CN114529520A - Positioning accuracy evaluation method - Google Patents
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Abstract
The invention provides a positioning accuracy evaluation method, which comprises the following steps: step 1: resampling the original image according to a geographical positioning result, and acquiring the resampled image and longitude and latitude corresponding to each pixel; and 2, step: carrying out edge extraction on the remote sensing image with the shoreline to obtain pixels corresponding to the shoreline in the remote sensing image; and 3, step 3: searching corresponding points in the high-resolution shoreline data for the shoreline pixels in the remote sensing image; and 4, step 4: calculating the arc length of the corresponding earth surface as a positioning deviation according to the longitude and latitude corresponding to the pixel and the longitude and latitude of the corresponding point in the high-resolution shoreline data for the shoreline pixel in the remote sensing image; and 5: and carrying out statistical analysis on the geographic positioning errors of the remote sensing images according to the multiple groups of positioning results. The method can be used for evaluating the geographic positioning accuracy of the remote sensing image without depending on other satellite images in the same scene and measuring ground control points in real time, is convenient to implement and can be generally suitable for evaluating the geographic positioning accuracy of the remote sensing image.
Description
Technical Field
The invention relates to the technical field of remote sensing image geographic positioning, in particular to a positioning accuracy evaluation method, and especially relates to a remote sensing image geographic positioning accuracy evaluation method based on high-resolution shoreline data.
Background
With the rapid development of remote sensing application, the geographic positioning accuracy of remote sensing images becomes one of important indexes for measuring the application value of the remote sensing images, so that quantitative evaluation on the positioning accuracy of the remote sensing images needs to be carried out.
The existing remote sensing image geographic positioning evaluation methods are all established on the basis of comparison of control points with the same name, and how to quickly and conveniently acquire the control points and match the control points has important significance on remote sensing image geographic positioning precision evaluation. The existing positioning precision evaluation method mostly depends on the measured data of a control point of a test field or other satellite image information, and the implementation is limited to a certain extent.
The control point acquisition method provided by the GJB 4407-2002 aerospace remote sensing image positioning precision detection method comprises the following steps: field testing methods, topographical map (library) reading point testing methods, and photogrammetry. The existing document 1[ aged shrimp, remote sensing image geometric positioning precision evaluation method research, Nanjing university of science and technology, Master thesis, 2013] provides a method for automatically selecting and matching control points based on SURF algorithm and domain edge information. In the prior art, a positioning accuracy evaluation method based on a field actual measurement control point and other satellite images is provided in a document 2[ Weidandan, Ganpui, Shangkun, etc., CBERS-04 star PAN/MUX image geometric positioning accuracy evaluation, radio engineering, 2018,48(5) ]. The existing document 3[ forest hong brother, falina, GeoEye-1 satellite uncontrolled autonomous positioning and positioning accuracy evaluation research under sparse control point, urban survey, 2018, 6] and the existing document 4[ Wangjiangbu, Zhangjie, Mayi, resource No. 02C satellite remote sensing image secondary product positioning accuracy evaluation, marine surveying and mapping, 2013,33(5) ] respectively provide a method for evaluating the positioning accuracy based on the control point obtained by a test field. Patent document No. CN104574347A discloses an on-orbit satellite image geometric positioning accuracy evaluation method based on multi-source remote sensing data, which adopts the following steps: step 1, adjusting an image to be evaluated and a reference image into two images under the same ellipsoid, a reference surface and resolution; step 2, down-sampling the two images and performing radiation enhancement treatment; step 3, carrying out rough matching on the two images by using an accelerating steady characteristic Surf algorithm, and rejecting mismatching point pairs by using epipolar geometric constraint; step 4, according to the rough matching result, performing geometric relation compensation on the image to be evaluated, and accurately blocking the image to be evaluated and the reference image after geometric compensation; step 5, aiming at the image to be evaluated and the reference image block pair, carrying out fine matching by using a Surf algorithm, and rejecting mismatching point pairs by epipolar geometric constraint; and 6, calculating the external geometric positioning precision, and meanwhile, calculating the internal geometric positioning precision according to the screened control point pairs in each direction.
The above documents still have the defect that the implementation is limited by depending on the measured data of the control point of the test field or other satellite image information.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a positioning precision evaluation method.
The invention provides a positioning accuracy evaluation method, which comprises the following steps:
step 1: resampling the original image according to the geographical positioning result, and acquiring the resampled image and longitude and latitude corresponding to each pixel;
step 2: carrying out edge extraction on the remote sensing image with the shoreline to obtain pixels corresponding to the shoreline in the remote sensing image;
and step 3: searching corresponding points in the high-resolution shoreline data for the shoreline pixels in the remote sensing image;
and 4, step 4: calculating the arc length of the corresponding earth surface as a positioning deviation according to the longitude and latitude corresponding to the pixel and the longitude and latitude of the corresponding point in the high-resolution shoreline data for the shoreline pixel in the remote sensing image;
and 5: and carrying out statistical analysis on the geographic positioning errors of the remote sensing images according to the multiple groups of positioning results.
