CN108507949A - A kind of river water quality monitoring method based on high score remote sensing satellite - Google Patents
A kind of river water quality monitoring method based on high score remote sensing satellite Download PDFInfo
- Publication number
- CN108507949A CN108507949A CN201810121841.6A CN201810121841A CN108507949A CN 108507949 A CN108507949 A CN 108507949A CN 201810121841 A CN201810121841 A CN 201810121841A CN 108507949 A CN108507949 A CN 108507949A
- Authority
- CN
- China
- Prior art keywords
- remote sensing
- high score
- sensing image
- river
- score remote
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
- G01N2021/177—Detector of the video camera type
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Image Processing (AREA)
Abstract
A kind of river water quality monitoring method based on high score remote sensing satellite obtains the high score remote sensing image a in river to be monitored from earth station the present invention is based on high score remote sensing satellite and transfers the digital topography map of river region to be monitored；Preprocessing module obtains the high score remote sensing image c that n width is located at the image for showing complete river to be monitored of different-waveband after being pre-processed to high score remote sensing image a；The actual mass concentration of each reference position point is made regression analysis with reflectivity of each reference position point on each high score remote sensing image c and obtains corresponding regression equation by controller, is then based on the relation function on each high score remote sensing image c between the reflectivity of corresponding position of actual mass concentration and each reference position of the Regression Equations of dispersion degree minimum about reference position point；Controller calculates reflectivity of the monitoring point on n high score remote sensing image c and calculates the mass concentration of the water quality parameter at monitoring point according to the relation function.
Description
Technical field
The present invention relates to a kind of river water quality monitoring methods based on high score remote sensing satellite.
Background technology
River water quality monitoring be one of main contents of environmental monitoring, be it is accurate, timely, comprehensively reflect water quality situation and
Development trend provides scientific basis for water environment management, pollution source apportionment, environmental planning etc., dirty to entire water environment protection, water
Dye controls and safeguards that water environment health aspect plays a crucial role.River water quality monitoring at this stage mainly has following two
Kind method：
One, traditional physics and chemistry monitoring
After investigating monitoring objective, monitoring network is determined, arrange the sampling time and determine sample frequency, periodically
Sampling carries out physico-chemical analysis, achievees the purpose that monitoring.The method has the disadvantage that：
1) whole process variable factor is too many, sample frequency, as long as going out at one in the determination of monitoring point and analysis method
Existing error may result in monitoring result distortion；
2) monitoring point can only be monitored, can not accomplishes global monitoring；
3) monitoring point quantity determines monitoring cost, and monitoring point quantity is few, and monitoring effect is bad, and monitoring point is more, and cost is big, right
Cost is difficult to control when broad river is monitored；
4) failure of error artificial in monitoring process and equipment can all have an impact monitoring data；
5) can only periodic monitoring, can not accomplish continuous long-term dynamic monitoring.
Two, biological monitoring
It is monitored using the variation of community of aquatic organism structure.Water quality condition changes, community of aquatic organism structure
It can occur to change accordingly.The method has the disadvantage that：
1) water pollution cannot qualitatively and quantitatively be measured；
2) it is not so good as Physico-chemical tests in terms of the sensitivity and specificity that detect；
3) certain biological detections take longer.
In conclusion there is also shortcomings for water quality monitoring mode at this stage, being not met by current monitoring needs
It wants.
Invention content
To overcome defect present in background technology, the present invention to provide a kind of urban river water quality supervision based on high score remote sensing satellite
Survey method.
