CN111142137A - Method and device for positioning point source target image control points - Google Patents
Method and device for positioning point source target image control points Download PDFInfo
- Publication number
- CN111142137A CN111142137A CN201811308640.3A CN201811308640A CN111142137A CN 111142137 A CN111142137 A CN 111142137A CN 201811308640 A CN201811308640 A CN 201811308640A CN 111142137 A CN111142137 A CN 111142137A
- Authority
- CN
- China
- Prior art keywords
- point source
- pixel
- source target
- target image
- point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a method and a device for positioning a point source target image control point, wherein the positioning method comprises the following steps: determining the initial position of a point source target on a remote sensing satellite image; selecting a set range around the initial position on the remote sensing satellite image as a selected area, and calculating the pixel value of each pixel to be detected in the selected area; and screening according to the pixel value of each pixel to be detected to obtain a point source target pixel in the selected area, and obtaining a point source target image control point according to the point source target pixel in the selected area. The technical scheme provided by the invention can automatically position the point source target image control points, and screen out the point source target image elements in the selected area in the positioning process, so that not only can the working efficiency be improved, but also the positioning precision of the point source target image control points can be improved.
Description
Technical Field
The invention belongs to the technical field of point source target image control point positioning, and particularly relates to a method and a device for positioning point source target image control points.
Background
During the in-orbit operation of the satellite imaging load and position attitude measurement sensor, the absolute positioning accuracy and the imaging quality of the sensor are influenced by the perturbation of a satellite orbit, a space environment, an atmospheric environment, an illumination condition and a device state and are in the process of dynamic change. The continuous accurate on-orbit geometry and radiometric calibration of the remote sensing satellite payload is a key measurement means for ensuring the satellite to accurately and reliably locate the target.
A high-precision imaging and positioning method for remote sensing satellites is characterized in that a high-resolution ground calibration field is established locally, in-orbit calibration is carried out based on a ground target field when satellites cross a local area, key geometric parameters and radiation parameters of satellite loads are continuously and dynamically solved, the variable quantity of the measurement parameters is improved and compensated through updated measurement parameters, and authenticity, reliability and accuracy of satellite positioning results are guaranteed through reliable calibration parameters.
The geometric positioning method commonly used in the geometric calibration process of the optical remote sensing satellite at present is a manual point selection method, the manual point selection method requires a worker to know and know the characteristics of a target in the actual location where the worker arrives, then the human eye observes a satellite remote sensing image, and a point source target image control point is found from the satellite remote sensing image according to the characteristics of the target. The time is wasted when the workers reach the actual location of the target, and the point source target image control points determined by different workers may be different, so that the positioning result precision of the optical remote sensing satellite geometric positioning method adopting the manual point selection method is lower.
Disclosure of Invention
The invention aims to provide a method and a device for positioning a point source target image control point, which are used for solving the problems of low efficiency and low precision caused by adopting a manual point selection method in the geometric positioning of an optical remote sensing satellite in the prior art.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a method for positioning a point source target image control point comprises the following steps:
determining the initial position of a point source target on a remote sensing satellite image;
selecting a set range around the initial position on the remote sensing satellite image as a selected area, and calculating the pixel value of each pixel to be detected in the selected area;
and screening according to the pixel value of each pixel to be detected to obtain a point source target pixel in the selected area, and obtaining a point source target image control point according to the point source target pixel in the selected area.
In order to solve the above technical problem, the present invention further provides a positioning apparatus for a point source target image control point, including a processor and a memory, where the memory stores a computer program for executing on the processor; and when the processor executes the computer program, the positioning method of the point source target image control point is executed.
According to the technical scheme provided by the invention, the initial position of a point source target on a remote sensing satellite is determined, then the pixels of the power source target are obtained by screening according to the pixel values of the pixels to be detected in the set direction around the initial position, and then the control points of the point source target image are positioned; the technical scheme provided by the invention can automatically position the point source target image control points, and screen out the point source target image elements in the selected area in the positioning process, so that not only can the working efficiency be improved, but also the positioning precision of the point source target image control points can be improved.
