CN114529520A - Positioning accuracy evaluation method - Google Patents

Positioning accuracy evaluation method Download PDF

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CN114529520A
CN114529520A CN202210089008.4A CN202210089008A CN114529520A CN 114529520 A CN114529520 A CN 114529520A CN 202210089008 A CN202210089008 A CN 202210089008A CN 114529520 A CN114529520 A CN 114529520A
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remote sensing
sensing image
shoreline
positioning
image
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CN114529520B (en
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郭玲玲
汪少林
代海山
周军
杨春燕
何军
金云飞
杨彬
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Shanghai Institute of Satellite Engineering
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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

定位精度评价方法Positioning Accuracy Evaluation Method

技术领域technical field

本发明涉及遥感图像地理定位技术领域,具体地,涉及一种定位精度评价方法,尤其是一种基于高分辨率岸线数据的遥感图像地理定位精度评价方法。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.

背景技术Background technique

随着遥感应用的迅速发展,遥感图像的地理定位精度成为衡量遥感图像应用价值的重要指标之一,这就需要开展对遥感图像定位精度进行定量化评价。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.

目前已有的遥感图像地理定位评价方法均建立在同名控制点位置比较基础之上,如何快速、便捷地获取控制点并匹配对于遥感图像地理定位精度评价具有重要意义。现有定位精度评价方法多依赖试验场控制点实测数据或者其他卫星影像信息,实施受到一定限制。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.

GJB 4407-2002《航天遥感图像定位精度检测方法》中给出的控制点获取方法包括:外业检测法、地形图(库)读点检测法和摄影测量法。现有文献1[陈艺虾,遥感图像几何定位精度评价方法研究,南京理工大学,硕士论文,2013]给出了基于SURF算法及领域边缘信息进行控制点自动选取并匹配的方法。现有文献2[魏丹丹,甘甫平,尚坤,等,CBERS-04星PAN/MUX图像几何定位精度评价,无线电工程,2018,48(5)]中给出了基于野外实测控制点与其他卫星影像的定位精度评价方法。现有文献3[林鸿弟,屈利娜,GeoEye-1卫星无控自主定位和稀疏控制点下定位精度评价研究,城市勘测,2018,6]与现有文献4[王建步,张杰,马毅,资源一号02C卫星遥感影像二级产品定位精度评价,海洋测绘,2013,33(5)]分别给出了基于试验场获取的控制点进行定位精度评价的方法。公开号CN104574347A的专利文献公开了一种基于多源遥感数据的在轨卫星图像几何定位精度评价方法,采用如下步骤:步骤1,将待评图像和参考图像调整为同一椭球体、基准面和分辨率下的两幅图像;步骤2,对以上两幅图像进行下采样,并进行辐射增强处理;步骤3,使用加速稳健特征Surf算法对以上两幅图像进行粗匹配,并用对极几何约束剔除误匹配点对;步骤4,根据粗匹配结果,对待评图像进行几何关系补偿,并对几何补偿后的待评图像和参考图像进行精确分块;步骤5,针对待评图像和参考图像块对,使用Surf算法进行精匹配,并用对极几何约束剔除误匹配点对;步骤6,计算外部几何定位精度,同时根据筛选出的各方向控制点对计算内部几何定位精度。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.

上述文献仍然存在多依赖试验场控制点实测数据或者其他卫星影像信息,实施受到一定限制的缺陷。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.

发明内容SUMMARY OF THE INVENTION

针对现有技术中的缺陷,本发明的目的是提供一种定位精度评价方法。Aiming at the defects in the prior art, the purpose of the present invention is to provide a positioning accuracy evaluation method.

根据本发明提供的一种定位精度评价方法,包括以下步骤:A positioning accuracy evaluation method provided according to the present invention includes the following steps:

步骤1:按照地理定位结果对原始图像进行重采样,获取重采样后的图像及逐像素对应的经度、纬度;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;

步骤2:对存在岸线的遥感图像进行边缘提取,获取遥感图像中岸线对应的像素;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;

步骤3:对遥感图像中岸线像素,搜素高分辨率岸线数据中的对应点;Step 3: For the shoreline pixels in the remote sensing image, search the corresponding points in the high-resolution shoreline data;

步骤4:对遥感图像中岸线像素,按照像素对应的经纬度与高分辨率岸线数据中的对应点的经纬度,计算对应的地球表面弧长作为定位偏差;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;

步骤5:根据多组定位结果统计分析遥感图像地理定位误差。Step 5: Statistically analyze the geolocation errors of remote sensing images according to multiple sets of location results.

优选的,所述步骤1中,重采样后的网格设置为按地理圆柱投影生成的等经度、等纬度间隔网格,将原始定位结果的离散点重采样为等经度、等纬度间隔网格上的点。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.