Technical proposal that the invention solves the above-mentioned problems is：
A kind of river water quality monitoring method based on high score remote sensing satellite, includes the following steps：
1.1, the remote sensing image data in the high score remote sensing satellite acquisition whole world and it is transferred to earth station, image acquiring module is from institute
It states earth station and obtains several panel heights of the river to be monitored in n wave band and divide remote sensing image a, and the height being located in the same band
Point equal capable assemblings of remote sensing image a are at the image in the complete river to be monitored of a width, n>0, and n is natural number；
1.2, map transfers the digital topography map that module transfers river region to be monitored from network；
1.3, preprocessing module pre-processes high score remote sensing image a, includes the following steps：
1.3.1, geometric correction and gray scale resampling
Length direction of the geometric correction module in digital topography map upper edge river to be monitored marks m in monitoring time
The changeless control point in position, and mapping point corresponding with the control point is marked on high score remote sensing image a；Exist respectively
The two-dimentional Cartesian coordinates different from establishing two on the high score remote sensing image a on the digital topography map, and with number
The coordinate of mapping point described in the coordinate at the control point and high score remote sensing image a establishes multinomial correction function on topographic map；So
Geometric correction is carried out with the multinomial correction function one by one to the pixel of high score remote sensing image a afterwards, while geometric correction
Gray scale resampling is carried out to high score remote sensing image a using bilinear interpolation method, then high score remote sensing image a is by geometric correction and ash
Become high score remote sensing image b, m after degree resampling>20, and m is natural number；
1.3.2, high score remote sensing image b splicings are inlayed
Module is inlayed in remote sensing image splicing to divide remote sensing image b respectively by displacement positioned at several panel heights of the same band
With method, histogram matching, the image mosaic method splicing based on jointing line inlays a width can show complete river to be monitored
The high score remote sensing image c of the image in road；Then each wave band respectively obtains a panel height and divides remote sensing image c；
1.3.3, histogram modification module stretches each remote sensing image c using Histogram Modification Methods, to enhance remote sensing
The clarity of image c；
1.4, controller automatic identification：
1.4.1, the length direction in digital topography map upper edge river to be monitored uniformly selects k reference position point and s
Monitoring point, k>20, s>0, and k and s are natural number；
1.4.2 the actual mass concentration of the several water quality parameter of each reference position point, is acquired on the spot；
1.4.3, controller extracts the reflectivity of each reference position point corresponding position on each high score remote sensing image c；
1.4.4, controller by the actual mass concentration of each reference position point and each reference position point in each high score remote sensing shadow
As the reflectivity on c makees linear, index, logarithm, multinomial, power regression analysis and obtains corresponding regression equation, then
Regression Equations based on dispersion degree minimum are about the actual mass concentration of reference position point and each reference position in each height
Divide the relation function between the reflectivity of corresponding position on remote sensing image c；
1.4.5, controller calculates reflectivity of the monitoring point on n high score remote sensing image c, according to the relation function,
The mass concentration of the water quality parameter at monitoring point is calculated, and with shade corresponding concentration height on high score remote sensing image c
It is identified, just obtains the spatial distribution map of the mass concentration of the water quality parameter of monitoring point in river to be monitored；
1.5, manual identified
Spatial distribution map described in real time inspection obtains the real-time monitoring conclusion of the water quality condition in river to be monitored；To each
The spatial distribution map of the river water quality parameter of period compares and analyzes, and obtains the water quality condition dynamic monitoring in river to be monitored
Conclusion.
Further, the n wave band is respectively blue wave band, green wave band, red wave band and near infrared band.
Further, the control point includes road junction, bridge and/or river inflection point.
Further, the water quality parameter includes chlorophyll, suspended matter, total phosphorus, total nitrogen and/or total organic carbon.
Further, it inlays by displacement matching method, histogram matching, the image mosaic method splicing based on jointing line
One width can show that the detailed process of the high score remote sensing image c of the image in complete river to be monitored is as follows：Divide a panel height to remote sensing
Image a divides remote sensing image a to move to adjacent another panel height so that and two panel heights divide the relative position of remote sensing image a constantly to change,
When two panel heights divide the lap of remote sensing image a to be completely superposed, then two panel heights divide remote sensing image a matchings to complete；Then it uses
Two panel heights are divided the hue adjustment of remote sensing image a to unanimously by histogram matching；Then with not with river on high score remote sensing image a
Image intersect principle pick out the jointing line that two panel heights divide remote sensing image a, two panel heights divide remote sensing image a to be spelled along jointing line
It connects, and jointing line is subjected to emergence processing；Finally, the high score remote sensing image a in the same band being set into a width can show
Show the high score remote sensing image c in complete river to be monitored.
Further, the detailed process for carrying out gray scale resampling to high score remote sensing image a using bilinear interpolation method is as follows：
The gray value of 4 adjacent picture elements on high score remote sensing image a around each pixel is made in x-axis direction and y-axis direction in linear
The gray value obtained after inserting is as the gray value after the pixel geometric correction.
Further, geometric correction the specific steps are：With each control point on digital topography map and in high score remote sensing image a
Different coordinates, respectively substitute into multinomial establish solve multinomial correction function coefficient equation group, and use least square
Method obtains the coefficient of multinomial correction function；The coefficient is substituted into after multinomial and obtains multinomial correction function；By high score
The coordinate of the pixel of remote sensing image a can be obtained the high score remote sensing image a's after geometric correction after substituting into multinomial correction function
Cell coordinate.
