Combined surveying and mapping data processing information system

Abstract

The invention belongs to the technical field of combined surveying and mapping and image data processing, and provides a combined surveying and mapping data processing information system, which specifically comprises the following steps of: firstly, carrying out combined surveying and mapping on a target area through a LiDAR optical ranging method and Landsat spectrum detection to obtain original data, and identifying an overlapping area of optical ranging point cloud data in the original data; and then reading an incident angle and laser reflection intensity of the point cloud data in the overlapped area, finally calculating variation fitting efficiency through the incident angle and the laser reflection intensity, and constructing a Kriging interpolation model according to the variation fitting efficiency and predicting spectral data. Error accumulation risks caused in a repeated scanning area of the light ranging method in a data merging process of combined surveying and mapping performed by the LiDAR light ranging method and the Landsat spectrum detection are effectively quantified, so that the data processing quality and the data processing accuracy of a data fusion step in a combined surveying and mapping application scene are improved; the final efficiency of land measurement is effectively improved, and the method is suitable for more application levels with high-precision data requirements.

Description

Technical Field

[0001] The invention belongs to the technical field of joint surveying and mapping and image data processing, and in particular relates to a joint surveying and mapping data processing information system. Background Art

[0002] In the context of growing demands in land resource management, urban planning, infrastructure construction, etc., joint surveying and mapping can effectively improve the efficiency, accuracy and data application level of land surveying. In the joint surveying and mapping process of land surveying, there is often a sparse correspondence problem in the collected data. Among them, the UAV LiDAR system can provide extremely high surface resolution, usually reaching an accuracy of 1 meter or even higher. This is because the UAV can fly at low altitude to capture very fine terrain details, and can even penetrate the vegetation to directly measure the surface, so as to build DSM digital surface models and digital elevation models with excellent ground resolution performance. However, satellite remote sensing technologies such as Landsat are difficult to capture fine surface details and have poor ground resolution, but they have multispectral or hyperspectral imaging capabilities to obtain ground information such as vegetation, soil or water conditions, and the data cost is low. Therefore, joint surveying and mapping usually combines the high-resolution data of LiDAR with low-resolution data such as Landsat, and uses Kriging interpolation to effectively interpolate spectral information to enhance the resolution performance of satellite remote sensing technology. However, in practical applications, there are a large number of overlapping areas in the image information obtained by the UAV LiDAR system. On the one hand, in order to ensure the integrity of the coverage range, there must be a certain overlap between the flight routes or information capture areas in the data collection process. This overlap ensures that a large number of areas have the opportunity to be scanned multiple times, and on the other hand, it reduces the error caused by the deviation of the scanning range caused by the flight attitude. However, in practical applications, the Kriging interpolation method is often used in these overlapping areas. This Kriging interpolation process directly induces local deviations in the interpolation model. This is because multiple scans may lead to inconsistent LiDAR data obtained for the same area due to sensor characteristics, changes in flight attitude, and environmental factors, including terrain undulations, obstacles, etc., and the errors in the scenes of multiple scans in the overlapping area accumulate, which ultimately leads to data distortion problems in the obtained DSM digital surface model and digital elevation model, thereby reducing the quality and effect of land resource planning or management. Therefore, a joint surveying and mapping data processing information system is urgently needed to timely identify and adjust the local deviation risk of the interpolation model induced by the Kriging interpolation method. Summary of the invention