Further, screening according to pixel values of pixels to be detected by adopting a characteristic parameter method to obtain point source target pixels in the selected area;
the characteristic parameter method comprises the following steps:
fitting a point source target pixel according to a Gaussian curved surface, and calculating characteristic parameters of the point source target pixel;
and screening non-point source target pixels by using the characteristic parameters of the point source target pixels to obtain the point source target pixels in the selected area.
The point source target mark pixels in the selected area are screened out by adopting a characteristic parameter method, so that the positioning precision of the point source target mark image control points can be improved.
Further, when the point source target pixel is fitted according to the Gaussian curved surface, the fitting formula is as follows:
formula (III) K, x0、y0σ, ξ, b are characteristic parameters, and are solved by adopting LM nonlinear least square algorithm.
Characteristic parameters in the fitting formula are solved through an LM nonlinear least square algorithm, and the calculation method is simple.
Further, when a characteristic parameter method is adopted to obtain the pixel of the point source target in the selected area, firstly, a template matching method is adopted to process the pixel to be detected in the selected area;
the template matching method comprises the following steps:
acquiring analog point source images at the sub-pixel positions with set quantity according to the pixel value of each pixel to be detected in the selected area;
fitting the area in the set range around each pixel to be detected in the selected area with the template of the analog point source image at each sub-pixel position respectively to obtain corresponding matching coefficients;
taking the maximum value in the matching coefficient of each pixel to be detected as the characteristic value of the pixel;
and connecting the connected pixels to be detected with the characteristic values larger than the set value to form connected domains, and taking the pixel to be detected with the maximum characteristic value in each connected domain as a central image point of the point source target image.
When the characteristic parameter method is adopted to obtain the point source target pixel in the selected area, the template matching method is firstly adopted to process the pixel to be detected in the selected area, the pixel to be detected in the selected area can be preliminarily screened, the data volume during calculation of the characteristic parameter method is reduced, and the efficiency of screening the point source target pixel is improved.
Further, let the kth matching coefficient of the pixel to be measured be rhoi,j(k) And then:
in the formula gi,jTo correspond to the distribution of pixel values within the defined area,
is an average value, g ', corresponding to pixel values in the set region'i,j(k) The pixel value distribution in the kth matching template of the pixel to be measured,
the average value of the pixel values in the kth matching template of the pixel to be detected is obtained, and w is the area size of the matching template.
Further, the method comprises the step of converting the point source target image elements to point source target image elements at a sub-pixel level.
The point source target image elements are converted into the point source target image elements at the sub-pixel level, so that the positioning precision of the point source target image control points can be further improved.
Further, the point source target image elements are converted into the point source target image elements at the sub-pixel level by adopting a weighted centroid method.
And by adopting a weighting execution method, the calculation process is simpler.
Further, when the point source target pixel is converted into a point source target pixel at a sub-pixel level by adopting a weighted centroid method, the central coordinate of the pixel (i, j) is set as (x)i,yj) Pixel value of UijThe centroid coordinate of the point source target pixel is
Then:
further, the set range is three times of the image space positioning error under the satellite uncontrolled condition.
And a proper setting range is selected, so that the positioning precision of the point source target image control point can be improved, and the workload in the positioning process can be reduced.
Drawings
FIG. 1 is a schematic diagram of a simulated point source image with a sub-pixel position of (0,0) according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a point source target pixel in an embodiment of a method of the invention;
FIG. 3 is a schematic diagram of a non-point source target pixel in an embodiment of a method of the invention.
Detailed Description
The technical solution of the present invention will be further explained with reference to the specific embodiments.