Preferably, in step 1, the resampled grid is set as an equal-longitude and equal-latitude interval grid generated by the projection of the geographic cylinder, and the discrete points of the original positioning result are resampled as points on the equal-longitude and equal-latitude interval grid.
Preferably, the step 2 comprises the following steps:
step 2.1: carrying out edge extraction on the remote sensing image to obtain a binary image of the edge of the remote sensing image;
step 2.2: projecting longitude and latitude discrete points in the high-resolution shore line data to the equal-longitude and equal-latitude interval grid in the step one to generate a binary image with the same resolution as the remote sensing image;
step 2.3: performing expansion operation in image morphology processing on the binary image in the step 2.2;
step 2.4: and (3) taking the binary image obtained in the step (2.3) as a mask, and filtering the edge of the remote sensing image in the step (2.1) to obtain a bank line pixel extracted from the remote sensing image.
Preferably, in the step 2.1, a Sobel operator is adopted to extract the remote sensing image edge.
Preferably, in the step 2.3, the structural element is D,
pixels where high resolution shorelines exist are marked as 1, otherwise 0.
Preferably, in step 3, the high-resolution shoreline data is acquired from a global self-consistent hierarchical high-resolution geographic database.
Preferably, in step 3, for the shore line pixels in the remote sensing image, the corresponding points in the high-resolution shore line data are searched according to the principle that the earth surface distance is closest.
Preferably, in step 4, if the latitude and longitude corresponding to the shore line pixel in the remote sensing image are a and b (unit: radian), and the α and longitude of the corresponding point in the high resolution shore line data are β, the positioning deviation d is calculated as:
d＝Aarccos(sin(a)cos(b)sin(α)cos(β)+sin(a)sin(b)sin(α)sin(β)+cos(a)cos(α))
wherein A is the earth mean radius.
Preferably, the method for calculating the positioning error δ in step 5 comprises:
wherein,
the average value of the positioning deviations is obtained.
Preferably, the number of sets of positioning deviations is not less than 50.
Compared with the prior art, the invention has the following beneficial effects:
1. the method can evaluate the geographic positioning precision of the remote sensing image without depending on the positioning data of other satellites in the same scene;
2. the method is based on a global self-consistent hierarchical high-resolution geographic database (GSHHG), and the geographic positioning accuracy of the remote sensing image is quantitatively evaluated by comparing the water and land boundary position in the remote sensing image with a high-resolution shoreline database;
3. the method is reasonable, simple in calculation and easy to implement, and can be generally applied to precision evaluation of remote sensing image theorem.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is an original remote sensing image of a satellite;
FIG. 3 is a remote sensing image after resampling;
FIG. 4 is a result of projecting high-resolution shoreline data onto a grid with the same resolution as the remote sensing image;
FIG. 5 shows pixels corresponding to a shoreline in a remote sensing image;
fig. 6 shows statistical results of positioning deviation based on high-resolution shoreline data.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1:
as shown in fig. 1, the positioning accuracy evaluation method provided in this embodiment includes the following steps:
step 1: and (2) resampling the original image according to the geographical positioning result, and acquiring the resampled image and longitude and latitude corresponding to each pixel, wherein in the step (1), the resampled grid is set as an equal-longitude and equal-latitude interval grid generated according to the geographical cylindrical projection, and the discrete points of the original positioning result are resampled into points on the equal-longitude and equal-latitude interval grid.
Step 2: and carrying out edge extraction on the remote sensing image with the bank line to obtain pixels corresponding to the bank line in the remote sensing image.
The step 2 comprises the following steps:
step 2.1: carrying out edge extraction on the remote sensing image to obtain a binary image of the edge of the remote sensing image;
step 2.2: projecting longitude and latitude discrete points in the high-resolution shoreline data to the grid with equal longitude and equal latitude intervals in the step one to generate a binary image with the same resolution as the remote sensing image, and in the step 2.1, extracting the edge of the remote sensing image by using a Sobel operator;
step 2.3: performing dilation operation in image morphology processing on the binary image in the step 2.2, wherein in the step 2.3, the structural element is D,
pixels where high resolution shorelines exist are marked as 1, otherwise 0;
step 2.4: and (3) taking the binary image obtained in the step (2.3) as a mask, and filtering the edge of the remote sensing image in the step (2.1) to obtain a bank line pixel extracted from the remote sensing image.
And step 3: and (3) searching corresponding points in the high-resolution shoreline data for the shoreline pixels in the remote sensing image, acquiring the high-resolution shoreline data from a global self-consistent hierarchical high-resolution geographic database in step 3, and searching corresponding points in the high-resolution shoreline data for the shoreline pixels in the remote sensing image according to the principle that the earth surface distance is closest.
