CN108169445A - A kind of effective monitoring lake water quality system - Google Patents
A kind of effective monitoring lake water quality system Download PDFInfo
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Abstract
The present invention provides a kind of effective monitoring lake water quality systems,Including remote sensing satellite,Multiple node monitoring devices,Remote sensing monitoring station and monitoring center,The remote sensing satellite is used to acquire the different remote sensing source images in lake,The node monitoring device is arranged in lake to be monitored,For obtaining each monitoring point water quality data in lake,And the monitoring data of monitoring are sent to monitoring center by wireless network,The remote sensing monitoring station is used to receive the different remote sensing source images in lake,Fusion treatment is carried out to remote sensing images,And blending image is sent to monitoring center by wireless network,The monitoring center is monitored lake water quality according to each monitoring point monitoring data and blending image,The node monitoring device includes water quality sensor and GPS chip,The water quality sensor is used to obtain monitoring point water quality data,The GPS chip is used to obtain monitoring location.Beneficial effects of the present invention are：Realize the accurate measurements of lake water quality.
Description
Technical field
The present invention relates to water quality monitoring technical fields, and in particular to a kind of effective monitoring lake water quality system.
Background technology
Lake is the important carrier of surface water resources, is the important factor for maintaining ecosystem health, has and adjusts rivers and creeks
Runoff, provides the multiple functions such as industry and drinking water source at development irrigation.In support socio-economic development and ecological environment is maintained to put down
Important function has been played in weighing apparatus.
In recent years, due to the activity of the mankind, lake water quality is destroyed, and how lake water quality is effectively monitored, right
Play an important roll in guarantee Sustainable Water Resources Development.
Invention content
In view of the above-mentioned problems, a kind of the present invention is intended to provide effective monitoring lake water quality system.
The purpose of the present invention is realized using following technical scheme：
A kind of effective monitoring lake water quality system is provided, including remote sensing satellite, multiple node monitoring devices, remote sensing prison
Survey station and monitoring center, for acquiring the different remote sensing source images in lake, the node monitoring device is set the remote sensing satellite
In lake to be monitored, for obtaining each monitoring point water quality data in lake, and the monitoring data of monitoring are passed through into wireless network
Monitoring center is sent to, remote sensing images are merged for receiving the different remote sensing source images in lake in the remote sensing monitoring station
Processing, and blending image is sent to monitoring center by wireless network, the monitoring center is according to each monitoring point monitoring data
Lake water quality is monitored with blending image, the node monitoring device includes water quality sensor and GPS chip, the water quality
For obtaining monitoring point water quality data, the GPS chip is used to obtain monitoring location sensor.
Beneficial effects of the present invention are：By remote sensing satellite and multiple node monitoring devices, the standard of lake water quality is realized
Really monitoring.
Description of the drawings
Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not form any limit to the present invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is the structure diagram of the present invention；
Reference numeral：
Remote sensing satellite 1, node monitoring device 2, remote sensing monitoring station 3, monitoring center 4.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of effective monitoring lake water quality system of the present embodiment is supervised including remote sensing satellite 1, multiple nodes
Device 2, remote sensing monitoring station 3 and monitoring center 4 are surveyed, the remote sensing satellite 1 is described for acquiring the different remote sensing source images in lake
Node monitoring device 2 is arranged in lake to be monitored, for obtaining each monitoring point water quality data in lake, and by the monitoring of monitoring
Data are sent to monitoring center 4 by wireless network, and the remote sensing monitoring station 3 is used to receive the different remote sensing source images in lake,
Fusion treatment is carried out, and blending image is sent to monitoring center 4, the monitoring center 4 by wireless network to remote sensing images
Lake water quality is monitored according to each monitoring point monitoring data and blending image, the node monitoring device 2 is passed including water quality
Sensor and GPS chip, for obtaining monitoring point water quality data, the GPS chip is used to obtain monitoring point the water quality sensor
Position.
