Remote sensing mapping data enhancement method and system for real estate mapping

Abstract

The present application relates to the technical field of image enhancement, in particular to a remote sensing surveying and mapping data enhancement method and system for real estate surveying and mapping, the method comprising: acquiring all target surveying and mapping images corresponding to a region to be surveyed and mapped; setting a virtual control point for each target surveying and mapping image and determining a correction coordinate corresponding to a pixel point in each target surveying and mapping image; screening out coincident pixel points representing a same geographical location from all target surveying and mapping images and locating missing pixel points with missing geographical locations; correcting and supplementing RGB values corresponding to the coincident pixel points and the missing pixel points; and constructing an enhanced overall surveying and mapping image according to the adjusted RGB values corresponding to the coincident pixel points and the missing pixel points and the unadjusted RGB values corresponding to other pixel points. The present application realizes enhancement of unmanned aerial vehicle remote sensing surveying and mapping data and improves the enhancement effect of unmanned aerial vehicle remote sensing surveying and mapping data.

Description

Technical Field

[0001] This invention relates to the field of image enhancement technology, and more specifically to a method and system for enhancing remote sensing mapping data used in real estate surveying. Background Technology

[0002] Compared to total station surveying, new UAV surveying technology offers advantages such as high flexibility, high efficiency, and low cost, and is therefore widely used in real estate surveying. However, due to lens distortion, UAV tilt, weather conditions, and other factors, the acquired image data often exhibits various forms of geometric distortion. To ensure the application of surveying data and the complete restoration of regional models, image data correction is often necessary to enhance the image data.

[0003] Currently, geometric correction theory is mainly used to correct image distortion. Specifically, ground control points are first selected in the survey area; distortion parameters are determined based on the transformation of the corresponding control points in the image; then, the correction positions of all pixels in the image are determined based on the transformation of the control points, i.e., the geometric correction model; then, pixels with positional changes and pixels with missing positions are supplemented by pixel resampling through interpolation to complete the geometric correction. However, for remote sensing images, the amount of information contained in each pixel is enormous, and the pixel resampling step, whether using nearest neighbor interpolation or bilinear interpolation, often leads to information loss. Due to the flexibility of UAVs, remote sensing images exhibit various distortions with different degrees, which often makes it impossible to unify the geometric correction model. Furthermore, the surveying process often involves a large number of images, resulting in poor data enhancement effects for UAV remote sensing surveying. Summary of the Invention

[0004] To address the technical problem of poor data enhancement effects in UAV remote sensing mapping, this invention proposes a method and system for enhancing remote sensing mapping data for real estate surveying.

[0005] In a first aspect, the present invention provides a method for enhancing remote sensing mapping data for real estate surveying, the method comprising:

[0006] Obtain all target mapping images corresponding to the area to be mapped, wherein the union of all target mapping images covers the area to be mapped, and different target control points are set in the area to be mapped;

[0007] Based on the target control points, virtual control points are set for each target mapping image. Based on the mapping coordinates and correction coordinates corresponding to the target control points, as well as the mapping coordinates corresponding to the virtual control points, the correction coordinates of the pixels between the virtual control points and the corresponding control point lines in each target mapping image are determined.

[0008] For pixels in the target mapping image other than control points and the pixels between them, determine the correction coordinates of the pixel based on the smallest quadrilateral to which the pixel belongs.

[0009] Based on the corrected coordinates of all pixels in all target images, the overlapping pixels representing the same geographical location are selected from all target images, and the missing pixels with missing geographical locations are located.

[0010] The RGB values ​​corresponding to overlapping and missing pixels are corrected and supplemented to obtain the adjusted RGB values ​​corresponding to overlapping and missing pixels.

[0011] Based on the adjusted RGB values ​​of overlapping and missing pixels, and the unadjusted RGB values ​​of other pixels, an enhanced overall mapping image is constructed.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the method for setting the target control point includes:

[0013] Starting from each initial control point in the area to be mapped, quadrilateral connection processing is performed to obtain the sub-network corresponding to each initial control point.

[0014] The union of the sub-networks corresponding to all initial control points is used to form an initial quadrangle network, and each initial control point in the initial quadrangle network is determined as a reference control point.

[0015] Each initial control point in the area to be surveyed, excluding all reference control points, is designated as a temporary control point;

[0016] Control points are supplemented based on reference control points and each temporary control point;

[0017] If the included angle between two adjacent sides in the initial quadrilateral network is greater than a preset angle, then these two adjacent sides are used as the sides of a parallelogram to construct a parallelogram, and the newly formed endpoints are added as new control points.

[0018] The average area of ​​all quadrilaterals in the initial four-corner network is determined as the area representative factor;

[0019] Quadrilaterals with areas greater than the area representation factor are selected from the initial quadrilateral network and used as supplementary quadrilaterals to be controlled.

[0020] Set a reduced version of the quadrilateral to be controlled within each quadrilateral to be controlled, and use each endpoint of the reduced version of the quadrilateral to be controlled as a new control point.

[0021] Each initial control point and each new control point added is designated as a target control point.

[0022] In conjunction with the first aspect above, in one possible implementation, the step of performing quadrilateral connection processing with each initial control point in the area to be mapped as the starting point to obtain a sub-network corresponding to each initial control point includes:

[0023] Any initial control point is designated as the marker control point. From the area to be surveyed, the three non-collinear initial control points closest to the marker control point are selected as standard control points. Based on these three standard control points and the marker control point, a quadrilateral is constructed, denoted as the marker quadrilateral.

[0024] Each side of the marked quadrilateral is defined as a marked line segment, and the two non-intersecting endpoints of the three initial control points on two adjacent marked line segments are used to form the initial control point set between the two adjacent marked line segments.

[0025] If the included angle between two adjacent marked line segments is less than a preset angle, then the set of initial control points between the two adjacent marked line segments is determined as the initial control point set. From all the initial control points in the area to be surveyed, except for the four endpoints of the marked quadrilateral, the initial control point closest to the initial control point set is selected as the reference control point. Then, the reference control point is connected to each initial control point in the initial control point set to obtain the quadrilateral between the two adjacent marked line segments.

[0026] The method for obtaining the initial control point closest to the set of marked initial control points is as follows: each initial control point in the area to be surveyed, excluding the four endpoints of the marked quadrilateral, is determined as a candidate control point; the distance between each candidate control point and each initial control point in the set of marked initial control points is determined as a reference distance; a set of reference distances corresponding to each candidate control point is obtained; the sum of all reference distances in the set of reference distances corresponding to each candidate control point is determined as the overall distance corresponding to each candidate control point; and the candidate control point with the smallest overall distance is recorded as the initial control point closest to the set of marked initial control points.

[0027] The subnetwork corresponding to the marked control point is formed by the union of the marked quadrilateral and the quadrilaterals between all adjacent marked line segments.

[0028] In conjunction with the first aspect above, in one possible implementation, the supplementation of control points based on the reference control point and each temporary control point includes:

[0029] Any temporary control point is designated as a control point to be determined.

[0030] Select the reference control point closest to the undetermined control point from all reference control points, and use it as the reference control point corresponding to the undetermined control point;

[0031] Connect the undetermined control point with its corresponding reference control point to obtain a reference line segment;

[0032] From all reference control points adjacent to the reference control point corresponding to the undetermined control point, select two reference control points as template control points. Starting from each template control point, draw a line segment with the same direction and length as the reference line segment, and record it as the target line segment to obtain two target line segments.

[0033] Add all endpoints of the two target line segments, excluding the reference control point, as new control points.

[0034] In conjunction with the first aspect above, in one possible implementation, setting virtual control points for each target mapping image based on target control points includes:

[0035] Any target mapping image is designated as a marked mapping image, wherein the edge connection method between target control points in the marked mapping image is copied from the edge connection method when the target control points are acquired;

[0036] If the shape formed by all target control points in the marked survey image is not a closed shape, then connect the target control points at the opening of the shape formed by all target control points in the marked survey image to make the shape formed by all target control points in the marked survey image a closed shape.

