CN117434568A - Intelligent positioning system based on remote sensing satellite - Google Patents
Intelligent positioning system based on remote sensing satellite Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- 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
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- G—PHYSICS
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- 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
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Abstract
The invention discloses an intelligent positioning system based on a remote sensing satellite, which relates to the technical field of satellite image processing and comprises a remote sensing image acquisition module, an image processing module, a three-dimensional positioning module and a correction evaluation module; the remote sensing image acquisition module is used for acquiring ground remote sensing image data and target point remote sensing image data through a high-resolution remote sensing satellite; the image processing module is used for performing filtering gain processing on the acquired ground remote sensing image data and target point remote sensing image data, and reducing the signal to noise ratio so as to improve the geometric positioning accuracy of the satellite image; the three-dimensional positioning module is used for constructing an RPC model according to the ground reference information, the ground remote sensing image data and the target point remote sensing image data; then carrying out three-dimensional positioning on the target point according to the RPC model; the correction evaluation module is used for acquiring positioning error data with the same satellite identifier to carry out parameter correction analysis and judging whether the corresponding high-resolution remote sensing satellite parameters need correction or not so as to improve geometric positioning accuracy.
Description
Technical Field
The invention relates to the technical field of satellite image processing, in particular to an intelligent positioning system based on a remote sensing satellite.
Background
The precise geometric positioning of the remote sensing image is the basis for further application of the remote sensing image. The remote sensing image is affected by various complex factors in the imaging process to generate geometric deformation, so that geometric positioning information of the image must be determined by establishing a geometric relationship between the coordinates of a ground point in an object space coordinate system and the coordinates of an image point on an image plane. In theory, under the support of accurate ground elevation data, the real geometric position of each pixel on the image can be recovered by using a strict imaging geometric model of the satellite remote sensing image, so that the geometric positioning of the image is realized.
However, the existing remote sensing satellite positioning system has the problems of low positioning efficiency and poor positioning accuracy; when noise signals influence, the image is easy to blur, and the geometric positioning accuracy of satellite images is limited; based on the defects, the invention provides an intelligent positioning system based on a remote sensing satellite.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides an intelligent positioning system based on a remote sensing satellite.
To achieve the above objective, an embodiment according to a first aspect of the present invention provides an intelligent positioning system based on a remote sensing satellite, which includes a remote sensing image acquisition module, a ground control point, an image processing module, an error checking module, and a correction evaluation module;
the remote sensing image acquisition module is connected with the control center; the method comprises the steps of obtaining ground remote sensing image data and target point remote sensing image data through a high-resolution remote sensing satellite; the ground control point is used for providing ground reference information to the control center;
the image processing module is connected with the control center and is used for performing filtering gain processing on the acquired ground remote sensing image data and target point remote sensing image data to reduce the signal to noise ratio; the method comprises the following specific steps:
step one: denoising and enhancing the ground remote sensing image data and the target point remote sensing image data;
step two: converting the ground remote sensing image data with color and the target point remote sensing image data into a gray level map; according to the graying ground remote sensing image data and the target point remote sensing image data, determining pixel values of a gray level image, and establishing a binary image;
step three: performing edge detection and matching on the acquired ground remote sensing image data and target point remote sensing image data; compensating errors caused by atmospheric influences of the ground remote sensing image data and the target point remote sensing image data, and inverting the real surface reflectivity of the target point;
step four: splicing two or more images through an image mosaic program, and cutting the acquired images through an image cutting program;
the image processing module is used for transmitting the processed ground remote sensing image data and target point remote sensing image data to the three-dimensional positioning module; the three-dimensional positioning module is used for constructing an RPC model according to the ground reference information, the ground remote sensing image data and the target point remote sensing image data; then, carrying out three-dimensional positioning on the target point according to the RPC model;
the error checking module is connected with the three-dimensional positioning module and is used for acquiring the three-dimensional positioning of the target point and performing error checking on the actual position of the target point to obtain positioning error data;
if the positioning error data is in the allowable range, marking the corresponding positioning error data with a qualified mark; otherwise, marking unqualified marks; the error checking module is used for uploading the positioning error data to the control center for display and storage through the bus communication unit;
the correction evaluation module is connected with the control center and is used for acquiring positioning error data with the same satellite identifier to perform parameter correction analysis and calculate and obtain a correction coefficient XZ of the corresponding high-resolution remote sensing satellite; and judging whether the corresponding high-resolution remote sensing satellite parameters need to be corrected or not.
