CN116579960B - A geospatial data fusion method - Google Patents

Abstract

The invention relates to the field of data processing. The invention provides a geospatial data fusion method and system, which acquires multiple remote sensing images, extracts the geospatial data in the multiple remote sensing images, and fuses the geospatial data in the multiple remote sensing images. Integrate, obtain the data source, calculate the geometric fit of the data source, and perform data fusion on multiple remote sensing images based on the geometric fit of the data source. The method described can efficiently fuse geospatial data without manual adjustment of some data, greatly improving the data fusion speed. The fused image can more accurately reflect the geospatial information and changes in ground objects in the area, and can also improve geographical location. The quality and accuracy of spatial data greatly reduce data deviations or errors caused by multiple different remote sensing images during the fusion process.

Description

A geospatial data fusion method

Technical field

The invention relates to the field of data processing, and in particular to a geospatial data fusion method.

Background technique

Geospatial data refers to various data related to geographical spatial location, including geographical location, geographical area, geographical phenomenon, topography and landforms, etc. Geospatial data generally has dimensions such as space, time and attributes, and is collected and obtained mainly through remote sensing technology, global positioning system, ground survey, etc. Geospatial data can be used to describe and analyze geographical phenomena, geographical laws and geographical processes, and is widely used in urban planning, natural resource management, disaster early warning and other fields.

Geospatial data fusion refers to the integration, integration and processing of geospatial data from different sources and types to obtain more accurate and complete geographic information data. With the widespread application of geographic information systems, more and more geospatial data are collected and generated, including remote sensing images, geographical location data, satellite images, etc. At the same time, geospatial data fusion methods are becoming increasingly diversified and Complexity, traditional data fusion methods mainly include model fusion, feature fusion, decision fusion, etc. However, due to differences in data formats, data accuracy, etc., in the process of fusion and integration of geospatial data, such as low data processing efficiency, poor fusion effect, etc. Problems such as poor performance are often difficult to solve. Current mainstream data fusion methods such as multi-source data fusion, multi-scale data fusion, and deep learning, although they have the characteristics of high processing efficiency and fast fusion speed, suffer from information loss and huge complexity. The processing effects in other aspects are still unsatisfactory. Therefore, proposing a geospatial data fusion method and application is the key to improving the accuracy and reliability of geospatial data fusion in the current field of geographic information technology research.

Contents of the invention

The purpose of the present invention is to propose a geospatial data fusion method and application to solve one or more technical problems existing in the existing technology, and at least provide a beneficial choice or creation condition.

The invention provides a geospatial data fusion method, which acquires multiple remote sensing images, extracts the geospatial data in the multiple remote sensing images, integrates the geospatial data in the multiple remote sensing images, obtains data sources, and calculates the data sources. The geometric joining degree is used to perform data fusion on multiple remote sensing images based on the geometric joining degree of the data source. The method described can efficiently fuse geospatial data without manual adjustment of some data, greatly improving the data fusion speed. The fused image can more accurately reflect the geospatial information and surface object changes in the area, and can also improve geographical location. The quality and accuracy of spatial data greatly reduce data deviations or errors caused by multiple different remote sensing images during the fusion process.

In order to achieve the above objectives, according to one aspect of the present invention, a geospatial data fusion method and application are provided. The method includes the following steps:

S100, acquire multiple remote sensing images and extract the geospatial data in the multiple remote sensing images;

S200, integrate geospatial data from multiple remote sensing images to obtain data sources;

S300, calculate the geometric coupling degree of the data source;

S400 performs data fusion on multiple remote sensing images based on the geometric fit of data sources.

Further, in step S100, the remote sensing image is a remote sensing digital image, and the remote sensing digital image is stored in digital form. The basic unit of the remote sensing digital image is a pixel, and the pixel has a corresponding brightness value.

