Control sheet generation and geometric positioning method fusing satellite-borne laser and orthoimage

Abstract

The invention discloses a control sheet generation and geometric positioning method fusing satellite-borne laser and an orthoimage. The method comprises the following steps: firstly, screening satellite-borne laser point clouds to obtain a high-quality laser point set; secondly, projecting high-quality satellite-borne laser points obtained by screening onto an orthoimage through coordinate transformation, and removing low-feature response points; and finally, generating a control sheet based on the orthoimage and the laser control point, and performing geometric correction on the target image by using the constructed control sheet to realize high-precision geometric positioning of the image. Different from the mode that only laser height measurement data is adopted for elevation control, the satellite-borne laser data and the orthoimage are fused, accurate horizontal height control over the remote sensing image is achieved, meanwhile, the defect that the DEM precision is limited is overcome, and the method is more suitable for geometric positioning of sub-meter images.

Description

Technical Field

[0001] The present invention relates to the field of photogrammetry and remote sensing, and in particular to a control film generation and geometric positioning method integrating satellite-borne laser and orthophoto, wherein the level control positioning combining satellite-borne laser and orthophoto is the key technology of the present invention. Background Art

[0002] With the continuous development and widespread application of remote sensing technology, high-resolution remote sensing images play an increasingly important role in many fields such as environmental monitoring, urban planning, precision agriculture and disaster emergency response. In order to ensure the effectiveness of these applications, the geometric positioning accuracy of remote sensing images is crucial. Especially in scenarios such as large-scale geographic information system construction, precise mapping and real-time monitoring, the geometric positioning accuracy of images directly affects the reliability of analysis results and the accuracy of decision-making. However, due to complex terrain, changes in ground environment and the difficulty of obtaining traditional ground control points, how to achieve high-precision geometric positioning of remote sensing images has become a key challenge in current remote sensing image processing and application.

[0003] Traditional remote sensing image geometric positioning and correction methods mainly rely on ground control points. However, obtaining high-quality ground control points usually requires a lot of manpower and material resources, especially in complex terrain or remote areas, where obtaining ground control points becomes more difficult and costly. At the same time, due to the sparsity and uneven distribution of ground control points, the accuracy of geometric correction of these methods is often difficult to guarantee when dealing with large areas or complex terrain.

[0004] Therefore, it is necessary to develop a high-precision geometric positioning method for remote sensing images in complex terrain or remote areas without ground control points, large areas or complex terrain. Summary of the invention

[0005] The purpose of the present invention is to provide a control film generation and geometric positioning method that integrates satellite-borne laser and orthophoto images, which is a method for integrating satellite-borne laser altimetry data and orthophoto images to generate control films, and then using them as control data to achieve precise leveling and height control positioning of remote sensing images to be processed. This method combines the high-precision ground coordinates of satellite-borne laser data with the optical feature information of orthophoto images to produce a control film containing a large number of laser three-dimensional control points, and thereby achieves high-precision geometric correction and positioning of remote sensing images. It is suitable for high-precision geometric positioning of remote sensing images in complex terrain or remote areas without ground control points, large areas or complex terrain; it solves the problem of difficulty in geometric positioning of remote sensing images caused by the difficulty and high cost of collecting ground control points in complex terrain or remote areas, and at the same time solves the problem that the accuracy of geometric correction is often difficult to guarantee when processing large areas or complex terrain.

[0006] In order to achieve the above-mentioned purpose, the technical solution of the present invention is: a control film generation and geometric positioning method integrating satellite-borne laser and orthophoto, characterized in that it comprises the following steps:

[0007] Step 1, preprocessing of onboard laser data;

[0008] Screen the satellite-borne laser point cloud, remove noise and abnormal points, retain high-precision and high-confidence laser points, and obtain a high-quality laser point set;

[0009] Step 2, generation of image control points;

[0010] Through coordinate transformation, the screened high-quality satellite laser points are converted to orthophotos, and the Harris response values ​​of the corresponding image points projected on the image by the local neighborhood image are calculated to evaluate whether the corresponding image points projected on the image by the laser points are obvious feature points, and the low feature response points are eliminated;

[0011] Step 3, construct the control piece;

[0012] Generate control slices based on orthophotos and laser control points. The control slices contain precise ground coordinates and image coordinate information.

