CN116593404A - Hyperspectral remote sensing monitoring data processing system based on surface water pollution factors - Google Patents
Hyperspectral remote sensing monitoring data processing system based on surface water pollution factors Download PDFInfo
- Publication number
- CN116593404A CN116593404A CN202310348645.3A CN202310348645A CN116593404A CN 116593404 A CN116593404 A CN 116593404A CN 202310348645 A CN202310348645 A CN 202310348645A CN 116593404 A CN116593404 A CN 116593404A
- Authority
- CN
- China
- Prior art keywords
- module
- remote sensing
- data processing
- monitoring
- surface water
- 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.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 66
- 238000012545 processing Methods 0.000 title claims abstract description 47
- 239000002352 surface water Substances 0.000 title claims abstract description 45
- 238000003911 water pollution Methods 0.000 title claims abstract description 33
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 23
- 238000004891 communication Methods 0.000 claims abstract description 10
- 230000003595 spectral effect Effects 0.000 claims description 7
- 238000000034 method Methods 0.000 abstract description 17
- 238000001228 spectrum Methods 0.000 abstract description 14
- 230000008569 process Effects 0.000 abstract description 12
- 230000009471 action Effects 0.000 abstract description 7
- 239000000463 material Substances 0.000 abstract description 4
- 238000003895 groundwater pollution Methods 0.000 abstract 1
- 238000005070 sampling Methods 0.000 description 6
- 230000007547 defect Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 4
- 230000005856 abnormality Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 230000031700 light absorption Effects 0.000 description 2
- 230000001915 proofreading effect Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 239000002351 wastewater Substances 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
-
- 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
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
-
- 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
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
-
- 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
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/20—Controlling water pollution; Waste water treatment
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Chemical & Material Sciences (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a hyperspectral remote sensing monitoring data processing system based on a surface water pollution factor. The system comprises an acquisition module, a calibration module, a data processing module, a network communication module and a monitoring module; the acquisition module adopts a remote control acquisition device loaded with a visible light camera and a hyperspectral camera, the calibration module comprises an RTK positioning module and an image comparison module, and the monitoring module comprises a ground station, a remote control station and a monitoring station. The invention has the advantages that the acquisition module acquires visible light image spectrum images in the water, the calibration module calibrates image information and positions, the data processing module processes image data, the network communication module wirelessly transmits the information, the monitoring module monitors and intervenes the acquisition action of the acquisition module and collects the processed monitoring data, thereby realizing quasi-real-time monitoring of the ground water pollution factor in a mode of saving manpower and material resources and improving the referenceability of the monitoring data.
Description
Technical Field
The invention relates to the technical field of water quality monitoring and evaluation, in particular to a hyperspectral remote sensing monitoring data processing system based on surface water pollution factors.
Background
The surface water quality monitoring and evaluation has important significance for balancing an ecological system and ensuring public life safety, various water body index requirements under different geographies and climate conditions are different, the real-time state of water quality can be determined by processing and analyzing water quality monitoring data, but the water quality evaluation result is not accurate enough due to the influence of various objective factors, the water quality grade of a water area cannot be reflected, at present, the sample is decomposed and extracted mainly by periodically sampling based on technical staff according to detection equipment, and the content of various elements in the water quality is determined, so that the water quality is evaluated.
However, the method for monitoring the quality of the surface water is inconvenient because of consuming large manpower and time. Moreover, the technician needs to spend a long time for storing, extracting, detecting and the like the sample according to the monitoring equipment, so that the sampling position cannot be simply and conveniently obtained, and the timeliness and the referenceability of the data result cannot be well ensured.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factor provided by the invention has the advantages that the specific acquisition position of the acquired image information can be known, the hyperspectral remote sensing image acquired by the acquisition module is checked pixel by the image comparison module, the reliability of the acquired information is ensured, the hyperspectral remote sensing technology is utilized, the quasi-real-time monitoring of the surface water pollution factor is realized in a mode of saving manpower and material resources, the referenceability of the monitoring data is improved, and the like.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factors comprises an acquisition module, a calibration module, a data processing module, a network communication module and a monitoring module; the acquisition module adopts a remote control acquisition device loaded with a visible light camera and a hyperspectral camera, the calibration module comprises an RTK positioning module and an image comparison module, and the monitoring module comprises a ground station, a remote control station and a monitoring station.
