Geographic information GIS database dynamic updating method and system

Abstract

The invention relates to the technical field of geographic information dynamic updating, in particular to a geographic information GIS database dynamic updating method and system, and the method comprises the steps: determining a continuous variation based on the difference between remote sensing grey-scale maps of the same region at each collection moment and a previous collection moment; determining a sensitive index based on the overall distribution condition of the continuous variation at all the acquisition moments in each region; determining the degree of interest based on the distribution condition of the gray values of all the pixel points in each region at each acquisition moment; and updating the geographic information GIS database on the basis of the discrete degree of the continuous variation of all the acquisition moments in the adjacent front and back period periods of any period period of each region in combination with the sensitive index and the interested degree. The method aims to distinguish the sensitivities of different areas, realize reasonable allocation of resources, and improve the resource utilization rate in the dynamic updating process of the geographic information GIS database.

Description

Technical Field

[0001] The present application relates to the field of dynamic updating technology of geographic information, and in particular to a method and system for dynamically updating a geographic information GIS database. Background Art

[0002] With the continuous development of science and technology, geographic information system (GIS) has become an indispensable tool in all walks of life in modern society. GIS database provides powerful technical support for urban planning, resource management, environmental protection, disaster warning and other fields by efficiently storing, processing and analyzing spatial data. In particular, image geographic information, as an important data type in GIS database, plays an irreplaceable role in navigation, remote sensing monitoring, map making and other fields. For example, satellite images and aerial images are widely used in surface change monitoring, agricultural precision planting, urban expansion analysis and other aspects. With the continuous increase of data sources and the increase of update frequency, the management and update of image geographic data has become particularly important.

[0003] Existing GIS database update methods face many technical challenges when processing image data. Current image data updates mostly rely on overall updates, that is, the new image data completely replaces the old data. This method requires processing a large amount of image data, resulting in a huge amount of calculation. Especially in the case of high-resolution remote sensing image data, the amount of data and the complexity of calculation grow exponentially. In many cases, only the image data of some areas has changed. Overall updates will cause a waste of resources during the update process, reducing the resource utilization rate during the dynamic update of geographic information GIS databases. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method and system for dynamically updating a geographic information GIS database. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for dynamically updating a geographic information GIS database, the method comprising the following steps:

[0006] Obtain remote sensing grayscale images of all acquisition times of the target area within a preset time period before the current time from the geographic information GIS database;

[0007] Based on the difference between the remote sensing grayscale image at each acquisition moment and the previous acquisition moment, an overlay image of the target area is constructed, the overlay image is segmented into multiple regions and the segmented form is mapped to the remote sensing grayscale image at each acquisition moment;

[0008] Based on the difference between the remote sensing grayscale images of the same area at each acquisition time and the previous acquisition time, the continuous change amount of the same area at each acquisition time is determined; based on the distribution of the continuous change amount of all acquisition times in each area in the frequency domain, the period of each area is determined; based on the difference between the continuous change amount of each acquisition time in any period of each area and the overall distribution of the continuous change amount of all acquisition times, the sensitivity index of any period of each area is determined;

[0009] Based on the distribution of the grayscale values ​​of all pixels in each area at each acquisition moment, the interest level of each area is determined; based on the discrete degree of the continuous change of all acquisition moments in the adjacent previous and next cycle periods of each area, the sensitivity weight of any cycle period of each area is determined, and the comprehensive sensitivity coefficient of each area is determined in combination with the sensitivity index and the interest level, and the geographic information GIS database after the current moment is updated.

[0010] Preferably, the process of constructing the superimposed image of the target area is:

[0011] The remote sensing grayscale image of the target area at each acquisition time and the previous acquisition time is used as the input of the frame difference method, and the frame difference image of the target area at each acquisition time is output;

[0012] The result of adding all the frame difference images of the target area is used as the superimposed image of the target area.

[0013] Preferably, the expression of the continuous change amount of the same area at each acquisition time is: i,j =exp(B i,j );where A i,j represents the continuous change of area j at the acquisition time i; B i,j It represents the Bhattacharyya distance of the grayscale histogram of area j between the acquisition time i and its previous acquisition time; exp() represents an exponential function with a natural constant as the base.

[0014] Preferably, the method for determining the period of each area is:

[0015] The continuous changes of all acquisition moments in each region are used as the input of the time-frequency conversion algorithm, and the spectrum of each region is output. The inverse of the frequency corresponding to the maximum peak in the spectrum is used as the period of each region.

