Land value space-time database design method

Abstract

The invention discloses a land value spatio-temporal database design method and system, which are applied to the technical field of spatial geographic information data processing and image correction and are used for acquiring spectral remote sensing image data. Performing geographic coordinate matching on the spectral remote sensing image data to generate coordinate spectral remote sensing image data; performing digital rasterization on the spectral remote sensing image data through a digital elevation model to generate point cloud data, and performing three-dimensional reconstruction according to the point cloud data to obtain a three-dimensional landform model; performing boundary region reconstruction processing on the three-dimensional landform model to obtain a reconstruction model; performing ground feature extraction on the reconstruction model to obtain ground feature data; performing spatio-temporal indexing on the ground feature data to construct a spatio-temporal database; according to the method, brightness correction can be carried out on edge defects of interval type and linear ground features according to predicted brightness changes, gray distortion of a three-dimensional image can be greatly reduced, and blur caused by interference is eliminated.

Description

Technical Field

[0001] The invention relates to the fields of spatial geographic information processing technology and image correction technology, and in particular to a land value spatiotemporal database design method. Background Art

[0002] In order to achieve accurate and timely acquisition and analysis of land resource value information, establishing a land resource mapping database is an effective way. However, traditional mapping database construction methods usually rely on field measurements and manual data collection, which leads to slow data updates and failure to reflect changes in land resources in a timely manner. In addition, when storing mapping data, conventional databases often find it difficult to deeply explore the spatial correlation and development trends in geographic information.

[0003] Therefore, in order to efficiently store and retrieve temporal geographic information, for example, a method and system for constructing a land resource mapping database provided by the Chinese invention patent with announcement number CN117611978B can integrate image information at different time points and generate multi-phase image data by performing time-series alignment and overlay processing on coordinate multispectral image data. By performing brightness correction on multi-phase images, the brightness difference between images at different time points can be eliminated to ensure data consistency. The brightness-corrected image data is subjected to three-dimensional modeling, thereby providing three-dimensional information of the surface, which can be used for terrain analysis, extraction of geomorphic features, and monitoring of environmental changes. The point cloud topography model is processed by residual reconstruction to repair possible data missing, noise or error, so as to generate a more accurate and complete topography model. However, when the loaded multi-phase spectral data is registered, time-series aligned and superimposed, the flares formed by strong light spots caused by the refraction and scattering of solar radiation in the atmosphere cause unclear information in some areas of the satellite remote sensing image. Because of the radiation of non-target pixels to target pixels or the interference between flares (the light intensity is weakened at this position and dark stripes appear), the edges of the objects in the image (the edges of the objects include the boundaries of the objects, terrain contours, geological linear structures and annular structures, etc.) become blurred, especially at the image boundaries where there are inevitably a large number of missing data, noise or errors that cannot be corrected. Summary of the invention The purpose of the present invention is to propose a land value spatiotemporal database design method and 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 designing a land value spatiotemporal database is provided, the method comprising the following steps: S100, acquiring spectral remote sensing image data; S200, performing geographic coordinate matching on the spectral remote sensing image data to generate coordinate spectral remote sensing image data; S300, digitally rasterizing the spectral remote sensing image data through a digital elevation model to generate point cloud data, and performing three-dimensional reconstruction based on the point cloud data to obtain a three-dimensional landform model; S400, performing boundary region reconstruction processing on the three-dimensional landform model to obtain a reconstructed model; S500, extracting ground objects from the reconstructed model to obtain ground object data; S600, performing spatiotemporal indexing on the feature data to construct a spatiotemporal database.

[0005] Further, in S100, the spectral remote sensing image data is any remote sensing image data of multispectral image data or hyperspectral image data of a preset region of interest (ROI).

[0006] Furthermore, in S200, the method of performing geographic coordinate matching on the spectral remote sensing image data to generate coordinate spectral remote sensing image data is: through the open source raster space data conversion library GDAL (Geospatial Data Abstraction Library), the conversion between the pixel coordinates of the spectral remote sensing image data and the geographic coordinates is realized to generate the coordinate spectral remote sensing image data, that is, the inverse geographic conversion.

[0007] Furthermore, in S300, the method for performing three-dimensional reconstruction based on point cloud data is: any one of a Poisson surface reconstruction algorithm, a three-dimensional point cloud surface reconstruction algorithm based on a Delaunay growth method, a greedy projection triangulation algorithm, or a surface reconstruction algorithm based on a B-spline curve.