优选的,所述步骤2包含如下步骤:Preferably, the step 2 includes the following steps:

步骤2.1:进行遥感图像边缘提取,获取遥感图像边缘二值图像;Step 2.1: Extract the edge of the remote sensing image to obtain a binary image of the edge of the remote sensing image;

步骤2.2:将高分辨率岸线数据中的经度、纬度离散点投影至步骤一中的等经度、等纬度间隔网格,生成一幅与遥感图像分辨率相同的二值图像;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;

步骤2.3:对步骤2.2中的二值图像进行图像形态学处理中膨胀操作;Step 2.3: Perform the dilation operation in the image morphological processing on the binary image in Step 2.2;

步骤2.4:将步骤2.3中得到的二值图像作为掩膜,对步骤2.1中的遥感图像边缘进行过滤,作为从遥感图像中提取出的岸线像素。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.

优选的,所述步骤2.1中,采用Sobel算子进行遥感图像边缘提取。Preferably, in the step 2.1, the Sobel operator is used to extract the edge of the remote sensing image.

优选的,所述步骤2.3中,结构元素为D,Preferably, in the step 2.3, the structural element is D,

Figure BDA0003488662030000031
Figure BDA0003488662030000031

存在高分辨率岸线的像素被标记为1,否则为0。Pixels with high-resolution shorelines are marked as 1, otherwise 0.

优选的,所述步骤3中,高分辨率岸线数据由全球自洽分层高分辨率地理数据库获取。Preferably, in the step 3, the high-resolution shoreline data is obtained from a global self-consistent hierarchical high-resolution geographic database.

优选的,所述步骤3中,对遥感图像中岸线像素,按照地表距离最近原则搜素高分辨率岸线数据中的对应点。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.

优选的,所述步骤4中,若遥感图像中岸线像素对应的纬度为a、经度为b(单位:弧度),高分辨率岸线数据中对应点的α、经度为β,定位偏差d计算公式为: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))d=Aarccos(sin(a)cos(b)sin(α)cos(β)+sin(a)sin(b)sin(α)sin(β)+cos(a)cos(α))

其中,A为地球平均半径。where A is the average radius of the earth.

优选的,步骤5中定位误差δ计算方法为:Preferably, the calculation method of the positioning error δ in step 5 is:

Figure BDA0003488662030000032
Figure BDA0003488662030000032

其中,

Figure BDA0003488662030000033
为多组定位偏差的平均值。in,
Figure BDA0003488662030000033
is the average of the positioning deviations of multiple groups.

优选的,定位偏差的组数不小于50。Preferably, the number of groups of positioning deviations is not less than 50.

与现有技术相比,本发明具有如下的有益效果:Compared with the prior art, the present invention has the following beneficial effects:

1、本发明能够不依赖其他卫星相同场景的定位数据,评价遥感图像地理定位精度;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、本发明基于全球自洽分层高分辨率地理数据库(GSHHG),利用遥感图像中的水陆分界位置与高分辨率岸线数据库对比,定量化评价遥感图像地理定位精度;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、本发明方法合理、计算简单、实施简易,能够普遍应用于遥感图像定理精度评估。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.

附图说明Description of drawings

通过阅读参照以下附图对非限制性实施例所作的详细描述,本发明的其它特征、目的和优点将会变得更明显: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:

图1为本发明的流程图;Fig. 1 is the flow chart of the present invention;

图2为某卫星原始遥感图像;Figure 2 is the original remote sensing image of a satellite;

图3为重采样后的遥感图像;Fig. 3 is the remote sensing image after resampling;

图4为高分辨率岸线数据投影至与遥感图像相同分辨率网格结果;Figure 4 is the result of projecting high-resolution shoreline data to a grid with the same resolution as the remote sensing image;

图5为遥感图像中岸线对应的像素;Fig. 5 is the pixel corresponding to the shoreline in the remote sensing image;

图6为基于高分辨率岸线数据的定位偏差统计结果。Figure 6 shows the statistical results of positioning deviation based on high-resolution shoreline data.

具体实施方式Detailed ways

下面结合具体实施例对本发明进行详细说明。以下实施例将有助于本领域的技术人员进一步理解本发明,但不以任何形式限制本发明。应当指出的是,对本领域的普通技术人员来说,在不脱离本发明构思的前提下,还可以做出若干变化和改进。这些都属于本发明的保护范围。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.

实施例1:Example 1:

如图1所示,本实施例提供的一种定位精度评价方法,包括以下步骤:As shown in FIG. 1, a positioning accuracy evaluation method provided by this embodiment includes the following steps:

步骤1:按照地理定位结果对原始图像进行重采样,获取重采样后的图像及逐像素对应的经度、纬度,步骤1中,重采样后的网格设置为按地理圆柱投影生成的等经度、等纬度间隔网格,将原始定位结果的离散点重采样为等经度、等纬度间隔网格上的点。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.

步骤2:对存在岸线的遥感图像进行边缘提取,获取遥感图像中岸线对应的像素。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.

步骤2包含如下步骤:Step 2 includes the following steps:

步骤2.1:进行遥感图像边缘提取,获取遥感图像边缘二值图像;Step 2.1: Extract the edge of the remote sensing image to obtain a binary image of the edge of the remote sensing image;

步骤2.2:将高分辨率岸线数据中的经度、纬度离散点投影至步骤一中的等经度、等纬度间隔网格,生成一幅与遥感图像分辨率相同的二值图像,步骤2.1中,采用Sobel算子进行遥感图像边缘提取;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;

步骤2.3:对步骤2.2中的二值图像进行图像形态学处理中膨胀操作,步骤2.3中,结构元素为D,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,

Figure BDA0003488662030000051
Figure BDA0003488662030000051

存在高分辨率岸线的像素被标记为1,否则为0;Pixels with high-resolution shorelines are marked as 1, otherwise 0;

步骤2.4:将步骤2.3中得到的二值图像作为掩膜,对步骤2.1中的遥感图像边缘进行过滤,作为从遥感图像中提取出的岸线像素。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.

步骤3:对遥感图像中岸线像素,搜素高分辨率岸线数据中的对应点,步骤3中,高分辨率岸线数据由全球自洽分层高分辨率地理数据库获取,对遥感图像中岸线像素,按照地表距离最近原则搜素高分辨率岸线数据中的对应点。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.

步骤4:对遥感图像中岸线像素,按照像素对应的经纬度与高分辨率岸线数据中的对应点的经纬度,计算对应的地球表面弧长作为定位偏差,步骤4中,若遥感图像中岸线像素对应的纬度为a、经度为b(单位:弧度),高分辨率岸线数据中对应点的α、经度为β,定位偏差d计算公式为: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))d=Aarccos(sin(a)cos(b)sin(α)cos(β)+sin(a)sin(b)sin(α)sin(β)+cos(a)cos(α))

其中,A为地球平均半径。where A is the average radius of the earth.

步骤5:根据多组定位结果统计分析遥感图像地理定位误差,步骤5中定位误差δ计算方法为: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:

Figure BDA0003488662030000052
Figure BDA0003488662030000052

其中,

Figure BDA0003488662030000053
为多组定位偏差的平均值。定位偏差的组数不小于50。in,
Figure BDA0003488662030000053
is the average of the positioning deviations of multiple groups. The number of groups of positioning deviation is not less than 50.

下面结合某卫星遥感图像对本发明方法进行验证,图2所示为某卫星原始遥感图像,由于该载荷成像过程中采用了45°旋转扫描反射镜,故而原始图像中存在像旋。图3为执行步骤1后得到的重采样后的遥感图像。步骤2中高分辨率岸线数据投影至与遥感图像相同分辨率网格结果如图4所示,执行步骤2后获取遥感图像中岸线对应的像素如图5所示。由图5可以看出,受云层等因素影响,在遥感图像中提取得到的岸线像素并不连续。对应对图5中的岸线像素,执行步骤3至步骤5,得到的1349组定位偏差统计结果如图6所示。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 .

本发明能够不依赖其他同场景卫星影像、无需实地测量地面控制点,实施方便且能普遍适用于遥感图像地理定位精度评价。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.

以上对本发明的具体实施例进行了描述。需要理解的是,本发明并不局限于上述特定实施方式,本领域技术人员可以在权利要求的范围内做出各种变化或修改,这并不影响本发明的实质内容。在不冲突的情况下,本申请的实施例和实施例中的特征可以任意相互组合。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,
Figure FDA0003488662020000021
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:
Figure FDA0003488662020000022
wherein,
Figure FDA0003488662020000023
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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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN119417891A (en) * 2024-09-29 2025-02-11 北京华云星地通科技有限公司 Satellite image positioning accuracy detection method, device, equipment and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103413272A (en) * 2013-07-22 2013-11-27 中国科学院遥感与数字地球研究所 Low-spatial-resolution multisource remote sensing image space consistency correction method
US20180144478A1 (en) * 2016-06-29 2018-05-24 Shenzhen University Method and system for sea background modeling and suppression on high-resolution remote sensing sea images
CN110378001A (en) * 2019-07-11 2019-10-25 中国空间技术研究院 A kind of Pillarless caving remote sensing satellite geometric positioning accuracy analysis method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103413272A (en) * 2013-07-22 2013-11-27 中国科学院遥感与数字地球研究所 Low-spatial-resolution multisource remote sensing image space consistency correction method
US20180144478A1 (en) * 2016-06-29 2018-05-24 Shenzhen University Method and system for sea background modeling and suppression on high-resolution remote sensing sea images
CN110378001A (en) * 2019-07-11 2019-10-25 中国空间技术研究院 A kind of Pillarless caving remote sensing satellite geometric positioning accuracy analysis method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
CHUNLI DAI ET AL.: "Coastline extraction from repeat high resolution satellite imagery", 《REMOTE SENSING OF ENVIRONMENT》, 26 April 2019 (2019-04-26), pages 260 - 270 *
张静等: "基于海岸线数据库的遥感图像海陆分割", 《电子测量技术》, vol. 43, no. 23, 8 December 2020 (2020-12-08), pages 115 - 120 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN115393438B (en) * 2022-08-29 2023-03-10 北京华云星地通科技有限公司 Method and system for detecting geographical positioning error of remote sensing image of satellite
CN119417891A (en) * 2024-09-29 2025-02-11 北京华云星地通科技有限公司 Satellite image positioning accuracy detection method, device, equipment and storage medium

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