Further, in step S100, the steps of acquiring multiple remote sensing images and extracting the geospatial data in the multiple remote sensing images are specifically: acquiring multiple remote sensing images through remote sensing monitoring, and combining the brightness values of the pixels in the remote sensing images with the remote sensing The spatial coordinates of the pixels in the image are used as the geospatial data in the remote sensing image. The geospatial data of each remote sensing image is sequentially extracted from multiple remote sensing images and saved.

Further, in step S200, the geospatial data in multiple remote sensing images are integrated to obtain the data source. The specific steps are:

S201, use rem(i) to represent the i-th remote sensing image among multiple remote sensing images, and record the number of multiple remote sensing images as N (that is, the specific number of multiple remote sensing images is N), then i=1,2, ..., N, initialize an integer variable j1, the initial value of variable j1 is 1, the value range of variable j1 is [1, N], traverse j1 starting from j1 = 1, and create a blank set lan{}, transfer to S202;

S202, record the number of all pixels in the current rem(j1) as M _j1 , and use alr(j) to represent the brightness value of the j-th pixel in the current rem(j1), j=1,2,…, M _j1 , let tha(j1) represent the average brightness value of all pixels in the current rem(j1), add the current tha(j1) value to the set lan{}, and go to S203;

S203, if the current value of j1 is less than N, increase the current value of j1 by 1 and go to S202; if the current value of j1 is equal to or greater than N, go to S204;

S204, use lan(i) to represent the i-th element in the set lan{}, i=1,2,...,N, and record the element with the largest value in the set lan{} as lan(M1), and record the set lan{} The element with the smallest median value is lan(M2). Create a blank set mis{}, and add all the remaining elements after removing the elements lan(M1) and lan(M2) from the set lan{} to the set mis{}. in to S205;

S205, if the current value of lan(j1) is greater than the value of roundup(tow), add the value of the current variable j1 to the set und{}; if the current value of lan(j1) is less than or equal to the value of roundup(tow) , then go to S206; where roundup(tow) is the value obtained by rounding up the value of tow;

S206, if the current value of j1 is less than N, increase the current value of j1 by 1 and go to S205; if the current value of j1 is equal to or greater than N, go to S207;

S207, record the number of all elements in the set und{} as N1, use und(i1) to represent the i1-th element in the set und{}, i1=1,2,...,N1, and then rem(und(1) ),rem(und(2)),…,rem(und(N1)) are saved as data sources.

The beneficial effect of this step is: since there is spectral information and spatial information in remote sensing images, the brightness value of pixels in remote sensing images can best reflect the geospatial information of the target area. At the same time, for multiple remote sensing images of the same area, when When the capture angles of the images are relatively similar, the average brightness values of all pixels in the images are relatively close. When the capture angles of the images are greatly different, the brightness values of all pixels in the images will fluctuate greatly. Therefore, the method in this step saves the key samples by filtering out the key samples from multiple remote sensing images (i.e. rem(und(1)), rem(und(2)),..., rem(und(N1))) As a data source, changes in the brightness value of pixels in key samples can reflect the key information of the target area. Screening out key samples can provide a better data fusion effect, and can also speed up the subsequent smoothing processing of some pixels.

Further, in step S300, the step of calculating the geometric coupling degree of the data source is specifically:

S301, initialize the integer variable k1. The initial value of the variable k1 is 1. The value range of the variable k1 is [1, N1]. N1 is the number of all elements in the set und{}. Go to S302;

S302, select rem(und(k1)) in the data source, that is, rem(und(k1)) is the und(k1)th remote sensing image among multiple remote sensing images, record the upper left corner of rem(und(k1)) The pixel point of is par1, the pixel point in the upper right corner of rem(und(k1)) is denoted as par2, the pixel point of the lower left corner within rem(und(k1)) is denoted as par3, and the pixel point in the right corner of rem(und(k1)) is denoted as par3. The pixel point in the lower corner is par4. Connect the pixel points par1 and par2 to get the straight line cap1. Connect the pixel points par2 and par3 to get the straight line cap2. Connect the pixel points par3 and par4 to get the straight line cap3. Connect the pixel points par4 and par1 to get the straight line cap4. Go to S303 ;

