CN114529520A - Positioning accuracy evaluation method - Google Patents

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

Positioning Accuracy Evaluation Method

technical field

The invention relates to the technical field of remote sensing image geolocation, in particular to a positioning accuracy evaluation method, especially a remote sensing image geolocation accuracy evaluation method based on high-resolution coastline data.

Background technique

With the rapid development of remote sensing applications, the geographic positioning accuracy of remote sensing images has become one of the important indicators to measure the application value of remote sensing images, which requires quantitative evaluation of the positioning accuracy of remote sensing images.

The existing remote sensing image geolocation evaluation methods are all based on the comparison of control points with the same name. How to obtain control points quickly and conveniently and match them is of great significance to the evaluation of remote sensing image geolocation accuracy. The existing positioning accuracy evaluation methods mostly rely on the measured data of the test site control points or other satellite image information, and the implementation is limited.

The control point acquisition methods given in GJB 4407-2002 "Location Accuracy Detection Method of Aerospace Remote Sensing Image" include: field detection method, topographic map (library) reading point detection method and photogrammetry method. Existing document 1 [Chen Yixia, Research on the Evaluation Method of Geometric Positioning Accuracy of Remote Sensing Image, Nanjing University of Science and Technology, Master's Thesis, 2013] provides a method for automatic selection and matching of control points based on SURF algorithm and domain edge information. Existing literature 2 [Wei Dandan, Gan Fuping, Shang Kun, et al., Evaluation of Geometric Positioning Accuracy of CBERS-04 Satellite PAN/MUX Image, Radio Engineering, 2018, 48(5)] gives the control points based on field measurements and other satellite images. The positioning accuracy evaluation method. Existing literature 3 [Lin Hongdi, Qu Lina, GeoEye-1 satellite uncontrolled autonomous positioning and positioning accuracy evaluation under sparse control points, Urban Survey, 2018, 6] and existing literature 4 [Wang Jianbu, Zhang Jie, Ma Yi, Resource 1 No. 02C Satellite Remote Sensing Image Secondary Product Positioning Accuracy Evaluation, Ocean Surveying and Mapping, 2013, 33(5)] respectively gave the method of positioning accuracy evaluation based on the control points obtained from the test site. The patent document of publication number CN104574347A discloses a method for evaluating the geometric positioning accuracy of in-orbit satellite images based on multi-source remote sensing data. The two images at the same rate; step 2, downsample the above two images, and perform radiation enhancement processing; step 3, use the accelerated robust feature Surf algorithm to rough match the above two images, and use the epipolar geometric constraint to eliminate false positives Matching point pairs; Step 4, according to the rough matching result, perform geometric relationship compensation on the image to be evaluated, and accurately block the image to be evaluated and the reference image after geometric compensation; Step 5, for the image to be evaluated and the reference image block pair, Use the Surf algorithm to perform precise matching, and use the epipolar geometric constraint to eliminate incorrect matching point pairs; step 6, calculate the external geometric positioning accuracy, and calculate the internal geometric positioning accuracy according to the selected control point pairs in each direction.

The above-mentioned literature still has the defect of relying on the measured data of the control points of the test site or other satellite image information, and the implementation is limited to a certain extent.

SUMMARY OF THE INVENTION

Aiming at the defects in the prior art, the purpose of the present invention is to provide a positioning accuracy evaluation method.

A positioning accuracy evaluation method provided according to the present invention includes the following steps:

Step 1: resample the original image according to the geolocation result, and obtain the resampled image and the corresponding longitude and latitude pixel by pixel;

Step 2: Extract the edge of the remote sensing image with coastline, and obtain the pixels corresponding to the coastline in the remote sensing image;

Step 3: For the shoreline pixels in the remote sensing image, search the corresponding points in the high-resolution shoreline data;

Step 4: For the shoreline pixels in the remote sensing image, according to the latitude and longitude corresponding to the pixel and the latitude and longitude of the corresponding point in the high-resolution shoreline data, calculate the corresponding arc length of the earth surface as the positioning deviation;

Step 5: Statistically analyze the geolocation errors of remote sensing images according to multiple sets of location results.

Preferably, in the step 1, the resampled grid is set to a grid of equal longitude and equal latitude interval generated by geographic cylindrical projection, and the discrete points of the original positioning result are resampled to a grid of equal longitude and equal latitude interval. point on.

Preferably, the step 2 includes the following steps:

Step 2.1: Extract the edge of the remote sensing image to obtain a binary image of the edge of the remote sensing image;

Step 2.2: Project the discrete points of longitude and latitude in the high-resolution shoreline data to the grid of equal longitude and latitude interval in step 1 to generate a binary image with the same resolution as the remote sensing image;

Step 2.3: Perform the dilation operation in the image morphological processing on the binary image in Step 2.2;

Step 2.4: Using the binary image obtained in step 2.3 as a mask, filter the edge of the remote sensing image in step 2.1 as the shoreline pixels extracted from the remote sensing image.

Preferably, in the step 2.1, the Sobel operator is used to extract the edge of the remote sensing image.

Preferably, in the step 2.3, the structural element is D,

Pixels with high-resolution shorelines are marked as 1, otherwise 0.

Preferably, in the step 3, the high-resolution shoreline data is obtained from a global self-consistent hierarchical high-resolution geographic database.

Preferably, in the step 3, for the shoreline pixels in the remote sensing image, the corresponding points in the high-resolution shoreline data are searched according to the principle of the closest distance to the surface.