[0003] The purpose of the present invention is to propose a joint surveying and mapping data processing information system to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0004] In order to achieve the above object, according to one aspect of the present invention, a method for processing information of joint surveying and mapping data is provided, the method comprising the following steps: The target area is jointly mapped by LiDAR optical ranging method and Landsat spectral detection to obtain raw data; the overlapping area of ​​optical ranging point cloud data in the raw data is identified; the incident angle and laser reflection intensity of the point cloud data in the overlapping area are read; the variation fitting efficiency is calculated by the incident angle and laser reflection intensity; the Kriging interpolation model is constructed based on the variation fitting efficiency and the spectral data is predicted; Furthermore, the target area is jointly mapped by the LiDAR optical ranging method and the Landsat spectral detection, and the method for obtaining the original data is as follows: the area to be jointly mapped is used as the target area; The target area is mapped by a drone equipped with a LiDAR optical ranging system. Each time the terrain is mapped, a point cloud of optical ranging data is obtained and rasterized. The Landsat spectral detection method is used to perform topographic mapping of the target area to obtain two-dimensional spectral data; Both the optical ranging point cloud data and the spectral data are raw data. The optical ranging point cloud data includes three-dimensional coordinates and GPS coordinates.

[0005] The joint mapping refers to three-dimensional terrain mapping based on LiDAR system and two-dimensional spectral mapping based on Landsat; The spectral data includes three-dimensional coordinates and GPS coordinates, and any three-dimensional coordinate contains the light spectrum value of each spectral band.

[0006] Furthermore, a method for identifying overlapping areas of optical ranging point cloud data in the original data is as follows: a UTM coordinate system is formed according to the GPS coordinates of each optical ranging point cloud data and a unified coordinate system of three-dimensional coordinates, each optical ranging point cloud data is aligned with the coordinate system to obtain a first UTM coordinate system, and each optical ranging point cloud data under the first UTM coordinate system is downsampled to obtain a second UTM coordinate system, so that the resolution of the optical ranging point cloud data is consistent with that of the spectral data; each coordinate in the second UTM coordinate system is marked as a resolution point; if a resolution point is included in a plurality of optical ranging point cloud data, the resolution point is defined as the overlapping point of the plurality of optical ranging point cloud data, and an overlapping area is formed by each overlapping point covered by and only by the same plurality of optical ranging point cloud data;.

[0007] The resolution of the two types of original data is made consistent through downsampling method to calculate the variogram.

[0008] The unified coordinate system and the aligned coordinate system are implemented by the open3d library in the python analysis software, so that all optical ranging point cloud data share a coordinate system. Through spatial alignment, the optical ranging point cloud data and the spectral data have a consistent coordinate system and projection.

[0009] Furthermore, the method for reading the incident angle and laser reflection intensity of the point cloud data in the overlapping area is: for any overlapping point in the overlapping area, the incident angle and laser reflection intensity are inherited from each corresponding optical ranging point cloud data; each three-dimensional coordinate includes the incident angle and laser reflection intensity.

[0010] The incident angle is calculated by the open3d library in the python analysis software, and the incident angle refers to the angle between the laser beam and the surface normal vector.

[0011] Furthermore, the method for calculating the variation fitting efficiency through the incident angle and the laser reflection intensity is: for any overlapping area, define each light ranging point cloud data constituting the overlapping area as a source data layer, take any source data layer as the current source data layer, and each resolution point of the current source data layer in the overlapping area as the source data point of the current source data layer; Get the incident angle of each source data point, and cluster all the incident angles using the KMeans clustering algorithm. The default value of the number of clusters n_clusters preset by the KMeans clustering algorithm is 3. If the proportion of the cardinality of any cluster to the total number of source data points exceeds the preset cardinality ratio, the number of clusters is increased by 1 and the KMeans clustering algorithm is used to re-cluster until the proportion of the cardinality of any cluster is less than or equal to the preset cardinality ratio; the preset cardinality ratio ranges from 30% to 50%. The cluster with the largest cluster cardinality is selected, and the sine value of the incident angle corresponding to the cluster center of the obtained cluster is the incident angle mass; For any cluster, the extreme difference of the laser reflection intensity of the source data points corresponding to the cluster is recorded as the light intensity drop, the minimum value of the incident angle among the source data points corresponding to the cluster is the basic incident angle, the absolute value of the difference between the sine value of the basic incident angle and the incident angle quality is the incident quality, and the ratio of the light intensity drop to the incident quality is taken as the coverage density quality of the cluster; The cluster with the largest cardinality has the corresponding coverage density quality recorded as the density quality critical value. The ratio of the cluster with coverage density quality greater than the coverage density quality to the number of clusters is calculated, and the product of the obtained ratio and the incident angle quality is taken as the variation fitting efficiency of the current source data layer.