[0008] In practical applications, due to the radiation of non-target pixels to target pixels, there are a lot of missing data, noisy or erroneous data at the boundaries, terrain contours, geological linear structures and annular structures of the spectral remote sensing images, and the locations where interference occurs between various flares (the light intensity is weakened at this location and dark stripes appear). As a result, the three-dimensional model obtained by the existing three-dimensional terrain model reconstruction technology inevitably becomes blurred at the boundary of the object or a large number of holes (corresponding perspective image loss) are generated at the edge of the object (the edge of the object is generally a common location for interference phenomena), which affects the accuracy of the three-dimensional reconstruction, resulting in inaccurate reconstruction results and serious distortion of the reconstructed three-dimensional model. To this end, the present application reconstructs the boundary of the object in the three-dimensional model by the following method to eliminate the above problems, specifically: Further, in S400, performing boundary region reconstruction processing on the three-dimensional landform model to obtain a reconstructed model includes the following steps: The three-dimensional landform model is grayed to obtain a grayscale model, that is, the grayscale value of each pixel on the grayscale model is the grayscale value of the pixel at the corresponding position of the spectral remote sensing image and the grayscale model after graying, and the grayscale model is segmented by the watershed algorithm to obtain each interval to be reconstructed composed of watershed lines; the average grayscale value of all pixels in the interval to be reconstructed is recorded as the grayscale mean of the interval to be reconstructed; the grayscale mean of the interval to be reconstructed with the smallest grayscale mean is taken as the reference grayscale; the grayscale mean of all pixels on the boundary of the interval to be reconstructed (i.e. the watershed line) is recorded as the boundary grayscale; the interval to be reconstructed with a boundary grayscale less than the reference grayscale is screened out and recorded as the interference interval; the light interference intensity of the interference interval is calculated, and the interference interval with a light interference intensity greater than the average light interference intensity of all interference intervals is marked as the affected interval; the affected intervals on the three-dimensional landform model are classified and reconstructed to obtain a reconstructed model. Among them, the method for calculating the light interference intensity of the interference interval is as follows: the average grayscale values ​​of all pixel points on the boundary of the interference interval in the most recent N time resolutions are collected in sequence and each collected average grayscale value is marked as follows in sequence: if the average grayscale value collected at the previous time resolution is less than the average grayscale value collected at the next time resolution, the difference between the average grayscale value collected at the next time resolution and the average grayscale value collected at the previous time resolution is calculated, and all these differences are marked as the brightening grayscale amplitude; if the average grayscale value collected at the previous time resolution is greater than the average grayscale value collected at the next time resolution, the difference between the average grayscale value collected at the previous time resolution and the average grayscale value collected at the next time resolution is calculated, and all these differences are marked as the darkening grayscale amplitude; the sum of all the darkening grayscale amplitudes is calculated as the darkening amplitude; the sum of all the brightening grayscale amplitudes is calculated as the brightening amplitude; the sum of the darkening amplitude and the brightening amplitude is taken as the total amplitude; the ratio of the darkening amplitude to the total amplitude is taken as the light interference intensity; The principle of light interference intensity is to infer the interference phenomenon of flares formed by strong light spots produced by the refraction and scattering of solar radiation in the atmosphere or the intensity of interference caused by noise on the edges of objects in spectral images by calculating the amplitude of grayscale changes within a preset time. If the value of light interference intensity is large, it means that it is in a state of strong interference, and the area near this boundary position will be greatly affected in the future, and reconstruction and repair must be performed; if the value is small, it means that the interference is weak, and the area near this boundary position will be affected in the future, and reconstruction and repair are not required.

[0009] Among them, N is the preset data, and the value range of N is [20, 100].