[0037] The closed shape formed by all target control points in the marked mapping image is defined as the marked closed shape;

[0038] The marked closed graphic is scaled or enlarged to generate a new graphic, and each endpoint of all the new graphics generated at this time is determined as a virtual control point.

[0039] In conjunction with the first aspect above, in one possible implementation, determining the correction coordinates of the pixels corresponding to the virtual control points and the corresponding control point lines in each target mapping image, based on the mapping coordinates and correction coordinates corresponding to the target control points, and the mapping coordinates corresponding to the virtual control points, includes:

[0040] The smallest graphic is selected from the marked closed graphic and all new graphics as a reference graphic. Any control point in the reference graphic is determined as the target origin, where the control point is the target control point or a virtual control point.

[0041] Connect the target origin with all its corresponding control points to obtain the first target straight line;

[0042] The line that passes through the origin of the target and is perpendicular to the first target line is defined as the second target line;

[0043] A marker coordinate system is constructed with the target origin as the origin, the first target line as the vertical axis, and the second target line as the horizontal axis.

[0044] Based on the mapping coordinates and correction coordinates of the target control point corresponding to each virtual control point, and the mapping coordinates corresponding to each virtual control point, the correction coordinates corresponding to each virtual control point are determined. The virtual control point and its corresponding target control point are corresponding points obtained by scaling or enlarging the graphic, and the mapping coordinates are coordinates in the marked coordinate system.

[0045] Connect the corresponding control points to obtain candidate line segments. Based on the surveyed coordinates and correction coordinates of the target control points on each candidate line segment, determine the correction coordinates of each non-control point on each candidate line segment. The non-control points on the candidate line segment are the pixels between the lines connecting the corresponding control points. The formula for the correction coordinates of the non-control points on the candidate line segment is:

[0046] ;

[0047] ;in, and These are the x and y coordinates of the correction coordinates corresponding to the j-th non-control point on the s-th candidate line segment, respectively; s is the sequence number of the candidate line segment; j is the sequence number of the non-control point on the s-th candidate line segment. It is the correction factor corresponding to the j-th non-control point on the s-th candidate line segment; yes The cosine value; yes The sine value; It is the angle between the survey line segment corresponding to the target control point on the s-th candidate line segment and the correction line segment; the survey line segment corresponding to the target control point is the line segment obtained by connecting the survey coordinates corresponding to the target control point and the target origin; the correction line segment corresponding to the target control point is the line segment obtained by connecting the correction coordinates corresponding to the target control point and the target origin. and These are the x and y coordinates of the mapping coordinates corresponding to the j-th non-control point on the s-th candidate line segment, respectively. It is the translation distance of the target control point on the s-th candidate line segment; It is the distance between the j-th non-control point on the s-th candidate line segment and the target origin; It is the distance between the target control point on the s-th candidate line segment and the target origin; It is the GPS distance between the target control point and the target origin on the s-th candidate line segment in the actual scenario.

[0048] In conjunction with the first aspect above, in one possible implementation, determining the correction coordinates of a pixel in the target mapping image, excluding control points and the pixels between their connecting lines, based on the smallest quadrilateral to which the pixel belongs, includes:

[0049] Any pixel in the target mapping image, excluding the control points and the pixels between their connecting lines, is designated as a marker pixel, and the smallest quadrilateral to which the marker pixel belongs is designated as a reference quadrilateral.

[0050] The straight line connecting any one set of corresponding control points in the two sets of corresponding control points in the reference quadrilateral is determined as the first temporary straight line, and the straight line connecting the other set of corresponding control points is determined as the second temporary straight line.

[0051] The straight line connecting any two adjacent non-corresponding control points in the reference quadrilateral is defined as the third temporary straight line;

[0052] The third temporary line is translated so that it passes through the marked pixel to obtain the fourth temporary line, and the intersection of the fourth temporary line and the first temporary line is determined as the first marked intersection point;

[0053] Connect the intersection of the marked pixel with the first mark to obtain the fifth temporary line, and determine the angle between the first temporary line and the fifth temporary line as the first temporary angle;

[0054] The angle between the first temporary line and the second temporary line is defined as the second temporary angle.

[0055] The intersection of the first temporary line and the second temporary line is determined as the second marked intersection point;

[0056] Based on the distance between the marked pixel and the first mark intersection point, the distance between the marked pixel and the second mark intersection point, and the first temporary angle and the second temporary angle, the formula for determining the correction coordinates corresponding to the marked pixel is as follows:

[0057] ;

[0058] ;

[0059] ;in, and These are the x and y coordinates of the corrected coordinates corresponding to the marked pixel; yes The cosine value; yes The sine value; equal ; and These are the x and y coordinates of the mapping coordinates corresponding to the marked pixel points, respectively. It is the translation distance of the control point on the reference quadrilateral that is closest to the marked pixel; It is the first temporary included angle; It is the second temporary included angle; It is the distance between the marked pixel and the intersection of the first marker; It is the distance between the marked pixel and the intersection of the second mark.

[0060] In conjunction with the first aspect above, in one possible implementation, the step of correcting and supplementing the RGB values ​​corresponding to overlapping and missing pixels to obtain adjusted RGB values ​​corresponding to overlapping and missing pixels includes:

[0061] The region consisting of consecutive overlapping pixels or consecutive missing pixels is defined as the reference region.

[0062] The region formed by the preset neighborhoods corresponding to all edge pixels of each reference region is determined as the overall neighborhood of each reference region.

[0063] The pixels in the overall neighborhood of each reference region, excluding the reference region itself, constitute the surrounding representative region of each reference region.

[0064] The formula for determining the information importance of each reference region based on its surrounding representative regions is as follows:

[0065] ;

[0066] ;in, This represents the importance of the information corresponding to the a-th reference region; a is the index of the reference region. It is a normalization function; It is the number of R values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region; It is the number of G values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region; It is the number of B values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region; t is the number of pixels in the surrounding representative area corresponding to the a-th reference area; t and v are the sequence numbers of different pixels in the surrounding representative area corresponding to the a-th reference area; It is the pixel difference between the t-th pixel and the v-th pixel in the surrounding representative area corresponding to the a-th reference area; It is an absolute value function; It is the R value corresponding to the t-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the R value corresponding to the v-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the G value corresponding to the t-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the G value corresponding to the v-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the B value corresponding to the t-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the B value corresponding to the v-th pixel in the surrounding representative region corresponding to the a-th reference region;

[0067] Based on the information importance corresponding to each reference region, determine the information importance factor corresponding to each pixel in the surrounding representative region corresponding to each reference region;

[0068] Based on the information importance factors of all pixels within the reference area representing the same geographical location, adjust the R value corresponding to the overlapping pixels representing the same geographical location.

[0069] Similarly, based on the information importance factors of all pixels within the reference area to which the overlapping pixels represent the same geographical location belong, the G and B values ​​corresponding to the overlapping pixels representing the same geographical location are adjusted.

[0070] Based on the information importance factor corresponding to all pixels within the reference region to which each missing pixel belongs, the formula for adjusting the R value corresponding to each missing pixel is as follows:

[0071] ;

[0072] ;in, It is the adjusted R value corresponding to the h-th missing pixel; h is the index of the missing pixel. is the number of pixels in the reference region to which the h-th missing pixel belongs; b is the index of the pixel in the reference region to which the h-th missing pixel belongs; It is the R value corresponding to the b-th pixel within the reference region to which the h-th missing pixel belongs; It is the information importance factor corresponding to the b-th pixel within the reference region to which the h-th missing pixel belongs; It is the natural exponential function; It is the distance between the h-th missing pixel and the b-th pixel in its reference region;

[0073] Similarly, based on the information importance factors of all pixels within the reference region to which each missing pixel belongs, the G and B values ​​corresponding to each missing pixel are adjusted.