The principle of satellite remote sensing：According to the theory of electromagnetic wave, distant object is radiated using various sensor apparatus and
The electromagnetic wave information of reflection is collected, handles, and is finally imaged, to which the various scenery in ground are detected and be identified
A kind of complex art.
The present invention is based on multi-source satellite remote sensing technologies, on the basis of analyzing different sensors monitoring water body situation ability,
Using the complementarity of Multiple Source Sensor, the integration capability of Satellite Remote Sensing water body situation is fully excavated, on a macroscopic scale
The complex method and technology for monitoring water body situation based on multiband, multidate, polymorphic type remotely-sensed data are established, basin water system is formed
Water body rapid extracting method, and utilize the water quality parameter inverting mould in the typical contaminated area of multi-source EO-1 hyperion satellite remote sensing date foundation
Type, reach quickly positioning waters, rapid extraction comprehensive water-body water quality information technical solution, provide under large scale macroscopic view letter
Breath realizes low cost, the big region aquatic monitoring of the wide ken and emergent aid decision.
Beneficial effects of the present invention are mainly manifested in：
1) monitoring range is wide, and satellite remote sensing date belongs to global data, can carry out global monitoring.
2) speed is fast, and after template is established, data are automatically analyzed by computer, improves flow path efficiency, at low cost, obtains remote sensing
After data, once and for all, the later stage, there is no maintenance costs caused by monitoring point increase etc..
3) long-term dynamics monitoring can be carried out, satellite remote sensing mainly provides the census information of tendency, is week with season
Phase monitoring, the Pollution Tendency for analyzing target area river water quality, it is auxiliary to provide direction using other monitoring results of unit
It helps.
4) error is small, for global analysis's technology in range, avoids monitoring point omission, not comprehensive enough problem.
Specific implementation mode
A kind of river water quality monitoring method based on high score remote sensing satellite, includes the following steps：
1.1, the remote sensing image data in the high score remote sensing satellite acquisition whole world and it is transferred to earth station, image acquiring module is from institute
It states earth station and obtains several panel heights of the river to be monitored in n wave band and divide remote sensing image a, and the height being located in the same band
Point equal capable assemblings of remote sensing image a are at the image in the complete river to be monitored of a width, n>0, and n is natural number；
1.2, map transfers the digital topography map that module transfers river region to be monitored from network；
1.3, preprocessing module pre-processes high score remote sensing image a, includes the following steps：
1.3.1, geometric correction and gray scale resampling
Length direction of the geometric correction module in digital topography map upper edge river to be monitored marks m in monitoring time
The changeless control point in position, and mapping point corresponding with the control point is marked on high score remote sensing image a；Exist respectively
The two-dimentional Cartesian coordinates different from establishing two on the high score remote sensing image a on the digital topography map, and with number
The coordinate of mapping point described in the coordinate at the control point and high score remote sensing image a establishes multinomial correction function (i.e. on topographic map
Binary polynomial of degree n correction function)；Then the pixel of high score remote sensing image a is carried out one by one with the multinomial correction function
Geometric correction carries out gray scale resampling, then high score using bilinear interpolation method while geometric correction to high score remote sensing image a
Remote sensing image a becomes high score remote sensing image b, m after geometric correction and gray scale resampling>20, and m is natural number；
1.3.2, high score remote sensing image b splicings are inlayed
Module is inlayed in remote sensing image splicing to divide remote sensing image b respectively by displacement positioned at several panel heights of the same band
With method, histogram matching, the image mosaic method splicing based on jointing line inlays a width can show complete river to be monitored
The high score remote sensing image c of the image in road；Then each wave band respectively obtains a panel height and divides remote sensing image c；
1.3.3, histogram modification module stretches each remote sensing image c using Histogram Modification Methods, to enhance remote sensing
The clarity of image c；
1.4, controller automatic identification：
1.4.1, the length direction in digital topography map upper edge river to be monitored uniformly selects k reference position point and s
Monitoring point, k>20, s>0, and k and s are natural number；
1.4.2 the actual mass concentration of the several water quality parameter of each reference position point, is acquired on the spot；Manually use instrument
The measurement of the actual mass concentration of water quality parameter is carried out with tool, for example, with leaf green analyzer measure chlorophyll mass concentration,
The quality that total phosphorus is measured using the mass concentration of suspended matter on-line determination instrument measurement suspended matter, using total phosphorus in-line analyzer is dense
Degree, utilizes the quality of total organic carbon detector measurement total organic carbon at the mass concentration that total nitrogen is measured using total nitrogen in-line analyzer
Concentration, above-mentioned instrument market are commercially available.