[0010] Further, classifying and reconstructing the affected intervals on the three-dimensional topographic model to obtain the reconstructed model includes the following steps: classifying and reconstructing each of the affected intervals on the three-dimensional topographic model in turn, specifically: selecting the interference interval with the smallest absolute value of the difference between the grayscale mean value of each interference interval adjacent to the boundary of the affected interval and the grayscale mean value of the current affected interval as the template area; (the template area is selected in order to select an area that is closest to the brightness of the affected interval before the affected interval, and the brightness change is relatively stable according to the light interference intensity as a reference area for repair, so that there is almost no visual difference after reconstruction and repair, and the distortion after reconstruction is smaller than the actual); If the light interference intensity of the template area is less than the average light interference intensity of all interference intervals, the repair type of the affected interval is classified as enhanced repair type; if the light interference intensity of the template area is greater than the average light interference intensity of all interference intervals, the repair type of the affected interval is classified as weakened repair type; If the restoration type is enhanced restoration, the average value of all brightening grayscale amplitudes in the template area is taken as the template brightening mean, and the grayscale values ​​of all pixels in the current affected interval are increased by the template brightening mean to obtain a reconstructed model; If the repair type is weakening repair type, the average value of all darkening grayscale amplitudes in the template area is taken as the template darkening mean value, and the grayscale values ​​of all pixels in the current affected interval are reduced by the template darkening mean value to obtain the reconstructed model.

[0011] The brightening grayscale amplitude of the template area is: obtain the average grayscale value of all pixels on the boundary of the template area in the most recent N time resolutions and mark each of the collected average grayscale values ​​in turn as follows: if the average grayscale value collected at the previous time resolution is less than the average grayscale value collected at the next time resolution, calculate the difference between the average grayscale value collected at the next time resolution and the average grayscale value collected at the previous time resolution, and mark all these differences as the brightening grayscale amplitude; Among them, the darkening grayscale amplitude of the template area is: obtain the average grayscale value of all pixels on the boundary of the template area in the most recent N time resolutions and mark each collected average grayscale value in turn as follows: if the average grayscale value collected at the previous time resolution is greater than the average grayscale value collected at the next time resolution, then calculate the difference between the average grayscale value collected at the previous time resolution and the average grayscale value collected at the next time resolution, and mark all these differences as the darkening grayscale amplitude.

[0012] The above method can correct the brightness of the edge defects of interval-type features with a certain area according to the predicted brightness changes, which can greatly reduce the grayscale distortion of the three-dimensional image and eliminate the blur of the remote sensing image caused by strong interference. However, for some linear terrain contours and geological linear structures, it is still easy to cause edge blur. Therefore, in order to solve the above problems, this application reconstructs the boundary through the following method: Preferably, classifying and reconstructing the affected intervals on the three-dimensional landform model to obtain a reconstructed model comprises the following steps: The interference interval with the smallest absolute value of the difference between the grayscale mean value of each interference interval adjacent to the boundary of the affected interval and the grayscale mean value of the current affected interval is selected as the template area; (the template area is selected to select an area that is closest to the brightness of the affected interval before the affected interval, and the brightness change is relatively stable according to the light interference intensity as a reference area for repair, so that there is almost no visual difference after reconstruction and repair, and the distortion after reconstruction is smaller than the actual one); The point with the largest grayscale value on the boundary of the template area is the maximum point; the point with the smallest grayscale value on the boundary line of the template area is the minimum point; the curve segment with the shortest length between the maximum point and the minimum point on the boundary of the template area is recorded as the reconstructed curve; the point with the smallest absolute value of the grayscale value difference with the maximum point on the boundary of the affected interval is recorded as the large projection point; the point with the smallest absolute value of the grayscale value difference with the minimum point on the boundary of the affected interval is recorded as the small projection point; delete the curve segment with the shortest length between the large projection point and the small projection point on the boundary of the affected interval; scale the reconstructed curve to a size where the length between the maximum point and the minimum point is equal to the length between the large projection point and the small projection point, and record it as the scaled curve; move the scaled curve to overlap between the maximum point and the large projection point, and overlap between the minimum point and the small projection point, so as to reconstruct the boundary of the affected interval; and obtain the reconstructed model.

[0013] Furthermore, in S500, the method for extracting objects from the reconstructed model to obtain object data is: using any one of the object extraction methods including ENVI threshold method, deep learning algorithm, analysis method based on mathematical morphology, texture feature analysis method, statistical analysis method, and regional growth method to obtain object data such as buildings, vegetation, and water bodies.

[0014] Furthermore, in S600, the method of constructing a spatiotemporal database by performing spatiotemporal indexing on the feature data includes: Perform three-dimensional occlusion detection on the ground object data and divide the object elements to obtain land data and terrain data respectively; The land data and terrain data are temporally processed to obtain temporal data; the temporal data are spatiotemporally indexed to construct a spatiotemporal database.

[0015] Among them, temporal processing is to add timestamp or time period attributes.

[0016] Among them, land data can be divided into three types: spatial feature data (positioning data), temporal attribute data (scale data) and thematic attribute data (non-positioning data).

[0017] Among them, terrain data includes terrain elements such as elevation models and contour lines.

[0018] Furthermore, in S600, the method of performing spatiotemporal indexing on temporal data to construct a spatiotemporal database specifically includes: Map the attribute values ​​of land data and terrain data into standardized codes and unify the units into geographic meta-attribute data; The spatiotemporal index of temporal data is designed through the quadtree index principle, and the temporal data and various geographic meta-attribute data are formed into a spatiotemporal database.

[0019] Preferably, the spatiotemporal database is deployed, specifically: the constructed spatiotemporal database is uploaded to the server through data migration tools, database backup and restoration, etc., and the spatiotemporal database is used for land value assessment.

[0020] Preferably, three-dimensional occlusion detection is implemented by the method in the following literature: Wu Di, Zhang Xudong, Zhang Jun, et al. Depth acquisition of light field images guided by 3D occlusion model [J]. Journal of Image and Graphics, 2021, 26(4):15.

[0021] The present invention also provides a land value spatiotemporal database design system, the system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the following system units: A remote sensing acquisition unit, used for acquiring spectral remote sensing image data; A coordinate matching unit, used for performing geographic coordinate matching on the spectral remote sensing image data to generate coordinate spectral remote sensing image data; A three-dimensional reconstruction unit is used to digitally rasterize the spectral remote sensing image data through a digital elevation model to generate point cloud data, and to perform three-dimensional reconstruction based on the point cloud data to obtain a three-dimensional landform model; A regional reconstruction unit, used for performing boundary region reconstruction processing on the three-dimensional landform model to obtain a reconstructed model; A ground object extraction unit, used for extracting ground objects from the reconstructed model to obtain ground object data; The database construction unit is used to construct a spatiotemporal database by spatiotemporal indexing of the feature data.

[0022] The beneficial effects of the present invention are as follows: the present invention provides a method and system for designing a spatiotemporal database of land value, which can correct the brightness of edge defects of interval-type and linear features according to predicted brightness changes, can significantly reduce the grayscale distortion of three-dimensional images, and eliminate blur caused by interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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 designing a spatiotemporal database of land value; Figure 2 The image shown is a three-dimensional model with dark stripes appearing due to interference phenomenon after reconstruction, which causes the light intensity to weaken at the boundary. Figure 3 The image shown is a 3D model image with a large number of holes and missing viewpoint images after reconstruction; Figure 4 The three-dimensional model image with dark stripes eliminated in Example 1 is shown; Figure 5 The three-dimensional model image in Example 2 is shown with the missing viewing angle image eliminated; Figure 6 Shown is a structural diagram of a land value spatiotemporal database design system. DETAILED DESCRIPTION

[0024] 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.

[0025] Example 1 like Figure 1 The flowchart of the land value spatiotemporal database design method according to the present invention is shown below. Figure 1 To illustrate a land value spatiotemporal database design method according to an implementation mode of the present invention.

[0026] The present invention proposes a land value spatiotemporal database design method, which specifically includes the following steps: S100, acquiring spectral remote sensing image data; S200, performing geographic coordinate matching on the spectral remote sensing image data to generate coordinate spectral remote sensing image data; S300, digitally rasterizing the spectral remote sensing image data through a digital elevation model to generate point cloud data, and performing three-dimensional reconstruction based on the point cloud data to obtain a three-dimensional landform model; S400, performing boundary region reconstruction processing on the three-dimensional landform model to obtain a reconstructed model; S500, extracting ground objects from the reconstructed model to obtain ground object data; S600, performing spatiotemporal indexing on the feature data to construct a spatiotemporal database.

[0027] Furthermore, in S100, the spectral remote sensing image data is a spectral remote sensing image in a Landsat-8-OLI_TIRS original remote sensing image dataset of a preset region of interest (ROI).

[0028] Furthermore, in S200, the method of performing geographic coordinate matching on the spectral remote sensing image data to generate coordinate spectral remote sensing image data is: through the open source raster space data conversion library GDAL (Geospatial Data Abstraction Library), the conversion between the pixel coordinates of the spectral remote sensing image data and the geographic coordinates is realized to generate the coordinate spectral remote sensing image data, that is, the inverse geographic conversion.

[0029] Furthermore, in S300, the method for performing three-dimensional reconstruction based on point cloud data is: Poisson surface reconstruction algorithm.

[0030] In practical applications, such as Figure 2 As shown in the figure, because of the radiation of non-target pixels to target pixels, there are a lot of missing data, noisy or erroneous data at the boundaries, terrain contours, geological linear structures and annular structures of the spectral remote sensing image, and the locations where interference occurs between various flares (the light intensity is weakened at this location and dark stripes appear). As a result, the three-dimensional model obtained by the existing three-dimensional landform model reconstruction technology inevitably becomes blurred at the boundaries of the objects or at the edges of the objects (the edges of the objects are generally the common locations of interference phenomena), such as Figure 3 As shown in the figure, a large number of holes will be generated (the corresponding perspective images are missing), which will affect the accuracy of 3D reconstruction, resulting in inaccurate reconstruction results and serious distortion of the reconstructed 3D model. To this end, the present application reconstructs the boundaries of the objects in the 3D model through the following method to eliminate the above problems, specifically: Further, in S400, the three-dimensional landform model is subjected to boundary area reconstruction processing to obtain a reconstructed model, including the following steps: graying the three-dimensional landform model to obtain a grayscale model, that is, setting the grayscale value of each pixel on the grayscale model to be the grayscale value of the pixel at the corresponding position of the spectral remote sensing image and the grayscale model after graying, segmenting the grayscale model by a watershed algorithm to obtain each interval to be reconstructed formed by watershed lines; recording the average grayscale value of all pixels in the interval to be reconstructed as the grayscale mean of the interval to be reconstructed; taking the grayscale mean of the interval to be reconstructed with the smallest grayscale mean as the reference grayscale; recording the grayscale mean of all pixels on the boundary of the interval to be reconstructed as the boundary grayscale; screening out the interval to be reconstructed whose boundary grayscale is less than the reference grayscale as the interference interval; calculating the light interference intensity of the interference interval, marking the interference interval whose light interference intensity is greater than the average light interference intensity of all interference intervals as the affected interval; and classifying and reconstructing the affected intervals on the three-dimensional landform model to obtain a reconstructed model. Among them, the method for calculating the light interference intensity of the interference interval is: the average grayscale values ​​of all pixel points on the boundary of the interference interval in the most recent N time resolutions are collected in turn, and each collected average grayscale value is marked as follows in turn: if the average grayscale value collected at the previous time resolution is less than the average grayscale value collected at the next time resolution, the difference between the average grayscale value collected at the next time resolution and the average grayscale value collected at the previous time resolution is calculated, and all these differences are marked as the brightening grayscale amplitude; if the average grayscale value collected at the previous time resolution is greater than the average grayscale value collected at the next time resolution, the difference between the average grayscale value collected at the previous time resolution and the average grayscale value collected at the next time resolution is calculated, and all these differences are marked as the darkening grayscale amplitude; the sum of all the darkening grayscale amplitudes is calculated as the darkening amplitude; the sum of all the brightening grayscale amplitudes is calculated as the brightening amplitude; the sum of the darkening amplitude and the brightening amplitude is taken as the total amplitude; the ratio of the darkening amplitude to the total amplitude is taken as the light interference intensity.

[0031] Furthermore, classifying and reconstructing the affected intervals on the three-dimensional topography model to obtain a reconstructed model includes the following steps: sequentially classifying and reconstructing each of the affected intervals on the three-dimensional topography model, specifically: selecting the interference interval with the smallest absolute value of the difference between the grayscale mean value of each interference interval adjacent to the boundary of the affected interval and the grayscale mean value of the current affected interval as the template area; If the light interference intensity of the template area is less than the average light interference intensity of all interference intervals, the repair type of the affected interval is classified as enhanced repair type; if the light interference intensity of the template area is greater than the average light interference intensity of all interference intervals, the repair type of the affected interval is classified as weakened repair type; If the restoration type is enhanced restoration, the average value of all brightening grayscale amplitudes in the template area is taken as the template brightening mean, and the grayscale values ​​of all pixels in the current affected interval are increased by the template brightening mean to obtain a reconstructed model; If the repair type is weakening repair type, the average value of all darkening grayscale amplitudes in the template area is taken as the template darkening mean value, and the grayscale values ​​of all pixels in the current affected interval are reduced by the template darkening mean value to obtain the reconstructed model.

[0032] like Figure 4 The three-dimensional model image of Example 1 with dark stripes eliminated is shown.

[0033] The brightening grayscale amplitude of the template area is: obtain the average grayscale value of all pixels on the boundary of the template area in the most recent N time resolutions and mark each of the collected average grayscale values ​​in turn as follows: if the average grayscale value collected at the previous time resolution is less than the average grayscale value collected at the next time resolution, calculate the difference between the average grayscale value collected at the next time resolution and the average grayscale value collected at the previous time resolution, and mark all these differences as the brightening grayscale amplitude; Among them, the darkening grayscale amplitude of the template area is: obtain the average grayscale value of all pixels on the boundary of the template area in the most recent N time resolutions and mark each collected average grayscale value in turn as follows: if the average grayscale value collected at the previous time resolution is greater than the average grayscale value collected at the next time resolution, then calculate the difference between the average grayscale value collected at the previous time resolution and the average grayscale value collected at the next time resolution, and mark all these differences as the darkening grayscale amplitude.

[0034] Among them, the value of N is 20.

[0035] Furthermore, in S500, the method of extracting ground objects from the reconstructed model to obtain ground object data is: using the ENVI threshold method to obtain building, vegetation, and water body data.

[0036] Furthermore, in S600, the method of constructing a spatiotemporal database by performing spatiotemporal indexing on the feature data includes: Perform three-dimensional occlusion detection on the ground object data and divide the object elements to obtain land data and terrain data respectively; The land data and terrain data are temporally processed to obtain temporal data; the temporal data are spatiotemporally indexed to construct a spatiotemporal database.

[0037] Among them, temporal processing is to add a timestamp attribute.

[0038] Among them, land data can be divided into three types: spatial feature data (positioning data), temporal attribute data (scale data) and thematic attribute data (non-positioning data).

[0039] Among them, terrain data: including elevation model and contour lines.

[0040] Furthermore, in S600, the method of performing spatiotemporal indexing on temporal data to construct a spatiotemporal database specifically includes: mapping the attribute values ​​of land data and terrain data into standardized codes and unifying the units into geographic meta-attribute data; performing spatiotemporal indexing design on the temporal data through the quadtree indexing principle, and forming a spatiotemporal database with the temporal data and each geographic meta-attribute data.

[0041] Preferably, the spatiotemporal database is deployed, specifically by uploading the constructed spatiotemporal database to the server through the MySQL Workbench data migration tool, and the spatiotemporal database is used for land value assessment.

[0042] Preferably, three-dimensional occlusion detection is implemented by the method in the following literature: Wu Di, Zhang Xudong, Zhang Jun, et al. Depth acquisition of light field images guided by 3D occlusion model [J]. Journal of Image and Graphics, 2021, 26(4):15.

[0043] Example 2 This embodiment 2 replaces the method of classifying and reconstructing the affected intervals on the three-dimensional landform model to obtain a reconstructed model based on the embodiment 1, specifically: Preferably, classifying and reconstructing the affected intervals on the three-dimensional landform model to obtain a reconstructed model comprises the following steps: Selecting the interference interval with the smallest absolute value of the difference between the grayscale mean value of each interference interval adjacent to the boundary of the affected interval and the grayscale mean value of the current affected interval as the template area; The point with the largest grayscale value on the boundary of the template area is the maximum point; the point with the smallest grayscale value on the boundary line of the template area is the minimum point; the curve segment with the shortest length between the maximum point and the minimum point on the boundary of the template area is recorded as the reconstructed curve; the point with the smallest absolute value of the grayscale value difference with the maximum point on the boundary of the affected interval is recorded as the large projection point; the point with the smallest absolute value of the grayscale value difference with the minimum point on the boundary of the affected interval is recorded as the small projection point; delete the curve segment with the shortest length between the large projection point and the small projection point on the boundary of the affected interval; scale the reconstructed curve to a size where the length between the maximum point and the minimum point is equal to the length between the large projection point and the small projection point, and record it as the scaled curve; move the scaled curve to overlap between the maximum point and the large projection point, and overlap between the minimum point and the small projection point, so as to reconstruct the boundary of the affected interval; and obtain the reconstructed model.