[0074] In conjunction with the first aspect above, in one possible implementation, the formula for adjusting the R value corresponding to the overlapping pixels representing the same geographical location based on the information importance factor corresponding to all pixels within the reference area to which the overlapping pixels representing the same geographical location belong is as follows:

[0075] ;

[0076] ;in, R is the adjusted value corresponding to the overlapping pixels of the same geographical location; E is the number of overlapping pixels of the same geographical location; f is the index of the overlapping pixels of the same geographical location; T is the number of pixels in the reference area to which the overlapping pixels of the same geographical location belong; k is the index of the pixels in the reference area to which the overlapping pixels of the same geographical location belong. It represents the unadjusted R value corresponding to the f-th overlapping pixel point at the same geographical location; It is an important factor representing the information corresponding to the f-th pixel within the reference area to which the f-th overlapping pixel belongs at the same geographical location; It is the natural exponential function; It represents the distance between the f-th overlapping pixel in the same geographical location and the f-th pixel in its reference area; It is an absolute value function; It is the R value corresponding to the f-th pixel within the reference area to which the f-th overlapping pixel belongs at the same geographical location.

[0077] In a second aspect, the present invention provides a remote sensing mapping data enhancement system for real estate surveying, the system comprising:

[0078] The mapping image acquisition module is used to acquire all target mapping images corresponding to the area to be mapped. The union of all target mapping images covers the area to be mapped, and different target control points are set in the area to be mapped.

[0079] The setting and determination module is used to set virtual control points for each target mapping image based on the target control points, and to determine the correction coordinates of the virtual control points and the pixels between the corresponding control points in each target mapping image based on the mapping coordinates and correction coordinates of the target control points and the mapping coordinates of the virtual control points.

[0080] The calibration coordinate determination module is used to determine the calibration coordinates of a pixel in the target mapping image, excluding the control points and the pixels between them, based on the smallest quadrilateral to which the pixel belongs.

[0081] The filtering and positioning module is used to filter out overlapping pixels representing the same geographical location from all target mapping images based on the correction coordinates corresponding to all pixels in all target mapping images, and to locate missing pixels with missing geographical locations.

[0082] The RGB adjustment module is used to correct and supplement the RGB values ​​corresponding to overlapping and missing pixels, so as to obtain the adjusted RGB values ​​corresponding to overlapping and missing pixels.

[0083] The mapping image enhancement module is used to construct an enhanced overall mapping image based on the adjusted RGB values ​​of overlapping and missing pixels, as well as the unadjusted RGB values ​​of other pixels.

[0084] Thirdly, a server is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the device to perform the methods of the first aspect or any possible implementation thereof.

[0085] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0086] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0087] The present invention has the following beneficial effects:

[0088] This invention provides a remote sensing mapping data enhancement method for real estate surveying. It enhances UAV remote sensing mapping data, solving the technical problem of poor enhancement effects and improving the overall enhancement performance. Specifically, addressing the issue that existing methods for correcting distortion in remote sensing images using geometric precision correction theory often result in poor enhancement due to the large amount of information contained in the images, the susceptibility to information loss, and the diverse distortion patterns, this invention first sets different target control points within the surveying area. It then comprehensively considers multiple features related to image distortion, such as the surveying and correction coordinates of the target control points, the surveying coordinates of virtual control points, quadrilaterals, overlapping pixels, and missing pixels. This enhances the surveying data, improving its quality and facilitating subsequent reconstruction and analysis of the surveyed area. Attached Figure Description

[0089] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0090] Figure 1 This is a flowchart of the remote sensing mapping data enhancement method for real estate surveying according to the present invention;

[0091] Figure 2 This is a schematic diagram of the composition structure of the remote sensing mapping data enhancement system for real estate surveying according to the present invention;

[0092] Figure 3 This is a schematic diagram of the structure of a computer device according to the present invention. Detailed Implementation

[0093] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solution proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0094] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0095] The GPS coordinate distance mesh structure between control points is a true mesh structure. However, due to distortion in the surveyed image, the mesh structure of the control points in the camera coordinate system is deformed. By correcting the control points towards the true structure and correcting the remaining pixels according to the correction direction of the neighboring control points, a more accurate image can be obtained.

[0096] However, during this process, due to the irregular layout of control points, if only local correction is considered, adjacent areas corrected in different directions will exhibit distortion. Therefore, the surveyed image should be considered as a whole. The distortion of the surveyed image becomes increasingly severe from the center to the edge. Thus, gradient control lines can be set by controlling points. As the control lines spread outward, the degree of pixel correction increases, thereby ensuring the overall smoothness of the image.

[0097] On the other hand, the process of obtaining a corrected image by completing pixel shift correction has problems such as coordinate overlap and pixel loss. Existing methods often involve interpolation, and overlapping pixels are averaged to obtain the final RGB value. However, remote sensing images contain a large amount of information, and interpolation often leads to pixelation and grayscale discontinuities, resulting in the loss of effective information. Therefore, this invention analyzes the effective information in the image and focuses on preserving the edges of effective information during interpolation and pixel fusion.

[0098] refer to Figure 1 The flowchart illustrates some embodiments of the remote sensing mapping data enhancement method for real estate surveying according to the present invention. This remote sensing mapping data enhancement method for real estate surveying includes the following steps:

[0099] Step S1: Obtain all target mapping images corresponding to the area to be mapped.

[0100] The area to be surveyed can be any area that will be surveyed. Target survey images can be survey images representing different parts of the area to be surveyed. The union of all target survey images can cover the area to be surveyed, and different target control points can be set within this area. Target control points can be control points ultimately set up to facilitate surveying.

[0101] It should be noted that multiple images of the area to be mapped can be acquired using an unmanned aerial vehicle (UAV) mapping system, and these images are denoted as target images. An UAV mapping system may include: an UAV, a flight control system, remote sensing equipment, a ground control station, and a data post-processing system. The UAV may include: the aircraft airframe, power supply, sensors, navigator, propulsion system, etc. The flight control system may include: a GPS (Global Positioning System) receiver, a speed sensor, an IMU / GPS system, etc. Remote sensing equipment may include: high-resolution digital cameras, multispectral imagers, lidar, infrared scanners, etc. The ground control station may include: flight monitoring and control, data acquisition, etc. The data post-processing system may include: processing sensor data, location calculation, etc.

[0102] The specific method for obtaining multiple survey images of the area to be surveyed can be as follows: design an S-shaped UAV flight path; design the layout of ground control points; and acquire multiple survey images at 30fps using a high-resolution digital camera during UAV flight.

[0103] Geometric correction involves calculating the geographic coordinates of each pixel in an image using control points according to a specific mathematical model. Therefore, the placement and accuracy of the control points directly affect the correction results. Control points should be set at locations with distinctive structural and color characteristics, and the GPS coordinates of any control point should be obtained.

[0104] Image distortion is mainly classified into trapezoidal distortion, pincushion distortion, and barrel distortion. Using control points as intersections on a grid yields the distortion network of the mapped image. However, uneven distribution of control points can lead to insufficient control points for network construction, necessitating the initial addition of control points. Distortion gradually worsens from the image center towards the edges; therefore, the mapped image is divided into several annular regions. Differences in distortion severity exist between these annular regions, representing distortion gradients. The method of correcting pixels in each annular region according to their respective distortion levels is known as the gradient geometry correction model.

[0105] As an example, the method for setting up a target control point may include the following steps:

[0106] The first step is to perform quadrilateral connection processing on each initial control point in the area to be surveyed, so as to obtain the sub-network corresponding to each initial control point.

[0107] The initial control points can be control points that are pre-set in the area to be surveyed based on human experience to facilitate surveying.