[0013] Step 4, performing high-precision geometric correction on the image to be processed;

[0014] The constructed control piece is used to perform geometric correction on the target image to achieve high-precision geometric positioning of the image.

[0015] In the above technical solution, in step 1, the satellite-borne laser data preprocessing method is as follows:

[0016] First, the laser points are screened based on the attribute labels. The attribute labels of the laser points are screened to ensure high confidence in the data.

[0017] Then, the elevation points are screened again in combination with the prior terrain data; the elevation errors of the laser points are further screened and eliminated in combination with the prior data as a reference, so as to obtain a more accurate and reliable laser point set.

[0018] In the above technical solution, the prior data includes SRTM (Shuttle Radar Topography Mission), global water body mask data, etc.

[0019] In the above technical solution, laser point screening based on attribute tags includes the following steps:

[0020] 1) First, screening based on reference DEM elevation labels;

[0021] In step 1, the laser points are screened according to the elevation labels of the reference DEM. The elevation difference threshold is set according to the accuracy of the reference DEM. Usually, the elevation difference threshold is set to twice the elevation root mean square error, which can be relaxed to three times in complex areas. The screening formula is:

[0022] ΔH=|H dem -H p |<3σ (1)

[0023] Among them, H dem H is the elevation label of the reference DEM, which describes the corresponding reference DEM elevation; p is the elevation of the corresponding laser elevation control point; σ is the root mean square error of the elevation of the reference DEM;

[0024] 2) Next, screening based on quality evaluation labels;

[0025] For public satellite laser point products, each point often has a quality evaluation label given when it is generated. Usually, in order to ensure the accuracy of the laser point, the highest quality laser points are selected (that is, the highest level laser points. Each laser point has a label to evaluate its quality, such as 1, 2, 3, and 4. The higher the level, the better the quality. Therefore, the laser point with level 4 is selected as the highest quality laser point). Different satellite laser data, or even different level products of the same satellite laser, may have different label formats.

[0026] In the above technical solution, the elevation points are re-screened in combination with the prior terrain data, including the following steps:

[0027] 1) First, combined with the screening of global water masks;

[0028] Considering that the laser points falling in water bodies are often of low accuracy and not suitable for image geometric correction, the global water mask data is used to filter out the laser points in the water;

[0029] 2) Next, combine with SRTM screening;

[0030] Assume that the geographic coordinates of a laser point are known to be (X, Y), and the affine transformation parameters of SRTM are X0, Y0, R x , R y , S x , S y , the geographic coordinates can be converted to the pixel coordinates of the image using the following formula:

[0031]

[0032] In the above formula, (X,Y) must be consistent with the SRTM ground coordinate unit; if not, the laser point geographic coordinates must be converted to the SRTM ground coordinate system first;

[0033] Through coordinate projection, each laser control point (x i ,y i ,z i )'s reference height H i , and then calculate the elevation difference:

[0034] ΔH i =z i -H i (4 )

[0035] To further eliminate gross error points, a statistical frequency histogram analysis can be performed on the elevation differences of all laser points in the analysis area, and gross elevation error points can be filtered out based on the frequency distribution. According to the regional characteristics and laser point quality, an appropriate frequency threshold can be selected to treat the laser points outside the two sides as gross error points.

[0036] In the above technical solution, in step 2, after the laser point is projected onto the image, the Harris response value is calculated using the local neighborhood image, and weak feature points with low response values ​​are eliminated;

[0037] The method for calculating the Harris response value R of each projection point includes the following steps:

[0038] First, calculate the gradient information of the image. Assuming the gray value function of the image is I(x, y), the gradient at the image pixel point (x, y) can be expressed as:

[0039] G=[I x I y ] (5)

[0040] in, are the gradients of the image in the x and y directions respectively; in order to calculate the feature changes in the local area, the autocorrelation matrix M can be constructed:

[0041]

[0042] Among them, w(x,y) is a weight function (usually a Gaussian function) used to weightedly calculate the gradient information within the neighborhood W;

[0043] According to the autocorrelation matrix M, the Harris corner response function R can be defined:

[0044] R = det(M) - k (trace(M)) 2 (7)

[0045] in, is the determinant of the matrix, is the trace of the matrix, k is an empirical constant, usually between [0.04, 0.06].