The method comprises the following steps:
the embodiment provides a method for positioning an image control point of a power source target, which comprises the following steps:
(1) determining the initial position of a point source target on a remote sensing satellite image;
in the embodiment, a point source target and a black bottom net are uniformly arranged in the range of a space flight checking field, and the RTK-GPS is used for measuring and recording the three-dimensional coordinates of a point source target reflector, and the three-dimensional coordinates are used as ground control points;
imaging the point source target at the satellite over-vertex moment to obtain a target image;
obtaining initial positions of point source targets on a satellite image by using position attitude data obtained by an attitude measurement system in a satellite overhead time range and calibration parameters during satellite sensor experiments;
(2) selecting a remote sensing satellite image in a set range around an initial position as a selected area, and calculating the pixel value of each pixel to be measured in the selected area, wherein the specific steps are as follows:
point source target pixels arranged in the selected area are pixels to be detected, and the set of all pixels to be detected is as follows:
P＝{(i,j)|1≤i≤M,1≤j≤N}
setting the pixel value of the pixel (i, j) to be detected in the selected area as gp(i, j), then:
wherein N isbM, N is the number of gray scale quantization steps of the satellite image, which is the number of rows and columns of the target image in the set region, x0And y0The peak positions of the point spread functions are respectively, and sigma and ξ are respectively standard deviations of the point spread functions on the axis of the image coordinate system X, Y and are obtained by the design parameters of the satellite;
the point spread function is an imaging result of an ideal point light source, and can be generally approximated as a two-dimensional high-speed function, and the point spread function in the above formula is expressed as:
the embodiment generates the analog point source images at 16 different sub-pixel positions at the interval of 0.25 pixel according to the change of the centroid position of the point source target image, namely x0And y0The decimal part of (A) has values of 0, 0.25, 0.5 and 0.75 pixels respectively, for example, x is shown in figure 10And y0When the decimal part of the image is taken as 0 pixel, simulating a point source image;
the size of the set range in the embodiment is three times of the image space positioning error under the satellite uncontrolled condition;
(3) screening point source target pixels in the selected area according to the pixel value of each pixel to be detected, and obtaining point source target image control points according to the screened point source target pixels;
in the process of screening the point source target pixels in the selected area, firstly, a template matching method is adopted to carry out rough selection on the point source target pixels in the selected area, and then a characteristic parameter method is adopted to carry out fine selection on the rough selection result;
the process of adopting a module matching method to roughly select the point source target pixels in the selected area comprises the following steps:
for each pixel to be detected in the selected area, taking each pixel to be detected as a center, respectively selecting areas with the same size as the selected area, and respectively taking the selected areas as the set areas of the corresponding pixels to be detected;
each pixel to be detected comprises a point source target pixel and a non-point source target pixel, wherein the point source target pixel is shown in figure 2, the non-point source target pixel is shown in figure 3, therefore, the non-point source target pixel of each pixel to be detected needs to be filtered, and the method comprises the following steps: respectively carrying out template matching on the set area of each pixel to be detected and the obtained analog point source images at the 16 different sub-pixel positions, and obtaining 16 corresponding matching coefficients for each pixel to be detected;
taking one of the pixels to be measured as an example, the kth matching coefficient of the pixel to be measured is rhoi,j(k) And then:
in the formula gi,jTo correspond to the distribution of pixel values within the defined area,
is an average value, g ', corresponding to pixel values in the set region'i,j(k) For the pixel value distribution in the kth matching template,
the average value of pixel values in the kth matching template is taken, and w is the area size of the matching template;
taking the maximum value of the matching coefficient in the pixel to be detected as the characteristic value of the pixel to be detected, and setting the characteristic value of the pixel to be detected as the m rhoi,jThen, there are:
mρi,j＝max(ρi,j(k))
sequentially obtaining the characteristic value of each pixel to be detected in the selected area according to the method;
screening out features in selected regions having values below a threshold value rhoTThe set of the remaining pixels to be tested is:
P′＝{(i,j)|1≤i≤M,1≤j≤N，mρi,j≥ρT}
a plurality of pixels to be detected belonging to the same point source target image exist in the set of the rest pixels to be detected, and the pixels to be detected generally have better connectivity; assigning the remaining pixels to be detected to be 1 and the screened pixels to be detected to be 0 to obtain a binary image B for the image after the primary screening, and communicating the pixels to be detected, which are assigned to be 1 and connected, to form a communication domain; and in the same communication area, the pixel to be detected with the largest characteristic value is taken as the central image point of the point source target image.