[0108] For example, obtaining the subnetwork corresponding to each initial control point may include the following sub-steps:

[0109] The first sub-step involves determining any initial control point as a marker control point, selecting the three non-collinear initial control points closest to the marker control point from the area to be surveyed, and using them as standard control points. Based on these three standard control points and the marker control point, a quadrilateral is constructed, denoted as the marker quadrilateral.

[0110] The four vertices of the marked quadrilateral can include: marked control points and three standard control points.

[0111] It should be noted that when constructing a quadrilateral, the perimeter of the quadrilateral can be made as small as possible.

[0112] The second sub-step involves determining each side of the marked quadrilateral as a marked line segment, and then using the two non-intersecting endpoints of the three initial control points on two adjacent marked line segments to form the initial control point set between the two adjacent marked line segments.

[0113] It should be noted that there are often three endpoints on two adjacent marked line segments. One endpoint is the intersection of the two adjacent marked line segments, and the other two endpoints are not the intersection of the two adjacent marked line segments. These two endpoints that are not the intersection of the two adjacent marked line segments can be called non-intersection endpoints.

[0114] The third sub-step involves determining the set of initial control points between two adjacent marked line segments as the initial control point set if the included angle between them is less than a preset angle. From all the initial control points in the area to be surveyed, excluding the four endpoints of the marked quadrilateral, the initial control point closest to the set of initial control points is selected as a reference control point. The reference control point is then connected to each initial control point in the set of initial control points to obtain the quadrilateral between the two adjacent marked line segments.

[0115] The preset angle can be a pre-set angle, which can be 180°. The method for obtaining the initial control point closest to the above-mentioned set of marked initial control points is as follows: each initial control point in the area to be surveyed, excluding the four endpoints of the marked quadrilateral, is determined as a candidate control point. The distance between each candidate control point and each initial control point in the set of marked initial control points is determined as a reference distance, thus obtaining a set of reference distances corresponding to each candidate control point. The sum of all reference distances in the set of reference distances corresponding to each candidate control point is determined as the overall distance corresponding to each candidate control point. The candidate control point with the smallest overall distance is recorded as the initial control point closest to the above-mentioned set of marked initial control points.

[0116] The fourth sub-step involves combining the marked quadrilateral with the union of all quadrilaterals between any two adjacent marked line segments to form the sub-network corresponding to the marked control point.

[0117] The second step is to form an initial quadrangle network by combining the subnetworks corresponding to all initial control points, and then determine each initial control point in the initial quadrangle network as a reference control point.

[0118] The third step is to designate each initial control point in the area to be surveyed, excluding all reference control points, as a temporary control point.

[0119] The fourth step, supplementing control points based on the reference control points and each temporary control point, may include the following sub-steps:

[0120] The first sub-step involves designating any temporary control point as a pending control point.

[0121] The second sub-step involves selecting the reference control point closest to the aforementioned undetermined control point from all reference control points, and using it as the reference control point corresponding to the aforementioned undetermined control point.

[0122] The third sub-step involves connecting the aforementioned undetermined control points with their corresponding reference control points to obtain the reference line segment.

[0123] The fourth sub-step involves selecting any two reference control points from all the reference control points adjacent to the aforementioned undetermined control point, and using them as template control points. This yields two template control points. Starting from each template control point, a line segment with the same direction and length as the aforementioned reference line segment is drawn and denoted as the target line segment, resulting in two target line segments.

[0124] In the initial quadrilateral network, two control points on the same edge can be adjacent control points; that is, two adjacent control points are often the two endpoints of the same edge within the initial quadrilateral network. A template control point can be an endpoint of the target line segment. The target line segment can be the same as the reference line segment.

[0125] The fifth sub-step involves adding all endpoints of the two target line segments, excluding the reference control points, as new control points.

[0126] Fifth step: If the included angle between two adjacent sides in the initial four-corner network is greater than the preset angle, then these two adjacent sides are used as the sides of the parallelogram to construct the parallelogram, and the newly formed endpoints are added as new control points.

[0127] The preset angle can be a pre-set angle, which can be 180°.

[0128] It should be noted that the edges in the initial quadrangle network in this embodiment of the invention are the line segments that constitute the initial quadrangle network.

[0129] The sixth step is to determine the average area of ​​all quadrilaterals in the initial four-corner network as the area representative factor.

[0130] Step 7: Select quadrilaterals with areas greater than the area representative factor from the initial quadrilateral network to supplement the quadrilaterals with control points.

[0131] Step 8: Set a smaller version of the quadrilateral to be controlled within each quadrilateral to be controlled, and add each endpoint of the smaller version of the quadrilateral to be controlled as a new control point.

[0132] Among them, the size of the reduced version of the quadrilateral to be supplemented by the control point can be half the size of the original quadrilateral to be supplemented by the control point.

[0133] It should be noted that the corresponding endpoints of the quadrilateral to be controlled and its scaled-down version can be connected.

[0134] Step 9: Record each initial control point and each new control point added as a target control point.

[0135] Step S2: Based on the target control points, set virtual control points for each target mapping image, and based on the mapping coordinates and correction coordinates corresponding to the target control points, as well as the mapping coordinates corresponding to the virtual control points, determine the correction coordinates of the pixels between the virtual control points and the corresponding control point lines in each target mapping image.

[0136] Among them, target control points and virtual control points can be collectively referred to as control points.

[0137] As an example, this step may include the following steps:

[0138] The first step is to designate any target mapping image as a marked mapping image.

[0139] The edge connection method between target control points in the above-mentioned marked mapping image can be copied from the edge connection method when the target control points were acquired.

[0140] The second step is to connect the target control points at the opening of the shape formed by all the target control points in the above-mentioned marked survey image if the shape is not a closed shape. This will make the shape formed by all the target control points in the above-mentioned marked survey image a closed shape.

[0141] The third step is to define the closed shape formed by all target control points in the above-mentioned marked survey image as the marked closed shape.

[0142] The fourth step is to scale or enlarge the marked closed shape to generate a new shape, and then determine each endpoint of all the new shapes generated at this time as a virtual control point.

[0143] For example, shrinking the marked closed shape to half its original size yields a new shape, and enlarging the marked closed shape to twice its original size yields another new shape. The endpoints of these two new shapes can be denoted as virtual control points.

[0144] Fifth, select the smallest shape from the marked closed shape and all new shapes as the reference shape, and determine any control point in the reference shape as the target origin.

[0145] The control point can be a target control point or a virtual control point.

[0146] Step 6: Connect the target origin with all its corresponding control points to obtain the first target straight line.

[0147] The target origin and its corresponding control points can be points obtained by scaling or enlarging the marked closed shape. The first target line can represent the line connecting the distortion gradients at the target origin.

[0148] The seventh step is to determine the line that passes through the aforementioned target origin and is perpendicular to the aforementioned first target line as the second target line.

[0149] Step 8: Construct a marker coordinate system with the aforementioned target origin as the origin, the aforementioned first target line as the vertical axis, and the aforementioned second target line as the horizontal axis.

[0150] Step 9: Based on the survey coordinates and correction coordinates of the target control point corresponding to each virtual control point, and the survey coordinates of each virtual control point, determine the correction coordinates corresponding to each virtual control point.

[0151] In this context, the virtual control point and its corresponding target control point can be points obtained by scaling or enlarging the image; for example, they can be points representing the position of the same object. The mapping coordinates corresponding to a pixel can represent the position of that pixel in the captured mapping image, and they can be coordinates in the marked coordinate system. The correction coordinates can be coordinates representing the actual position of the object obtained after correcting the mapping coordinates.

[0152] It should be noted that since target control points are location points set within the area to be surveyed for ease of mapping, their actual positions within the area are often known. For example, using GPS (Global Positioning System), the GPS coordinates of the target control point can be obtained, thus providing the GPS distance to the target control point. Scaling the GPS distance of the target control point to the image size yields the corrected coordinates of the target control point.