[0016] Preferably, the method for determining the sensitivity index of any period of each region is:

[0017] The mean of the continuous changes of all the acquisition moments in any periodic period of each region is calculated, and the acquisition moment corresponding to the continuous change with the smallest difference from the mean of the continuous change in the periodic period is taken as the close moment, and the reciprocal of the time interval between the close moment and the middle moment of the periodic period is taken as the sensitivity index of any periodic period of each region.

[0018] Preferably, the method for determining the interest level of each area is:

[0019] The mean grayscale value of all pixels in each region at each acquisition time is calculated, recorded as the mean grayscale value of each region at each acquisition time, and the average of the mean grayscale values ​​of each region at all acquisition times is taken as the interest level of each region.

[0020] Preferably, the sensitivity weight of any periodic period of each region is a normalized value of the variance of the continuous changes of all acquisition moments in the adjacent periodic periods before and after any periodic period of each region.

[0021] Preferably, the expression of the comprehensive sensitivity coefficient of each area is: In the formula, C j Indicates the comprehensive sensitivity coefficient of area j; IN j represents the interest level of region j; Indicates the mean value of the interest level of all regions; D j,n represents the sensitivity index of the nth period in region j; E j,n N represents the sensitivity weight of the nth period in region j; j represents all periodic periods in region j; norm() represents the normalization function; exp() represents the exponential function with a natural constant as the base.

[0022] Preferably, the updating of the geographic information GIS database after the current moment includes:

[0023] Update period T′ of region j j The expression is: Where, T j represents the period of region j; represents the ceiling function;

[0024] The process of updating the remote sensing image within the same preset time after the current moment is: starting from the current moment, when the integer multiple of the update period of area j is reached, a new remote sensing image of the target area at the moment corresponding to the integer multiple of the update period is obtained, and the image of area j in the corresponding target area in the geographic information GIS database is replaced with the image of area j in the new remote sensing image.

[0025] In the second aspect, an embodiment of the present application also provides a geographic information GIS database dynamic update system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements the steps of any one of the above-mentioned methods for dynamically updating a geographic information GIS database.

[0026] This application has at least the following beneficial effects:

[0027] This application constructs a continuous change quantity by analyzing the differences between remote sensing grayscale images of the same area at different acquisition times. The beneficial effect is that the continuous change quantity can quantify the degree of information change of each area at different acquisition times, which helps to identify the change pattern of regional information and reduce the focus on areas that are not sensitive to changes, thereby saving resources in the update process of the geographic information database; this application constructs a sensitivity index by analyzing the distribution of the continuous change quantity of each area within a period. The beneficial effect is that according to the sensitivity index, the update frequency of each area can be determined to ensure that highly sensitive areas are updated more promptly, reduce the update frequency of low-sensitivity areas, and save the update process of the geographic information database. resources in the process; the present application constructs the degree of interest by analyzing the distribution of the gray values ​​of all pixels in each area, which has the beneficial effect of helping to identify the importance of the area, thereby realizing the effective allocation of resources; the present application constructs the comprehensive sensitivity coefficient by comprehensively analyzing the degree of interest, the sensitivity index, and the discrete degree of the continuous change at all acquisition moments in adjacent cycle time periods, which has the beneficial effect of reasonably allocating resources according to the comprehensive sensitivity coefficient, giving priority to high-sensitivity areas, thereby improving the utilization of resources. The present application realizes the reasonable allocation of resources by distinguishing the sensitivity of different areas, and improves the resource utilization in the dynamic update process of the geographic information GIS database. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A flowchart of a method for dynamically updating a geographic information GIS database provided in one embodiment of the present application;

[0030] Figure 2 A flow chart of area division provided for one embodiment of the present application;

[0031] Figure 3A schematic diagram of a sensitivity index acquisition process provided for an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of a method and system for dynamically updating a geographic information GIS database proposed by the present application, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0034] The following is a detailed description of a specific solution of a method and system for dynamically updating a geographic information GIS database provided by the present application in conjunction with the accompanying drawings.

[0035] See also Figure 1 , which shows a flowchart of a method for dynamically updating a geographic information GIS database provided by an embodiment of the present application, the method comprising the following steps:

[0036] Step S1: Obtain remote sensing grayscale images of all acquisition times of the target area within a preset time period before the current time from the geographic information GIS database.

[0037] (1) In the geographic information GIS database, a coordinate frame is used to obtain remote sensing images of the target area at all acquisition times within a preset time period before the current time, where the remote sensing image sampling interval is set to H.