Preferably, the remote control collector of the collecting module is used for collecting images in a water area to be monitored, wherein the visible light camera is used for shooting visible light images of the surface water, and the hyperspectral camera is used for shooting spectral images of the surface water.
Preferably, the calibration module is used for calibrating the information acquired by the acquisition module. The RTK positioning module is used for positioning the acquisition module in real time so as to acquire the specific acquisition position of the acquired image information.
The technical scheme is adopted: the target region is imaged simultaneously in tens to hundreds of consecutive and finely divided spectral bands in the ultraviolet, visible, near infrared and mid-infrared regions of the electromagnetic spectrum by means of a hyperspectral camera mounted on an acquisition platform. At the same time of obtaining surface water image information, the spectrum information is obtained. The imaging technology is combined with the spectrum detection technology, and when the spatial characteristics of the target are imaged, tens or even hundreds of narrow wave bands are formed for each spatial pixel through dispersion so as to carry out continuous spectrum coverage. The data thus formed can be visually described by a "three-dimensional data block", and the hyperspectral image integrates the image information and the spectral information of the sample. The image information can reflect the external quality characteristics of the sample such as size, shape, defects and the like, and the image can obviously reflect a certain defect under a certain specific wavelength due to different components and different light absorption, and the spectrum information can fully reflect the differences of the physical structure and chemical components in the sample, so that the condition of pollution factors in the surface water can be known, and the tedious work of a technician for storing, extracting, detecting the sample according to monitoring equipment and the like is omitted.
Preferably, the image comparison module in the calibration module is used for performing pixel-by-pixel calibration on the hyperspectral remote sensing image acquired by the acquisition module.
Preferably, the image comparison module in the calibration module performs pixel-by-pixel calibration on the hyperspectral remote sensing image, and then performs pixel-by-pixel offset on the image to obtain a corrected hyperspectral remote sensing image.
The technical scheme is adopted: the image calibration module moves the (i+1) th row of pixels of the reference image pixel by pixel relative to the (i) th row of pixels of the reference image according to a preset movement rule, calculates error values of the (i+1) th row of pixels of the reference image and the (i) th row of pixels of the reference image according to a calibration formula every time a pixel position is moved.
The proofreading formula is as follows: e= [ Σ (Rp-Cq) 2]/m2;
wherein E is an error value of an ith row pixel of the reference image and an ith row pixel of the reference image, rp is a value of a p-th pixel in the ith row pixel, cq is a value of a q-th pixel in the ith row pixel of the reference image, and the q-th pixel in the ith row pixel of the reference image is aligned with a p-th pixel column in the ith row pixel; m is the number of pixels in column alignment with the ith row of pixels in the (i+1) th row of pixels.
Determining the offset of the (i+1) -th row pixels relative to the (i+1) -th row pixels as the relative offset of the (i+1) -th row pixels when the error value is minimum;
the offset of the i+1th row of pixels relative to the i th row of pixels is positive offset or negative offset, and the positive value and the negative value of the offset of the i+1th row of pixels relative to the i th row of pixels are determined according to a preset positive offset direction;
an absolute offset of each row of pixels of the reference image is calculated. Comprising the following steps: calculating the sum of the relative offsets of all rows between the ith row of pixels and the predetermined target row of pixels to obtain a sum value; the absolute offset of the i-th row of pixels is the sum of the sum and the relative offset of the i-th row of pixels;
according to the absolute offset of each row of pixels of the reference wave band, moving the corresponding row in the hyperspectral remote sensing image to be corrected wave band by wave band to obtain a corrected hyperspectral remote sensing image;
the number of pixels of the ith row of pixels is the absolute value of the absolute offset of the ith row of pixels of the reference wave band, and the moving direction of the ith row of pixels of each wave band is the same as the offset direction corresponding to the absolute offset of the ith row of pixels of the reference wave band; wherein i is a positive integer greater than or equal to 1;
determining a wave band from the hyperspectral remote sensing image to be corrected as a reference wave band, determining the relative offset of each line through the minimum error of the adjacent lines, and determining the absolute offset of each line (namely the offset of each line relative to the pixels of the target line) through the relative offset of each line, so that the corresponding line in the hyperspectral remote sensing image to be corrected is moved wave band by wave band according to the absolute offset of each line, and the corrected hyperspectral remote sensing image is obtained. Therefore, the influence of image abnormality caused by the image information collected by the sampling module in the moving process on the data processing work can be avoided.