[0012] The cardinality of a cluster refers to the number of elements in a cluster.

[0013] Since there is an over-reliance on the quality of the incident angle in the calculation process of the above variation fitting efficiency, it will lead to the problem of quantization distortion in the area with drastic terrain changes, especially when the same area with drastic terrain changes is scanned repeatedly many times, the distortion problem will be aggravated. However, the existing technology cannot solve the problem of over-reliance on the quality of the incident angle. In order to better solve this problem and eliminate the phenomenon of aggravated distortion, the present invention proposes a more preferred solution as follows: Preferably, the method for calculating the variation fitting efficiency by the incident angle and the laser reflection intensity is: for any overlapping area, define each optical ranging point cloud data constituting the overlapping area as a source data layer, take any source data layer as the current source data layer, and each resolution point of the current source data layer in the overlapping area as the source data point of the current source data layer; Get the incident angle of each source data point, define the sine value of the incident angle corresponding to the source data point as the incident efficiency EXF, record the standard deviation of all incident efficiencies as the efficiency deviation EXPZ, preset a floating point number as the deviation conversion efficiency aff, and its value range is aff∈[0.5,3]; define the efficiency deviation interval of a source data point as [EXF-aff·EXPZ,EXF+aff·EXPZ], obtain other source data points whose incident efficiency is within the efficiency deviation interval corresponding to the source data point and record them as subordinate points, record the number of subordinate points as the efficiency subordinate quantity ESbd of the source data point, and the difference between the maximum value of the laser reflection intensity in the subordinate point and the laser reflection intensity of the source data point is the subordinate difference ESDis; mark the source data points whose efficiency subordinate quantity is greater than or equal to 1 as having a subordinate relationship; mark the source data point with the maximum value of the efficiency subordinate quantity as the pointer data point; The constraint conditions for the bias conversion efficiency are as follows: if the number of source data points with subordinate relationships accounts for less than 50% of all source data points, the bias conversion result is defined as false, otherwise the bias conversion result is defined as true; the default value of the bias conversion efficiency is 0.5. When the bias conversion result is false, the bias conversion efficiency is increased by 0.1 until the bias conversion result is true; if the bias conversion result is still false when the bias conversion efficiency reaches the maximum value of the value range, the variation fitting efficiency is set to 0; The absolute value of the difference between the laser reflection intensity of the pointer data point and any source data point is recorded as the pointer difference PXDis of the source data point. The variation fitting efficiency VFEff of the current source data layer is calculated based on the source data points with subordinate relationships and their pointer differences: ; Among them, HS i1<> is the harmonic mean function with i1 as the independent variable, where i1 is the serial number of the calling element in the harmonic mean function, and the harmonic mean of the calling element is returned by the harmonic mean function. The calling element refers to each source data point with a subordinate relationship; exp() is an exponential function with a natural constant as the base, max{} is the maximum value function, ESbd i1 ESDis represents the efficiency dependency of the i1th source data point with a dependency relationship, i1 and PXDis i1 They respectively represent the subordination difference and pointer difference of the i1th source data point with subordination relationship.

[0014] Beneficial effect: Since the variation fitting efficiency is calculated based on the source data layers of the repeated area, it can effectively quantify the risk of error accumulation caused by the repeated scanning area of ​​the optical ranging method during the data merging process of the joint mapping of LiDAR optical ranging method and Landsat spectral detection, and provide a reasonable weight allocation strategy with less error chance for the further step of merging variation functions.