[0038] It should be noted that the preset duration and the remote sensing image sampling interval H are both manually set. In this embodiment, the preset duration is 365 days, and the remote sensing image sampling interval H is 1 day. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0039] (2) Furthermore, in order to eliminate the noise in the remote sensing image, the remote sensing image is used as the input of the filtering algorithm, and the remote sensing image after the noise is removed is output.

[0040] It should be understood that there are many commonly used filtering algorithms. In this embodiment, a Gaussian filtering algorithm is used to denoise the remote sensing image. The implementer may also use other filtering algorithms such as a median filtering algorithm or a mean filtering algorithm. This embodiment does not impose any special restrictions on the selection of the filtering algorithm.

[0041] Among them, the Gaussian filtering algorithm is a well-known technology in the field of image processing, and its specific denoising process is not repeated here.

[0042] (3) Furthermore, in order to simplify the analysis, the remote sensing images of the target area at all acquisition times are converted into remote sensing grayscale images.

[0043] Step S2: Based on the difference between the remote sensing grayscale image at each acquisition moment and the previous acquisition moment, an overlay image of the target area is constructed, the overlay image is segmented into multiple regions and the segmented form is mapped to the remote sensing grayscale image at each acquisition moment.

[0044] The purpose of updating geographic information is to ensure the timeliness of information changes and to record and update the changed information. In remote sensing images, because the area involved in each region is generally large, when updating the image, since the image corresponding to the area has only partially changed, only the information of the changed part is updated.

[0045] Perform local analysis on the remote sensing images of a certain area, analyze the interest level of different local areas and the sensitivity of image information changes, and then formulate targeted update strategies based on the interest level of different local areas and the sensitivity of image information changes, and perform local updates on remote sensing images, thereby reducing the computational loss of database updates. Specifically:

[0046] (1) The remote sensing grayscale image of the target area at each acquisition time and the previous acquisition time is used as the input of the frame difference method, and the frame difference image of the target area at each acquisition time is output;

[0047] It should be noted that the principle of the frame difference method is to use the absolute value of the difference between the grayscale value of the corresponding pixel in the remote sensing grayscale image of the target area at each acquisition time and the remote sensing grayscale image of the target area at the previous acquisition time as the new pixel value of the corresponding pixel, and form a so-called frame difference image.

[0048] Among them, the frame difference method is a well-known technology in the field of image processing, and its specific principle will not be described in detail.

[0049] (2) Further, the result of adding all the frame difference images of the target area is used as the superimposed image of the target area.

[0050] It should be understood that the adding of the frame difference images at all acquisition moments specifically refers to adding the pixel values ​​of corresponding pixel points in the frame difference images at all acquisition moments to obtain a final superimposed image.

[0051] (3) Furthermore, since in the target area, the same structures such as forests, roads, and crop fields generally have the same change trend, this embodiment uses the overlay image of the target area as the input of the tile segmentation algorithm and outputs multiple regions in the overlay image, that is, the overlay image is divided into multiple image blocks. Each image block can also be called a region, so the overlay image is divided into multiple regions here.

[0052] According to the segmentation method of the superimposed image, the segmentation forms of multiple regions in the superimposed image are mapped to the remote sensing grayscale images at all acquisition moments, so that the remote sensing grayscale images at each acquisition moment also correspond to multiple regions.

[0053] The tile segmentation algorithm is a well-known technology in the field of image processing, and its specific segmentation principle will not be described in detail in this embodiment.

[0054] Preferably, the area division flow chart provided in this embodiment is as follows: Figure 2 shown.

[0055] S3: Based on the difference between the remote sensing grayscale images of the same area at each acquisition time and the previous acquisition time, the continuous change of the same area at each acquisition time is determined; based on the distribution of the continuous change of all acquisition times of each area in the frequency domain, the period of each area is determined; based on the difference between the continuous change of each acquisition time in any period of each area and the overall distribution of the continuous change of all acquisition times, the sensitivity index of any period of each area is determined.

[0056] In different areas of the target region, not all areas have significant periodic change characteristics in the information changes of remote sensing images. Generally speaking, there are two types of information changes in different areas. One is that the information in the area changes completely randomly, and the other is that the information in the area changes regularly within a fixed periodic time period. The more regular the time of regional information changes, the more sensitive the area is to external information changes, and more attention should be paid to it in the process of updating geographic information. On the contrary, if the regional information changes more randomly, it means that the area is less sensitive to external information changes and can be given less attention.

[0057] Therefore, this embodiment determines the continuous change amount of the same area at each acquisition time by analyzing the difference between the remote sensing grayscale images of the same area at different acquisition times, determines the period of each area based on the distribution of the continuous change amount of all acquisition times in each area in the frequency domain, and determines the sensitivity index of any period of each area based on the difference between the continuous change amount of each acquisition time in any period of each area and the overall distribution of the continuous change amount of all acquisition times, so as to judge the sensitivity of the corresponding area to external information. Specifically:

[0058] (1) Based on the difference between the remote sensing grayscale image of the same area at each acquisition time and the previous acquisition time, the continuous change amount of the same area at each acquisition time is determined, specifically:

[0059] The expression of the continuous change of the same area at each acquisition time is: A i,j =exp(B i,j );where A i,j represents the continuous change of area j at the acquisition time i; B i,j It represents the Bhattacharyya distance of the grayscale histogram of area j between the acquisition time i and its previous acquisition time; exp() represents an exponential function with a natural constant as the base.

[0060] It should be noted that the grayscale histogram acquisition process and the Bhattacharyya distance calculation steps are both well-known technologies, and the specific grayscale histogram acquisition process and the specific Bhattacharyya distance calculation process are not repeated here.

[0061] According to the continuous change amount of the same area at each acquisition time, it can be understood that if the difference between the corresponding parts of the area between the current acquisition time and the previous acquisition time on the remote sensing grayscale map is greater, that is, the Bhattacharyya distance of the grayscale histogram of the area between the current acquisition time and the previous acquisition time is greater, then the continuous change amount is greater, indicating that the area is more sensitive to changes in external information; conversely, if the difference between the corresponding parts of the area between the current acquisition time and the previous acquisition time on the remote sensing grayscale map is smaller, that is, the Bhattacharyya distance of the grayscale histogram of the area between the current acquisition time and the previous acquisition time is smaller, then the continuous change amount is smaller, indicating that the area is less sensitive to changes in external information.

[0062] (2) Further, based on the distribution of the continuous changes of all acquisition moments in each region in the frequency domain, the period of each region is determined, specifically:

[0063] The continuous changes of all acquisition moments in each region are used as the input of the time-frequency conversion algorithm, and the spectrum of each region is output. The inverse of the frequency corresponding to the maximum peak in the spectrum is used as the period of each region.

[0064] It should be noted that there are many commonly used time-frequency conversion algorithms. Discrete Fourier transform is used in this embodiment, and the implementer also uses other time-frequency conversion algorithms such as wavelet transform. This embodiment does not impose any special restrictions on the selection of time-frequency conversion algorithms.

[0065] Among them, discrete Fourier transform is a well-known technology in the field of signal processing, and the specific process of converting time domain signals into frequency domain is not repeated here.

[0066] (3) Further, based on the difference between the continuous change amount at each acquisition time in any period of each region and the overall distribution of the continuous change amount at all acquisition times, the sensitivity index of each region in any period of each region is determined to judge the sensitivity of the region to changes in external information, specifically:

[0067] The mean of the continuous changes of all the acquisition moments in any periodic period of each region is calculated, and the acquisition moment corresponding to the continuous change with the smallest difference from the mean of the continuous change in the periodic period is taken as the close moment, and the reciprocal of the time interval between the close moment and the middle moment of the periodic period is taken as the sensitivity index of any periodic period of each region.

[0068] Among them, the periodic period refers to the time period occupied by the period. If a signal changes periodically, it will have many periods with the same regular changes.

[0069] According to the sensitivity index of any periodic period in each area, it can be understood that when the period is shorter, and the difference between the continuous change amount of each acquisition moment in the periodic period and the mean of the continuous change amount of all acquisition moments in the periodic period is smaller, and the distance between the similar moments and the middle moment of the periodic period is closer, the sensitivity index is larger, indicating that the area is more sensitive to changes in external information; conversely, when the period is longer, and the difference between the continuous change amount of each acquisition moment in the periodic period and the mean of the continuous change amount of all acquisition moments in the periodic period is larger, and the distance between the similar moments and the middle moment of the periodic period is farther, the sensitivity index is smaller, indicating that the area is less sensitive to changes in external information.

[0070] Step S4: Based on the distribution of the grayscale values ​​of all pixels in each area at each acquisition moment, determine the interest level of each area; based on the discrete degree of the continuous change of all acquisition moments in the adjacent previous and next cycle periods of any cycle period of each area, determine the sensitivity weight of any cycle period of each area, and combine the sensitivity index and the interest level to determine the comprehensive sensitivity coefficient of each area.