Preferably, the data processing module is used for processing the calibrated time sequence hyperspectral remote sensing image data of the water area to be monitored and obtaining the surface water pollution factor index.
Preferably, the ground station in the monitoring module is used for automatically planning the route of the acquisition module, and the remote control station in the monitoring module is used for manually controlling the route of the acquisition module.
The technical scheme is adopted: when the data processing module processes the calibrated time sequence hyperspectral remote sensing image data of the water area to be monitored, the data processing module firstly acquires the calibrated time sequence hyperspectral remote sensing image data of the water area to be monitored. And inverting each hyperspectral remote sensing image data in the time sequence hyperspectral remote sensing image data to obtain the optical characteristic data of each hyperspectral remote sensing image data. And calculating the surface water pollution factor index of each hyperspectral remote sensing image data based on the optical characteristic data of each hyperspectral remote sensing image data. And constructing a surface water pollution factor index time sequence based on the wastewater pollution indexes of the hyperspectral remote sensing image data. And carrying out space-time mode analysis on the surface water pollution factor index time sequence to obtain an analysis result.
(III) beneficial effects
Compared with the prior art, the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factor has the following beneficial effects:
1. according to the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factor, the acquisition module, the calibration module, the data processing module, the network communication module and the monitoring module are arranged, the acquisition module acquires visible light image spectrum images in the water, the calibration module calibrates image information and positions, the data processing module processes image data, the network communication module wirelessly transmits information, the monitoring module monitors and intervenes the acquisition action of the acquisition module and collects the processed monitoring data, so that the quasi-real-time monitoring of the surface water pollution factor is realized in a mode of saving manpower and material resources, and the referenceof the monitoring data is improved.
2. According to the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factors, the calibration module is arranged, the RTK positioning module is used for positioning the acquisition module in real time so as to acquire the specific acquisition position of the acquired image information, the image comparison module is used for performing pixel-by-pixel correction on the hyperspectral remote sensing image acquired by the acquisition module, the influence of image abnormality caused by the acquisition of the image information by the sampling module in the moving process on the data processing work can be avoided, the reliability of the acquired information is ensured, and the monitoring data is more reliable.
Drawings
FIG. 1 is a schematic diagram of the overall structure of a hyperspectral remote sensing monitoring data processing system based on surface water pollution factors, which is provided by the invention;
FIG. 2 is a schematic diagram of the workflow of an image comparison module of the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factor;
fig. 3 is a schematic diagram of a working flow of a data processing module of the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factor.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factor comprises an acquisition module, a calibration module, a data processing module, a network communication module and a monitoring module; the acquisition module adopts a remote control acquisition device loaded with a visible light camera and a hyperspectral camera, the calibration module comprises an RTK positioning module and an image comparison module, and the monitoring module comprises a ground station, a remote control station and a monitoring station.
Example 1: the remote control collector of the collection module is used for collecting images in a water area to be monitored, wherein the visible light camera is used for shooting visible light images of the surface water, and the hyperspectral camera is used for shooting spectral images of the surface water. The calibration module is used for calibrating the information acquired by the acquisition module. The RTK positioning module is used for positioning the acquisition module in real time so as to acquire the specific acquisition position of the acquired image information. The target region is imaged simultaneously in tens to hundreds of consecutive and finely divided spectral bands in the ultraviolet, visible, near infrared and mid-infrared regions of the electromagnetic spectrum by means of a hyperspectral camera mounted on an acquisition platform. At the same time of obtaining surface water image information, the spectrum information is obtained. The imaging technology is combined with the spectrum detection technology, and when the spatial characteristics of the target are imaged, tens or even hundreds of narrow wave bands are formed for each spatial pixel through dispersion so as to carry out continuous spectrum coverage. The data thus formed can be visually described by a "three-dimensional data block", and the hyperspectral image integrates the image information and the spectral information of the sample. The image information can reflect the external quality characteristics of the sample such as size, shape, defects and the like, and the image can obviously reflect a certain defect under a certain specific wavelength due to different components and different light absorption, and the spectrum information can fully reflect the differences of the physical structure and chemical components in the sample, so that the condition of pollution factors in the surface water can be known, and the tedious work of a technician for storing, extracting, detecting the sample according to monitoring equipment and the like is omitted.
Example 2: the image comparison module in the calibration module is used for performing pixel-by-pixel calibration on the hyperspectral remote sensing image acquired by the acquisition module. The image comparison module in the calibration module performs pixel-by-pixel calibration on the hyperspectral remote sensing image and then performs pixel-by-pixel offset on the hyperspectral remote sensing image to obtain a corrected hyperspectral remote sensing image. The image calibration module moves the (i+1) th row of pixels of the reference image pixel by pixel relative to the (i) th row of pixels of the reference image according to a preset movement rule, and calculates error values of the (i+1) th row of pixels of the reference image and the (i) th row of pixels of the reference image according to a calibration formula every one pixel position moved.
The proofreading formula is: e= [ Σ (Rp-Cq) 2]/m2;
wherein E is the error value of the (i+1) -th line pixel of the reference image and the (i) -th line pixel of the reference image, rp is the value of the (p) -th pixel in the (i) -th line pixel, cq is the value of the (q) -th pixel in the (i+1) -th line pixel, and the (q) -th pixel in the (i+1) -th line pixel is aligned with the (p) -th pixel column in the (i) -th line pixel; m is the number of pixels in column alignment with the ith row of pixels in the (i+1) th row of pixels.
Determining the offset of the (i+1) -th row pixels relative to the (i+1) -th row pixels as the relative offset of the (i+1) -th row pixels when the error value is minimum;
the offset of the (i+1) th row of pixels relative to the (i) th row of pixels is positive offset or negative offset, and the positive value and the negative value of the offset of the (i+1) th row of pixels relative to the (i) th row of pixels are determined according to a preset positive offset direction;
an absolute offset of each row of pixels of the reference image is calculated. Comprising the following steps: calculating the sum of the relative offsets of all rows between the ith row of pixels and the predetermined target row of pixels to obtain a sum value; the absolute offset of the i-th row of pixels is the sum of the sum value and the relative offset of the i-th row of pixels;
according to the absolute offset of each row of pixels of the reference wave band, moving the corresponding row in the hyperspectral remote sensing image to be corrected wave band by wave band to obtain a corrected hyperspectral remote sensing image;
the pixel number of the i-th row of pixels is the absolute value of the absolute offset of the i-th row of pixels of the reference wave band, and the moving direction of the i-th row of pixels of each wave band is the same as the offset direction corresponding to the absolute offset of the i-th row of pixels of the reference wave band; wherein i is a positive integer greater than or equal to 1;
determining a wave band from the hyperspectral remote sensing image to be corrected as a reference wave band, determining the relative offset of each line through the minimum error of the adjacent lines, and determining the absolute offset of each line (namely the offset of each line relative to the pixels of the target line) through the relative offset of each line, so that the corresponding line in the hyperspectral remote sensing image to be corrected is moved wave band by wave band according to the absolute offset of each line, and the corrected hyperspectral remote sensing image is obtained. Therefore, the influence of image abnormality caused by the image information collected by the sampling module in the moving process on the data processing work can be avoided.