S303. In the current rem(und(k1)), select the pixel with the smallest brightness value and record it as soc. Select a straight line with the shortest distance from the pixel soc among the straight lines cap1, cap2, cap3, and cap4 and record it. is capA, select two straight lines perpendicular to the straight line capA from the straight lines cap1, cap2, cap3, and cap4 and record them as capC1 and capC2 respectively. Select the straight line with the shortest distance from the pixel point soc among the straight lines capC1 and capC2. And record it as capB, go to S304;

S304. Draw a perpendicular line through the pixel point soc to the straight line capA to obtain the vertical foot exaA. Draw a perpendicular line through the pixel point soc to the straight line capB to obtain the vertical foot exaB. Note the intersection of the straight line capA and the straight line capB as dau, and connect soc and exaA in sequence. , dau, exaB get the square area gro, record all the pixels falling in the square area gro in the current rem(und(k1)) as geometric pixels, create a blank set fut{}, and correspond all the geometric pixels The brightness values are all added to the set fut{} in turn (each pixel corresponds to a brightness value), M2 is the number of all elements in the set fut{}, and fut(k2) represents the number of elements in the set fut{}. The k2th element, k2=1,2,...,M2; eliminate all geometric pixels in the current rem(und(k1)), and record the remaining pixels as the first pixels; calculate the current The geometric coupling degree of rem(und(k1)) Geo_Re(rem(und(k1))):

In the formula, fut_A is the element with the smallest value in the set fut{}, soc_B is the brightness value of the pixel with the smallest brightness value among all the first pixel points, k3 is the accumulated variable, and fut(k3) is the first element in the set fut{}. k3 elements, hav is the average of the brightness values of all first pixels, min{} means taking the minimum value of the number in {}, max{} means taking the maximum value of the number in {}, go to S305;

S305, if the value of the current variable k1 is less than N1, increase the value of k1 by 1 and go to S302; if the value of the current variable k1 is equal to or greater than N1, go to S306;

S306, create a blank set Geo{}, and add Geo_Re(rem(und(1))), Geo_Re(rem(und(2))),..., Geo_Re(rem(und(N1))) to the set in sequence In Geo{}, the average of all elements in the set Geo{} is GeoA.

The beneficial effect of this step is that in the process of image registration, using key samples in the data source is an effective method. These proofs are often representative samples and can be used to calculate the geometric matching between different images. The matching degree at these geometric levels can indicate the matching degree of different spatial positions between different remote sensing images, and can be used to determine the fusion position with high matching degree. These high-matching fusion positions are locally processed to eliminate possible splicing errors, thereby achieving seamless fusion of images. At the same time, due to quality differences or different shooting angles in remote sensing images during imaging, a single remote sensing image can only reflect the local characteristics of the target area. The method in this step uses key samples in the data source to calculate the geometry of the key samples. The degree of fusion and geometric fusion can indicate whether the fusion degree of different spatial positions in different remote sensing images is sufficiently matched. Local processing of fusion positions with high matching degrees can not only improve the completeness of the reflection of the overall characteristics of the target area, but also This makes the fused image have better geographical information and improves the accuracy and reliability of remote sensing image fusion.

Further, in step S400, the method for data fusion of multiple remote sensing images according to the geometric fit of the data sources is specifically:

S401, initialize an integer variable j2. The initial value of variable j2 is 1. The value range of variable j2 is [1, N]. N is the number of multiple remote sensing images. Start traversing j2 from j2 = 1 and go to S402;

S402, record the pixel with the largest brightness value in the current rem(j2) as pag(j2), mark the critical pixel in rem(j2) whose brightness value is greater than cla as the second pixel, and go to S403; where, cla =GeoA*pag(j2), the definition of critical pixel point in rem(j2) is: the pixel point whose distance from the edge of rem(j2) is less than T (that is, the critical pixel point is from the edge of rem(j2) pixels whose distance is less than T); T is the distance of [3,50] pixels;

S403, if the current value of variable j2 is less than N, increase the value of variable j2 by 1 and go to S402.