[0046] In the above technical solution, the method for determining whether a projection point is a weak feature point is:

[0047] According to the Harris response value R of each projection point, set the threshold R threshold =5%·R max , when the projection point satisfies R ≥ R threshold , then this projection point is an obvious feature point and is retained as an image control point; otherwise, this projection point is a weak feature point and is removed.

[0048] In the above technical solution, in step 3, for each image control point, firstly, an image block of size n×n pixels is cropped from the orthophoto with the control point as the center. The size of n depends on the orthophoto resolution, ensuring that the image block is larger than 1km 2 ;

[0049] Each control piece is composed of the image control point, the corresponding image block, and the image coordinates and object coordinates of the image control point.

[0050] In the above technical solution, in step 4, based on the control piece generated in step 3, the control piece is matched with the image to be processed by an image matching method, and the matching method is as follows:

[0051] First, the same-name feature points between the control image and the image to be processed are identified and extracted through an automatic or semi-automatic image matching algorithm;

[0052] Then, based on these matching points, a regional network is constructed and rigorous adjustment is performed;

[0053] Then, during the adjustment process, a robust statistical method is used to dynamically remove image control points with large errors or poor matching quality to reduce the impact of errors on model accuracy.

[0054] Finally, through adjustment calculation, high-precision geometric positioning image results are obtained. It should be noted that the satellite-borne laser mainly provides elevation control, while the orthophoto provides texture features and plane control.

[0055] The present invention has the following advantages:

[0056] (1) A new image geometric positioning solution is provided; the present invention achieves high-precision geometric positioning of images by fusing satellite-borne laser data and orthophotos and using the generated control slices, making full use of the advantages of multi-source data and further expanding the application scope of laser altimetry data (suitable for high-precision geometric positioning of remote sensing images in complex terrain or remote areas without ground control points, large areas or complex terrain). The control slices generated by fusing with orthophotos achieve level control, rather than just elevation control.

[0057] (2) Compared with the positioning method based on orthophoto + DEM, the method of the present invention overcomes the defect of limited DEM accuracy (the geometric positioning accuracy of the prior art can only reach the meter level), and is more suitable for the geometric positioning of sub-meter images (the geometric positioning accuracy of the method of the present invention is one order of magnitude higher than the positioning accuracy of the prior art), ensuring the geometric accuracy of the entire image (the plane accuracy of the positioning of the present invention can be achieved within 1 pixel, and the elevation accuracy can be achieved about 0.5 meters) and consistency (mainly ensuring the geometric consistency within the entire image and ensuring that the local accuracy and global accuracy of the image are relatively consistent due to the abundant image control points);

[0058] (3) This method has strong iterativeness. With the continuous improvement of the plane accuracy of the satellite-borne laser radar and the continuous improvement of the positioning accuracy of the acquired orthophotos, the positioning effect of the control piece produced based on the method of the present invention will also be continuously optimized and improved. Therefore, with the advancement of technology and the improvement of data quality, the positioning accuracy and application scope of this method will only continue to increase, ensuring its long-term effectiveness and competitiveness in future applications.

[0059] (4) The present invention achieves overall correction of the image by integrating satellite-borne laser altimetry data with orthophoto images. This overcomes the limitation of the prior art that only a very small area of ​​the image (the area with laser points) can be corrected when only laser altimetry data is used for correction, and the overall correction of the image cannot be achieved.

[0060] (5) The method of the present invention uses open source orthophotos, which are sub-meter images in themselves, and further improves the geometric positioning effect by integrating satellite-borne laser altimetry data. It overcomes the problem that the existing geometric positioning method based on open source DEM is not suitable for high-precision geometric positioning of sub-meter images due to the limited resolution and accuracy of open source DEM. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is the overall technical flow chart of the present invention;

[0062] Figure 2 A high-quality laser point distribution map extracted from a certain area in an embodiment of the present invention;

[0063] Figure 3 It is a partial control slice file diagram obtained by fusing satellite-borne laser altimetry and orthophoto in an embodiment of the present invention;

[0064] Figure 4 is a distribution diagram of image control points for image correction to be processed in an embodiment of the present invention;

[0065] Figure 5 It is a distribution map of ground control points used for evaluating the geometric positioning accuracy of images in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following is a detailed description of the implementation of the present invention in conjunction with the accompanying drawings, but they do not constitute a limitation of the present invention and are only used as examples. At the same time, the advantages of the present invention are made clearer and easier to understand through the description.