Further, in the first step, denoising and enhancing are performed on the ground remote sensing image data and the target point remote sensing image data, and the specific steps are as follows:
s1: converting the received image information into a digital signal, and filtering the converted digital signal to obtain digital signal amplitude information; acquiring amplitude information of the digital signal according to a preset acquisition interval duration to generate an amplitude information group WPi;
s2: calculating a highest amplitude early warning value and a lowest amplitude early warning value according to the received i amplitude information; wherein i is more than or equal to 10; the method comprises the following steps:
calculating the average amplitude value of the i amplitude information as WPavg; traversing the amplitude information group WPi, obtaining a maximum value mark of the WPi as WPmax, and obtaining a minimum value mark of the WPi as WPmin;
calculating the highest early warning value W1 of the amplitude by combining the average amplitude WPavg and the maximum value WPmax; the specific calculation formula is as follows: w1=wpmax+ (WPmax-WPavg) × ƒ, wherein ƒ is an early warning threshold;
calculating an amplitude minimum early warning value W2 by combining the average amplitude WPavg and the minimum amplitude WPmin; the specific calculation formula is as follows: w2=wpmin- (WPavg-WPmin) × ƒ;
s3: acquiring the (i+1) th amplitude information; and labeled WP (i+1); comparing WP (i+1) with an amplitude highest warning value W1 and an amplitude lowest warning value W2; if WP (i+1) is more than or equal to W1 or WP (i+1) is less than or equal to W2, generating a regulating signal; otherwise, generating a normal signal;
s4: when receiving the adjusting signal, the image processing module adjusts the gain of the digital signal by controlling the programmable gain amplifying circuit, and adjusts the amplitude of the digital signal to be between the lowest amplitude early warning value W2 and the highest amplitude early warning value W1; let i=i+1 then and so on.
Further, the specific analysis process of the correction evaluation module is as follows:
collecting positioning error data with the same satellite identifier stored in a control center; the positioning error data carries a qualified mark and a unqualified mark;
when the unqualified mark is monitored, counting down automatically, wherein the count down is D1, and D1 is a preset value; counting down by one after each positioning error data is acquired; continuously monitoring the unqualified marks in the countdown stage, automatically returning the countdown to the original value if the new unqualified marks are monitored, and carrying out countdown again according to D1, otherwise, returning the countdown to zero, and stopping counting;
counting the occurrence times of unqualified marks in the countdown stage to be P2, counting the times of automatic normalization of the countdown to be P3, and counting the length of the countdown stage to be L1;
calculating to obtain a correction coefficient XZ of the corresponding high-resolution remote sensing satellite by using a formula XZ= (P2×a3+P3×a4)/(L1×a5+u), wherein a3, a4 and a5 are coefficient factors, and u is a preset compensation factor;
comparing the correction coefficient XZ with a preset correction threshold value; if XZ is more than or equal to a preset correction threshold, determining that the geometric positioning accuracy error of the corresponding high-resolution remote sensing satellite is larger, and generating a correction signal.
In the fourth step, two or more images are spliced by an image mosaic program, and the acquired images are cut by an image cutting program, wherein the specific process is as follows:
establishing a corresponding image set according to the acquired ground remote sensing image data and target point remote sensing image data; in the image set, determining a corresponding reference image, and taking the reference image as a standard of an output mosaic image;
and determining the matching contrast, pixel size and data type of the mosaic image according to the reference of the output mosaic image, and processing the tone of the image by using a histogram equalization mode and a color smoothing processing mode.