1.4.3, controller extracts the reflectivity of each reference position point corresponding position on each high score remote sensing image c；
1.4.4, controller by the actual mass concentration of each reference position point and each reference position point in each high score remote sensing shadow
As the reflectivity on c makees linear, index, logarithm, multinomial, power regression analysis and obtains corresponding regression equation, then
Regression Equations based on dispersion degree minimum are about the actual mass concentration of reference position point and each reference position in each height
Divide the relation function between the reflectivity of corresponding position on remote sensing image c；
1.4.5, controller calculates reflectivity of the monitoring point on n high score remote sensing image c, according to the relation function,
The mass concentration of the water quality parameter at monitoring point is calculated, and with shade corresponding concentration height on high score remote sensing image c
It is identified, just obtains the spatial distribution map of the mass concentration of the water quality parameter of monitoring point in river to be monitored；
1.5, manual identified
Spatial distribution map described in real time inspection obtains the real-time monitoring conclusion of the water quality condition in river to be monitored；To each
The spatial distribution map of the river water quality parameter of period compares and analyzes, and obtains the water quality condition dynamic monitoring in river to be monitored
Conclusion.
Further, the n wave band is respectively blue wave band, green wave band, red wave band and near infrared band.
Further, the control point includes road junction, bridge and/or river inflection point.
Further, the water quality parameter includes chlorophyll, suspended matter, total phosphorus, total nitrogen and/or total organic carbon.
Further, it inlays by displacement matching method, histogram matching, the image mosaic method splicing based on jointing line
One width can show that the detailed process of the high score remote sensing image c of the image in complete river to be monitored is as follows：Divide a panel height to remote sensing
Image a divides remote sensing image a to move to adjacent another panel height so that and two panel heights divide the relative position of remote sensing image a constantly to change,
When two panel heights divide the lap of remote sensing image a to be completely superposed, then two panel heights divide remote sensing image a matchings to complete；Then it uses
Two panel heights are divided the hue adjustment of remote sensing image a to unanimously by histogram matching；Then with not with river on high score remote sensing image a
Image intersect principle pick out the jointing line that two panel heights divide remote sensing image a, two panel heights divide remote sensing image a to be spelled along jointing line
It connects, and jointing line is subjected to emergence processing；Finally, the high score remote sensing image a in the same band being set into a width can show
Show the high score remote sensing image c in complete river to be monitored.
Further, the detailed process for carrying out gray scale resampling to high score remote sensing image a using bilinear interpolation method is as follows：
The gray value of 4 adjacent picture elements on high score remote sensing image a around each pixel is in x-axis direction and y-axis direction (two-dimentional cartesian
X-axis direction on coordinate system and y-axis direction) on make the gray value obtained after linear interpolation as the ash after the pixel geometric correction
Angle value.
Further, geometric correction the specific steps are：With each control point on digital topography map and in high score remote sensing image a
Different coordinates, respectively substitute into multinomial establish solve multinomial correction function coefficient equation group, and use least square
Method obtains the coefficient of multinomial correction function；The coefficient is substituted into after multinomial and obtains multinomial correction function；By high score
The coordinate of the pixel of remote sensing image a can be obtained the high score remote sensing image a's after geometric correction after substituting into multinomial correction function
Cell coordinate.
The present invention is based on satellite remote sensings to be monitored water quality, have that viewpoint is high, the ken is wide, data acquisition is fast and repeat,
The data of the characteristics of being observed continuously, acquisition is digitlization, can be directly entered the Computerized image processing system of user, this scheme tool
There is the advantage that conventional monitoring methods are incomparable.
The present invention solve it is of the existing technology can not carry out it is global with long-term trend monitoring, monitoring device it is excessive and
Excessively rely on, monitor promptness is low, flow is cumbersome for monitoring, artificial sample when may result in personnel's accident, maintenance cost height etc.
Problem.