[0067] Satellite-borne laser altimetry data is gradually becoming an important control source for the geometric correction of remote sensing images due to its high-precision elevation information. By combining satellite-borne laser data with high-resolution optical images, the geometric positioning accuracy of the image can be significantly improved, thereby reducing the dependence on traditional ground control points, especially in complex terrain or areas where ground control points are difficult to obtain. However, in current technical applications, satellite-borne laser altimetry data is usually only used for elevation control, and its potential in all-round geometric correction has not been fully explored. Research on how to organically integrate satellite-borne laser data with orthophotos to achieve high-precision horizontal and vertical positioning and correction of remote sensing images is still in its preliminary stages. Existing methods have problems such as low data utilization and limited fusion accuracy in the fusion process, and fail to give full play to the unique advantages of satellite-borne laser data in improving image correction accuracy;

[0068] Control sheets are a new type of photogrammetry control data source. They refer to image data that contains precise geometric positioning parameters and aerial triangulation encryption point information after aerial triangulation processing. Because they come with the ground coordinates of the encrypted points and the corresponding image data, they can be used directly as control information for image correction. Satellite-borne laser altimetry data only has ground coordinates and no corresponding image position information, while orthophotos have ground texture information. By geometrically aligning the two and fusing them, and using the orthophoto texture information and satellite-borne laser geometric coordinate information, high-precision control sheets can be generated. However, how to fuse satellite-borne laser points and orthophotos to generate control sheets is an urgent problem to be solved.

[0069] The present invention provides a method that can fuse satellite-borne laser and orthophoto to realize control slice generation and high-precision geometric positioning of images. The core idea is to use satellite-borne laser data and open-source orthophoto as basic data sources, use the attribute labels of satellite-borne laser data itself and prior data to screen and obtain high-quality laser point data, use orthophoto as base map and satellite-borne laser to align and extract high-feature response image control points, generate control slices by fusing satellite-borne laser data and open-source orthophoto, use the generated control slices to realize high-precision level control positioning of the image to be processed, and then use them as control data to realize accurate level control positioning of the remote sensing image to be processed. The specific method is: first, screen the satellite-borne laser point cloud to obtain a high-quality laser point set; second, project the screened high-quality satellite-borne laser points onto the orthophoto through coordinate transformation, and remove low-feature response points; finally, generate control slices based on orthophoto and laser control points, and use the constructed control slices to perform geometric correction on the target image to realize high-precision geometric positioning of the image. Different from using only laser altimetry data for elevation control, the method of the present invention realizes precise leveling and height control of remote sensing images by fusing satellite-borne laser data with orthophotos, while overcoming the defect of limited DEM accuracy. It is more suitable for the geometric positioning of sub-meter images, and is suitable for high-precision geometric correction and positioning of remote sensing images in complex terrain or remote areas without ground control points, large areas or complex terrain.

[0070] The present invention is mainly implemented by the following technical scheme: a control film generation and geometric positioning method integrating satellite-borne laser and orthophoto, comprising the following steps:

[0071] Step 1: Preprocessing of satellite laser data: Screen the satellite laser point cloud, remove noise and abnormal points, retain high-precision and high-confidence laser points, and obtain a high-quality laser point set.

[0072] Step 2: Generate image control points. Through coordinate transformation, the screened high-quality satellite laser points are converted to orthophotos, and their Harris response values ​​are calculated using local neighborhood images to evaluate whether they are obvious feature points and eliminate low feature response points.

[0073] Step 3: Construct control slices. Generate control slices based on orthophotos and laser control points. The control slices contain precise ground coordinates and image coordinate information.