The present embodiment realizes the accurate measurements of lake water quality by remote sensing satellite 1 and multiple node monitoring devices 2.
Preferably, the remote sensing monitoring station 3 includes first processing module and Second processing module, the first processing module
For being merged to different source images, the blending image of remote sensing images is obtained, the Second processing module is used for described
The syncretizing effect of blending image is evaluated.
This preferred embodiment remote sensing monitoring station 3 realizes the accurate fusion of lake remote sensing images and to the accurate of syncretizing effect
Evaluation.
Preferably, the remote sensing satellite 1 is for acquiring the different remote sensing source images in lake, specially：Acquire two width remote sensing
Source images RU, MH, wherein, RU is high-definition picture, and MH is low-resolution image；
The first processing module includes the first fusion submodule, the second fusion submodule and comprehensive fusion submodule, institute
The first fusion submodule is stated for obtaining the Single cell fusion of remote sensing images as a result, the second fusion submodule is used to obtain remote sensing
The secondary fusion results of image, the comprehensive fusion submodule obtain remote sensing figure according to Single cell fusion result and secondary fusion results
The blending image of picture；
The first fusion submodule is used to obtain the Single cell fusion of remote sensing images as a result, being specially：
A, remote sensing images are transformed into HIS color space from rgb color space, wherein, R, G, B represent red, green respectively
Color, blue component, H, I, S represent tone, brightness, saturation degree component respectively；B, it is bright with high-definition picture in HIS space
The luminance component that component substitutes low-resolution image is spent, then low-resolution image inversion is changed into rgb color space, and will be corresponding
Gray level image as Single cell fusion result DT1(i, j), wherein, i, j are respectively the line number and row number of pixel in image, i=1,
2 ..., N, j=1,2 ..., M.
The second fusion submodule is used to obtain the secondary fusion results of remote sensing images, specially：
It is merged using following formula：
In formula, DT2(i, j) represents secondary fusion results, and RU (i, j) represents gray values of the source images RU in pixel (i, j), MH (i,
J) source images MH is represented in the gray value of pixel (i, j), i, j are respectively the line number and row number of pixel in image, i=1, and 2 ...,
N, j=1,2 ..., M,For weight coefficient,
The comprehensive fusion submodule obtains the fusion figure of remote sensing images according to Single cell fusion result and secondary fusion results
Picture, specially：In formula, DT (i, j) is represented
The blending image of remote sensing images, i, j are respectively the line number and row number of pixel in image, i=1,2 ..., N, j=1,2 ..., M.
This preferred embodiment obtains blending image by Single cell fusion result and secondary fusion results, has obtained high quality
Blending image specifically, the first fusion submodule merges remote sensing images in HIS color space, is more in line with the mankind's
Visual characteristic, the existing high spatial resolution of Single cell fusion result images of acquisition, and have the coloration identical with original image and satisfy
And degree；Remote sensing images to be fused are considered as two two-dimensional matrixes by the second fusion submodule, by spatial position pair in two images
The pixel value answered carries out pixel value of the sum of the weighting summation, weighting as new images on the spatial position after being handled, obtain
More rich information has been arrived, multispectral image can be decomposed into multiple gray level images by the fusion for multispectral image, when use,
Then fusion treatment is carried out respectively, then they are synthesized into a multispectral image；Comprehensive fusion submodule combination Single cell fusion
As a result blending image is obtained with secondary fusion results, has obtained the good blending image of syncretizing effect；Fusion is carried out in Pixel-level, is protected
Information as much as possible has been stayed, has increased the information content that each pixel includes in image, is provided for next step image procossing
More characteristic informations can be easier identification potential target.
Preferably, the Second processing module includes the first evaluation submodule, the second evaluation submodule and overall merit
Module, the first evaluation submodule are used to obtain the first evaluation parameter of blending image, and the second evaluation submodule is used for
The second evaluation parameter of blending image is obtained, the overall merit submodule is according to the first evaluation parameter and the second evaluation parameter pair
Blending image is evaluated.