In the present invention, high score remote sensing image a, high score remote sensing image b, high score remote sensing image c refer both to high score remote sensing image, are
It is named for the ease of distinguishing the high score remote sensing image in different step, there is no meanings for letter itself.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, protection of the invention
Range is not construed as being only limitted to the concrete form that embodiment is stated, protection scope of the present invention also includes art technology
Personnel according to present inventive concept it is conceivable that equivalent technologies mean.
Claims (7)
1. a kind of river water quality monitoring method based on high score remote sensing satellite, it is characterised in that：Include the following steps：
1.1, high score remote sensing satellite acquires the remote sensing image data in the whole world and is transferred to earth station, and image acquiring module is from described
Face station obtains several panel heights of the river to be monitored in n wave band and divides remote sensing image a, and the high score in the same band is distant
The equal capable assemblings of image a are felt into the image in the complete river to be monitored of a width, n>0, and n is natural number；
1.2, map transfers the digital topography map that module transfers river region to be monitored from network；
1.3, preprocessing module pre-processes high score remote sensing image a, includes the following steps：
1.3.1, geometric correction and gray scale resampling
Length direction of the geometric correction module in digital topography map upper edge river to be monitored marks the m positions in monitoring time
Changeless control point, and mapping point corresponding with the control point is marked on high score remote sensing image a；Respectively described
The two-dimentional Cartesian coordinates different from establishing two on the high score remote sensing image a on digital topography map, and with digital terrain
The coordinate of mapping point described in the coordinate at the control point and high score remote sensing image a establishes multinomial correction function on figure；Then with
The multinomial correction function carries out geometric correction one by one to the pixel of high score remote sensing image a, is used while geometric correction
Bilinear interpolation method carries out gray scale resampling to high score remote sensing image a, then high score remote sensing image a is by geometric correction and gray scale weight
Become high score remote sensing image b, m after sampling>20, and m is natural number；
1.3.2, high score remote sensing image b splicings are inlayed
Module is inlayed in remote sensing image splicing to divide remote sensing image b to be matched respectively by shifting positioned at several panel heights of the same band
Method, histogram matching, the image mosaic method splicing based on jointing line inlays a width can show complete river to be monitored
Image high score remote sensing image c；Then each wave band respectively obtains a panel height and divides remote sensing image c；
1.3.3, histogram modification module stretches each remote sensing image c using Histogram Modification Methods, to enhance remote sensing image c
Clarity；
1.4, controller automatic identification：
1.4.1, the length direction in digital topography map upper edge river to be monitored uniformly selects k reference position point and s monitoring
Point, k>20, s>0, and k and s are natural number；
1.4.2 the actual mass concentration of the several water quality parameter of each reference position point, is acquired on the spot；
1.4.3, controller extracts the reflectivity of each reference position point corresponding position on each high score remote sensing image c；
1.4.4, controller by the actual mass concentration of each reference position point and each reference position point on each high score remote sensing image c
Reflectivity make linear, index, logarithm, multinomial, power regression analysis and obtain corresponding regression equation, be then based on from
The Regression Equations of the degree of dissipating minimum are about the actual mass concentration of reference position point and each reference position in each high score remote sensing
Relation function on image c between the reflectivity of corresponding position；
1.4.5, controller calculates reflectivity of the monitoring point on n high score remote sensing image c, according to the relation function, calculates
Go out the mass concentration of the water quality parameter at monitoring point, and is carried out on high score remote sensing image c with shade corresponding concentration height
Mark, just obtains the spatial distribution map of the mass concentration of the water quality parameter of monitoring point in river to be monitored；
1.5, manual identified
Spatial distribution map described in real time inspection obtains the real-time monitoring conclusion of the water quality condition in river to be monitored；To each time
The spatial distribution map of the river water quality parameter of section compares and analyzes, and obtains the water quality condition dynamic monitoring knot in river to be monitored
By.
2. a kind of river water quality monitoring method based on high score remote sensing satellite as described in claim 1, it is characterised in that：It is described
N wave band is respectively blue wave band, green wave band, red wave band and near infrared band.
3. a kind of river water quality monitoring method based on high score remote sensing satellite as claimed in claim 2, it is characterised in that：It is described
Control point includes road junction, bridge and/or river inflection point.
4. a kind of river water quality monitoring method based on high score remote sensing satellite as claimed in claim 3, it is characterised in that：It is described
Water quality parameter includes chlorophyll, suspended matter, total phosphorus, total nitrogen and/or total organic carbon.