[0074] Step 4: Perform high-precision geometric correction on the image to be processed. Use the constructed control slice to perform geometric correction on the target image to achieve high-precision geometric positioning of the image.

[0075] In step 1, the preprocessing of satellite laser data is mainly divided into two small steps, namely, filtering based on attribute labels and filtering elevation points based on prior terrain. First, the attribute labels of laser points are filtered to ensure high confidence in the data. Then, combined with prior data such as SRTM (Shuttle Radar Topography Mission) and global water mask data as reference, the elevation errors of laser points are further screened and eliminated to obtain a more accurate and reliable laser point set.

[0076] (1) Laser point screening based on attribute tags

[0077] Referring to the laser point data preprocessing technology of ICESat-2, laser points usually have multiple attribute tags, including control point quality confidence tags, etc. The screening technology process is as follows:

[0078] 1) Filtering based on reference DEM elevation labels

[0079] In step 1, the laser points are screened according to the elevation labels of the reference DEM. The elevation difference threshold is set according to the accuracy of the reference DEM. Usually, the elevation difference threshold is set to twice the elevation root mean square error, which can be relaxed to three times in complex areas. The screening formula is:

[0080] ΔH=|H dem -H p |<3σ (1)

[0081] Among them, H dem H is the elevation label of the reference DEM, which describes the corresponding reference DEM elevation; p is the elevation of the corresponding laser elevation control point; σ is the root mean square error of the elevation of the reference DEM.

[0082] 2) Screening based on quality evaluation labels

[0083] For publicly available satellite-borne laser point products, each point often has a quality evaluation label given when it is generated. Usually, in order to ensure the accuracy of the laser points, the highest quality laser points are selected.

[0084] It should be noted that different satellite laser data, or even different levels of products of the same satellite laser, may have different label formats. Taking the ATL03 and ATL08 products of ICESat-2 as examples, the main attribute labels of the former are as described in the present invention, while the latter includes multiple labels such as cloud coverage of photons and slope of segmented photons. Therefore, the present invention only uses the most common attribute labels as an example for explanation.

[0085] (2) Re-screening based on prior data

[0086] Due to the large number of satellite-borne laser points, there are certain errors in the attribute labels provided by the data provider when processing the laser points. On this basis, some prior data are used for secondary screening to further ensure the quality of the laser points.

[0087] 1) Combined with the screening of global water masks

[0088] Considering that the laser points falling in water bodies often have low accuracy and are not suitable for image geometric correction, the global water mask data is used to filter out the laser points in the water.

[0089] 2) Combined with SRTM screening

[0090] For SRTM TIFF image files, they contain geographic reference information, such as geographic coordinate system (such as WGS 84) and image affine transformation parameters. Affine transformation parameters are used to describe the mapping relationship between image coordinates and geographic coordinates, usually including six parameters: two parameters for translation (X0 and Y0, respectively, the X and Y coordinates of the upper left corner of the image), two parameters for rotation (R x and R y , which is usually close to 0 because images are usually aligned with the coordinate axes), and two parameters for scaling (S x and S y , respectively the horizontal and vertical pixel resolutions of the image).

[0091] Assume that the geographic coordinates of a laser point are known to be (X, Y), and the affine transformation parameters of SRTM are X0, Y0, R x , R y , S x , S y , the geographic coordinates can be converted to the pixel coordinates of the image using the following formula:

[0092]

[0093] In the above formula, (X,Y) must be consistent with the SRTM ground coordinate unit. If not, the laser point geographic coordinates must be converted to the SRTM ground coordinate system.

[0094] Through coordinate projection, each laser control point (x i ,y i ,z i )'s reference height H i , and then calculate the elevation difference:

[0095] ΔH i =z i -H i (4).

[0096] To further eliminate gross error points, a statistical frequency histogram analysis can be performed on the elevation differences of all laser points in the area to be analyzed, and gross elevation error points can be filtered out based on the frequency distribution. Since the number of laser points generated by the photon counter is large and the point density varies in different areas, it is not appropriate to simply use the zero value on both sides of the main frequency as the filtering standard. A more reasonable method is to set the frequency limit of gross error points based on a certain proportion of the total number of photons. Specifically, according to the regional characteristics and the quality of the laser points, an appropriate frequency threshold can be selected to regard the laser points outside the two sides as gross error points.