Example 3: the data processing module is used for processing the calibrated time sequence hyperspectral remote sensing image data of the water area to be monitored and obtaining the surface water pollution factor index. The ground station in the monitoring module is used for automatically planning the route of the acquisition module, and the remote control station in the monitoring module is used for manually controlling the route of the acquisition module. When the data processing module processes the calibrated time sequence hyperspectral remote sensing image data of the water area to be monitored, the data processing module firstly acquires the calibrated time sequence hyperspectral remote sensing image data of the water area to be monitored. And inverting each hyperspectral remote sensing image data in the time sequence hyperspectral remote sensing image data to obtain the optical characteristic data of each hyperspectral remote sensing image data. And calculating the surface water pollution factor index of each hyperspectral remote sensing image data based on the optical characteristic data of each hyperspectral remote sensing image data. And constructing a surface water pollution factor index time sequence based on the wastewater pollution indexes of the hyperspectral remote sensing image data. And carrying out space-time pattern analysis on the surface water pollution factor index time sequence to obtain an analysis result.
When the system is used, the acquisition module acquires visible light image spectrum images in the water, the calibration module calibrates image information and positions, the data processing module processes image data, the network communication module wirelessly transmits information, the monitoring module monitors and intervenes the acquisition action of the acquisition module, and the processed monitoring data are collected.
In summary, according to the hyperspectral remote sensing monitoring data processing system based on the surface water pollution factor, by arranging the acquisition module, the calibration module, the data processing module, the network communication module and the monitoring module, the acquisition module acquires the visible light image spectrum image in the water, the calibration module calibrates the image information and the position, the data processing module processes the image data, the network communication module wirelessly transmits the information, the monitoring module monitors and intervenes the acquisition action of the acquisition module and collects the processed monitoring data, so that the quasi-real-time monitoring of the surface water pollution factor is realized in a mode of saving manpower and material resources, and the referenceability of the monitoring data is improved. Through setting up calibration module, RTK positioning module carries out real-time positioning to the collection module to learn the specific acquisition position of the image information who gathers, image contrast module carries out the pixel by pixel to the hyperspectral remote sensing image who gathers by the collection module and proofreads, can avoid the sampling module to gather the unusual influence that and then lead to the fact data processing work of image information that the image information caused in the removal in-process, has ensured the reliability of the information of gathering, and then makes monitoring data more reliable.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The hyperspectral remote sensing monitoring data processing system based on the surface water pollution factor is characterized by comprising an acquisition module, a calibration module, a data processing module, a network communication module and a monitoring module; the acquisition module adopts a remote control acquisition device loaded with a visible light camera and a hyperspectral camera, the calibration module comprises an RTK positioning module and an image comparison module, and the monitoring module comprises a ground station, a remote control station and a monitoring station.
2. The hyperspectral remote sensing monitoring data processing system based on surface water pollution factors as claimed in claim 1, wherein: the remote control collector of the collection module is used for collecting images in a water area to be monitored, wherein the visible light camera is used for shooting visible light images of the surface water, and the hyperspectral camera is used for shooting spectral images of the surface water.
3. The hyperspectral remote sensing monitoring data processing system based on surface water pollution factors as claimed in claim 2, wherein: the calibration module is used for calibrating the information acquired by the acquisition module. The RTK positioning module is used for positioning the acquisition module in real time so as to acquire the specific acquisition position of the acquired image information.
4. The hyperspectral remote sensing monitoring data processing system based on surface water pollution factors as claimed in claim 3, wherein: the image comparison module in the calibration module is used for performing pixel-by-pixel calibration on the hyperspectral remote sensing image acquired by the acquisition module.
5. The hyperspectral remote sensing monitoring data processing system based on surface water pollution factors as claimed in claim 4, wherein: and an image comparison module in the calibration module performs pixel-by-pixel calibration on the hyperspectral remote sensing image and then performs pixel-by-pixel offset on the hyperspectral remote sensing image to obtain a corrected hyperspectral remote sensing image.
6. The hyperspectral remote sensing monitoring data processing system based on surface water pollution factors as claimed in claim 5, wherein: the data processing module is used for processing the calibrated time sequence hyperspectral remote sensing image data of the water area to be monitored and obtaining the surface water pollution factor index.