5. a kind of river water quality monitoring method based on high score remote sensing satellite as claimed in claim 4, it is characterised in that：Pass through
Displacement matching method, histogram matching, the image mosaic method splicing based on jointing line are inlayed a width and can be shown and completely wait for
The detailed process for monitoring the high score remote sensing image c of the image in river is as follows：Divide remote sensing image a to adjacent another width one panel height
High score remote sensing image a movements so that two panel heights divide the relative position of remote sensing image a constantly to change, when two panel heights divide remote sensing image a
Lap when being completely superposed, then two panel heights divide remote sensing image a matchings to complete；Then use histogram matching by two panel heights
Divide the hue adjustment of remote sensing image a to unanimously；Then it is selected with the principle that do not intersect with the image in river on high score remote sensing image a
Go out the jointing line that two panel heights divide remote sensing image a, two panel heights divide remote sensing image a along seam splicing, and jointing line is carried out plumage
Change is handled；Finally, the height in complete river to be monitored can be shown by the high score remote sensing image a in the same band being set into a width
Divide remote sensing image c.
6. a kind of river water quality monitoring method based on high score remote sensing satellite as claimed in claim 5, it is characterised in that：Using
The detailed process that bilinear interpolation method carries out high score remote sensing image a gray scale resampling is as follows：Each on high score remote sensing image a
The gray value of 4 adjacent picture elements around pixel makees the gray value conduct obtained after linear interpolation in x-axis direction and y-axis direction
Gray value after the pixel geometric correction.
7. a kind of river water quality monitoring method based on high score remote sensing satellite as claimed in claim 6, it is characterised in that：Geometry
Correction the specific steps are：With each control point on digital topography map and high score remote sensing image a different coordinates, substitute into respectively
Multinomial establishes the equation group for the coefficient for solving multinomial correction function, and uses least square method, show that multinomial corrects letter
Several coefficients；The coefficient is substituted into after multinomial and obtains multinomial correction function；By the seat of the pixel of high score remote sensing image a
It can be obtained the cell coordinate of the high score remote sensing image a after geometric correction after mark substitution multinomial correction function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810121841.6A CN108507949B (en) | 2018-02-07 | 2018-02-07 | River water quality monitoring method based on high-resolution remote sensing satellite |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810121841.6A CN108507949B (en) | 2018-02-07 | 2018-02-07 | River water quality monitoring method based on high-resolution remote sensing satellite |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108507949A true CN108507949A (en) | 2018-09-07 |
| CN108507949B CN108507949B (en) | 2020-11-13 |
Family
ID=63375632
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810121841.6A Active CN108507949B (en) | 2018-02-07 | 2018-02-07 | River water quality monitoring method based on high-resolution remote sensing satellite |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108507949B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110988286A (en) * | 2019-12-18 | 2020-04-10 | 松辽水资源保护科学研究所 | Intelligent water resource long-term detection system |
| CN111241991A (en) * | 2020-01-08 | 2020-06-05 | 上海普适导航科技股份有限公司 | Water quality classification method based on remote sensing image |
| CN111274938A (en) * | 2020-01-19 | 2020-06-12 | 四川省自然资源科学研究院 | Web-oriented dynamic monitoring method and system for high-resolution remote sensing river water quality |
| CN111735503A (en) * | 2020-07-26 | 2020-10-02 | 榆林学院 | Water resource monitoring system based on big data and monitoring method thereof |
| CN112051222A (en) * | 2020-08-30 | 2020-12-08 | 山东锋士信息技术有限公司 | River and lake water quality monitoring method based on high-resolution satellite image |