[0071] Based on step S2, the remote sensing grayscale image at each acquisition time is divided into multiple areas, but not all areas need to be paid equal attention. For areas with a long change time interval and a small change degree, that is, areas that are not sensitive to changes in external information, the analysis of the area can be appropriately reduced, and the areas with a large change degree and a fast change speed, that is, areas that are not sensitive to changes in external information, should be analyzed in detail. Therefore, this embodiment determines the interest level of each area by analyzing the distribution of the grayscale values ​​of all pixels in each area at each acquisition time. Based on the discrete degree of the continuous change amount of all acquisition moments in the adjacent previous and next cycle periods of any cycle period of each area, the sensitivity weight of any cycle period of each area is determined, and the comprehensive sensitivity coefficient of each area is determined in combination with the sensitivity index and the interest level, so as to identify the area sensitive to external information, focus on analyzing the area, and reduce the waste in the resource update process. Specifically:

[0072] (1) Based on the distribution of the grayscale values ​​of all pixels in each area at each acquisition time, the degree of interest of each area is determined, specifically:

[0073] The mean grayscale value of all pixels in each region at each acquisition time is calculated, recorded as the mean grayscale value of each region at each acquisition time, and the average of the mean grayscale values ​​of each region at all acquisition times is taken as the interest level of each region.

[0074] (2) Further, based on the discrete degree of the continuous change of all acquisition moments in the adjacent and preceding cycle periods of any cycle period in each region, the sensitivity weight of any cycle period in each region is determined, specifically:

[0075] The variance of the continuous changes of all acquisition moments in the adjacent and preceding cycle periods of any cycle period in each region is normalized and used as the sensitivity weight of any cycle period in each region.

[0076] (3) Further, based on the sensitivity weight of each region in any period, and in combination with the sensitivity index and the interest level, a comprehensive sensitivity coefficient of each region is determined, specifically:

[0077] The comprehensive sensitivity coefficient C of region j j The expression is: In the formula, IN j represents the interest level of region j; Indicates the mean value of the interest level of all regions; D j,n represents the sensitivity index of the nth period in region j; E j,n N represents the sensitivity weight of the nth period in region j; jrepresents all periodic periods in region j; norm() represents the normalization function; exp() represents the exponential function with a natural constant as the base.

[0078] According to the comprehensive sensitivity coefficient of each region, it can be understood that the more regular the information changes in the region, the more sensitive the region is to changes in external information. Therefore, the larger the sensitivity index, the larger the sensitivity weight, the greater the difference between the interest level of the region and the mean interest level of all regions, the larger the comprehensive sensitivity coefficient of the region, and the more necessary it is to make timely adjustments to the geographic information of the region; conversely, if the information changes in the region are more random, the less sensitive the region is to changes in external information. Therefore, the smaller the sensitivity index, the smaller the sensitivity weight, the smaller the difference between the interest level of the region and the mean interest level of all regions, the smaller the comprehensive sensitivity coefficient of the region, and the less necessary it is to adjust the geographic information of the region.

[0079] Preferably, the schematic diagram of the sensitivity index acquisition process provided in this embodiment is as follows: Figure 3 shown.

[0080] Step S5: Based on the comprehensive sensitivity coefficient of each area, the geographic information GIS database after the current moment is updated.

[0081] Based on step S4, the comprehensive sensitivity coefficient of each region is obtained. Therefore, based on the period and comprehensive sensitivity coefficient of each region, the geographic information GIS database is updated. Specifically:

[0082] Update period T′ of region j j The expression is: Where, T j represents the period of region j; represents the ceiling function;

[0083] Taking the current moment as the starting point, the remote sensing images within the same preset time after the current moment are updated. Specifically, taking the current moment as the starting point, when the integer multiple of the update period of area j is reached, a new remote sensing image of the target area at the moment corresponding to the integer multiple of the update period is obtained, and the image of area j in the corresponding target area in the geographic information GIS database is replaced with the image of area j in the new remote sensing image.

[0084] It should be noted that since geographic information usually needs to be collected through satellite images, aerial photography, ground measurements, etc., it often takes a long time, and geographic information does not change in real time. For example, topographic maps may only need to be updated every few years because the changes are very slow, and the changes in geographic information have a certain periodicity, and the periods in different regions are different. Therefore, this embodiment obtains remote sensing images within a preset time period before the current moment, and uses historical data to predict the information update period of different regions in each region within the same preset time period after the current moment, and updates the geographic information at times that are integer multiples of the period, thereby improving resource utilization and reducing resource waste during the update process.