7. The hyperspectral remote sensing monitoring data processing system based on surface water pollution factors as claimed in claim 6, wherein: the ground station in the monitoring module is used for automatically planning the route of the acquisition module, and the remote control station in the monitoring module is used for manually controlling the route of the acquisition module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310348645.3A CN116593404A (en) | 2023-04-04 | 2023-04-04 | Hyperspectral remote sensing monitoring data processing system based on surface water pollution factors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310348645.3A CN116593404A (en) | 2023-04-04 | 2023-04-04 | Hyperspectral remote sensing monitoring data processing system based on surface water pollution factors |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116593404A true CN116593404A (en) | 2023-08-15 |
Family
ID=87597950
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310348645.3A Pending CN116593404A (en) | 2023-04-04 | 2023-04-04 | Hyperspectral remote sensing monitoring data processing system based on surface water pollution factors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116593404A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117030634A (en) * | 2023-10-09 | 2023-11-10 | 深圳市盘古环保科技有限公司 | Quick detection and repair method for groundwater pollution |
-
2023
- 2023-04-04 CN CN202310348645.3A patent/CN116593404A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117030634A (en) * | 2023-10-09 | 2023-11-10 | 深圳市盘古环保科技有限公司 | Quick detection and repair method for groundwater pollution |
| CN117030634B (en) * | 2023-10-09 | 2023-12-12 | 深圳市盘古环保科技有限公司 | Quick detection and repair method for groundwater pollution |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111626985A (en) | Poultry body temperature detection method based on image fusion and poultry house inspection system | |
| CN101419058B (en) | Plant haulm diameter measurement device and measurement method based on machine vision | |
| CN107764976B (en) | Rapid diagnosis method and online monitoring system for soil nitrogen | |
| CN108956505B (en) | Method and device for detecting concentration of chlorophyll a in small water body based on Sentinel-2 image | |
| CN108507949A (en) | A kind of river water quality monitoring method based on high score remote sensing satellite | |
| CN109799199B (en) | Cooperative inversion method for chlorophyll a concentration | |
| CN116593404A (en) | Hyperspectral remote sensing monitoring data processing system based on surface water pollution factors | |
| CN111832518A (en) | Space-time fusion-based TSA remote sensing image land utilization method | |
| CN114202675A (en) | Crop growth parameter determination method and system based on multispectral remote sensing image of unmanned aerial vehicle | |
| CN114265418A (en) | Unmanned aerial vehicle inspection and defect positioning system and method for photovoltaic power station | |
| CN113325868A (en) | Crop real-time identification system and method based on unmanned aerial vehicle | |
| CN111896549A (en) | Building crack monitoring system and method based on machine learning | |
| CN112557307B (en) | Space-air-ground integrated lake and reservoir water quality monitoring fusion data method | |
| CN110953993A (en) | Detection device and method for sag and distance limit of power transmission line | |
| CN111241991B (en) | Water quality classification method based on remote sensing image | |
| CN116046692A (en) | Soil heavy metal pollution monitoring method and device based on hyperspectrum | |
| CN116148188A (en) | Air-space-ground integrated lake water quality tracing method, system, equipment and storage medium | |
| CN114140695B (en) | Prediction method and system for tea tree nitrogen diagnosis and quality index determination based on unmanned aerial vehicle multispectral remote sensing | |
| CN114965501A (en) | Peanut disease detection and yield prediction method based on canopy parameter processing | |
| CN114397277A (en) | Unmanned aerial vehicle water chlorophyll remote sensing detection system | |
| CN114577734A (en) | Low-altitude multispectral remote sensing water quality monitoring method for surface water body | |
| CN102788796B (en) | Nutrient diagnosis device and nutrient diagnosis method for nitrogen of crops based on multi-information integration of high spectral images and fluorescent images | |
| CN112434569A (en) | Thermal imaging system of unmanned aerial vehicle | |
| CN115236006B (en) | Processing method and system for electronic river length spectrum detection data | |
| CN115372281A (en) | Monitoring system and method for physical structure and chemical composition of soil |
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 |