Therefore, point source target image identification based on template matching is completed, and due to the fact that an ideal point source does not exist in the ground object, a large number of non-point source target images can be screened out by using the method, and a small number of pixels to be detected are reserved;
after a large number of non-point source target image points are screened out by the template matching method, mismatching points still exist, and further screening is needed.
And (3) fine selection is carried out on the rough selection result by adopting a characteristic parameter method:
under an ideal condition, the image pixel values of the point source target are distributed as discrete values of a two-dimensional Gaussian function, so that the point source target image can be fitted through a Gaussian curved surface, and the characteristic parameters of the point source target image are solved;
when a Gaussian curved surface is adopted to fit a point source target image, a fitting formula is set as follows:
formula (III) K, x0、y0And sigma, ξ and b are unknowns and are solved by adopting an LM nonlinear least square algorithm, and an initial value of each unknowns needs to be provided when the LM nonlinear least square algorithm is adopted for solving, wherein x0、y0InitialThe value is the position of the pixel to be measured in (x)0，y0) The pixel value of (b) is used as the initial value of K, and the initial value of b is b0The initial value of sigma and ξ can be set as the prior value sigma used in generating the matching template0、ξ0(ii) a Obtained by calculation
εminA value of (d);
checking whether the image is a point source target image by using different parameters or parameter combinations, e.g. parameter combinations
And (4) verifying, namely, screening out non-point source target images by setting a reasonable threshold value to obtain the point source target images.
In order to obtain a more accurate point source target image, after obtaining the power source target image, in this embodiment, a region is obtained by taking the position of a point source target image element as a center, and the position of a point source target image centroid image point is solved by using a weighted centroid method, so as to obtain a sub-pixel-level point source target centroid position, specifically:
let the center coordinate of the (i, j) th pixel be (x)i,yj) Pixel value of UijThe coordinates of the centroid of the point source image can be calculated by using the following formula:
the embodiment of the device is as follows:
the embodiment provides a positioning device for a point source target image control point, which comprises a processor and a memory, wherein the memory is stored with a computer program for being executed on the processor; when the processor executes the computer program, the positioning method of the point source target image control point provided in the above method embodiments is executed.
Claims (10)
1. A method for positioning a point source target image control point is characterized by comprising the following steps:
determining the initial position of a point source target on a remote sensing satellite image;
selecting a set range around the initial position on the remote sensing satellite image as a selected area, and calculating the pixel value of each pixel to be detected in the selected area;
and screening according to the pixel value of each pixel to be detected to obtain a point source target pixel in the selected area, and obtaining a point source target image control point according to the point source target pixel in the selected area.
2. The method for positioning the point source target image control point according to claim 1, wherein the point source target pixels in the selected area are obtained by screening according to the pixel values of the pixels to be detected by using a characteristic parameter method;
the characteristic parameter method comprises the following steps:
fitting a point source target pixel according to a Gaussian curved surface, and calculating characteristic parameters of the point source target pixel;
and screening non-point source target pixels by using the characteristic parameters of the point source target pixels to obtain the point source target pixels in the selected area.
3. The method for positioning the point source target image control point according to claim 2, wherein when the point source target pixel is fitted according to the gaussian surface, the fitting formula is as follows:
formula (III) K, x0、y0σ, ξ, b are characteristic parameters, and are solved by adopting LM nonlinear least square algorithm.
4. The method for positioning the point source target image control point according to claim 2 or 3, wherein when the point source target image elements in the selected area are obtained by using a characteristic parameter method, the image elements to be detected in the selected area are processed by using a template matching method;
the template matching method comprises the following steps:
acquiring analog point source images at the sub-pixel positions with set quantity according to the pixel value of each pixel to be detected in the selected area;
fitting the area in the set range around each pixel to be detected in the selected area with the template of the analog point source image at each sub-pixel position respectively to obtain corresponding matching coefficients;
taking the maximum value in the matching coefficient of each pixel to be detected as the characteristic value of the pixel;
and connecting the connected pixels to be detected with the characteristic values larger than the set value to form connected domains, and taking the pixel to be detected with the maximum characteristic value in each connected domain as a central image point of the point source target image.