[0044] like Figure 5 The three-dimensional model image in Example 2 is shown with the missing perspective image eliminated.

[0045] The embodiment of the present invention provides a land value spatiotemporal database design system, such as Figure 6 Shown is a structural diagram of a land value spatiotemporal database design system of the present invention. A land value spatiotemporal database design 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 land value spatiotemporal database design system embodiment are implemented.

[0046] 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 remote sensing acquisition unit, used for acquiring spectral remote sensing image data; A coordinate matching unit, used for performing geographic coordinate matching on the spectral remote sensing image data to generate coordinate spectral remote sensing image data; A three-dimensional reconstruction unit is used to digitally rasterize the spectral remote sensing image data through a digital elevation model to generate point cloud data, and to perform three-dimensional reconstruction based on the point cloud data to obtain a three-dimensional landform model; A regional reconstruction unit, used for performing boundary region reconstruction processing on the three-dimensional landform model to obtain a reconstructed model; A ground object extraction unit, used for extracting ground objects from the reconstructed model to obtain ground object data; The database construction unit is used to construct a spatiotemporal database by spatiotemporal indexing of the feature data.

[0047] The land value spatiotemporal database design system can be run on computing devices such as desktop computers, notebooks, PDAs, and cloud servers. The land value spatiotemporal database design system can be operated on systems including, but not limited to, processors and memories. Those skilled in the art can understand that the example is only an example of a land value spatiotemporal database design system and does not constitute a limitation on a land value spatiotemporal database design system. It can include more or fewer components than the example, or combine certain components, or different components. For example, the land value spatiotemporal database design system can also include input and output devices, network access devices, buses, etc.

[0048] 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 land value spatiotemporal database design system operating system, and uses various interfaces and lines to connect the various parts of the entire land value spatiotemporal database design system operating system.

[0049] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the land value spatiotemporal database design 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, 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.

[0050] 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 designing a spatiotemporal database of land value, characterized in that: The method comprises the following steps: S100, acquiring spectral remote sensing image data; S200, performing geographic coordinate matching on the spectral remote sensing image data to generate coordinate spectral remote sensing image data; S300, digitally rasterizing the spectral remote sensing image data through a digital elevation model to generate point cloud data, and performing three-dimensional reconstruction based on the point cloud data to obtain a three-dimensional landform model; S400, performing boundary region reconstruction processing on the three-dimensional landform model to obtain a reconstructed model; S500, extracting ground objects from the reconstructed model to obtain ground object data; S600, performing spatiotemporal indexing on the feature data to construct a spatiotemporal database.

2. A land value spatiotemporal database design method according to claim 1, characterized in that: In S400, performing boundary area reconstruction processing on the three-dimensional landform model to obtain a reconstructed model includes the following steps: The three-dimensional landform model is grayed to obtain a grayscale model, that is, the grayscale value of each pixel on the grayscale model is the grayscale value of the pixel at the corresponding position of the spectral remote sensing image and the grayscale model after graying, and the grayscale model is segmented by a watershed algorithm to obtain each interval to be reconstructed composed of watershed lines; the average grayscale value of all pixels in the interval to be reconstructed is recorded as the grayscale mean of the interval to be reconstructed; the grayscale mean of the interval to be reconstructed with the smallest grayscale mean is recorded as the reference grayscale; the grayscale mean of all pixels on the boundary of the interval to be reconstructed is recorded as the boundary grayscale; the interval to be reconstructed with a boundary grayscale less than the reference grayscale is screened out and recorded as an interference interval; the light interference intensity of the interference interval is calculated, and the interference interval with a light interference intensity greater than the average light interference intensity of all interference intervals is marked as an affected interval; the affected intervals on the three-dimensional landform model are classified and reconstructed to obtain a reconstructed model.