[0015] Furthermore, the method of constructing the Kriging interpolation model and predicting the spectral data according to the variation fitting efficiency is as follows: for any overlapping area, the variation function of each corresponding optical ranging point cloud data is calculated, the variation fitting efficiency of each variation function is used as a weighted value, and each variation function is combined by a weighted average method to obtain a normalized variation function; The variation function is obtained by calculating using a KD tree algorithm or a Ball-tree algorithm; the source data layer has a one-to-one correspondence with the optical ranging point cloud data.

[0016] The specific mathematical expression equation for merging each variogram by weighted average is: ; Where k1 is the cumulative variable, γ(h) represents the variation value corresponding to the distance h in the merged variation function, and nk1 represents the number of optical ranging point cloud data covered in the overlapping area, that is, the number of variation functions; VFEff k1 is the variation fitting efficiency corresponding to the k1th optical ranging point cloud data, that is, the variation fitting efficiency obtained by the source data layer, and The value of is 1; k1 (h) represents the variation function corresponding to the k1th optical ranging point cloud data.

[0017] The Kriging equation group and the spectral data in the original data are constructed according to the combined variance function. Each grid point in the overlapping area in the first UTM coordinate system is taken as the prediction position, and the spectral data of each prediction position is calculated by the OrdinaryKriging algorithm.

[0018] The OrdinaryKriging is called through the PyKrige library of the Python program.

[0019] Preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0020] The present invention also provides a joint surveying and mapping data processing information system, the joint surveying and mapping data processing information system comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the joint surveying and mapping data processing information method when executing the computer program, the joint surveying and mapping data processing information system can be run in computing devices such as desktop computers, notebook computers, PDAs, and cloud data centers, and the executable system may include, but is not limited to, a processor, a memory, and a server cluster, and the processor executes the computer program to run in the following system units: A joint surveying and mapping raw data acquisition unit is used to obtain raw data by jointly surveying and mapping the target area through the LiDAR optical ranging method and Landsat spectral detection; An overlap region identification unit, used to identify overlap regions of optical ranging point cloud data in the original data; An analysis factor acquisition unit is used to read the incident angle and laser reflection intensity of the point cloud data in the overlapped area; A variation fitting analysis unit, used for calculating variation fitting efficiency through incident angle and laser reflection intensity; The spectral value prediction unit is used to build a Kriging interpolation model and predict spectral data based on the variation fitting efficiency.

[0021] The beneficial effects of the present invention are as follows: the present invention provides a joint surveying and mapping data processing information system, which effectively quantifies the risk of error accumulation caused by repeated scanning areas of the light ranging method in the data merging process of the joint surveying and mapping by the LiDAR light ranging method and the Landsat spectral detection through the incident angle and light intensity of the LiDAR point cloud data, and provides a reasonable weight allocation strategy with a smaller error chance for the step of further merging the variance function, thereby improving the data processing quality and data processing accuracy of the data fusion step in the joint surveying and mapping application scenario, so that the subsequent construction of the DSM digital surface model or the construction of the digital elevation model reduces the risk of data distortion, effectively improving the final efficiency of land measurement and adapting to more application levels with high-precision data requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings: Figure 1 Shown is a flow chart of a method for processing information of joint surveying and mapping data; Figure 2 Shown is a structural diagram of a joint surveying and mapping data processing information system. DETAILED DESCRIPTION

[0023] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0024] like Figure 1 The figure shows a flow chart of a method for processing information of joint surveying and mapping data. Figure 1 A method for processing information of joint surveying and mapping data according to an embodiment of the present invention is described below. The method comprises the following steps:

[0025] Example 1 The target area is jointly mapped by LiDAR optical ranging method and Landsat spectral detection to obtain raw data; the overlapping area of ​​optical ranging point cloud data in the raw data is identified; the incident angle and laser reflection intensity of the point cloud data in the overlapping area are read; the variation fitting efficiency is calculated by the incident angle and laser reflection intensity; the Kriging interpolation model is constructed based on the variation fitting efficiency and the spectral data is predicted; Furthermore, the target area is jointly mapped by the LiDAR optical ranging method and the Landsat spectral detection, and the method for obtaining the original data is as follows: the area to be jointly mapped is used as the target area; The target area is mapped by a drone equipped with a LiDAR optical ranging system. Each time the terrain is mapped, a point cloud of optical ranging data is obtained and rasterized. The Landsat spectral detection method is used to perform topographic mapping of the target area to obtain two-dimensional spectral data; Both the optical ranging point cloud data and the spectral data are raw data. The optical ranging point cloud data includes three-dimensional coordinates and GPS coordinates.

[0026] Furthermore, a method for identifying overlapping areas of optical ranging point cloud data in the original data is as follows: a UTM coordinate system is formed according to the GPS coordinates of each optical ranging point cloud data and a unified coordinate system of three-dimensional coordinates, each optical ranging point cloud data is aligned with the coordinate system to obtain a first UTM coordinate system, and each optical ranging point cloud data under the first UTM coordinate system is downsampled to obtain a second UTM coordinate system, so that the resolution of the optical ranging point cloud data is consistent with that of the spectral data; each coordinate in the second UTM coordinate system is marked as a resolution point; if a resolution point is included in a plurality of optical ranging point cloud data, the resolution point is defined as the overlapping point of the plurality of optical ranging point cloud data, and an overlapping area is formed by each overlapping point covered by and only by the same plurality of optical ranging point cloud data;.

[0027] Furthermore, the method for reading the incident angle and laser reflection intensity of the point cloud data in the overlapping area is: for any overlapping point in the overlapping area, the incident angle and laser reflection intensity are inherited from each corresponding optical ranging point cloud data; each three-dimensional coordinate includes the incident angle and laser reflection intensity.

[0028] Furthermore, the method for calculating the variation fitting efficiency through the incident angle and the laser reflection intensity is: for any overlapping area, define each light ranging point cloud data constituting the overlapping area as a source data layer, take any source data layer as the current source data layer, and each resolution point of the current source data layer in the overlapping area as the source data point of the current source data layer; Get the incident angle of each source data point, and cluster all the incident angles using the KMeans clustering algorithm. The default value of the number of clusters n_clusters preset by the KMeans clustering algorithm is 3. If the proportion of the cardinality of any cluster to the total number of source data points exceeds the preset cardinality ratio, the number of clusters is increased by 1 and the KMeans clustering algorithm is used to re-cluster until the proportion of the cardinality of any cluster is less than or equal to the preset cardinality ratio; the cardinality ratio value range is 30%; The cluster with the largest cluster cardinality is selected, and the sine value of the incident angle corresponding to the cluster center of the obtained cluster is the incident angle mass; For any cluster, the extreme difference of the laser reflection intensity of the source data points corresponding to the cluster is recorded as the light intensity drop, the minimum value of the incident angle among the source data points corresponding to the cluster is the basic incident angle, the absolute value of the difference between the sine value of the basic incident angle and the incident angle quality is the incident quality, and the ratio of the light intensity drop to the incident quality is taken as the coverage density quality of the cluster; The cluster with the largest cardinality has the corresponding coverage density quality recorded as the density quality critical value. The ratio of the cluster with coverage density quality greater than the coverage density quality to the number of clusters is calculated, and the product of the obtained ratio and the incident angle quality is taken as the variation fitting efficiency of the current source data layer.

[0029] Furthermore, the method of constructing the Kriging interpolation model and predicting the spectral data according to the variation fitting efficiency is as follows: for any overlapping area, the variation function of each corresponding optical ranging point cloud data is calculated, the variation fitting efficiency of each variation function is used as a weighted value, and each variation function is combined by a weighted average method to obtain a normalized variation function; The variation function is obtained by calculating using the KD tree algorithm, and the source data layer has a one-to-one correspondence with the optical ranging point cloud data.

[0030] The specific mathematical expression equation for merging each variogram by weighted average is: ; Where k1 is the cumulative variable, γ(h) represents the variation value corresponding to the distance h in the merged variation function, and nk1 represents the number of optical ranging point cloud data covered in the overlapping area, that is, the number of variation functions; VFEff k1 is the variation fitting efficiency corresponding to the k1th optical ranging point cloud data, that is, the variation fitting efficiency obtained by the source data layer, and The value of is 1; k1 (h) represents the variation function corresponding to the k1th optical ranging point cloud data.

[0031] The Kriging equation group and the spectral data in the original data are constructed according to the combined variance function. Each grid point in the overlapping area in the first UTM coordinate system is taken as the prediction position, and the spectral data of each prediction position is calculated by the OrdinaryKriging algorithm.

[0032] Example 2 Example 2 adopts the same joint surveying and mapping data processing information method as Example 1, the difference being that the method for calculating the variation fitting efficiency through the incident angle and the laser reflection intensity is: for any overlapping area, the light ranging point cloud data constituting the overlapping area is defined as a source data layer, any source data layer is defined as the current source data layer, and each resolution point of the current source data layer in the overlapping area is defined as a source data point of the current source data layer; Get the incident angle of each source data point, define the sine value of the incident angle corresponding to the source data point as the incident efficiency EXF, record the standard deviation of all incident efficiencies as the efficiency deviation EXPZ, preset a floating point number as the deviation conversion efficiency aff, and its value range is aff∈[0.5,3]; define the efficiency deviation interval of a source data point as [EXF-aff·EXPZ,EXF+aff·EXPZ], obtain other source data points whose incident efficiency is within the efficiency deviation interval corresponding to the source data point and record them as subordinate points, record the number of subordinate points as the efficiency subordinate quantity ESbd of the source data point, and the difference between the maximum value of the laser reflection intensity in the subordinate point and the laser reflection intensity of the source data point is the subordinate difference ESDis; mark the source data points whose efficiency subordinate quantity is greater than or equal to 1 as having a subordinate relationship; mark the source data point with the maximum value of the efficiency subordinate quantity as the pointer data point; The constraint conditions for the bias conversion efficiency are as follows: if the number of source data points with subordinate relationships accounts for less than 50% of all source data points, the bias conversion result is defined as false, otherwise the bias conversion result is defined as true; the default value of the bias conversion efficiency is 0.5. When the bias conversion result is false, the bias conversion efficiency is increased by 0.1 until the bias conversion result is true; if the bias conversion result is still false when the bias conversion efficiency reaches the maximum value of the value range, the variation fitting efficiency is set to 0; The absolute value of the difference between the laser reflection intensity of the pointer data point and any source data point is recorded as the pointer difference PXDis of the source data point. The variation fitting efficiency VFEff of the current source data layer is calculated based on the source data points with subordinate relationships and their pointer differences: ; Among them, HS i1 <> is the harmonic mean function with i1 as the independent variable, ESbd i1 ESDis represents the efficiency dependency of the i1th source data point with a dependency relationship, i1 and PXDis i1 They respectively represent the subordination difference and pointer difference of the i1th source data point with subordination relationship.

[0033] An embodiment of the present invention provides a joint surveying and mapping data processing information system, such as Figure 2 The figure shows a structure diagram of a joint surveying and mapping data processing information system of the present invention. A joint surveying and mapping data processing information system of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned joint surveying and mapping data processing information method embodiment are implemented.

[0034] The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the system: A joint surveying and mapping raw data acquisition unit is used to obtain raw data by jointly surveying and mapping the target area through the LiDAR optical ranging method and Landsat spectral detection; An overlap region identification unit, used to identify overlap regions of optical ranging point cloud data in the original data; An analysis factor acquisition unit is used to read the incident angle and laser reflection intensity of the point cloud data in the overlapped area; A variation fitting analysis unit, used for calculating variation fitting efficiency through incident angle and laser reflection intensity; The spectral value prediction unit is used to build a Kriging interpolation model and predict spectral data based on the variation fitting efficiency.

[0035] The joint surveying and mapping data processing information system can be run on computing devices such as desktop computers, laptops, PDAs, and cloud servers. The system that can run the joint surveying and mapping data processing information system may include, but is not limited to, processors and memories. Those skilled in the art will understand that the example is only an example of a joint surveying and mapping data processing information system and does not constitute a limitation on a joint surveying and mapping data processing information system. It may include more or fewer components than the example, or a combination of certain components, or different components. For example, the joint surveying and mapping data processing information system may also include input and output devices, network access devices, buses, etc.

[0036] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the operation system of the joint surveying and mapping data processing information system, and uses various interfaces and lines to connect the various parts of the entire joint surveying and mapping data processing information system operation system.

[0037] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the joint surveying and mapping data processing information system by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0038] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.

Claims

1. A method for processing information of joint surveying and mapping data, characterized in that: The method comprises the following steps: The target area is jointly mapped by LiDAR optical ranging method and Landsat spectral detection to obtain raw data; the overlapping area of ​​optical ranging point cloud data in the raw data is identified; the incident angle and laser reflection intensity of the point cloud data in the overlapping area are read; the variation fitting efficiency is calculated by the incident angle and laser reflection intensity; the Kriging interpolation model is constructed based on the variation fitting efficiency and the spectral data is predicted; The method for calculating the variation fitting efficiency by using the incident angle and the laser reflection intensity is as follows: for any overlapping area, the light ranging point cloud data constituting the overlapping area is defined as the source data layer, any source data layer is defined as the current source data layer, and each analytical point of the current source data layer in the overlapping area is defined as the source data point of the current source data layer; Get the incident angle of each source data point, and cluster all the incident angles using the KMeans clustering algorithm, where the default value of the number of clusters n_clusters preset by the KMeans clustering algorithm is 3. If the proportion of the cardinality of any cluster to the total number of source data points exceeds the preset cardinality ratio, the number of clusters is increased by 1 and the KMeans clustering algorithm is used to re-cluster until the proportion of the cardinality of any cluster is less than or equal to the preset cardinality ratio; The cluster with the largest cluster cardinality is selected, and the sine value of the incident angle corresponding to the cluster center of the obtained cluster is the incident angle mass; For any cluster, the extreme difference of the laser reflection intensity of the source data points corresponding to the cluster is recorded as the light intensity drop, the minimum value of the incident angle among the source data points corresponding to the cluster is the basic incident angle, the absolute value of the difference between the sine value of the basic incident angle and the incident angle quality is the incident quality, and the ratio of the light intensity drop to the incident quality is taken as the coverage density quality of the cluster; The cluster with the largest cardinality has the corresponding coverage density quality recorded as the density quality critical value. The ratio of the cluster with coverage density quality greater than the coverage density quality to the number of clusters is calculated, and the product of the obtained ratio and the incident angle quality is taken as the variation fitting efficiency of the current source data layer.

2. A method for processing information of joint surveying and mapping data according to claim 1, characterized in that: The method of obtaining raw data by jointly mapping a target area through the LiDAR optical ranging method and the Landsat spectral detection is as follows: the area to be jointly mapped is used as the target area; The target area is mapped by a drone equipped with a LiDAR optical ranging system. Each time the topography is mapped, a light ranging point cloud data is obtained and rasterized. The target area is mapped by a Landsat spectral detection method to obtain two-dimensional spectral data. Both the optical ranging point cloud data and the spectral data are raw data. The optical ranging point cloud data includes three-dimensional coordinates and GPS coordinates.

3. A method for processing information of joint surveying and mapping data according to claim 1, characterized in that: The method for identifying the overlapping area of ​​the optical ranging point cloud data in the original data is: forming a UTM coordinate system according to the GPS coordinates of each optical ranging point cloud data and the three-dimensional coordinate unified coordinate system, and aligning each optical ranging point cloud data with the coordinate system to obtain a first UTM coordinate system; Downsampling each optical ranging point cloud data in the first UTM coordinate system to obtain a second UTM coordinate system, so that the resolution of the optical ranging point cloud data is consistent with that of the spectral data; Each coordinate in the second UTM coordinate system is a resolution point; if a resolution point is included in several optical ranging point cloud data, the resolution point is defined as the overlapping point of the several optical ranging point cloud data, and an overlapping area is formed by and only by each overlapping point covered by the same several optical ranging point cloud data.

4. A method for processing information of joint surveying and mapping data according to claim 1, characterized in that: The method for reading the incident angle and laser reflection intensity of the point cloud data in the overlapped area is: for any overlapped point in the overlapped area, the incident angle and laser reflection intensity are inherited from each corresponding optical ranging point cloud data; each three-dimensional coordinate includes the incident angle and laser reflection intensity.

5. The method for processing information of joint surveying and mapping data according to claim 1, characterized in that: The method of calculating the variation fitting efficiency through the incident angle and laser reflection intensity can be replaced as follows: after obtaining the incident angle of each source data point, define the sine value of the incident angle corresponding to the source data point as the incident efficiency EXF, record the standard deviation of all incident efficiencies as the efficiency deviation EXPZ, preset a floating point number as the deviation conversion efficiency aff, and its value range is aff∈[0.5,3]; define the efficiency deviation interval of a source data point as [EXF-aff·EXPZ,EXF+aff·EXPZ], obtain other source data points whose incident efficiencies are within the efficiency deviation interval corresponding to the source data point and record them as subordinate points, record the number of subordinate points as the efficiency subordinate quantity ESbd of the source data point, and the difference between the maximum value of the laser reflection intensity in the subordinate point and the laser reflection intensity of the source data point is the subordinate difference ESDis; The source data points whose marking efficiency dependency is greater than or equal to 1 are in a subordinate relationship; the source data point whose marking efficiency dependency has the maximum value is the pointer data point; The constraint condition for the deviation conversion efficiency is: if the number of source data points with subordinate relationships accounts for less than 50% of all source data points, the deviation conversion result is defined as false, otherwise the deviation conversion result is defined as true; The default value of the deviation conversion efficiency is 0.

5. When the deviation conversion result is false, the deviation conversion efficiency is increased by 0.1 until the deviation conversion result is true. If the deviation conversion result is still false when the deviation conversion efficiency reaches the maximum value of the value range, the variation fitting efficiency is set to 0. The absolute value of the difference between the laser reflection intensity of the pointer data point and any source data point is recorded as the pointer difference PXDis of the source data point. The variation fitting efficiency VFEff of the current source data layer is calculated based on the source data points with subordinate relationships and their pointer differences: ； Among them, HS i1 <> is the harmonic mean function with i1 as the independent variable, ESbd i1 ESDis represents the efficiency dependency of the i1th source data point with a dependency relationship, i1 and PXDis i1 They respectively represent the subordination difference and pointer difference of the i1th source data point with subordination relationship.

6. A method for processing information of joint surveying and mapping data according to claim 1, characterized in that: The method of constructing a Kriging interpolation model and predicting spectral data based on the variation fitting efficiency is as follows: for any overlapping area, the variation function of each optical ranging point cloud data is calculated, and the variation fitting efficiency of each variation function is used as a weighted value. The weighted average method is used to merge each variation function to obtain the normalized variation function; The Kriging equation group and the spectral data in the original data are constructed according to the combined variance function. Each grid point in the overlapping area in the first UTM coordinate system is taken as the prediction position, and the spectral data of each prediction position is calculated by the OrdinaryKriging algorithm.

7. A joint surveying and mapping data processing information system, characterized in that: The joint surveying and mapping data processing information system includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the joint surveying and mapping data processing information method described in any one of claims 1 to 6 are implemented. The joint surveying and mapping data processing information system runs on desktop computers, laptop computers, PDAs, and computing devices in cloud data centers.

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