5. The method for positioning point source target image control points according to claim 4, wherein the kth matching coefficient of the pixel to be measured is defined as pi,j(k) And then:
in the formula gi,jTo correspond to the distribution of pixel values within the defined area,
is an average value, g ', corresponding to pixel values in the set region'i,j(k) For the pixel value distribution in the kth matching template,
is the average of the pixel values in the kth matching template, and w is the region size of the matching template.
6. The method for locating a point source target image-control point according to claim 1, further comprising the step of converting point source target pixel elements to point source target pixel elements at a sub-pixel level.
7. The method for positioning point source target image control points according to claim 6, wherein the point source target image elements are transformed to point source target image elements at sub-pixel level using a weighted centroid method.
8. The method for positioning point source target image control points according to claim 7, wherein when the point source target image elements are converted to the point source target image elements at sub-pixel level by using the weighted centroid method, the center coordinates of the image elements (i, j) are set as (x)i,yj) Pixel value of UijThe centroid coordinate of the point source target pixel is
Then:
9. the method for positioning the image-controlled point of the point source target according to claim 1, wherein the setting range is three times of the image-side positioning error under the satellite-uncontrolled condition.
10. A positioning apparatus for a point source target image control point, comprising a processor and a memory, the memory having stored thereon a computer program for execution on the processor; the computer program is executed by the processor to perform the method for locating a target image-controlled point of a point source according to any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811308640.3A CN111142137B (en) | 2018-11-05 | 2018-11-05 | Method and device for positioning point source target image control points |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811308640.3A CN111142137B (en) | 2018-11-05 | 2018-11-05 | Method and device for positioning point source target image control points |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111142137A true CN111142137A (en) | 2020-05-12 |
| CN111142137B CN111142137B (en) | 2021-04-09 |
Family
ID=70516313
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811308640.3A Active CN111142137B (en) | 2018-11-05 | 2018-11-05 | Method and device for positioning point source target image control points |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111142137B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111753887A (en) * | 2020-06-09 | 2020-10-09 | 军事科学院系统工程研究院后勤科学与技术研究所 | Point source target image control point detection model training method and device |
| CN111751809A (en) * | 2020-06-09 | 2020-10-09 | 军事科学院系统工程研究院后勤科学与技术研究所 | Method for calculating adjustment angle of point source target reflector |
| CN113538593A (en) * | 2021-06-22 | 2021-10-22 | 北京大学 | Unmanned aerial vehicle remote sensing time resolution calibration method based on vehicle-mounted mobile target |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103020997A (en) * | 2012-11-28 | 2013-04-03 | 国家测绘地理信息局卫星测绘应用中心 | Satellite target extraction method |
| CN103575395A (en) * | 2013-11-08 | 2014-02-12 | 中国科学院遥感与数字地球研究所 | External-field absolute radiation calibration method and system |
| CN104406574A (en) * | 2014-12-01 | 2015-03-11 | 中国能源建设集团山西省电力勘测设计院 | Field plane-height image control point laid target for unmanned aerial vehicle photogrammetric survey and layout method of target |
| KR20160095691A (en) * | 2015-02-03 | 2016-08-12 | 인하대학교 산학협력단 | A gis-based pervious/impervious map production method for urban areas using multiple spatial data |
-
2018
- 2018-11-05 CN CN201811308640.3A patent/CN111142137B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103020997A (en) * | 2012-11-28 | 2013-04-03 | 国家测绘地理信息局卫星测绘应用中心 | Satellite target extraction method |
| CN103575395A (en) * | 2013-11-08 | 2014-02-12 | 中国科学院遥感与数字地球研究所 | External-field absolute radiation calibration method and system |