[0097] In step 2, the high-quality laser points obtained in step 1 are projected onto the orthophoto through coordinate transformation using formulas (2) and (3). After the laser points are projected onto the image, the Harris response value is calculated using the local neighborhood image, and weak feature points with low response values ​​are removed. First, the gradient information of the image is calculated. Assuming that the gray value function of the image is I(x, y), the gradient at the image pixel point (x, y) can be expressed as:

[0098] G=[I x I y ] (5)

[0099] in, are the gradients of the image in the x and y directions respectively. In order to calculate the feature changes in the local area, the autocorrelation matrix M can be constructed:

[0100]

[0101] Among them, w(x,y) is a weight function (usually a Gaussian function) used to weightedly calculate the gradient information within the neighborhood W.

[0102] According to the autocorrelation matrix M, the Harris corner response function R can be defined:

[0103] R = det(M) - k (trace(M)) 2 (7)

[0104] in, is the determinant of the matrix, is the trace of the matrix, k is an empirical constant, usually between [0.04, 0.06]. According to the Harris response value R of each projection point, the threshold R is set threshold =5%·R max , when the projection point satisfies R ≥ R threshold It is retained as an image control point.

[0105] In step 3, for each image control point, firstly, an image block of size n×n pixels is cropped from the orthophoto with the control point as the center. The size of n depends on the orthophoto resolution, ensuring that the image block is larger than 1km. 2 Each control slice is composed of the image control point, the corresponding image block, and the image coordinates and object coordinates of the image control point.

[0106] In step 4, based on the control piece generated in step 3, it is matched with the image to be processed by the image matching method. First, the feature points with the same name between the control piece and the image to be processed are identified and extracted through an automatic or semi-automatic image matching algorithm. Then, based on these matching points, a regional network is constructed and a strict adjustment process is performed. During the adjustment process, a robust statistical method is used to dynamically eliminate image control points with large errors or poor matching quality to reduce the impact of errors on model accuracy. Finally, through adjustment calculation, the image results of high-precision geometric positioning are obtained. It should be noted that the satellite-borne laser mainly provides elevation control, while the orthophoto provides texture features and plane control.

[0107] Example

[0108] The present invention is now described in detail by taking the application of the present invention to a remote area for geometric positioning of images as an example, which also has a guiding role in the application of the present invention to other areas for geometric positioning of images.

[0109] In a remote area of ​​this embodiment, it is difficult and costly to obtain ground control points, and it is impossible to use ground control points for geometric positioning of images.

[0110] This example selects the ICESat-2 ATL03 laser point data of a certain area and 1-meter resolution open source orthophotos for the generation of control slices. At the same time, the IKONOS satellite 1-meter resolution images and aerial ground control points of the area were collected to verify the geometric positioning accuracy of the method. Since the spacing between each group of laser orbits of ICESat-2 is 3 kilometers and the orbital spacing is about 1 kilometer, in order to ensure sufficient coverage of the area by satellite-borne laser data, this example downloads a total of 21 laser point data sets from January 2022 to October 2023. The overall process of geometric positioning of images using the present invention in this embodiment is shown in the attached figure. Figure 1 As shown, the core steps include the following aspects:

[0111] Step 1: Preprocess the downloaded 21 ATL03 satellite laser point datasets. The initial data contains 7,846,683 laser points. For ATL03 data products, the attributes of laser points only include quality confidence labels. By filtering this label, 318,912 laser points were retained; secondly, the collected 30-meter resolution SRTM DEM and water mask data were used for secondary screening, and 221,743 laser points were retained; finally, in order to meet the density requirements of subsequent control points, a 50×50-meter grid was used for thinning, and 3,351 laser points were finally retained.

[0112] Step 2: Project the high-quality satellite laser points selected in step 1 onto the orthophoto through the general imaging model. Then, the Harris operator is used to detect and remove low-feature response points, and finally 2163 image control points are extracted. Their spatial distribution is shown in the attached figure. Figure 2 As shown ( Figure 2 The red dots distributed on the image are all image control points).