Further, three-dimensional positioning is carried out on the target point according to the RPC model; the method comprises the following steps:
extracting each frame of image in the target point remote sensing image data, and identifying target characteristics from each frame of image based on a target detection and identification algorithm of deep learning;
marking the target features in a box form, marking the positions and the sizes of the boxes in the picture, and carrying out three-dimensional positioning on the target points.
Further, the correction evaluation module is used for transmitting a correction signal to the control center; and the control center controls the alarm module to give an alarm after receiving the correction signal to remind a manager to correct the related parameters of the high-resolution remote sensing satellite.
Compared with the prior art, the invention has the beneficial effects that:
the remote sensing image acquisition module is used for acquiring ground remote sensing image data and target point remote sensing image data through a high-resolution remote sensing satellite; the image processing module is used for performing filtering gain processing on the acquired ground remote sensing image data and target point remote sensing image data, reducing the signal to noise ratio and image noise, and further improving the geometric positioning accuracy of the satellite image; the three-dimensional positioning module is used for constructing an RPC model according to the ground reference information, the ground remote sensing image data and the target point remote sensing image data; then, carrying out three-dimensional positioning on the target point according to the RPC model, thereby improving the positioning accuracy;
the error checking module is used for acquiring and comparing the three-dimensional positioning of the target point with the actual position of the target point to obtain positioning error data; if the positioning error data is in the allowable range, marking the corresponding positioning error data with a qualified mark; otherwise, marking unqualified marks; the correction evaluation module is used for acquiring positioning error data with the same satellite identifier to perform parameter correction analysis, calculating to obtain a correction coefficient XZ of the corresponding high-resolution remote sensing satellite, and judging whether the corresponding high-resolution remote sensing satellite parameter needs correction or not; so as to improve the geometric positioning precision of the high-resolution remote sensing satellite.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of an intelligent positioning system based on remote sensing satellites of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, 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.
As shown in fig. 1, an intelligent positioning system based on a remote sensing satellite comprises a remote sensing image acquisition module, a control center, a ground control point, an image processing module, a three-dimensional positioning module, an error checking module, a correction evaluation module and an alarm module;
the remote sensing image acquisition module is connected with the control center and is used for acquiring ground remote sensing image data and target point remote sensing image data through a high-resolution remote sensing satellite; the ground control point is used for providing reference information of the ground to the control center;
the image processing module is connected with the control center and is used for performing filtering gain processing on the acquired ground remote sensing image data and target point remote sensing image data, reducing the signal to noise ratio and reducing the image noise; thereby improving the geometric positioning precision of the satellite image; the specific processing steps are as follows:
step one: denoising and enhancing the acquired ground remote sensing image data and target point remote sensing image data by using a denoising and enhancing program, specifically comprising the following steps:
s1: converting the received image information into a digital signal, and filtering the converted digital signal to obtain digital signal amplitude information; acquiring amplitude information of the digital signal according to a preset acquisition interval duration to generate an amplitude information group WPi;
s2: calculating a highest amplitude early warning value and a lowest amplitude early warning value according to the received i amplitude information; wherein i is more than or equal to 10; the method comprises the following steps:
calculating the average amplitude value of the i amplitude information as WPavg; traversing the amplitude information group WPi, obtaining a maximum value mark of the WPi as WPmax, and obtaining a minimum value mark of the WPi as WPmin;
calculating the highest early warning value W1 of the amplitude by combining the average amplitude WPavg and the maximum value WPmax; the specific calculation formula is as follows: w1=wpmax+ (WPmax-WPavg) × ƒ, wherein ƒ is an early warning threshold;
calculating an amplitude minimum early warning value W2 by combining the average amplitude WPavg and the minimum amplitude WPmin; the specific calculation formula is as follows: w2=wpmin- (WPavg-WPmin) × ƒ;
s3: acquiring the (i+1) th amplitude information; and labeled WP (i+1); comparing WP (i+1) with an amplitude highest warning value W1 and an amplitude lowest warning value W2; if WP (i+1) is more than or equal to W1 or WP (i+1) is less than or equal to W2, generating a regulating signal; otherwise, generating a normal signal;
s4: when receiving the adjusting signal, the image processing module adjusts the gain of the digital signal by controlling the programmable gain amplifying circuit, and adjusts the amplitude of the digital signal to be between the lowest amplitude early warning value W2 and the highest amplitude early warning value W1; let i=i+1 then, and so on;
step two: converting the ground remote sensing image data with color and the target point remote sensing image data into a gray level map; according to the graying ground remote sensing image data and the target point remote sensing image data, determining pixel values of a gray level image, and establishing a binary image;
step three: performing edge detection and matching on the acquired ground remote sensing image data and target point remote sensing image data; compensating errors caused by atmospheric influences of the ground remote sensing image data and the target point remote sensing image data, and inverting the real surface reflectivity of the target point;
step four: splicing two or more images through an image mosaic program, and cutting the acquired images through an image cutting program; the specific process is as follows:
establishing a corresponding image set according to the acquired ground remote sensing image data and target point remote sensing image data; in the image set, determining a corresponding reference image, and taking the reference image as a standard of an output mosaic image;
determining matching contrast, pixel size and data type of the mosaic image according to the reference of the output mosaic image, and processing the tone of the image by using a histogram equalization mode and a color smoothing processing mode;
the image processing module is used for transmitting the processed ground remote sensing image data and target point remote sensing image data to the three-dimensional positioning module; the three-dimensional positioning module is used for constructing an RPC model according to the ground reference information, the ground remote sensing image data and the target point remote sensing image data; the construction program of the RPC model is the prior art;
then carrying out three-dimensional positioning on the target point according to the RPC model; the method comprises the following steps:
extracting each frame of image in the remote sensing image data of the target point, and identifying target characteristics from each frame of image based on a target detection and identification algorithm of deep learning;
marking the target features in a box form, marking the positions and the sizes of the boxes in the picture, and carrying out three-dimensional positioning on the target points;
the error checking module is connected with the three-dimensional positioning module and is used for acquiring the three-dimensional positioning of the target point for error checking, and specifically comprises the following steps:
obtaining three-dimensional positioning of a target point and comparing the three-dimensional positioning with the actual position of the target point to obtain positioning error data; if the positioning error data is in the allowable range, marking the corresponding positioning error data with a qualified mark; otherwise, marking unqualified marks;
the error checking module is used for uploading the positioning error data to the control center for display and storage through the bus communication unit;
the correction evaluation module is connected with the control center and is used for acquiring positioning error data with the same satellite identifier to carry out parameter correction analysis and judging whether the corresponding high-resolution remote sensing satellite parameters need correction or not; the specific analysis process is as follows:
collecting positioning error data with the same satellite identifier stored in a control center; the positioning error data carries a qualified mark and a unqualified mark;
when the unqualified mark is monitored, counting down automatically, wherein the count down is D1, and D1 is a preset value; for example, D1 takes a value of 10; counting down by one after each positioning error data is acquired; continuously monitoring the unqualified marks in the countdown stage, automatically returning the countdown to the original value if the new unqualified marks are monitored, and carrying out countdown again according to D1, otherwise, returning the countdown to zero, and stopping counting;
counting the occurrence times of unqualified marks in the countdown stage to be P2, counting the times of automatic normalization of the countdown to be P3, and counting the length of the countdown stage to be L1; calculating to obtain a correction coefficient XZ of the corresponding high-resolution remote sensing satellite by using a formula XZ= (P2×a3+P3×a4)/(L1×a5+u), wherein a3, a4 and a5 are coefficient factors, and u is a preset compensation factor;
comparing the correction coefficient XZ with a preset correction threshold value, and if XZ is larger than or equal to the preset correction threshold value, judging that the geometric positioning accuracy error of the corresponding high-resolution remote sensing satellite is larger, and generating a correction signal when the satellite parameters need to be corrected; the correction evaluation module is used for transmitting a correction signal to the control center;
and after receiving the correction signal, the control center controls the alarm module to give an alarm to remind a manager to correct the related parameters of the high-resolution remote sensing satellite so as to improve the geometric positioning accuracy of the high-resolution remote sensing satellite.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