| CN104406574A (en) * | 2014-12-01 | 2015-03-11 | 中国能源建设集团山西省电力勘测设计院 | Field plane-height image control point laid target for unmanned aerial vehicle photogrammetric survey and layout method of target |
| KR20160095691A (en) * | 2015-02-03 | 2016-08-12 | 인하대학교 산학협력단 | A gis-based pervious/impervious map production method for urban areas using multiple spatial data |
Non-Patent Citations (2)
| Title |
|---|
| FU-FEI GU等: "Translational Motion Compensation and Micro-Doppler Feature Extraction of Space Spinning Targets", 《IEEE GEOSCIENCE AND REMOTE SENSING LETTERS》 * |
| 徐伟伟等: "基于反射点源的高分辨率光学卫星传感器在轨调制传递函数检测", 《光学学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111753887A (en) * | 2020-06-09 | 2020-10-09 | 军事科学院系统工程研究院后勤科学与技术研究所 | Point source target image control point detection model training method and device |
| CN111751809A (en) * | 2020-06-09 | 2020-10-09 | 军事科学院系统工程研究院后勤科学与技术研究所 | Method for calculating adjustment angle of point source target reflector |
| CN111751809B (en) * | 2020-06-09 | 2023-11-14 | 军事科学院系统工程研究院后勤科学与技术研究所 | Method for calculating adjustment angle of point source target reflector |
| CN111753887B (en) * | 2020-06-09 | 2024-05-28 | 军事科学院系统工程研究院后勤科学与技术研究所 | Point source target image control point detection model training method and device |
| CN113538593A (en) * | 2021-06-22 | 2021-10-22 | 北京大学 | Unmanned aerial vehicle remote sensing time resolution calibration method based on vehicle-mounted mobile target |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111142137B (en) | 2021-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111142137B (en) | Method and device for positioning point source target image control points | |
| CN109458980B (en) | Antenna downward inclination angle measurement method based on linear regression fitting | |
| CN112017224B (en) | SAR data area network adjustment processing method and system | |
| CN105046251B (en) | A kind of automatic ortho-rectification method based on environment No.1 satellite remote-sensing image | |
| CN111145227B (en) | Iterative integral registration method for space multi-view point cloud of underground tunnel | |
| CN101750606B (en) | Automatic and moderate orthographic projection correction method of satellite remote sensing image | |
| US11334986B2 (en) | System and method for processing images of agricultural fields for remote phenotype measurement | |
| US8855439B2 (en) | Method for determining a localization error in a georeferenced image and related device | |
| CN106679676B (en) | A kind of monoscopic multifunctional optical sensor and implementation method | |
| CN106887016B (en) | Automatic relative registration method for GF-4 satellite sequence images | |
| CN110363758B (en) | Optical remote sensing satellite imaging quality determination method and system | |
| US20230358536A1 (en) | Method for Determining a Position and/or Orientation of a Measuring Device | |
| CN108398123B (en) | Total station and dial calibration method thereof | |
| CN104050643A (en) | Remote-sensing image relative correction method and system integrating geometry and radiation | |
| CN108230375A (en) | Visible images and SAR image registration method based on structural similarity fast robust | |
| Clarke et al. | Estimator for the random error in subpixel target location and its use in the bundle adjustment | |
| CN106405581A (en) | Evaluation method for coupling direction precision, caused by satellite structure deformation, of multiple types of loads | |
| CN111814666B (en) | Single tree parameter extraction method, system, medium and equipment under complex forest stand | |
| CN105510901A (en) | Optical satellite image time-varying error calibrating method and system based on multiple calibration fields | |
| CN105571598A (en) | Satellite laser altimeter footprint camera pose measuring method | |
| CN110514286B (en) | Method for measuring micro-vibration of optical axis of remote sensing satellite camera | |
| CN114063024A (en) | Calibration method and device of sensor, electronic equipment and storage medium | |
| CN116778344A (en) | Land arrangement boundary line dividing method based on visual technology | |
| CN112241981A (en) | Method and device for verifying precision of secondary measurement data of crop planting area | |
| Hearst et al. | Extraction of small spatial plots from geo-registered UAS imagery of crop fields |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |