| CN110888948B (en) * | 2019-01-07 | 2020-09-01 | 好活（昆山）网络科技有限公司 | Regional big data dynamic correction method |
| CN112149891B (en) * | 2020-09-16 | 2023-05-23 | 南京信息工程大学 | Simple typhoon moving direction prediction method |
| CN112987133B (en) * | 2021-05-14 | 2021-08-13 | 郭洪涛 | Ground wind vector nowcasting method based on SVR and VAR |
| CN113538526A (en) * | 2021-06-10 | 2021-10-22 | 国家卫星气象中心(国家空间天气监测预警中心) | Based on TV _ l1Wind cloud four-cloud wind guide estimation method and system based on optical flow method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101969207A (en) * | 2010-09-16 | 2011-02-09 | 国网电力科学研究院 | Photovoltaic ultra-short term power predicting method based on satellite remote sensing and meteorology telemetry technology |
| CN102938057A (en) * | 2012-10-19 | 2013-02-20 | 株洲南车时代电气股份有限公司 | Vehicle shadow eliminating method and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040086152A1 (en) * | 2002-10-30 | 2004-05-06 | Ramakrishna Kakarala | Event detection for video surveillance systems using transform coefficients of compressed images |
| US7924545B2 (en) * | 2008-12-10 | 2011-04-12 | Helmut Fluhrer | Apparatus for controlling atmospheric humidity |
| US20110149268A1 (en) * | 2009-12-17 | 2011-06-23 | Marchant Alan B | Dynamic 3d wind mapping system and method |
-
2013
- 2013-04-09 CN CN201310120689.7A patent/CN103198447B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101969207A (en) * | 2010-09-16 | 2011-02-09 | 国网电力科学研究院 | Photovoltaic ultra-short term power predicting method based on satellite remote sensing and meteorology telemetry technology |
| CN102938057A (en) * | 2012-10-19 | 2013-02-20 | 株洲南车时代电气股份有限公司 | Vehicle shadow eliminating method and device |
Non-Patent Citations (8)
| Title |
|---|
| Cloud-drift and water vapor winds in the polar regions from MODIS;Jeffrey R.Key等;《Geoscience and Remote Sensing,IEEE Transactions on》;20030228;第41卷(第2期);第482-492页 * |
| jeong-Hyun Park等.Sensitivity of Satellite-Derived Wind Retrieval Over Cloudy Scenes to Target Selection in Tracking and Pixel Selection in Height Assignment.《Geoscience and Remote Sensing,IEEE Transactions on 》.2012,第50卷(第5期),第2063-2073页. * |
| Study on inversing cloud motion wind from visible and infrared satellite image;LONG Zhi-yong;《Geoscience and Remote Sensing (IITA-GRS),2010 Second IITA International Conference on》;20100831;第1卷;第506-509页 * |
| 不同导风系统的云迹风资料及其在台风预报中同化应用的对比分析;河惠卿;《中国博士学位论文全文数据库 基础科学辑》;20080915;第2008年卷(第9期);第A009-13页 * |
| 卫星云迹风微机客观导出系统;王振会等;《南京气象学院学报》;19960315;第19卷(第1期);全文 * |
| 卫星导风在台风路径预报中的应用进展;詹奕哲等;《地球科学进展》;20110410;第26卷(第4期);第386-393页 * |
| 基于卫星云图的风矢场(云导风)度量模型与算法探讨;江惠坤;《www.njnet.edu.cn/news/16》;20120921;第1-5页，图一-图二 * |
| 基于卫星云图的风矢场度量模型;张博等;《科技传播》;20130123;第2013卷(第2期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103198447A (en) | 2013-07-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103198447B (en) | A kind of wind arrow field real-time metrics method based on satellite cloud picture | |
| US11333796B2 (en) | Spatial autocorrelation machine learning-based downscaling method and system of satellite precipitation data | |
| CN113706482B (en) | High-resolution remote sensing image change detection method | |
| Yang et al. | Solar irradiance forecasting using spatio-temporal empirical kriging and vector autoregressive models with parameter shrinkage | |
| CN102968631B (en) | The automatic geometric of mountain area multispectral remote sensing satellite image is corrected and ortho-rectification method | |
| Cros et al. | Extracting cloud motion vectors from satellite images for solar power forecasting | |
| Kouyama et al. | Horizontal structure of planetary-scale waves at the cloud top of Venus deduced from Galileo SSI images with an improved cloud-tracking technique | |
| CN103744428A (en) | Unmanned surface vehicle path planning method based on neighborhood intelligent water drop algorithm | |
| CN108734725B (en) | Probability data correlation filtering extended target tracking method based on Gaussian process | |
| CN115880364B (en) | Robot pose estimation method based on laser point cloud and visual SLAM | |
| CN107341513B (en) | Multi-source ocean surface temperature remote sensing product fusion method based on stable fixed order filtering model | |
| Dissawa et al. | Cloud motion tracking for short-term on-site cloud coverage prediction | |
| Zaher et al. | Comparative study of algorithms for cloud motion estimation using sky-imaging data | |
| CN108335261A (en) | A kind of Optical remote satellite orthography garland region automatic testing method | |
| Dissawa et al. | Cross-correlation based cloud motion estimation for short-term solar irradiation predictions | |
| Singh et al. | A visual-inertial system to determine accurate solar insolation and optimal PV panel orientation at a point and over an area | |
| CN117350157A (en) | Space-time mapping region irradiance estimation and distributed photovoltaic power prediction method | |
| CN116045970A (en) | Multi-platform information collaborative navigation enhancement method based on external condition constraint | |
| CN106097277B (en) | A kind of rope substance point-tracking method that view-based access control model measures | |
| Walker Brown et al. | New insights into f CO 2 variability in the tropical eastern Pacific Ocean using SMOS SSS | |
| CN105652284A (en) | Determination method and device of long-wave downward radiation | |
| CN112268564A (en) | Unmanned aerial vehicle landing space position and attitude end-to-end estimation method | |
| CN116879886A (en) | Early warning method for graded communication of micro-change monitoring radar on landslide surface | |
| CN107808393B (en) | Target tracking method with anti-interference performance in intelligent video monitoring field | |
| Zhang et al. | Increasing SLAM Pose Accuracy by Ground-to-Satellite Image Registration |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20150909 |