[0153] For example, determining the correction coordinates for each virtual control point, based on the mapping coordinates and correction coordinates of the target control point corresponding to each virtual control point, and the mapping coordinates of each virtual control point, may include the following sub-steps:

[0154] The first sub-step involves determining the first and second parameters of each virtual control point based on the surveyed coordinates and correction coordinates of the target control point corresponding to each virtual control point, using the affine transformation formula corresponding to the target control point.

[0155] For example, the affine transformation formula for the target control point corresponding to the virtual control point can be:

[0156] ;in, yes The cosine value. yes The sine value of . i is the index of the virtual control point. This is the first parameter corresponding to the i-th virtual control point, which is equal to the angle between the surveyed line segment and the correction line segment corresponding to the target control point. The surveyed line segment corresponding to the target control point is the line segment connecting the surveyed coordinates of the target control point to the target origin. The correction line segment corresponding to the target control point is the line segment connecting the correction coordinates of the target control point to the target origin. and These are the x-coordinate and y-coordinate of the mapping coordinates of the target control point corresponding to the i-th virtual control point, respectively. It is the second parameter corresponding to the i-th virtual control point, which is equal to the translation distance of the target control point corresponding to the i-th virtual control point. and These are the x and y coordinates of the correction coordinates of the target control point corresponding to the i-th virtual control point, respectively.

[0157] The second sub-step involves determining the correction coordinates of each virtual control point based on its mapping coordinates, the first parameter, and the second parameter, using the affine transformation formula corresponding to each virtual control point.

[0158] For example, the affine transformation formula for a virtual control point can be:

[0159] ;in, yes The cosine value. yes The sine value of . i is the index of the virtual control point. It is the first parameter corresponding to the i-th virtual control point, which is equal to the angle between the survey line segment and the correction line segment corresponding to the target control point of the i-th virtual control point. and These are the x-coordinate and y-coordinate of the surveying coordinates corresponding to the i-th virtual control point, respectively. It is the second parameter corresponding to the i-th virtual control point, which is equal to the translation distance of the target control point corresponding to the i-th virtual control point. and These are the x and y coordinates of the correction coordinates corresponding to the i-th virtual control point, respectively.

[0160] Step 10: Connect the corresponding control points to obtain candidate line segments. Based on the survey coordinates and correction coordinates of the target control points on each candidate line segment, determine the correction coordinates of each non-control point on each candidate line segment.

[0161] The corresponding control points can be points obtained by scaling or enlarging the image; for example, they can be points representing the position of the same object. Non-control points on the candidate line segment can represent the pixels between the lines connecting the corresponding control points. Non-control points can be pixels other than control points. The formula for the correction coordinates of the non-control points on the candidate line segment can be:

[0162] ;

[0163] ;in, and These are the x and y coordinates of the correction coordinates corresponding to the j-th non-control point on the s-th candidate line segment, respectively. s is the index of the candidate line segment. j is the index of the non-control point on the s-th candidate line segment. It is the correction factor corresponding to the j-th non-control point on the s-th candidate line segment. yes The cosine value. yes The sine value. It is the angle between the surveyed line segment corresponding to the target control point and the correction line segment on the s-th candidate line segment. The surveyed line segment corresponding to the target control point is the line segment obtained by connecting the surveyed coordinates corresponding to the target control point to the target origin. The correction line segment corresponding to the target control point is the line segment obtained by connecting the correction coordinates corresponding to the target control point to the target origin. and These are the x and y coordinates of the mapping coordinates corresponding to the j-th non-control point on the s-th candidate line segment, respectively. It is the translation distance of the target control point on the s-th candidate line segment. It is the distance between the j-th non-control point on the s-th candidate line segment and the target origin. It is the distance between the target control point on the s-th candidate line segment and the target origin. It is the GPS distance between the target control point and the target origin on the s-th candidate line segment in the actual scenario.

[0164] Step S3: For pixels in the target mapping image other than control points and the pixels between them, determine the correction coordinates of the pixel based on the smallest quadrilateral to which the pixel belongs.

[0165] As an example, this step may include the following steps:

[0166] The first step is to identify any pixel in the target mapping image, excluding the control points and the pixels between them, as a marker pixel, and to identify the smallest quadrilateral to which the marker pixel belongs as a reference quadrilateral.

[0167] The four endpoints of the reference quadrilateral often include two sets of corresponding control points. One set of corresponding control points can be points obtained by scaling or enlarging the graphic, for example, points representing the position of the same object.

[0168] The second step is to determine the line connecting any one set of corresponding control points in the two sets of corresponding control points in the above reference quadrilateral as the first temporary line, and the line connecting the other set of corresponding control points as the second temporary line.

[0169] The third step is to determine the line connecting any two adjacent non-corresponding control points in the above reference quadrilateral as the third temporary line.

[0170] Fourth step, translate the third temporary line so that it passes through the marked pixel point to obtain the fourth temporary line, and determine the intersection point of the fourth temporary line and the first temporary line as the first marked intersection point.

[0171] Fifth step, connect the intersection of the above marked pixel point and the above first mark to obtain the fifth temporary line, and determine the angle between the above first temporary line and the above fifth temporary line as the first temporary angle.

[0172] The sixth step is to determine the angle between the first temporary line and the second temporary line as the second temporary angle.

[0173] Step 7: Determine the intersection of the first temporary line and the second temporary line as the second marked intersection point.

[0174] Step 8: Based on the distance between the marked pixel and the intersection point of the first mark, the distance between the marked pixel and the intersection point of the second mark, and the first temporary angle and the second temporary angle, the formula corresponding to the correction coordinates of the marked pixel is determined as follows:

[0175] ;

[0176] ;

[0177] ;in, and These are the x and y coordinates of the correction coordinates corresponding to the marked pixel points, respectively. yes The cosine value. yes The sine value. equal . and These are the x and y coordinates of the mapping coordinates corresponding to the marked pixel points, respectively. It is the translation distance of the control point on the reference quadrilateral that is closest to the marked pixel. It is the first temporary included angle. It is the second temporary included angle. It is the distance between the marked pixel and the intersection point of the first mark. It is the distance between the marked pixel and the intersection of the second mark.

[0178] It should be noted that the comprehensive correction method, which connects control points to control point lines and then to the area of ​​the connected lines, can obtain the correction coordinates of all pixels. At the same time, since the control points are laid out according to the distribution of information in the surveying area, it can avoid information loss caused by separating effective information for correction to a certain extent, thus ensuring the correction effect.

[0179] Step S4: Based on the correction coordinates of all pixels in all target mapping images, select overlapping pixels representing the same geographical location from all target mapping images, and locate missing pixels with missing geographical locations.

[0180] It's important to note that pixel movement can cause some pixels to overlap in their calibration coordinates, resulting in missing pixels in the calibrated image. For overlapping pixels, if they belong to low-information background areas such as grass or forests, and the RGB values ​​of the overlapping pixels are small, then the average RGB value of the overlapping pixels can be used as the value for that pixel in the calibrated image. However, if the RGB values ​​of the overlapping pixels are large, and some pixels contain more important information, then the pixel with higher information content should be prioritized to avoid blurring the calibrated image. For missing pixels, more important neighboring pixels should also be used as a reference to ensure information transfer.

[0181] As an example, pixels with the same corrected coordinates in all target images can be identified as overlapping pixels representing the same geographical location. In reality, since the captured area is continuous, the corrected coordinates of all pixels in all target images should normally also be continuous. However, due to image distortion, some location points in the captured area may be lost. Therefore, the location points corresponding to the missing corrected coordinates can be selected from all target images. For example, if there are three corrected coordinates (1,1), (1,2), and (1,4), then the missing location point (1,3) can represent the missing pixel.

[0182] Step S5: Correct and supplement the RGB values ​​corresponding to overlapping and missing pixels to obtain the adjusted RGB values ​​corresponding to overlapping and missing pixels.