| CN112365274A (en) * | 2020-12-01 | 2021-02-12 | 苏州深蓝空间遥感技术有限公司 | High-precision water pollution tracing method based on multi-source data |
| CN112763425A (en) * | 2020-12-14 | 2021-05-07 | 广东省科学院广州地理研究所 | Estuary turbid zone identification method and identification system |
| CN113109281A (en) * | 2021-04-13 | 2021-07-13 | 中国科学院成都生物研究所 | Water quality parameter quantitative inversion model based on hyperspectral remote sensing and construction method thereof |
| CN113324923A (en) * | 2021-06-07 | 2021-08-31 | 郑州大学 | Remote sensing water quality inversion method combining time-space fusion and deep learning |
| CN115014294A (en) * | 2022-05-18 | 2022-09-06 | 河海大学 | River channel agile imaging method for east-west satellite |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101493409A (en) * | 2008-12-11 | 2009-07-29 | 中山大学 | Method for automatically extracting water pollution information |
| CN103218780A (en) * | 2013-03-27 | 2013-07-24 | 中国科学院电子学研究所 | Orthorectification method of non-control satellite-borne SAR (synthetic aperture radar) image based on inverted RD positioning model |
| CN103413272A (en) * | 2013-07-22 | 2013-11-27 | 中国科学院遥感与数字地球研究所 | Low-spatial-resolution multisource remote sensing image space consistency correction method |
| CN103760112A (en) * | 2014-01-23 | 2014-04-30 | 中国科学院南京地理与湖泊研究所 | Lake water body blue-green algae abundance estimation method based on remote sensing |
| CN104636629A (en) * | 2015-03-05 | 2015-05-20 | 香港中文大学深圳研究院 | Method and device for analyzing emergent water quality pollution event |
| CN105158172A (en) * | 2015-08-22 | 2015-12-16 | 中国城市科学研究会 | Analysis method of remote sensing inversion of water color parameters of inland class II water |
| CN106442422A (en) * | 2016-11-18 | 2017-02-22 | 中国水利水电科学研究院 | Method for indirect remote-sensing monitoring of water quality of macrophytic regions of macrophytic lakes |
| CN106940290A (en) * | 2017-01-22 | 2017-07-11 | 广州地理研究所 | Reservoir water body ammonia-nitrogen content evaluation method based on high-spectral data |
-
2018
- 2018-02-07 CN CN201810121841.6A patent/CN108507949B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101493409A (en) * | 2008-12-11 | 2009-07-29 | 中山大学 | Method for automatically extracting water pollution information |
| CN103218780A (en) * | 2013-03-27 | 2013-07-24 | 中国科学院电子学研究所 | Orthorectification method of non-control satellite-borne SAR (synthetic aperture radar) image based on inverted RD positioning model |
| CN103413272A (en) * | 2013-07-22 | 2013-11-27 | 中国科学院遥感与数字地球研究所 | Low-spatial-resolution multisource remote sensing image space consistency correction method |
| CN103760112A (en) * | 2014-01-23 | 2014-04-30 | 中国科学院南京地理与湖泊研究所 | Lake water body blue-green algae abundance estimation method based on remote sensing |
| CN104636629A (en) * | 2015-03-05 | 2015-05-20 | 香港中文大学深圳研究院 | Method and device for analyzing emergent water quality pollution event |
| CN105158172A (en) * | 2015-08-22 | 2015-12-16 | 中国城市科学研究会 | Analysis method of remote sensing inversion of water color parameters of inland class II water |
| CN106442422A (en) * | 2016-11-18 | 2017-02-22 | 中国水利水电科学研究院 | Method for indirect remote-sensing monitoring of water quality of macrophytic regions of macrophytic lakes |
| CN106940290A (en) * | 2017-01-22 | 2017-07-11 | 广州地理研究所 | Reservoir water body ammonia-nitrogen content evaluation method based on high-spectral data |
Non-Patent Citations (7)
| Title |
|---|
| MOHAMMAD HAJI GHOLIZADEH ET AL.: "A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques", 《SENSORS》 * |
| TUNG-CHING SU ET AL.: "Application of Multispectral Sensors Carried on Unmanned Aerial Vehicle (UAV) to Trophic State Mapping of Small Reservoirs: A Case Study of Tain-Pu Reservoir in Kinmen, Taiwan", 《REMOTE SENSING》 * |
| 叶金印 等: "气象遥感图像及格点场重采样插值方法", 《计算机工程与应用》 * |
| 周艺 等: "遥感技术在内陆水体水质监测中的应用", 《水科学进展》 * |
| 张兵 等: "太湖水体3中典型水质参数的高光谱遥感反演", 《湖泊科学》 * |
| 江辉 等: "基于RBF神经网络的鄱阳湖表层水体总悬浮颗粒物浓度遥感反演", 《生态环境学报》 * |
| 谭兆: "遥感影像与地形图自动配准方法的研究", 《科技创新导报》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110988286A (en) * | 2019-12-18 | 2020-04-10 | 松辽水资源保护科学研究所 | Intelligent water resource long-term detection system |
| CN111241991A (en) * | 2020-01-08 | 2020-06-05 | 上海普适导航科技股份有限公司 | Water quality classification method based on remote sensing image |
| CN111274938A (en) * | 2020-01-19 | 2020-06-12 | 四川省自然资源科学研究院 | Web-oriented dynamic monitoring method and system for high-resolution remote sensing river water quality |