an intelligent positioning system based on remote sensing satellites is characterized in that when in operation, a remote sensing image acquisition module is used for acquiring ground remote sensing image data and target point remote sensing image data through high-resolution remote sensing satellites; the ground control point is used for providing reference information of the ground; the image processing module is used for performing filtering gain processing on the acquired ground remote sensing image data and target point remote sensing image data, reducing the signal to noise ratio and reducing the image noise, thereby improving the geometric positioning accuracy of the satellite image; the image processing module is used for transmitting the processed ground remote sensing image data and target point remote sensing image data to the three-dimensional positioning module; the three-dimensional positioning module is used for constructing an RPC model according to the ground reference information, the ground remote sensing image data and the target point remote sensing image data; then, the target point is positioned in three dimensions according to the RPC model, so that the positioning accuracy is improved;
the error checking module is used for obtaining the three-dimensional positioning of the target point and comparing the three-dimensional positioning with the actual position of the target point to obtain positioning error data; if the positioning error data is in the allowable range, marking the corresponding positioning error data with a qualified mark; otherwise, marking unqualified marks; the correction evaluation module is used for acquiring positioning error data with the same satellite identifier, carrying out parameter correction analysis, calculating to obtain a correction coefficient XZ of the corresponding high-resolution remote sensing satellite, and judging whether the corresponding high-resolution remote sensing satellite parameter needs correction or not; so as to improve the geometric positioning precision of the high-resolution remote sensing satellite.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. An intelligent positioning system based on a remote sensing satellite is characterized by comprising a remote sensing image acquisition module, a ground control point, an image processing module, an error checking module and a correction evaluation module;
the remote sensing image acquisition module is connected with the control center; the method comprises the steps of obtaining ground remote sensing image data and target point remote sensing image data through a high-resolution remote sensing satellite; the ground control point is used for providing ground reference information to the control center;
the image processing module is connected with the control center and is used for performing filtering gain processing on the acquired ground remote sensing image data and target point remote sensing image data to reduce the signal to noise ratio; the method comprises the following specific steps:
step one: denoising and enhancing the ground remote sensing image data and the target point remote sensing image data;
step two: converting the ground remote sensing image data with color and the target point remote sensing image data into a gray level map; according to the graying ground remote sensing image data and the target point remote sensing image data, determining pixel values of a gray level image, and establishing a binary image;
step three: performing edge detection and matching on the acquired ground remote sensing image data and target point remote sensing image data; compensating errors caused by atmospheric influences of the ground remote sensing image data and the target point remote sensing image data, and inverting the real surface reflectivity of the target point;
step four: splicing two or more images through an image mosaic program, and cutting the acquired images through an image cutting program;
the image processing module is used for transmitting the processed ground remote sensing image data and target point remote sensing image data to the three-dimensional positioning module; the three-dimensional positioning module is used for constructing an RPC model according to the ground reference information, the ground remote sensing image data and the target point remote sensing image data; then, carrying out three-dimensional positioning on the target point according to the RPC model;
the error checking module is connected with the three-dimensional positioning module and is used for acquiring the three-dimensional positioning of the target point and performing error checking on the actual position of the target point to obtain positioning error data;
if the positioning error data is in the allowable range, marking the corresponding positioning error data with a qualified mark; otherwise, marking unqualified marks; the error checking module is used for uploading the positioning error data to the control center for display and storage through the bus communication unit;
the correction evaluation module is connected with the control center and is used for acquiring positioning error data with the same satellite identifier to perform parameter correction analysis and calculate and obtain a correction coefficient XZ of the corresponding high-resolution remote sensing satellite; and judging whether the corresponding high-resolution remote sensing satellite parameters need to be corrected or not.