[0183] As an example, this step may include the following steps:

[0184] The first step is to define the region consisting of consecutive overlapping pixels or consecutive missing pixels as the reference region.

[0185] For example, a corrected image can be obtained based on the corrected coordinates corresponding to all pixels in all target mapping images, where the coordinates of the points in the corrected image can be represented by the corrected coordinates. The region formed by consecutive overlapping pixels with corresponding corrected coordinates in the corrected image can be defined as a reference region; and the region formed by consecutive missing pixels with corresponding corrected coordinates in the corrected image can also be defined as a reference region.

[0186] The second step is to determine the overall neighborhood corresponding to each reference region by defining the region formed by the preset neighborhoods corresponding to all edge pixels of each reference region.

[0187] The preset neighborhood can be a pre-set neighborhood, which can be an eight-neighbor neighborhood.

[0188] The third step is to construct the surrounding representative area of ​​each reference area by taking the pixels in the overall neighborhood of each reference area that are not in the reference area itself.

[0189] The fourth step is to determine the information importance corresponding to each reference region based on the surrounding representative regions:

[0190] ;

[0191] ;in, This represents the information importance corresponding to the a-th reference region. 'a' is the index of the reference region. It is a normalization function. It is the number of R values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region. It is the number of G values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region. It is the number of B values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region. t represents the number of pixels in the surrounding representative region corresponding to the a-th reference region. t and v are the indexes of the different pixels in the surrounding representative region corresponding to the a-th reference region. It is the pixel difference between the t-th pixel and the v-th pixel in the surrounding representative region corresponding to the a-th reference region. It is an absolute value function. It is the R value corresponding to the t-th pixel in the surrounding representative region corresponding to the a-th reference region. It is the R value corresponding to the v-th pixel in the surrounding representative area corresponding to the a-th reference area. It is the G value corresponding to the t-th pixel in the surrounding representative area corresponding to the a-th reference area. It is the G value corresponding to the v-th pixel in the surrounding representative area corresponding to the a-th reference area. It is the B value corresponding to the t-th pixel in the surrounding representative area corresponding to the a-th reference area. It is the B value corresponding to the v-th pixel in the surrounding representative area corresponding to the a-th reference area.

[0192] The fifth step, determining the information importance factor for each pixel within the surrounding representative region corresponding to each reference region based on the information importance of each reference region, may include the following sub-steps:

[0193] The first sub-step involves determining any one reference region as the marked reference region, determining any one pixel within the surrounding representative region corresponding to the marked reference region as the reference pixel, and determining the region within the surrounding representative region corresponding to the marked reference region other than the reference pixel as the target sub-region.

[0194] The second sub-step involves determining the importance of the information corresponding to the marked reference area based on the surrounding representative areas.

[0195] The third sub-step involves determining the degree of unimportance corresponding to the reference pixel based on the target sub-region. The method for obtaining this degree of unimportance can refer to the method for obtaining information importance described above. Specifically, the target sub-region is regarded as the surrounding representative region corresponding to the marked reference region. The second sub-step of the fifth step of step S5 is executed, and the information importance obtained at this time is recorded as the degree of unimportance corresponding to the reference pixel.

[0196] The fourth sub-step is to determine the absolute value of the difference between the unimportance level of the reference pixel and the information importance level of the marked reference region as the information importance factor of the reference pixel.

[0197] Step 6: Based on the information importance factors of all pixels within the reference region representing the same geographical location, the formula for adjusting the R value of the overlapping pixels representing the same geographical location can be:

[0198] ;

[0199] ;in, R is the adjusted value representing overlapping pixels at the same geographical location. E is the number of overlapping pixels at the same geographical location. f is the index of the overlapping pixels at the same geographical location. T is the number of pixels within the reference region to which the overlapping pixels at the same geographical location belong. k is the index of the pixels within the reference region to which the overlapping pixels at the same geographical location belong. It represents the unadjusted R value corresponding to the f-th overlapping pixel point at the same geographical location. It is an important factor representing the information corresponding to the f-th pixel within the reference area to which the f-th overlapping pixel belongs at the same geographical location. It is a natural exponential function. It represents the distance between the f-th overlapping pixel in the same geographical location and the f-th pixel in its reference area. It is an absolute value function. It is the R value corresponding to the f-th pixel within the reference area to which the f-th overlapping pixel belongs at the same geographical location.

[0200] It should be noted that, Can be used as The weight. When A larger value usually indicates a smaller difference in R values ​​and a smaller distance, which often indicates a larger weight for the f-th overlapping pixel.

[0201] Step 7: Similarly, based on the information importance factors of all pixels within the reference area representing the same geographical location, adjust the G and B values ​​corresponding to the overlapping pixels representing the same geographical location.

[0202] Step 8: Based on the information importance factors of all pixels within the reference region to which each missing pixel belongs, adjust the R value corresponding to each missing pixel using the following formula:

[0203] ;

[0204] ;in, It is the adjusted R value corresponding to the h-th missing pixel. h is the index of the missing pixel. is the number of pixels in the reference region to which the h-th missing pixel belongs. b is the index of the pixel in the reference region to which the h-th missing pixel belongs. It is the R value corresponding to the b-th pixel within the reference region to which the h-th missing pixel belongs. It is the information importance factor corresponding to the b-th pixel within the reference region to which the h-th missing pixel belongs. It is a natural exponential function. It is the distance between the h-th missing pixel and the b-th pixel in its reference region.

[0205] Step 9: Similarly, adjust the G and B values ​​corresponding to each missing pixel based on the information importance factors of all pixels in the reference region to which each missing pixel belongs.

[0206] Step S6: Construct the enhanced overall mapping image based on the adjusted RGB values ​​of overlapping and missing pixels, and the unadjusted RGB values ​​of other pixels.

[0207] As an example, the RGB values ​​of all missing and overlapping pixels can be calculated, while the RGB values ​​of the remaining pixels are retained, resulting in a true corrected image of any surveyed image. By using the SIFT algorithm to register and stitch all corrected images, a complete image of the area to be measured can be obtained, serving as surveying data for subsequent analysis and modeling.

[0208] In summary, this invention corrects the entire image by establishing a gradient geometric correction model. For missing or overlapping parts, it performs numerical calculations by analyzing the importance of the corresponding information, thereby ensuring the integrity of the surveyed image and enhancing the surveyed data.

[0209] refer to Figure 2 Based on the same inventive concept as the above-described method embodiments, this invention provides a remote sensing mapping data enhancement system for real estate surveying. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When executed by the processor, the computer program implements the steps of the remote sensing mapping data enhancement method for real estate surveying, specifically including:

[0210] The mapping image acquisition module 201 is used to acquire all target mapping images corresponding to the area to be mapped, wherein the union of all target mapping images covers the area to be mapped, and different target control points are set in the area to be mapped.

[0211] The setting and determining module 202 is used to set virtual control points for each target mapping image based on the target control points, and to determine the correction coordinates of the virtual control points and the pixels between the corresponding control points in each target mapping image based on the mapping coordinates and correction coordinates corresponding to the target control points and the mapping coordinates corresponding to the virtual control points.

[0212] The calibration coordinate determination module 203 is used to determine the calibration coordinates of a pixel in the target mapping image, excluding the control points and the pixels between them, based on the smallest quadrilateral to which the pixel belongs.

[0213] The filtering and positioning module 204 is used to filter out overlapping pixels representing the same geographical location from all target mapping images based on the correction coordinates corresponding to all pixels in all target mapping images, and to locate missing pixels with missing geographical locations.

[0214] The RGB adjustment module 205 is used to correct and supplement the RGB values ​​corresponding to overlapping pixels and missing pixels to obtain the adjusted RGB values ​​corresponding to overlapping pixels and missing pixels.

[0215] The mapping image enhancement module 206 is used to construct an enhanced overall mapping image based on the adjusted RGB values ​​corresponding to overlapping and missing pixels, and the unadjusted RGB values ​​corresponding to other pixels.