[0085] Based on the same inventive concept as the above method, an embodiment of the present application also provides a geographic information GIS database dynamic update system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned geographic information GIS database dynamic update methods.

[0086] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0088] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for dynamically updating a geographic information GIS database, characterized in that: The method comprises the following steps: Obtain remote sensing grayscale images of all acquisition times of the target area within a preset time period before the current time from the geographic information GIS database; Based on the difference between the remote sensing grayscale image at each acquisition moment and the previous acquisition moment, an overlay image of the target area is constructed, the overlay image is segmented into multiple regions and the segmented form is mapped to the remote sensing grayscale image at each acquisition moment; Based on the difference between the remote sensing grayscale images of the same area at each acquisition time and the previous acquisition time, the continuous change amount of the same area at each acquisition time is determined; based on the distribution of the continuous change amount of all acquisition times in each area in the frequency domain, the period of each area is determined; based on the difference between the continuous change amount of each acquisition time in any period of each area and the overall distribution of the continuous change amount of all acquisition times, the sensitivity index of any period of each area is determined; Based on the distribution of the grayscale values ​​of all pixels in each area at each acquisition moment, the interest level of each area is determined; based on the discrete degree of the continuous change of all acquisition moments in the adjacent previous and next cycle periods of each area, the sensitivity weight of any cycle period of each area is determined, and the comprehensive sensitivity coefficient of each area is determined in combination with the sensitivity index and the interest level, and the geographic information GIS database after the current moment is updated.

2. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The construction process of the superimposed image of the target area is as follows: The remote sensing grayscale image of the target area at each acquisition time and the previous acquisition time is used as the input of the frame difference method, and the frame difference image of the target area at each acquisition time is output; The result of adding all the frame difference images of the target area is used as the superimposed image of the target area.

3. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The expression of the continuous change amount of the same area at each acquisition time is: i,j =exp(B i,j );where A i,j represents the continuous change of area j at the acquisition time i; B i,j It represents the Bhattacharyya distance of the grayscale histogram of area j between the acquisition time i and its previous acquisition time; exp() represents an exponential function with a natural constant as the base.

4. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The method for determining the period of each area is: The continuous changes of all acquisition moments in each region are used as the input of the time-frequency conversion algorithm, and the spectrum of each region is output. The inverse of the frequency corresponding to the maximum peak in the spectrum is used as the period of each region.

5. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The method for determining the sensitivity index of each region in any period is as follows: The mean of the continuous changes of all the acquisition moments in any periodic period of each region is calculated, and the acquisition moment corresponding to the continuous change with the smallest difference from the mean of the continuous change in the periodic period is taken as the close moment, and the reciprocal of the time interval between the close moment and the middle moment of the periodic period is taken as the sensitivity index of any periodic period of each region.

6. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The method for determining the interest level of each region is as follows: The mean grayscale value of all pixels in each region at each acquisition time is calculated, recorded as the mean grayscale value of each region at each acquisition time, and the average of the mean grayscale values ​​of each region at all acquisition times is taken as the interest level of each region.

7. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The sensitivity weight of any periodic period of each region is a normalized value of the variance of the continuous changes of all acquisition moments in the periodic periods before and after any periodic period of each region.

8. A method for dynamically updating a geographic information GIS database as claimed in claim 1, characterized in that: The expression of the comprehensive sensitivity coefficient of each area is: In the formula, C j Indicates the comprehensive sensitivity coefficient of region j; IN j represents the interest level of region j; Indicates the mean value of the interest level of all regions; D j，n represents the sensitivity index of the nth period of region j; E j，n represents the sensitivity weight of the nth period in region j; Nj represents all periodic periods in region j; norm() represents the normalization function; exp() represents the exponential function with a natural constant as the base.

9. A method for dynamically updating a geographic information GIS database as claimed in claim 8, characterized in that: The geographic information GIS database after the current moment is updated, including: Update period T′ of region j j The expression is: Where, T j represents the period of region j; represents the ceiling function; The process of updating the remote sensing image within the same preset time after the current moment is: starting from the current moment, when the integer multiple of the update period of area j is reached, a new remote sensing image of the target area at the moment corresponding to the integer multiple of the update period is obtained, and the image of area j in the corresponding target area in the geographic information GIS database is replaced with the image of area j in the new remote sensing image.

10. A geographic information GIS database dynamic update system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for dynamically updating a geographic information GIS database as described in any one of claims 1 to 9 are implemented.

Images