The first evaluation submodule is used to obtain the first evaluation parameter of blending image, specially：It is calculated using following formula
First evaluation parameter of blending image：In formula
In, RX1Represent the first evaluation parameter, L represents the quantity of the gray level of blending image, pkRepresent k-th of gray level in blending image
Pixel account for the ratio of total pixel in blending image, piece represents the gray value mean value of all pixels of blending image；
The second evaluation submodule is used to obtain the second evaluation parameter of blending image, specially：It is calculated using following formula
Second evaluation parameter of blending image：
In formula, RX2Represent the second evaluation parameter, FN (i, j) represents standard reference image, and i, j are respectively the line number of pixel in image
And row number, i=1,2 ..., N, j=1,2 ..., M；
The overall merit submodule evaluates blending image according to the first evaluation parameter and the second evaluation parameter：Meter
Calculate the assessment parameter of blending image：In formula, RX expressions are melted
Close the assessment parameter of image；Assessment parameter is bigger, represents that syncretizing effect is better.
This preferred embodiment realizes the accurate evaluation of remote sensing image fusion effect, specifically, the first evaluation parameter is based on
Blending image is evaluated, and has fully considered the contrast between the information content of image and image pixel, and the second evaluation parameter is based on
Blending image and standard reference image are evaluated, and have fully considered the signal-to-noise ratio of image, and assessment parameter is commented with reference to first
The advantages of valency parameter and the second evaluation parameter, realizes the accurate evaluation of syncretizing effect, so as to ensure that monitoring lake water quality water
It is flat.
Prize monitoring carries out lake water quality using effective monitoring lake water quality system of the invention, chooses 5 lakes and carries out
Simulated experiment, respectively lake 1, lake 2, lake 3, lake 4, lake 5, count monitoring efficiency and monitoring cost, together
Existing monitoring system is compared, and generation is had the beneficial effect that shown in table：
| Monitoring efficiency improves | Monitoring cost reduces | |
| Lake 1 | 29% | 27% |
| Lake 2 | 27% | 26% |
| Lake 3 | 26% | 26% |
| Lake 4 | 25% | 24% |
| Lake 5 | 24% | 22% |
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention rather than the present invention is protected
The limitation of range is protected, although being explained in detail with reference to preferred embodiment to the present invention, those of ordinary skill in the art should
Work as understanding, technical scheme of the present invention can be modified or replaced equivalently, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (7)
1. a kind of effective monitoring lake water quality system, which is characterized in that including remote sensing satellite, multiple node monitoring devices, distant
Feel monitoring station and monitoring center, the remote sensing satellite is used to acquire the different remote sensing source images in lake, the node monitoring device
It is arranged in lake to be monitored, for obtaining each monitoring point water quality data in lake, and the monitoring data of monitoring is passed through wireless
To monitoring center, the remote sensing monitoring station carries out remote sensing images for receiving the different remote sensing source images in lake transmission of network
Fusion treatment, and blending image is sent to monitoring center by wireless network, the monitoring center monitors according to each monitoring point
Data and blending image are monitored lake water quality.
2. effective monitoring lake water quality system according to claim 1, which is characterized in that the remote sensing monitoring station includes
First processing module and Second processing module, the first processing module obtain distant for being merged to different source images
Feel the blending image of image, the Second processing module is used to evaluate the syncretizing effect of the blending image.
3. effective monitoring lake water quality system according to claim 2, which is characterized in that the remote sensing satellite is used to adopt
Collect the different remote sensing source images in lake, specially：Two width remote sensing source images RU, MH are acquired, wherein, RU is high-definition picture,
MH is low-resolution image；
The first processing module includes the first fusion submodule, the second fusion submodule and comprehensive fusion submodule, and described the
One fusion submodule is used to obtain the Single cell fusion of remote sensing images as a result, the second fusion submodule is used to obtain remote sensing images
Secondary fusion results, the comprehensive fusion submodule obtains remote sensing images according to Single cell fusion result and secondary fusion results
Blending image.
4. effective monitoring lake water quality system according to claim 3, which is characterized in that the first fusion submodule
For obtaining the Single cell fusion of remote sensing images as a result, being specially：
A, remote sensing images are transformed into HIS color space from rgb color space, wherein, R, G, B represent red, green, blue respectively
Colouring component, H, I, S represent tone, brightness, saturation degree component respectively；B, in HIS space, with high-definition picture luminance component
The luminance component of low-resolution image is substituted, then rgb color space is changed into low-resolution image inversion, and by corresponding gray scale
Image is as Single cell fusion result DT1(i, j), wherein, i, j are respectively the line number and row number of pixel in image, i=1, and 2 ...,
N, j=1,2 ..., M；
The second fusion submodule is used to obtain the secondary fusion results of remote sensing images, specially：
It is merged using following formula：In formula
In, DT2(i, j) represents secondary fusion results, and RU (i, j) represents source images RU in the gray value of pixel (i, j), MH (i, j) table
Show gray values of the source images MH in pixel (i, j), i, j are respectively the line number and row number of pixel in image, i=1,2 ..., N, j=
1,2 ..., M,For weight coefficient,
5. effective monitoring lake water quality system according to claim 4, which is characterized in that the comprehensive fusion submodule
The blending image of remote sensing images is obtained according to Single cell fusion result and secondary fusion results, specially： In formula, DT (i, j) represents the blending image of remote sensing images,
I, j are respectively the line number and row number of pixel in image, i=1,2 ..., N, j=1,2 ..., M.
6. effective monitoring lake water quality system according to claim 5, which is characterized in that the Second processing module packet
The first evaluation submodule, the second evaluation submodule and overall merit submodule are included, the first evaluation submodule melts for obtaining
The first evaluation parameter of image is closed, the second evaluation submodule is used to obtain the second evaluation parameter of blending image, described comprehensive
Evaluation submodule is closed to evaluate blending image according to the first evaluation parameter and the second evaluation parameter；
The first evaluation submodule is used to obtain the first evaluation parameter of blending image, specially：It is calculated and merged using following formula
First evaluation parameter of image：In formula, RX1
Represent the first evaluation parameter, L represents the quantity of the gray level of blending image, pkRepresent the picture of k-th of gray level in blending image
Element accounts for the ratio of total pixel in blending image, and H represents the gray value mean value of all pixels of blending image.
7. effective monitoring lake water quality system according to claim 6, which is characterized in that the second evaluation submodule
For obtaining the second evaluation parameter of blending image, specially：The second evaluation parameter of blending image is calculated using following formula：In formula, RX2Represent the second evaluation ginseng
Number, FN (i, j) represent standard reference image, and i, j are respectively the line number and row number of pixel in image, i=1,2 ..., N, j=1,
2,…,M；
The overall merit submodule evaluates blending image according to the first evaluation parameter and the second evaluation parameter：Calculating is melted
Close the assessment parameter of image：In formula, RX represents fusion figure
The assessment parameter of picture；Assessment parameter is bigger, represents that syncretizing effect is better.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
| CN112082962A (en) * | 2020-09-04 | 2020-12-15 | 安徽思环科技有限公司 | Water quality ultraviolet-visible spectrum denoising and correcting method based on compressed sensing |
| CN114965918A (en) * | 2022-04-20 | 2022-08-30 | 重庆两江生态渔业发展有限公司 | Water quality analysis method based on satellite remote sensing image |
| WO2022217589A1 (en) * | 2021-04-16 | 2022-10-20 | 长沙有色冶金设计研究院有限公司 | Water quality image analysis method and system based on deep learning, and device and medium |
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Cited By (4)
| 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 |
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| WO2022217589A1 (en) * | 2021-04-16 | 2022-10-20 | 长沙有色冶金设计研究院有限公司 | Water quality image analysis method and system based on deep learning, and device and medium |
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Application publication date: 20180615 |