[0216] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. For example, as shown... Figure 3As shown, the computer device 300 includes: a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302, wherein when the processor 302 executes the computer program 303, the computer device can execute any of the remote sensing mapping data enhancement methods for real estate mapping described above.

[0217] Based on the same inventive concept as the above-described method embodiments, the present invention provides a server, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the device to execute any of the above-described remote sensing mapping data enhancement methods for real estate surveying.

[0218] Based on the same inventive concept as the above-described method embodiments, the present invention provides a computer program product comprising: computer program code, which, when run on a computer, causes the computer to execute any of the above-described remote sensing mapping data enhancement methods for real estate surveying.

[0219] Based on the same inventive concept as the above-described method embodiments, the present invention provides a computer-readable storage medium storing computer program code, which, when executed on a computer, causes the computer to perform any of the above-described remote sensing mapping data enhancement methods for real estate surveying.

[0220] In summary, existing methods for remote sensing image distortion correction using geometric precision correction theory suffer from poor enhancement results due to the large amount of information contained in the surveyed images, the susceptibility to information loss, and the diverse nature of image distortion. This invention analyzes the distortion of control points in the surveyed images, establishes a gradient geometric correction model, and obtains a corrected image. By considering the characteristics of different distortion types leading to overlapping points and new pixels in the corrected image, the information content of the correction point locations is determined, ensuring that the correction points retain as much information as possible. This results in a corrected image that improves the quality of surveying data and facilitates subsequent reconstruction and analysis of the surveyed area.

[0221] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for enhancing remote sensing mapping data for real estate surveying, characterized in that, Includes the following steps: Obtain all target mapping images corresponding to the area to be mapped, wherein the union of all target mapping images covers the area to be mapped, and different target control points are set in the area to be mapped; Based on the target control points, virtual control points are set for each target mapping image. Based on the mapping coordinates and correction coordinates corresponding to the target control points, as well as the mapping coordinates corresponding to the virtual control points, the correction coordinates of the pixels between the virtual control points and the corresponding control point lines in each target mapping image are determined. For pixels in the target mapping image other than control points and the pixels between them, determine the correction coordinates of the pixel based on the smallest quadrilateral to which the pixel belongs. Based on the corrected coordinates of all pixels in all target images, the overlapping pixels representing the same geographical location are selected from all target images, and the missing pixels with missing geographical locations are located. The RGB values ​​corresponding to overlapping and missing pixels are corrected and supplemented to obtain the adjusted RGB values ​​corresponding to overlapping and missing pixels. Based on the adjusted RGB values ​​of overlapping and missing pixels, and the unadjusted RGB values ​​of other pixels, construct the enhanced overall mapping image. The step of setting virtual control points for each target mapping image based on target control points includes: Any target mapping image is designated as a marked mapping image, wherein the edge connection method between target control points in the marked mapping image is copied from the edge connection method when the target control points are acquired; If the shape formed by all target control points in the marked survey image is not a closed shape, then connect the target control points at the opening of the shape formed by all target control points in the marked survey image to make the shape formed by all target control points in the marked survey image a closed shape. The closed shape formed by all target control points in the marked mapping image is defined as the marked closed shape; The marked closed graphic is scaled or enlarged to generate a new graphic, and each endpoint of all the new graphics generated at this time is determined as a virtual control point; The determination of the correction coordinates of the virtual control points and the corresponding pixels between the virtual control points in each target image, based on the surveyed coordinates and correction coordinates of the target control points and the surveyed coordinates of the virtual control points, includes: The smallest graphic is selected from the marked closed graphic and all new graphics as a reference graphic. Any control point in the reference graphic is determined as the target origin, where the control point is the target control point or a virtual control point. Connect the target origin with all its corresponding control points to obtain the first target straight line; The line that passes through the origin of the target and is perpendicular to the first target line is defined as the second target line; A marker coordinate system is constructed with the target origin as the origin, the first target line as the vertical axis, and the second target line as the horizontal axis. Based on the mapping coordinates and correction coordinates of the target control point corresponding to each virtual control point, and the mapping coordinates corresponding to each virtual control point, the correction coordinates corresponding to each virtual control point are determined. The virtual control point and its corresponding target control point are corresponding points obtained by scaling or enlarging the graphic, and the mapping coordinates are coordinates in the marked coordinate system. Connect the corresponding control points to obtain candidate line segments. Based on the surveyed coordinates and correction coordinates of the target control points on each candidate line segment, determine the correction coordinates of each non-control point on each candidate line segment. Here, the corresponding control points are the points obtained by scaling or enlarging the graphic; the non-control points on the candidate line segments are the pixels between the lines connecting the corresponding control points. The formula for the correction coordinates of the non-control points on the candidate line segments is: ； ;in, and These are the x and y coordinates of the correction coordinates corresponding to the j-th non-control point on the s-th candidate line segment, respectively; s is the sequence number of the candidate line segment; j is the sequence number of the non-control point on the s-th candidate line segment. It is the correction factor corresponding to the j-th non-control point on the s-th candidate line segment; yes The cosine value; yes The sine value; It is the angle between the survey line segment corresponding to the target control point on the s-th candidate line segment and the correction line segment; the survey line segment corresponding to the target control point is the line segment obtained by connecting the survey coordinates corresponding to the target control point and the target origin; the correction line segment corresponding to the target control point is the line segment obtained by connecting the correction coordinates corresponding to the target control point and the target origin. and These are the x and y coordinates of the mapping coordinates corresponding to the j-th non-control point on the s-th candidate line segment, respectively. It is the translation distance of the target control point on the s-th candidate line segment; It is the distance between the j-th non-control point on the s-th candidate line segment and the target origin; It is the distance between the target control point on the s-th candidate line segment and the target origin; It is the GPS distance between the target control point and the target origin on the s-th candidate line segment in the actual scenario.

2. The remote sensing mapping data enhancement method for real estate surveying according to claim 1, characterized in that, The methods for setting target control points include: Starting from each initial control point in the area to be surveyed, a quadrilateral connection process is performed to obtain a sub-network corresponding to each initial control point. The initial control points are control points pre-set in the area to be surveyed. The union of the sub-networks corresponding to all initial control points is used to form an initial quadrangle network, and each initial control point in the initial quadrangle network is determined as a reference control point. Each initial control point in the area to be surveyed, excluding all reference control points, is designated as a temporary control point; Control points are supplemented based on reference control points and each temporary control point; If the included angle between two adjacent sides in the initial quadrilateral network is greater than a preset angle, then these two adjacent sides are used as the sides of a parallelogram to construct a parallelogram, and the newly formed endpoints are added as new control points. The average area of ​​all quadrilaterals in the initial four-corner network is determined as the area representative factor; Quadrilaterals with areas greater than the area representation factor are selected from the initial quadrilateral network and used as supplementary quadrilaterals to be controlled. Set a reduced version of the quadrilateral to be controlled within each quadrilateral to be controlled, and use each endpoint of the reduced version of the quadrilateral to be controlled as a new control point. Each initial control point and each new control point added is designated as a target control point.

3. The remote sensing mapping data enhancement method for real estate surveying according to claim 2, characterized in that, The step of performing quadrilateral connection processing with each initial control point in the area to be mapped as the starting point to obtain a sub-network corresponding to each initial control point includes: Any initial control point is designated as the marker control point. From the area to be surveyed, the three non-collinear initial control points closest to the marker control point are selected as standard control points. Based on these three standard control points and the marker control point, a quadrilateral is constructed, denoted as the marker quadrilateral. Each side of the marked quadrilateral is defined as a marked line segment, and the two non-intersecting endpoints of the three initial control points on two adjacent marked line segments are used to form the initial control point set between the two adjacent marked line segments. If the included angle between two adjacent marked line segments is less than a preset angle, then the set of initial control points between the two adjacent marked line segments is determined as the initial control point set. From all the initial control points in the area to be surveyed, except for the four endpoints of the marked quadrilateral, the initial control point closest to the initial control point set is selected as the reference control point. Then, the reference control point is connected to each initial control point in the initial control point set to obtain the quadrilateral between the two adjacent marked line segments. The method for obtaining the initial control point closest to the set of marked initial control points is as follows: each initial control point in the area to be surveyed, excluding the four endpoints of the marked quadrilateral, is determined as a candidate control point; the distance between each candidate control point and each initial control point in the set of marked initial control points is determined as a reference distance; a set of reference distances corresponding to each candidate control point is obtained; the sum of all reference distances in the set of reference distances corresponding to each candidate control point is determined as the overall distance corresponding to each candidate control point; and the candidate control point with the smallest overall distance is recorded as the initial control point closest to the set of marked initial control points. The subnetwork corresponding to the marked control point is formed by the union of the marked quadrilateral and the quadrilaterals between all adjacent marked line segments.

4. A method for enhancing remote sensing mapping data for real estate surveying according to claim 2, characterized in that, The supplementation of control points based on reference control points and each temporary control point includes: Any temporary control point is designated as a control point to be determined. Select the reference control point closest to the undetermined control point from all reference control points, and use it as the reference control point corresponding to the undetermined control point; Connect the undetermined control point with its corresponding reference control point to obtain a reference line segment; From all reference control points adjacent to the reference control point corresponding to the undetermined control point, select two reference control points as template control points. Starting from each template control point, draw a line segment with the same direction and length as the reference line segment, and record it as the target line segment to obtain two target line segments. Add all endpoints of the two target line segments, excluding the reference control point, as new control points.

5. A method for enhancing remote sensing mapping data for real estate surveying according to claim 1, characterized in that, For pixels in the target mapping image other than control points and the pixels between them, the correction coordinates corresponding to the pixel are determined based on the smallest quadrilateral to which the pixel belongs, including: Any pixel in the target mapping image, excluding the control points and the pixels between their connecting lines, is designated as a marker pixel, and the smallest quadrilateral to which the marker pixel belongs is designated as a reference quadrilateral. The straight line connecting any one set of corresponding control points in the two sets of corresponding control points in the reference quadrilateral is determined as the first temporary straight line, and the straight line connecting the other set of corresponding control points is determined as the second temporary straight line. The straight line connecting any two adjacent non-corresponding control points in the reference quadrilateral is defined as the third temporary straight line; The third temporary line is translated so that it passes through the marked pixel to obtain the fourth temporary line, and the intersection of the fourth temporary line and the first temporary line is determined as the first marked intersection point; Connect the intersection of the marked pixel with the first mark to obtain the fifth temporary line, and determine the angle between the first temporary line and the fifth temporary line as the first temporary angle; The angle between the first temporary line and the second temporary line is defined as the second temporary angle. The intersection of the first temporary line and the second temporary line is determined as the second marked intersection point; Based on the distance between the marked pixel and the first mark intersection point, the distance between the marked pixel and the second mark intersection point, and the first temporary angle and the second temporary angle, the formula for determining the correction coordinates corresponding to the marked pixel is as follows: ； ； ;in, and These are the x and y coordinates of the corrected coordinates corresponding to the marked pixel; yes The cosine value; yes The sine value; equal ; and These are the x and y coordinates of the mapping coordinates corresponding to the marked pixel points, respectively. It is the translation distance of the control point on the reference quadrilateral that is closest to the marked pixel; It is the first temporary included angle; It is the second temporary included angle; It is the distance between the marked pixel and the intersection of the first marker; It is the distance between the marked pixel and the intersection of the second mark.

6. A method for enhancing remote sensing mapping data for real estate surveying according to claim 1, characterized in that, The step of correcting and supplementing the RGB values ​​corresponding to overlapping and missing pixels to obtain the adjusted RGB values ​​corresponding to overlapping and missing pixels includes: The region consisting of consecutive overlapping pixels or consecutive missing pixels is defined as the reference region. The region formed by the preset neighborhoods corresponding to all edge pixels of each reference region is determined as the overall neighborhood of each reference region. The pixels in the overall neighborhood of each reference region, excluding the reference region itself, constitute the surrounding representative region of each reference region. The formula for determining the information importance of each reference region based on its surrounding representative regions is as follows: ； ;in, This represents the importance of the information corresponding to the a-th reference region; a is the index of the reference region. It is a normalization function; It is the number of R values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region; It is the number of G values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region; It is the number of B values ​​corresponding to all pixels in the surrounding representative region corresponding to the a-th reference region; t is the number of pixels in the surrounding representative area corresponding to the a-th reference area; t and v are the sequence numbers of different pixels in the surrounding representative area corresponding to the a-th reference area; It is the pixel difference between the t-th pixel and the v-th pixel in the surrounding representative area corresponding to the a-th reference area; It is an absolute value function; It is the R value corresponding to the t-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the R value corresponding to the v-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the G value corresponding to the t-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the G value corresponding to the vth pixel in the surrounding representative region corresponding to the ath reference region; It is the B value corresponding to the t-th pixel in the surrounding representative region corresponding to the a-th reference region; It is the B value corresponding to the v-th pixel in the surrounding representative region corresponding to the a-th reference region; Based on the information importance corresponding to each reference region, determine the information importance factor corresponding to each pixel in the surrounding representative region corresponding to each reference region; Based on the information importance factors of all pixels within the reference area representing the same geographical location, adjust the R value corresponding to the overlapping pixels representing the same geographical location. Similarly, based on the information importance factors of all pixels within the reference area to which the overlapping pixels representing the same geographical location belong, the G and B values ​​corresponding to the overlapping pixels representing the same geographical location are adjusted. Based on the information importance factor corresponding to all pixels within the reference region to which each missing pixel belongs, the formula for adjusting the R value corresponding to each missing pixel is as follows: ； ;in, It is the adjusted R value corresponding to the h-th missing pixel; h is the index of the missing pixel. is the number of pixels in the reference region to which the h-th missing pixel belongs; b is the index of the pixel in the reference region to which the h-th missing pixel belongs; It is the R value corresponding to the b-th pixel within the reference region to which the h-th missing pixel belongs; It is the information importance factor corresponding to the b-th pixel within the reference region to which the h-th missing pixel belongs; It is the natural exponential function; It is the distance between the h-th missing pixel and the b-th pixel in its reference region; Similarly, based on the information importance factors of all pixels within the reference region to which each missing pixel belongs, the G and B values ​​corresponding to each missing pixel are adjusted.

7. A remote sensing mapping data enhancement method for real estate surveying according to claim 6, characterized in that, The formula for adjusting the R value of overlapping pixels representing the same geographical location based on the information importance factor of all pixels within the reference area to which the overlapping pixels represent the same geographical location belong is as follows: ； ;in, R is the adjusted value corresponding to the overlapping pixels of the same geographical location; E is the number of overlapping pixels of the same geographical location; f is the index of the overlapping pixels of the same geographical location; T is the number of pixels in the reference area to which the overlapping pixels of the same geographical location belong; k is the index of the pixels in the reference area to which the overlapping pixels of the same geographical location belong. It represents the unadjusted R value corresponding to the f-th overlapping pixel point at the same geographical location; It is an important factor representing the information corresponding to the f-th pixel within the reference area to which the f-th overlapping pixel belongs at the same geographical location; It is the natural exponential function; It represents the distance between the f-th overlapping pixel in the same geographical location and the f-th pixel in its reference area; It is an absolute value function; It is the R value corresponding to the f-th pixel within the reference area to which the f-th overlapping pixel belongs at the same geographical location.

8. A remote sensing mapping data augmentation system for real estate surveying, characterized in that, It includes a processor and a memory, the processor being used to process instructions stored in the memory to implement a remote sensing mapping data enhancement method for real estate mapping according to any one of claims 1-7.

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