Preferably, in step 4, if the latitude and longitude corresponding to the shoreline pixels in the remote sensing image are a and the longitude is b (unit: radian), the α and longitude of the corresponding points in the high-resolution shoreline data are β, and the positioning deviation is d. The calculation formula is:

d=Aarccos(sin(a)cos(b)sin(α)cos(β)+sin(a)sin(b)sin(α)sin(β)+cos(a)cos(α))

where A is the average radius of the earth.

Preferably, the calculation method of the positioning error δ in step 5 is:

为多组定位偏差的平均值。in,

is the average of the positioning deviations of multiple groups.

Preferably, the number of groups of positioning deviations is not less than 50.

Compared with the prior art, the present invention has the following beneficial effects:

1. The present invention can evaluate the geographic positioning accuracy of remote sensing images without relying on the positioning data of the same scene of other satellites;

2. Based on the global self-consistent hierarchical high-resolution geographic database (GSHHG), the present invention utilizes the comparison between the land-water boundary position in the remote sensing image and the high-resolution coastline database to quantitatively evaluate the geographic positioning accuracy of the remote sensing image;

3. The method of the present invention is reasonable, simple in calculation and simple in implementation, and can be widely applied to the theorem accuracy evaluation of remote sensing images.

Description of drawings

Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

Fig. 1 is the flow chart of the present invention;

Figure 2 is the original remote sensing image of a satellite;

Fig. 3 is the remote sensing image after resampling;

Figure 4 is the result of projecting high-resolution shoreline data to a grid with the same resolution as the remote sensing image;

Fig. 5 is the pixel corresponding to the shoreline in the remote sensing image;

Figure 6 shows the statistical results of positioning deviation based on high-resolution shoreline data.

Detailed ways

The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the inventive concept. These all belong to the protection scope of the present invention.

Example 1:

As shown in FIG. 1, a positioning accuracy evaluation method provided by this embodiment includes the following steps:

Step 1: Resample the original image according to the geolocation result, and obtain the resampled image and the pixel-by-pixel corresponding longitude and latitude. In Step 1, the resampled grid is set to equal longitude, Equal latitude interval grid, which resamples the discrete points of the original positioning result to points on an equal longitude and latitude interval grid.

Step 2: Extract the edge of the remote sensing image with coastline, and obtain the pixels corresponding to the coastline in the remote sensing image.

Step 2 includes the following steps:

Step 2.1: Extract the edge of the remote sensing image to obtain a binary image of the edge of the remote sensing image;

Step 2.2: Project the discrete points of longitude and latitude in the high-resolution shoreline data to the grid of equal longitude and latitude interval in step 1 to generate a binary image with the same resolution as the remote sensing image. In step 2.1, Using Sobel operator to extract remote sensing image edge;

Step 2.3: Perform the dilation operation in the image morphological processing on the binary image in step 2.2. In step 2.3, the structural element is D,

Pixels with high-resolution shorelines are marked as 1, otherwise 0;

Step 2.4: Using the binary image obtained in step 2.3 as a mask, filter the edge of the remote sensing image in step 2.1 as the shoreline pixels extracted from the remote sensing image.

Step 3: Search the corresponding points in the high-resolution shoreline data for the shoreline pixels in the remote sensing image. In Step 3, the high-resolution shoreline data is obtained from the global self-consistent hierarchical Middle coastline pixels, search for corresponding points in high-resolution coastline data according to the principle of the closest distance to the surface.

Step 4: For the shoreline pixels in the remote sensing image, according to the latitude and longitude corresponding to the pixel and the latitude and longitude of the corresponding point in the high-resolution shoreline data, calculate the corresponding arc length of the earth's surface as the positioning deviation. The latitude corresponding to the line pixel is a, the longitude is b (unit: radian), the α and longitude of the corresponding point in the high-resolution shoreline data are β, and the calculation formula of the positioning deviation d is:

d=Aarccos(sin(a)cos(b)sin(α)cos(β)+sin(a)sin(b)sin(α)sin(β)+cos(a)cos(α))

where A is the average radius of the earth.

Step 5: Statistically analyze the geographic positioning error of the remote sensing image according to the multiple sets of positioning results. The calculation method of the positioning error δ in Step 5 is:

为多组定位偏差的平均值。定位偏差的组数不小于50。in,

is the average of the positioning deviations of multiple groups. The number of groups of positioning deviation is not less than 50.

The method of the present invention is verified below in combination with a satellite remote sensing image. Figure 2 shows the original remote sensing image of a satellite. Since a 45° rotating scanning mirror is used in the payload imaging process, there is image rotation in the original image. FIG. 3 is a resampled remote sensing image obtained after step 1 is performed. In step 2, the high-resolution shoreline data is projected to the grid of the same resolution as the remote sensing image, as shown in Figure 4. After performing step 2, the pixels corresponding to the shoreline in the remote sensing image are obtained as shown in Figure 5. It can be seen from Figure 5 that, affected by factors such as cloud layers, the shoreline pixels extracted from remote sensing images are not continuous. Corresponding to the shoreline pixels in FIG. 5 , steps 3 to 5 are performed, and 1349 sets of statistical results of positioning deviations are obtained as shown in FIG. 6 .

The invention can not rely on other satellite images of the same scene, and does not need to measure ground control points on the spot, is convenient to implement, and can be generally applied to remote sensing image geolocation accuracy evaluation.

Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above-mentioned specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essential content of the present invention. The embodiments of the present application and features in the embodiments may be arbitrarily combined with each other 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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