And 4, step 4: for the shoreline pixels in the remote sensing image, calculating the corresponding arc length on the earth surface as positioning deviation according to the longitude and latitude corresponding to the pixels and the longitude and latitude of the corresponding point in the high-resolution shoreline data, and in step 4, if the latitude corresponding to the shoreline pixels in the remote sensing image is a and the longitude is b (unit: radian), the alpha and longitude of the corresponding point in the high-resolution shoreline data is beta, and the calculation formula of the positioning deviation d is as follows:
d＝Aarccos(sin(a)cos(b)sin(α)cos(β)+sin(a)sin(b)sin(α)sin(β)+cos(a)cos(α))
wherein A is the earth mean radius.
And 5: and (3) carrying out statistical analysis on the geographic positioning errors of the remote sensing images according to a plurality of groups of positioning results, wherein the positioning error delta calculation method in the step 5 comprises the following steps:
wherein,
the average value of the positioning deviations is obtained. The number of sets of positioning deviations is not less than 50.
The method is verified by combining a satellite remote sensing image, and an original remote sensing image of a satellite is shown in fig. 2, and because a 45-degree rotary scanning reflector is adopted in the load imaging process, image rotation exists in the original image. Fig. 3 is a resampled remote sensing image obtained after step 1 is executed. The result of projecting the high-resolution shore line data in step 2 to the grid with the same resolution as the remote sensing image is shown in fig. 4, and the pixel corresponding to the shore line in the remote sensing image obtained after step 2 is executed is shown in fig. 5. As can be seen from fig. 5, the shoreline pixels extracted from the remote sensing image are not continuous due to the influence of the cloud layer and other factors. Corresponding to the shoreline pixels in fig. 5, steps 3 to 5 are performed, and the obtained 1349 sets of positioning deviation statistics are shown in fig. 6.
The method can be used for evaluating the geographical positioning accuracy of the remote sensing image without depending on other satellite images in the same scene and measuring ground control points in real time, is convenient to implement and can be generally suitable for evaluating the geographical positioning accuracy of the remote sensing image.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A positioning accuracy evaluation method is characterized by comprising the following steps:
step 1: resampling the original image according to the geographical positioning result, and acquiring the resampled image and longitude and latitude corresponding to each pixel;
step 2: carrying out edge extraction on the remote sensing image with the shoreline to obtain pixels corresponding to the shoreline in the remote sensing image;
and step 3: searching corresponding points in the high-resolution shoreline data for the shoreline pixels in the remote sensing image;
and 4, step 4: calculating the arc length of the corresponding earth surface as a positioning deviation according to the longitude and latitude corresponding to the pixel and the longitude and latitude of the corresponding point in the high-resolution shoreline data for the shoreline pixel in the remote sensing image;
and 5: and carrying out statistical analysis on the geographic positioning errors of the remote sensing images according to the multiple groups of positioning results.
2. The method according to claim 1, wherein in step 1, the resampled grid is set as an equal-longitude and equal-latitude interval grid generated by a projection of a geographic cylinder, and the discrete points of the original positioning result are resampled as points on the equal-longitude and equal-latitude interval grid.
3. The positioning accuracy evaluation method according to claim 1, wherein the step 2 includes the steps of:
step 2.1: carrying out edge extraction on the remote sensing image to obtain a binary image of the edge of the remote sensing image;
step 2.2: projecting longitude and latitude discrete points in the high-resolution shore line data to the equal-longitude and equal-latitude interval grid in the step one to generate a binary image with the same resolution as the remote sensing image;
step 2.3: performing expansion operation in image morphology processing on the binary image in the step 2.2;
step 2.4: and (3) taking the binary image obtained in the step (2.3) as a mask, and filtering the edge of the remote sensing image in the step (2.1) to obtain a bank line pixel extracted from the remote sensing image.
4. The method for evaluating the positioning accuracy according to claim 3, characterized in that in the step 2.1, a Sobel operator is adopted to extract the edges of the remote sensing image.
5. The method according to claim 3, wherein in step 2.3, the structural element is D,
pixels where high resolution shorelines exist are marked as 1, otherwise 0.
6. The method according to claim 1, wherein in step 3, the high-resolution shoreline data is acquired from a global self-consistent hierarchical high-resolution geographic database.
7. The method for evaluating positioning accuracy according to claim 1, wherein in the step 3, for the shore line pixels in the remote sensing image, corresponding points in the high-resolution shore line data are searched according to the principle that the earth surface distance is closest.
8. The method according to claim 1, wherein in the step 4, if the latitude and longitude of the corresponding shore line pixel in the remote sensing image are a and b (unit: radian), and the α and longitude of the corresponding point in the high resolution shore line data are β, the positioning deviation d is calculated as:
d＝Aarccos(sin(a)cos(b)sin(α)cos(β)+sin(a)sin(b)sin(α)sin(β)+cos(a)cos(α))
wherein A is the earth mean radius.
9. The positioning accuracy evaluation method according to claim 1, wherein the positioning error δ calculation method in step 5 is:
wherein,
the average value of the positioning deviations is obtained.
10. The method of evaluating positioning accuracy according to claim 7, wherein the number of sets of positioning deviation is not less than 50.
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| CN115393438A (en) * | 2022-08-29 | 2022-11-25 | 北京华云星地通科技有限公司 | Satellite remote sensing image geographical positioning error detection method and system |
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