[0113] Step 3: Generate a control sheet based on the orthophoto and laser control points. The control sheet contains accurate ground coordinates and image coordinate information. The generated control sheet file is as follows: Figure 3 As shown (in Figure 3 In the Hobart txt file, the object coordinates and image coordinates of the image control points in each control slice are recorded; Hobart1-Hobart23 are partial images corresponding to the control slice itself).

[0114] Step 4: Match the generated control slices with the IKONOS satellite image. The initial number of control slices was 2163. Since some image control points were outside the range of the IKONOS image, the actual number of control slices that could be used for matching was 1979. Finally, 341 control slices were successfully matched. Geometric correction was performed based on the successfully matched control slices, and gross error points were eliminated through iterative calculation. Finally, 278 accurate image control points were obtained, as shown in the attached figure. Figure 4 As shown ( Figure 4 The blue dots in the figure are precise image control points).

[0115] For the final geometrically positioned image, the example uses the following Figure 5 The distribution of ground control points is shown in Figure 2 for accuracy evaluation ( Figure 5 The triangle point in the figure represents the ground control point, and the number in the lower right corner of the triangle point is the number of the ground control point), and is compared with several other schemes. The accuracy comparison is shown in Table 1 below.

[0116] Table 1 Evaluation and comparative analysis of image geometric positioning accuracy of the method of the present invention

[0117]

[0118] The above-mentioned accuracy comparison experiments are divided into four groups. Experiment 1 performs geometric positioning processing on the IKONOS image without any control data; Experiment 2 uses STRM data as control data to perform positioning processing on the IKONOS image. Both Experiments 1 and 2 use 17 ground control points as check points. The results show that the root mean square errors of the plane and elevation are both within the range of 2.5 meters.

[0119] Experiment 3 simulates the scenario with ground control points, using 6 ground control points for geometric correction, and the remaining 11 ground control points as checkpoint data. The plane error and elevation error are both controlled within 0.25 meters. After the control slice generated by the method of the present invention (Experiment 4) processes the IKONOS image, the plane accuracy can reach the sub-meter level (within one pixel), and the elevation accuracy is not much different from the ground control point, showing that the present invention can achieve high-precision geometric correction and positioning of remote sensing images and has extremely high engineering practicality. For the industry, the plane positioning accuracy is within one pixel, and the elevation reaches the decimeter level, which has met the higher accuracy requirements, fully proving the effectiveness of the present invention. At the same time, the geometric positioning accuracy of the prior art (Experiment 1, Experiment 2) is about 2.5 meters, while the geometric positioning accuracy of the method of the present invention is at the decimeter level, which is an order of magnitude higher than the geometric positioning accuracy of the prior art. It can be seen that, relative to the prior art, the present invention not only greatly improves the geometric positioning accuracy when performing geometric positioning of remote sensing images in complex terrain or remote areas, large areas or complex terrain, but also saves costs (the present invention does not require the setting of ground control points), improves work efficiency, and expands the applicability of remote sensing image geometric positioning in complex terrain or remote areas, large areas or complex terrain areas without ground control points.

[0120] The present invention mainly solves the problem that ground control point collection is difficult and costly in complex terrain or remote areas, and proposes a method for generating control slices by fusing satellite-borne laser data with orthophotos, and performing precise leveling and height control positioning of the image to be processed based on the generated control slices. This method achieves high-precision geometric correction and positioning of remote sensing images by combining the high-precision elevation information of satellite-borne laser data with the optical characteristics of orthophotos, making full use of the complementarity of laser data and image data, and effectively improving the positioning accuracy and geometric correction effect of remote sensing images in complex terrain areas. Leveling and height control positioning combining satellite-borne laser and orthophotos is the key technology of the present invention.

[0121] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

[0122] Other parts not described belong to the prior art.