| CN111735503A (en) * | 2020-07-26 | 2020-10-02 | 榆林学院 | Water resource monitoring system based on big data and monitoring method thereof |
| CN112051222A (en) * | 2020-08-30 | 2020-12-08 | 山东锋士信息技术有限公司 | River and lake water quality monitoring method based on high-resolution satellite image |
| CN112365274B (en) * | 2020-12-01 | 2022-08-23 | 苏州深蓝空间遥感技术有限公司 | High-precision water pollution tracing method based on multi-source data |
| CN112365274A (en) * | 2020-12-01 | 2021-02-12 | 苏州深蓝空间遥感技术有限公司 | High-precision water pollution tracing method based on multi-source data |
| CN112763425A (en) * | 2020-12-14 | 2021-05-07 | 广东省科学院广州地理研究所 | Estuary turbid zone identification method and identification system |
| CN112763425B (en) * | 2020-12-14 | 2021-12-28 | 广东省科学院广州地理研究所 | Estuary turbid zone identification method and identification system |
| CN113109281A (en) * | 2021-04-13 | 2021-07-13 | 中国科学院成都生物研究所 | Water quality parameter quantitative inversion model based on hyperspectral remote sensing and construction method thereof |
| CN113109281B (en) * | 2021-04-13 | 2022-11-29 | 中国科学院成都生物研究所 | Water quality parameter quantitative inversion model based on hyperspectral remote sensing and construction method thereof |
| CN113324923A (en) * | 2021-06-07 | 2021-08-31 | 郑州大学 | Remote sensing water quality inversion method combining time-space fusion and deep learning |
| CN113324923B (en) * | 2021-06-07 | 2023-07-07 | 郑州大学 | Remote sensing water quality inversion method combining space-time fusion and deep learning |
| CN115014294A (en) * | 2022-05-18 | 2022-09-06 | 河海大学 | River channel agile imaging method for east-west satellite |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108507949B (en) | 2020-11-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108507949A (en) | A kind of river water quality monitoring method based on high score remote sensing satellite | |
| CN110378909B (en) | Single wood segmentation method for laser point cloud based on Faster R-CNN | |
| CN102084794B (en) | Method and device for early detecting crop pests based on multisensor information fusion | |
| Lelovics et al. | Design of an urban monitoring network based on Local Climate Zone mapping and temperature pattern modelling | |
| CN111368817B (en) | Method and system for quantitatively evaluating thermal effect based on earth surface type | |
| CN110689563A (en) | Data processing method for extracting illegal building information in remote sensing image | |
| CN109977813A (en) | A kind of crusing robot object localization method based on deep learning frame | |
| CN108875794A (en) | Image visibility detection method based on transfer learning | |
| CN103018196A (en) | Fast detection method for rape water demand information | |
| CN112577906B (en) | Urban green land soil moisture content detection method | |
| CN102636486A (en) | Method for automatically detecting three-true-leaf period of cotton | |
| CN102855485A (en) | Automatic wheat earing detection method | |
| CN106683059A (en) | Night light data sequence construction method and apparatus | |
| CN116881812A (en) | Method for estimating forest carbon reserves based on multisource remote sensing data and random forest method | |
| CN108550154A (en) | A kind of method of accurately measuring karst earth's surface bare rock accounting | |
| CN117710831A (en) | Rural black and odorous water body monitoring method based on satellite remote sensing | |
| CN104282006A (en) | High-resolution image splicing method based on CE-2 data | |
| CN116778343B (en) | Target image feature extraction method for comprehensive identification | |
| Liu et al. | Fractional coverage rather than green chromatic coordinate is a robust indicator to track grassland phenology using smartphone photography | |
| CN106770372A (en) | White glass detection method of surface flaw based on micro-vision | |
| CN116593404A (en) | Hyperspectral remote sensing monitoring data processing system based on surface water pollution factors | |
| CN112365274A (en) | High-precision water pollution tracing method based on multi-source data | |
| Yamamoto et al. | Measurement of growth information of a strawberry plant using a natural interaction device | |
| Cao et al. | A deep learning method for detecting leaf phenology from phenocam imagery | |
| CN104457842A (en) | Pineapple non-destructive testing device and method based on hyperspectral synchronous scanning imaging technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |