A joint mapping data processing information system

Abstract

The present invention belongs to the technical fields of joint surveying and mapping and image data processing, and proposes a joint surveying and mapping data processing information system, specifically: First, the target area of joint surveying and mapping is carried out through the LiDAR optical ranging method and Landsat spectral detection to obtain the original data, identify the overlapping area of the optical ranging point cloud data in the original data, then read the incident angle and laser reflection intensity of the point cloud data in the overlapping area, and finally calculate the variation fitting efficiency through the incident angle and laser reflection intensity, and construct a Kriging interpolation model based on the variation fitting efficiency and predict the spectral data. It effectively quantifies the risk of error accumulation in the repeated scanning area of the optical ranging method during the data merging process of joint surveying and mapping by the LiDAR optical ranging method and Landsat spectral detection, thereby improving the data processing quality and data processing accuracy of the data fusion step under the joint surveying and mapping application scenario, effectively improving the final efficiency of land surveying and adapting to more application levels with high-precision data requirements.

Description

Technical Field

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

[0002] In the context of the increasing demands in aspects such as land resource management, urban planning, and infrastructure construction, joint surveying and mapping can effectively improve the efficiency, accuracy, and more data application levels of land measurement. During the joint surveying and mapping process of land measurement, there often exists 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 a low altitude to capture very detailed terrain details and can even penetrate vegetation to directly measure the ground surface, thereby enabling the construction of a DSM digital surface model and a digital elevation model with excellent ground resolution performance. However, satellite remote sensing technologies such as Landsat are difficult to capture detailed surface details and have poor ground resolution, but they have the ability to obtain information on the ground conditions such as vegetation, soil, or water bodies through multi-spectral or hyper-spectral imaging and have low data costs. Therefore, joint surveying and mapping usually combines the high-resolution data of LiDAR with the low-resolution data such as Landsat and uses the Kriging interpolation method to effectively interpolate spectral information to enhance the resolution performance of satellite remote sensing technology. However, in practical applications, due to the existence of a large number of overlapping regions 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 during the data collection process. This overlap ensures that a large number of areas have the opportunity for multiple scans. On the other hand, it is to reduce the error caused by the deviation of the scanning range due to the flight attitude. However, in practical applications, the Kriging interpolation method is often used in these overlapping regions. The direct process of this Kriging interpolation method will induce local deviations in the interpolation model. This is because multiple scans may result in inconsistent LiDAR data for the same area due to sensor characteristics, changes in flight attitude, and environmental factors, including terrain undulations, obstacles, etc. The errors accumulate in the scenario of multiple scans within the overlapping region, ultimately leading 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, there is an urgent need for a joint surveying and mapping data processing information system to timely identify and adjust the risk of local deviations in 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 creation condition.

[0004] To achieve the above object, according to one aspect of the present invention, there is provided a method for processing information on combined surveying and mapping data, the method comprising the following steps:

[0005] Jointly survey the target area through the LiDAR optical ranging method and Landsat spectral detection to obtain raw data; identify the overlapping areas of the optical ranging point cloud data in the raw data; read the incident angle and laser reflection intensity of the point cloud data in the overlapping areas; calculate the variation fitting efficiency through the incident angle and laser reflection intensity; construct a Kriging interpolation model based on the variation fitting efficiency and predict the spectral data;

[0006] Further, the method for jointly surveying the target area through the LiDAR optical ranging method and Landsat spectral detection to obtain raw data is: the area to be jointly surveyed is used as the target area;

[0007] Use a drone equipped with a LiDAR optical ranging system to conduct topographic survey on the target area, obtain a piece of optical ranging point cloud data each time of topographic survey, and perform rasterization processing;

[0008] Conduct topographic survey on the target area through the Landsat spectral detection method to obtain two-dimensional spectral data;

[0009] Both the optical ranging point cloud data and the spectral data are raw data, and the optical ranging point cloud data includes three-dimensional coordinates and GPS coordinates.

[0010] Wherein the combined surveying and mapping refers to three-dimensional topographic survey based on the LiDAR system and two-dimensional spectral survey based on Landsat;

[0011] The spectral data includes three-dimensional coordinates and GPS coordinates, and each three-dimensional coordinate contains spectral values of each spectral band.

[0012] Further, the method for identifying the overlapping areas of the optical ranging point cloud data in the raw data is: unify the coordinate systems according to the GPS coordinates and three-dimensional coordinates of each optical ranging point cloud data to form a UTM coordinate system, align each optical ranging point cloud data with the coordinate system respectively to obtain the first UTM coordinate system, perform downsampling processing on each optical ranging point cloud data in the first UTM coordinate system to obtain the second UTM coordinate system, and make the resolution of the optical ranging point cloud data consistent with that of the spectral data; each coordinate in the second UTM coordinate system is denoted as an analysis point; if an analysis point is included in several optical ranging point cloud data, then define this analysis point as the overlapping point of these several optical ranging point cloud data, and each overlapping point covered by only the same several optical ranging point cloud data constitutes an overlapping area;

[0013] Make the resolutions of the two types of raw data consistent through the downsampling method to calculate the variogram.

[0014] The unified coordinate system and the aligned coordinate system are implemented by the Open3D library in the Python analysis software, enabling all the light ranging point cloud data to share a common coordinate system. Through spatial alignment, the light ranging point cloud data and the spectral data have a consistent coordinate system and projection.

[0015] Furthermore, the method for reading the incident angle and the laser reflection intensity of the point cloud data in the overlapping region is as follows: for any overlapping point in the overlapping region, the incident angle and the laser reflection intensity are respectively inherited from the corresponding light ranging point cloud data; the incident angle and the laser reflection intensity are included in each three-dimensional coordinate.

[0016] Among them, 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.

[0017] Furthermore, the method for calculating the variation fitting efficiency through the incident angle and the laser reflection intensity is as follows: for any overlapping region, define each light ranging point cloud data that constitutes the overlapping region as the source data layer. Taking any source data layer as the current source data layer, the source data points of the current source data layer in the overlapping region are the source data points of the current source data layer;

[0018] Obtain the incident angles of each source data point, and cluster all the incident angles through the KMeans clustering algorithm. The default value of the preset number of clusters n_clusters in the KMeans clustering algorithm is 3. If the proportion of the cardinality of any cluster in the total number of source data points exceeds the preset cardinality ratio, increase the number of clusters by 1 and re-cluster through the KMeans clustering algorithm 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%;

[0019] Select the cluster with the largest cardinality of the cluster. The sine value of the incident angle corresponding to the cluster center of the obtained cluster is the incident angle quality;

[0020] For any cluster, the range of the laser reflection intensity of the source data points corresponding to the cluster is denoted as the light intensity drop. The minimum value of the incident angles of 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. The ratio of the light intensity drop to the incident quality is used as the coverage density quality of the cluster;

[0021] The coverage density quality corresponding to the cluster with the largest cardinality of the cluster is denoted as the density quality critical value. Calculate the ratio of the number of clusters with a coverage density quality greater than the coverage density quality to the number of clusters. The product of the obtained ratio and the incident angle quality is used as the variation fitting efficiency of the current source data layer.

[0022] Among them, the cardinality of the cluster refers to the number of elements in a cluster.

[0023] Since there is a phenomenon of over-reliance on the quality of the incident angle in the calculation process of the above mutation fitting efficiency, it will lead to the problem of quantization distortion in dealing with areas with drastic terrain changes. Especially in the case of a large number of repeated scans in areas with the same drastic terrain changes, the distortion problem will be exacerbated. However, the existing technology cannot solve this problem of over-reliance on the incident angle quality. In order to better solve this problem and eliminate the phenomenon of aggravated distortion, the present invention proposes a more preferred solution as follows:

[0024] Preferably, the method for calculating the mutation fitting efficiency by the incident angle and the laser reflection intensity is as follows: For any overlapping area, define the respective light ranging point cloud data constituting the overlapping area as the source data layer. Taking any source data layer as the current source data layer, the source data points of the current source data layer at each parsing point in the overlapping area are the source data points of the current source data layer;

[0025] Obtain the incident angles 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 amount EXPZ, and 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, and record the number of subordinate points as the efficiency subordinate quantity ESbd of the source data point. The difference between the maximum value of the laser reflection intensity among the subordinate points and the laser reflection intensity of the source data point is the subordinate difference degree ESDis; Mark the source data points with the efficiency subordinate quantity 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;

[0026] Among them, the constraint condition for the value of the deviation conversion efficiency is: If the proportion of the source data points with a subordinate relationship in all source data points is less than 50%, then define the deviation conversion result as false, otherwise define the deviation conversion result as true; The default value of the deviation conversion efficiency value is 0.5. When the deviation conversion result is false, increase the deviation conversion efficiency 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, set the mutation fitting efficiency to 0;

[0027] Take the difference between the laser reflection intensity of the pointer data point and any source data point, and record the absolute value of the obtained difference as the pointer difference degree PXDis of the source data point. Calculate the mutation fitting efficiency VFEff of the current source data layer according to the source data points with a subordinate relationship and their pointer difference degrees:

[0028] ;

[0029] 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 through the harmonic mean function. The calling element refers to each source data point with a subordinate relationship; exp() is the exponential function with the natural constant as the base, max{} is the maximum value function, ESbd i1 represents the efficiency subordinate quantity of the i1-th source data point with a subordinate relationship, ESDis i1 and PXDis i1 respectively represent the subordinate difference degree and pointer difference degree of the i1-th source data point with a subordinate relationship.

[0030] Beneficial effects: Since the mutation fitting efficiency is calculated overall based on each source data layer in the overlapping area, it can effectively quantify the risk of error accumulation in the repeated scanning area of the LiDAR optical ranging method during the data merging process of joint mapping with the Landsat spectral detection, and provide a more reasonable and less error-prone weight allocation strategy for further merging the steps of the variogram.

[0031] Furthermore, the method for constructing the Kriging interpolation model and predicting spectral data based on the mutation fitting efficiency is as follows: For any overlapping area, calculate the variogram of its corresponding LiDAR point cloud data at each optical ranging point, and use the mutation fitting efficiency of each variogram as the weighting value. The variograms are merged through the weighted average method to obtain the merged variogram;

[0032] Among them, the variogram is calculated by using the K-D tree algorithm or the Ball-tree algorithm; there is a one-to-one correspondence between the source data layer and the LiDAR point cloud data.

[0033] The specific mathematical expression equation for merging each variogram through weighted average is: ; where k1 is the cumulative variable, γ(h) represents the variogram value corresponding to the distance h in the merged variogram, nk1 represents the number of LiDAR point cloud data covered in the overlapping area, that is, the number of variograms; VFEff k1 is the mutation fitting efficiency corresponding to the k1-th LiDAR point cloud data, that is, the mutation fitting efficiency obtained from the source data layer, and the value of is 1; γ k1 (h) represents the variogram corresponding to the k1-th LiDAR point cloud data.

[0034] Based on the merged variogram, construct the Kriging equations and the spectral data in the original data. Take each grid point in the overlapping area in the first UTM coordinate system as the prediction position, and calculate the spectral data of each prediction position through the OrdinaryKriging algorithm.

[0035] Among them, Ordinary Kriging is called through the PyKrige library of a Python program.

[0036] Preferably, for all undefined variables in the present invention, if there is no clear definition, they can all be artificially set thresholds.

[0037] The present invention also provides a joint mapping data processing information system. The joint 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 in the joint mapping data processing information method are implemented. The joint mapping data processing information system can run on computing devices such as desktop computers, laptop computers, palm computers, and cloud data centers. The operable system may include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program and runs in the following system units:

[0038] The joint mapping raw data acquisition unit is used to jointly map the target area through the LiDAR optical ranging method and Landsat spectral detection to obtain raw data;

[0039] The overlapping area recognition unit is used to identify the overlapping areas of the optical ranging point cloud data in the raw data;

[0040] The analysis factor acquisition unit is used to read the incident angle and laser reflection intensity of the point cloud data in the overlapping area;

[0041] The variation fitting analysis unit is used to calculate the variation fitting efficiency through the incident angle and laser reflection intensity;

[0042] The spectral value prediction unit is used to construct a Kriging interpolation model based on the variation fitting efficiency and predict spectral data.

[0043] The beneficial effects of the present invention are as follows: The present invention provides a joint mapping data processing information system. By using the incident angle and light intensity of LiDAR point cloud data, it effectively quantifies the risk of error accumulation caused by repeated scanning areas in the optical ranging method during the data merging process of jointly mapping by the LiDAR optical ranging method and Landsat spectral detection, provides a more reasonable and less error-prone weight allocation strategy for further merging the variogram steps, and then improves the data processing quality and data processing accuracy of the data fusion step in the joint mapping application scenario, reduces the risk of data distortion when constructing the DSM digital surface model or the digital elevation model subsequently, and effectively improves the final efficiency of land measurement and adapts to more application levels with high-precision data requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By elaborating on the embodiments shown in the accompanying drawings in detail, the above and other features of the present invention will become more apparent. In the drawings of the present invention, the same reference numerals denote the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings:

[0045] Figure 1 Shown is a flowchart of a method for processing joint surveying and mapping data information;

[0046] Figure 2 Shown is a structural diagram of a joint surveying and mapping data processing information system. Detailed implementation manners

[0047] The following will clearly and completely describe the concept, specific structure, and technical effects generated by the present invention in combination with embodiments and drawings to fully understand the purpose, solution, and effects of the present invention. It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other.

[0048] As Figure 1 Shown is a flowchart of a method for processing joint surveying and mapping data information. The following will elaborate on a method for processing joint surveying and mapping data information according to the embodiments of the present invention. The method includes the following steps: Figure 1 to

[0049] Embodiment 1

[0050] Perform joint surveying of the target area through the LiDAR optical ranging method and Landsat spectral detection to obtain raw data; identify the overlapping area of the optical ranging point cloud data in the raw data; read the incident angle and laser reflection intensity of the point cloud data in the overlapping area; calculate the variation fitting efficiency through the incident angle and laser reflection intensity; construct a Kriging interpolation model based on the variation fitting efficiency and predict spectral data;

[0051] Furthermore, the method for performing joint surveying of the target area through the LiDAR optical ranging method and Landsat spectral detection to obtain raw data is as follows: the area to be jointly surveyed is used as the target area;

[0052] Perform topographic surveying of the target area by using a drone equipped with a LiDAR optical ranging system. Each time topographic surveying is performed, a piece of optical ranging point cloud data is obtained and rasterized;

[0053] Perform topographic surveying of the target area through the Landsat spectral detection method to obtain two-dimensional spectral data;

[0054] 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.

[0055] Further, the method for identifying the overlapping region of the optical ranging point cloud data in the raw data is as follows: According to the GPS coordinates and three-dimensional coordinates of each optical ranging point cloud data, the coordinate system is unified to form a UTM coordinate system. Each optical ranging point cloud data is aligned with the coordinate system to obtain the first UTM coordinate system. The downsampling process is performed on each optical ranging point cloud data in the first UTM coordinate system to obtain the second UTM coordinate system, making the resolution of the optical ranging point cloud data consistent with that of the spectral data. Each coordinate in the second UTM coordinate system is recorded as an analysis point. If an analysis point is included in several optical ranging point cloud data, then this analysis point is defined as the overlapping point of these several optical ranging point cloud data. Each overlapping point covered by only the same several optical ranging point cloud data constitutes an overlapping region.

[0056] Further, the method for reading the incident angle and laser reflection intensity of the point cloud data in the overlapping region is as follows: For any overlapping point in the overlapping region, the incident angle and laser reflection intensity are respectively inherited from each corresponding optical ranging point cloud data. Each three-dimensional coordinate includes the incident angle and laser reflection intensity.

[0057] Further, the method for calculating the mutation fitting efficiency through the incident angle and laser reflection intensity is as follows: For any overlapping region, define each optical ranging point cloud data that constitutes this overlapping region as the source data layer. Taking any source data layer as the current source data layer, each analysis point of the current source data layer in the overlapping region is the source data point of the current source data layer.

[0058] Obtain the incident angles of each source data point, and perform clustering on all incident angles through the KMeans clustering algorithm. The default value of the preset number of clusters n_clusters in the KMeans clustering algorithm is 3. If the proportion of the cardinality of any cluster in the total amount of source data points exceeds the preset cardinality ratio, then increase the number of clusters by 1 and perform clustering again through the KMeans clustering algorithm until the proportion of the cardinality of any cluster is less than or equal to the preset cardinality ratio. The range of the cardinality ratio is 30%.

[0059] Select the cluster with the maximum cardinality of the cluster. The sine value of the incident angle corresponding to the cluster center of the obtained cluster is the incident angle quality.

[0060] For any cluster, the range of the laser reflection intensity of the source data points corresponding to this cluster is recorded as the light intensity drop. The minimum value of the incident angles of the source data points corresponding to this 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. The ratio of the light intensity drop to the incident quality is used as the coverage density quality of the cluster.

[0061] The cluster corresponding to the maximum cardinality of the cluster is denoted as the density mass critical value of the coverage density mass. Calculate the ratio of the number of clusters with a coverage density mass greater than the coverage density mass, and the product of the obtained ratio and the incident angle mass is used as the variation fitting efficiency of the current source data layer.

[0062] Further, the method of constructing a Kriging interpolation model based on the variation fitting efficiency and predicting spectral data is as follows: For any overlapping region, calculate the variogram of the corresponding light ranging point cloud data at each light ranging point. Using the variation fitting efficiency of each variogram as the weighting value, the individual variograms are merged through a weighted average method to obtain a combined variogram;

[0063] Among them, the variogram is obtained by calculation using the K-D tree algorithm, and there is a one-to-one correspondence between the source data layer and the light ranging point cloud data.

[0064] The specific mathematical expression equation for merging each variogram through weighted average is: ; where k1 is the cumulative variable, γ(h) represents the variation value corresponding to the distance h in the combined variogram, nk1 represents the number of light ranging point cloud data covered in the overlapping region, that is, the number of variograms; VFEff k1 is the variation fitting efficiency corresponding to the k1-th light ranging point cloud data, that is, the variation fitting efficiency obtained from the source data layer, and the value of is 1; γ k1 (h) represents the variogram corresponding to the k1-th light ranging point cloud data.

[0065] Based on the combined variogram, construct the Kriging equations and the spectral data in the original data. Take each grid point in the overlapping region in the first UTM coordinate system as the prediction position, and calculate the spectral data at each prediction position through the OrdinaryKriging algorithm.

[0066] Example 2

[0067] Example 2 uses the same method for processing joint surveying and mapping data information as in Example 1. The difference is that the method of calculating the variation fitting efficiency through the incident angle and laser reflection intensity is as follows: For any overlapping region, define the individual light ranging point cloud data that make up the overlapping region as the source data layer. Taking any source data layer as the current source data layer, the source data points of the current source data layer at each parsing point in the overlapping region are the source data points of the current source data layer;

[0068] Obtain the incident angles of each source data point. Define the sine value of the incident angle corresponding to the source data point as the incident efficiency EXF. Denote the standard deviation of all incident efficiencies as the efficiency deviation amount 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 this source data point and denote them as subordinate points. Denote the number of subordinate points as the efficiency subordination amount ESbd of this source data point. The difference between the maximum value of the laser reflection intensity among the subordinate points and the laser reflection intensity of this source data point is the subordination difference degree ESDis. Mark the source data points with an efficiency subordination amount greater than or equal to 1 as having a subordination relationship. Mark the source data point with the maximum efficiency subordination amount as the pointer data point.

[0069] Among them, the constraint condition for the value of the deviation conversion efficiency is: If the proportion of the source data points with a subordination relationship among all source data points is less than 50%, then define the deviation conversion result as false, otherwise define the deviation conversion result as true. The default value of the deviation conversion efficiency value is 0.5. When the deviation conversion result is false, increase the deviation conversion efficiency 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, then set the variation fitting efficiency to 0.

[0070] Take the difference between the laser reflection intensity of the pointer data point and that of any source data point, and denote the absolute value of the obtained difference as the pointer difference degree PXDis of this source data point. Calculate the variation fitting efficiency VFEff of the current source data layer according to the source data points with a subordination relationship and their pointer difference degrees:

[0071] ;

[0072] Among them, HS i1 <> is the harmonic mean function with i1 as the independent variable, ESbd i1 represents the efficiency subordination amount of the i1-th source data point with a subordination relationship, ESDis i1 and PXDis i1 respectively represent the subordination difference degree and the pointer difference degree of the i1-th source data point with a subordination relationship.

[0073] An integrated mapping data processing information system provided by an embodiment of the present invention, as Figure 2 shown is a structural diagram of an integrated mapping data processing information system of the present invention. An integrated 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, it implements the steps in the above-mentioned method embodiment of an integrated mapping data processing information method.

[0074] The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to run in the following units of the system:

[0075] A joint mapping raw data acquisition unit, configured to perform joint mapping of a target area through a LiDAR optical ranging method and Landsat spectral detection to obtain raw data;

[0076] A coincidence area identification unit, configured to identify the coincidence area of the optical ranging point cloud data in the raw data;

[0077] An analysis factor acquisition unit, configured to read the incident angle and laser reflection intensity of the point cloud data in the coincidence area;

[0078] A variation fitting analysis unit, configured to calculate the variation fitting efficiency through the incident angle and laser reflection intensity;

[0079] A spectral value prediction unit, configured to construct a Kriging interpolation model based on the variation fitting efficiency and predict spectral data.

[0080] The joint mapping data processing information system can run on computing devices such as desktop computers, laptop computers, palmtop computers, and cloud servers. The joint mapping data processing information system that can run may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above examples are merely examples of the joint mapping data processing information system, and do not constitute a limitation on the joint mapping data processing information system. It may include more or fewer components than the examples, or combine certain components, or different components. For example, the joint mapping data processing information system may further include input / output devices, network access devices, buses, etc.

[0081] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center for the operation system of the joint mapping data processing information system, and connects all parts of the operable system of the joint mapping data processing information system through various interfaces and lines.

[0082] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory, the processor realizes various functions of the joint mapping data processing information system. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0083] Although the description of the present invention has been quite detailed and several embodiments have been particularly described, 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 embodiments foreseeable by the inventor for the purpose of providing a useful description, and non-substantive modifications to the present invention that are not currently foreseeable may still represent equivalent modifications 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; The incident angle of each source data point is obtained, and all the incident angles are clustered by 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 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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