Claims

1. A control film generation and geometric positioning method integrating satellite-borne laser and orthophoto, characterized in that: The following steps are included: Step 1, preprocessing of onboard laser data; Screen the satellite-borne laser point cloud, remove noise and abnormal points, retain high-precision and high-confidence laser points, and obtain a high-quality laser point set; Step 2, generation of image control points; Through coordinate transformation, the screened high-quality satellite laser points are converted to orthophotos, and the Harris response values ​​of the corresponding image points projected on the image by the local neighborhood image are calculated to evaluate whether the corresponding image points projected on the image by the laser points are obvious feature points, and the low feature response points are eliminated; Step 3, construct the control piece; Generate control slices based on orthophotos and laser control points. The control slices contain precise ground coordinates and image coordinate information. Step 4, performing high-precision geometric correction on the image to be processed; The constructed control piece is used to perform geometric correction on the target image to achieve high-precision geometric positioning of the image.

2. The control slice generation and geometric positioning method of integrating satellite-borne laser and orthophoto according to claim 1 is characterized in that: In step 1, the onboard laser data preprocessing method is as follows: First, laser points are screened based on attribute labels to ensure high confidence in the data; Then, the elevation points are screened again in combination with the prior terrain data; the elevation errors of the laser points are further screened and eliminated in combination with the prior data as a reference, so as to obtain a more accurate and reliable laser point set.

3. The control slice generation and geometric positioning method of integrating satellite-borne laser and orthophoto according to claim 2 is characterized in that: Prior data include SRTM and global water mask data.

4. The control slice generation and geometric positioning method of integrating satellite-borne laser and orthophoto according to claim 3 is characterized in that: Laser point screening based on attribute tags includes the following steps: 1) Based on the reference DEM elevation label, the screening formula is: ΔH=|H dem -H p |<3σ (1) Among them, H dem H is the elevation label of the reference DEM, which describes the corresponding reference DEM elevation; p is the elevation of the corresponding laser elevation control point; σ is the root mean square error of the elevation of the reference DEM; 2) Screening based on quality evaluation labels In order to ensure the accuracy of the laser points, the highest quality laser points are selected.

5. The control slice generation and geometric positioning method of integrating satellite-borne laser and orthophoto according to claim 4 is characterized in that: Combined with the prior terrain data, the elevation points are screened again, including the following steps: 1) Combined with the screening of global water masks Use global water mask data to filter out laser points in water; 2) Combined with SRTM screening Assume that the geographic coordinates of a laser point are (X, Y), and the affine transformation parameters of SRTM are X0, Y0, R x , R y , S x , S y , the geographic coordinates are converted to the pixel coordinates of the image using the following formula: In the above formula, (X, Y) must be consistent with the SRTM ground coordinate unit; If they are inconsistent, first convert the geographic coordinates of the laser point to the ground coordinate system where the SRTM is located; Through coordinate projection, each laser control point (x i ,y i , z i )'s reference height H i , and then calculate the elevation difference: ΔH i =z i -H i (4)。 6. The control slice generation and geometric positioning method of integrating satellite-borne laser and orthophoto according to claim 5 is characterized in that: The method to determine whether a projection point is a weak feature point is: According to the Harris response value R of each projection point, set the threshold R threshold =5%·R max , when the projection point satisfies R ≥ R threshold , then this projection point is an obvious feature point and is retained as an image control point; otherwise, this projection point is a weak feature point and is removed.

7. The control slice generation and geometric positioning method of integrating satellite-borne laser and orthophoto according to claim 6 is characterized in that: In step 3, for each image control point, firstly, an image block of size n×n pixels is cropped from the orthophoto with the control point as the center. The size of n depends on the orthophoto resolution. The image block is guaranteed to be larger than 1km. 2 ; Each control piece is composed of the image control point, the corresponding image block, and the image coordinates and object coordinates of the image control point.

8. The control slice generation and geometric positioning method of integrating satellite-borne laser and orthophoto according to claim 7 is characterized in that: In step 4, based on the control piece generated in step 3, the control piece is matched with the image to be processed by an image matching method. The matching method is as follows: First, the same-name feature points between the control image and the image to be processed are identified and extracted through an automatic or semi-automatic image matching algorithm; Then, based on these matching points, a regional network is constructed and rigorous adjustment is performed; Then, during the adjustment process, a robust statistical method is used to dynamically remove image control points with large errors or poor matching quality to reduce the impact of errors on model accuracy. Finally, through adjustment calculation, high-precision geometric positioning image results are obtained.

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