2. The intelligent positioning system based on remote sensing satellites as set forth in claim 1, wherein the denoising enhancement is performed on the ground remote sensing image data and the target point remote sensing image data in the first step, the specific steps are as follows:
s1: converting the received image information into a digital signal, and filtering the converted digital signal to obtain digital signal amplitude information; acquiring amplitude information of the digital signal according to a preset acquisition interval duration to generate an amplitude information group WPi;
s2: calculating a highest amplitude early warning value and a lowest amplitude early warning value according to the received i amplitude information; wherein i is more than or equal to 10; the method comprises the following steps:
calculating the average amplitude value of the i amplitude information as WPavg; traversing the amplitude information group WPi, obtaining a maximum value mark of the WPi as WPmax, and obtaining a minimum value mark of the WPi as WPmin;
calculating the highest early warning value W1 of the amplitude by combining the average amplitude WPavg and the maximum value WPmax; the specific calculation formula is as follows: w1=wpmax+ (WPmax-WPavg) × ƒ, wherein ƒ is an early warning threshold;
calculating an amplitude minimum early warning value W2 by combining the average amplitude WPavg and the minimum amplitude WPmin; the specific calculation formula is as follows: w2=wpmin- (WPavg-WPmin) × ƒ;
s3: acquiring the (i+1) th amplitude information; and labeled WP (i+1); comparing WP (i+1) with an amplitude highest warning value W1 and an amplitude lowest warning value W2; if WP (i+1) is more than or equal to W1 or WP (i+1) is less than or equal to W2, generating a regulating signal; otherwise, generating a normal signal;
s4: when receiving the adjusting signal, the image processing module adjusts the gain of the digital signal by controlling the programmable gain amplifying circuit, and adjusts the amplitude of the digital signal to be between the lowest amplitude early warning value W2 and the highest amplitude early warning value W1; let i=i+1 then and so on.
3. The intelligent positioning system based on remote sensing satellites according to claim 1, wherein the specific analysis process of the correction evaluation module is as follows:
collecting positioning error data with the same satellite identifier stored in a control center; the positioning error data carries a qualified mark and a unqualified mark;
when the unqualified mark is monitored, counting down automatically, wherein the count down is D1, and D1 is a preset value; counting down by one after each positioning error data is acquired; continuously monitoring the unqualified marks in the countdown stage, automatically returning the countdown to the original value if the new unqualified marks are monitored, and carrying out countdown again according to D1, otherwise, returning the countdown to zero, and stopping counting;
counting the occurrence times of unqualified marks in the countdown stage to be P2, counting the times of automatic normalization of the countdown to be P3, and counting the length of the countdown stage to be L1;
calculating to obtain a correction coefficient XZ of the corresponding high-resolution remote sensing satellite by using a formula XZ= (P2×a3+P3×a4)/(L1×a5+u), wherein a3, a4 and a5 are coefficient factors, and u is a preset compensation factor;
comparing the correction coefficient XZ with a preset correction threshold value; if XZ is more than or equal to a preset correction threshold, determining that the geometric positioning accuracy error of the corresponding high-resolution remote sensing satellite is larger, and generating a correction signal.
4. The intelligent positioning system based on remote sensing satellites according to claim 1, wherein in the fourth step, two or more images are spliced by an image mosaic program, and simultaneously the acquired images are cut by an image cutting program, the specific process is as follows:
establishing a corresponding image set according to the acquired ground remote sensing image data and target point remote sensing image data; in the image set, determining a corresponding reference image, and taking the reference image as a standard of an output mosaic image;
and determining the matching contrast, pixel size and data type of the mosaic image according to the reference of the output mosaic image, and processing the tone of the image by using a histogram equalization mode and a color smoothing processing mode.
5. The intelligent positioning system based on remote sensing satellites according to claim 1, wherein the target point is positioned in three dimensions according to the RPC model; the method comprises the following steps:
extracting each frame of image in the target point remote sensing image data, and identifying target characteristics from each frame of image based on a target detection and identification algorithm of deep learning;
marking the target features in a box form, marking the positions and the sizes of the boxes in the picture, and carrying out three-dimensional positioning on the target points.
6. A remote sensing satellite based intelligent positioning system according to claim 3, wherein the correction evaluation module is configured to transmit a correction signal to the control center; and the control center controls the alarm module to give an alarm after receiving the correction signal to remind a manager to correct the related parameters of the high-resolution remote sensing satellite.
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