The beneficial effect of this step is: in the process of data fusion, the pixels located at the edge of the remote sensing image are particularly critical. These pixels play a decisive role in the quality of the remote sensing image generated after fusion. The method of this step uses the geometric coupling degree. , filter out the second pixel point among the critical pixel points. The surrounding pixels of the second pixel point restore the geometric shape information in the target area to a high degree. Local pixel-level processing of the second pixel point can not only improve The detail and local quality of remote sensing images can also more accurately extract key information of target areas, providing a more reliable data basis for subsequent geographical information analysis and application.

Further, in step S400, performing data fusion on multiple remote sensing images according to the geometric fit of the data sources also includes: performing filtering and smoothing preprocessing on the second pixels in the multiple remote sensing images using the neighborhood mean method, and merging all the pixels. The remote sensing images after filtering and smoothing preprocessing are subjected to data fusion; the data fusion method is any one of pixel-level image fusion, feature-level image fusion, and decision-level image fusion.

The present invention also provides a geospatial data fusion system. The geospatial data fusion system includes: a processor, a memory and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the steps in a geospatial data fusion method. The geospatial data fusion system can run on desktop computers, notebook computers, mobile phones, mobile phones, tablet computers, handheld computers and In computing devices such as cloud data centers, executable systems may include, but are not limited to, processors, memories, and server clusters. The processor executes the computer program and runs in the following system units:

The image acquisition unit is used to acquire multiple remote sensing images and extract the geospatial data in the multiple remote sensing images;

The data integration unit is used to integrate geospatial data from multiple remote sensing images to obtain data sources;

Parameter calculation unit, used to calculate the geometric fit of the data source;

The data fusion unit is used to fuse multiple remote sensing images based on the geometric fit of the data sources.

The beneficial effects of the present invention are: the method can efficiently integrate geospatial data without manual adjustment of part of the data, greatly improving the data fusion speed, and the fused image can more accurately reflect the geospatial information and location within the area. It can also improve the quality and accuracy of geospatial data and significantly reduce data deviations or errors caused by multiple different remote sensing images during the fusion process.

Description of the drawings

The above and other features of the present invention will be more apparent from the detailed description of the embodiments shown in the accompanying drawings. In the drawings of the present invention, the same reference numerals designate the same or similar elements. It will be apparent that the appended drawings in the following description The drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts. In the drawings:

Figure 1 shows a flow chart of a geospatial data fusion method;

Figure 2 shows the system structure diagram of a geospatial data fusion system.

Detailed ways

The following will give a clear and complete description of the concept, specific structure and technical effects of the present invention in conjunction with the embodiments and drawings, so as to fully understand the purpose, solutions and effects of the present invention. It should be noted that, as long as there is no conflict, the embodiments and features in the embodiments of this application can be combined with each other.

In the description of the present invention, several means one or more, plural means two or more, greater than, less than, more than, etc. are understood to exclude the original number, and above, below, within, etc. are understood to include the original number. If there is a description of first and second, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of indicated technical features. relation.

Figure 1 shows a flow chart of a geospatial data fusion method according to the present invention. A geospatial data fusion method according to an embodiment of the present invention will be described below with reference to Figure 1 .

The present invention proposes a geospatial data fusion method, which includes the following steps:

S100, acquire multiple remote sensing images and extract the geospatial data in the multiple remote sensing images;

S200, integrate geospatial data from multiple remote sensing images to obtain data sources;

S300, calculate the geometric coupling degree of the data source;

S400 performs data fusion on multiple remote sensing images based on the geometric fit of data sources.

Further, in step S100, the remote sensing image is a remote sensing digital image, and the remote sensing digital image is stored in digital form. The basic unit of the remote sensing digital image is a pixel, and the pixel has a corresponding brightness value.

Further, in step S100, the steps of acquiring multiple remote sensing images and extracting the geospatial data in the multiple remote sensing images are specifically: acquiring multiple remote sensing images through remote sensing monitoring, and combining the brightness values of the pixels in the remote sensing images with the remote sensing The spatial coordinates of the pixels in the image are used as the geospatial data in the remote sensing image. The geospatial data of each remote sensing image is sequentially extracted from multiple remote sensing images and saved.

Further, in step S200, the geospatial data in multiple remote sensing images are integrated to obtain the data source. The specific steps are:

S201, use rem(i) to represent the i-th remote sensing image among multiple remote sensing images, and record the number of multiple remote sensing images as N (that is, the specific number of multiple remote sensing images is N), then i=1,2, ..., N, initialize an integer variable j1, the initial value of variable j1 is 1, the value range of variable j1 is [1, N], traverse j1 starting from j1 = 1, and create a blank set lan{}, transfer to S202;

S202, record the number of all pixels in the current rem(j1) as M _j1 , and use alr(j) to represent the brightness value of the j-th pixel in the current rem(j1), j=1,2,…, M _j1 , let tha(j1) represent the average brightness value of all pixels in the current rem(j1), add the current tha(j1) value to the set lan{}, and go to S203;

S203, if the current value of j1 is less than N, increase the current value of j1 by 1 and go to S202; if the current value of j1 is equal to or greater than N, go to S204;

S204, use lan(i) to represent the i-th element in the set lan{}, i=1,2,...,N, and record the element with the largest value in the set lan{} as lan(M1), and record the set lan{} The element with the smallest median value is lan(M2). Create a blank set mis{}, and add all the remaining elements after removing the elements lan(M1) and lan(M2) from the set lan{} to the set mis{}. in to S205;

S205, if the current value of lan(j1) is greater than the value of roundup(tow), add the value of the current variable j1 to the set und{}; if the current value of lan(j1) is less than or equal to the value of roundup(tow) , then go to S206; where roundup(tow) is the value obtained by rounding up the value of tow;

S206, if the current value of j1 is less than N, increase the current value of j1 by 1 and go to S205; if the current value of j1 is equal to or greater than N, go to S207;

S207, record the number of all elements in the set und{} as N1, use und(i1) to represent the i1-th element in the set und{}, i1=1,2,...,N1, and then rem(und(1) ),rem(und(2)),…,rem(und(N1)) are saved as data sources.

Further, in step S300, the step of calculating the geometric coupling degree of the data source is specifically:

S301, initialize the integer variable k1. The initial value of the variable k1 is 1. The value range of the variable k1 is [1, N1]. N1 is the number of all elements in the set und{}. Go to S302;

S302, select rem(und(k1)) in the data source, that is, rem(und(k1)) is the und(k1)th remote sensing image among multiple remote sensing images, record the upper left corner of rem(und(k1)) The pixel point of is par1, the pixel point in the upper right corner of rem(und(k1)) is denoted as par2, the pixel point of the lower left corner within rem(und(k1)) is denoted as par3, and the pixel point in the right corner of rem(und(k1)) is denoted as par3. The pixel point in the lower corner is par4. Connect the pixel points par1 and par2 to get the straight line cap1. Connect the pixel points par2 and par3 to get the straight line cap2. Connect the pixel points par3 and par4 to get the straight line cap3. Connect the pixel points par4 and par1 to get the straight line cap4. Go to S303 ;

S303. In the current rem(und(k1)), select the pixel with the smallest brightness value and record it as soc. Select a straight line with the shortest distance from the pixel soc among the straight lines cap1, cap2, cap3, and cap4 and record it. is capA, select two straight lines perpendicular to the straight line capA from the straight lines cap1, cap2, cap3, and cap4 and record them as capC1 and capC2 respectively. Select the straight line with the shortest distance from the pixel point soc among the straight lines capC1 and capC2. And record it as capB, go to S304;