3. A land value spatiotemporal database design method according to claim 2, characterized in that: In S400, a method for calculating the light interference intensity of an interference interval is provided; the average grayscale values ​​of all pixel points on the boundary of the interference interval in the most recent N time resolutions are collected in turn, and each collected average grayscale value is marked as follows in turn: if the average grayscale value collected at the previous time resolution is less than the average grayscale value collected at the next time resolution, the difference between the average grayscale value collected at the next time resolution and the average grayscale value collected at the previous time resolution is calculated, and all these differences are marked as the brightening grayscale amplitude; if the average grayscale value collected at the previous time resolution is greater than the average grayscale value collected at the next time resolution, the difference between the average grayscale value collected at the previous time resolution and the average grayscale value collected at the next time resolution is calculated, and all these differences are marked as the darkening grayscale amplitude; the sum of all the darkening grayscale amplitudes is calculated as the darkening amplitude; the sum of all the brightening grayscale amplitudes is calculated as the brightening amplitude; the sum of the darkening amplitude and the brightening amplitude is taken as the total amplitude; the ratio of the darkening amplitude to the total amplitude is taken as the light interference intensity, wherein the value range of N is [20, 100].

4. A land value spatiotemporal database design method according to claim 2, characterized in that: In S400, classifying and reconstructing the affected intervals on the three-dimensional landform model to obtain a reconstructed model includes the following steps: The affected intervals on the three-dimensional topographic model are classified and reconstructed in turn, specifically: the interference interval with the smallest absolute value of the difference between the grayscale mean of each interference interval adjacent to the boundary of the affected interval and the grayscale mean of the current affected interval is selected as the template area.

5. A land value spatiotemporal database design method according to claim 4, characterized in that: In S400, if the light interference intensity of the template area is less than the average light interference intensity of all interference intervals, the repair type of the affected interval is classified as the enhanced repair type; if the light interference intensity of the template area is greater than the average light interference intensity of all interference intervals, the repair type of the affected interval is classified as the weakened repair type; If the restoration type is enhanced restoration, the average value of all brightening grayscale amplitudes in the template area is taken as the template brightening mean, and the grayscale values ​​of all pixels in the current affected interval are increased by the template brightening mean to obtain a reconstructed model; If the repair type is weakening repair type, the average value of all darkening grayscale amplitudes in the template area is taken as the template darkening mean value, and the grayscale values ​​of all pixels in the current affected interval are reduced by the template darkening mean value to obtain the reconstructed model.

6. A method for designing a spatiotemporal database of land value according to claim 5, characterized in that: In S400, the brightening grayscale amplitude of the template area is: obtain the average grayscale value of all pixels on the boundary of the template area in the most recent N time resolutions and mark each collected average grayscale value in turn as follows: if the average grayscale value collected at the previous time resolution is less than the average grayscale value collected at the next time resolution, calculate the difference between the average grayscale value collected at the next time resolution and the average grayscale value collected at the previous time resolution, and mark all these differences as brightening grayscale amplitudes.

7. A method for designing a spatiotemporal database of land value according to claim 4, characterized in that: In S400, the method of performing classification and reconstruction processing on the affected intervals on the three-dimensional landform model to obtain a reconstructed model is replaced by the following steps: The interference interval with the smallest absolute value of the difference between the grayscale mean value of each interference interval adjacent to the boundary of the affected interval and the grayscale mean value of the current affected interval is selected as the template area; the point with the largest grayscale value on the boundary of the template area is the maximum point; the point with the smallest grayscale value on the boundary line of the template area is the minimum point; the curve segment with the smallest length between the maximum point and the minimum point on the boundary of the template area is recorded as the reconstructed curve; the point with the smallest absolute value of the grayscale value difference with the maximum point on the boundary of the affected interval is recorded as the large projection point; the point with the smallest absolute value of the grayscale value difference with the minimum point on the boundary of the affected interval is recorded as the small projection point; delete the curve segment with the smallest length between the large projection point and the small projection point on the boundary of the affected interval; scale the reconstructed curve to a size where the length between the maximum point and the minimum point is equal to the length between the large projection point and the small projection point, and record it as the scaled curve; move the scaled curve to overlap between the maximum point and the large projection point, and overlap between the minimum point and the small projection point, so as to reconstruct the boundary of the affected interval; and obtain the reconstructed model.

8. A land value spatiotemporal database design system, characterized in that: The land value spatiotemporal database design system comprises: 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 any one of the land value spatiotemporal database design methods in claims 1-7 are implemented.

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