S304. Draw a perpendicular line through the pixel point soc to the straight line capA to obtain the vertical foot exaA. Draw a perpendicular line through the pixel point soc to the straight line capB to obtain the vertical foot exaB. Note the intersection of the straight line capA and the straight line capB as dau, and connect soc and exaA in sequence. , dau, exaB get the square area gro, record all the pixels falling in the square area gro in the current rem(und(k1)) as geometric pixels, create a blank set fut{}, and correspond all the geometric pixels The brightness values are all added to the set fut{} in turn (each pixel corresponds to a brightness value), M2 is the number of all elements in the set fut{}, and fut(k2) represents the number of elements in the set fut{}. The k2th element, k2=1,2,...,M2; eliminate all geometric pixels in the current rem(und(k1)), and record the remaining pixels as the first pixels; calculate the current The geometric coupling degree of rem(und(k1)) Geo_Re(rem(und(k1))):

In the formula, fut_A is the element with the smallest value in the set fut{}, soc_B is the brightness value of the pixel with the smallest brightness value among all the first pixel points, k3 is the accumulated variable, and fut(k3) is the first element in the set fut{}. k3 elements, hav is the average of the brightness values of all first pixels, min{} means taking the minimum value of the number in {}, max{} means taking the maximum value of the number in {}, go to S305;

S305, if the value of the current variable k1 is less than N1, increase the value of k1 by 1 and go to S302; if the value of the current variable k1 is equal to or greater than N1, go to S306;

S306, create a blank set Geo{}, and add Geo_Re(rem(und(1))), Geo_Re(rem(und(2))),..., Geo_Re(rem(und(N1))) to the set in sequence In Geo{}, the average of all elements in the set Geo{} is GeoA.

Further, in step S400, the method for data fusion of multiple remote sensing images according to the geometric fit of the data sources is specifically:

S401, initialize an integer variable j2. The initial value of variable j2 is 1. The value range of variable j2 is [1, N]. N is the number of multiple remote sensing images. Start traversing j2 from j2 = 1 and go to S402;

S402, record the pixel with the largest brightness value in the current rem(j2) as pag(j2), mark the critical pixel in rem(j2) whose brightness value is greater than cla as the second pixel, and go to S403; where, cla =GeoA*pag(j2), the definition of critical pixel point in rem(j2) is: the pixel point whose distance from the edge of rem(j2) is less than T (that is, the critical pixel point is from the edge of rem(j2) pixels whose distance is less than T); T is the distance of [3,50] pixels;

S403, if the current value of variable j2 is less than N, increase the value of variable j2 by 1 and go to S402.

Further, in step S400, performing data fusion on multiple remote sensing images according to the geometric fit of the data sources also includes: performing filtering and smoothing preprocessing on the second pixels in the multiple remote sensing images using the neighborhood mean method, and merging all the pixels. The remote sensing images after filtering and smoothing preprocessing are subjected to data fusion; the data fusion method is any one of pixel-level image fusion, feature-level image fusion, and decision-level image fusion.

The geospatial data fusion system includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above geospatial data fusion system is implemented. The steps in the embodiment of the spatial data fusion method. The geospatial data fusion system can run on computing devices such as desktop computers, notebook computers, mobile phones, portable phones, tablet computers, handheld computers, and cloud data centers, Runnable systems may include, but are not limited to, processors, memories, and server clusters.

An embodiment of the present invention provides a geospatial data fusion system. As shown in Figure 2, the geospatial data fusion system of this embodiment includes: a processor, a memory, and a processor that is stored in the memory and can be stored in the memory. A computer program running on a processor. When the processor executes the computer program, it implements the steps in one of the above geospatial data fusion method embodiments. The processor executes the computer program and runs in the unit of the following system:

The image acquisition unit is used to acquire multiple remote sensing images and extract the geospatial data in the multiple remote sensing images;

The data integration unit is used to integrate geospatial data from multiple remote sensing images to obtain data sources;

Parameter calculation unit, used to calculate the geometric fit of the data source;

The data fusion unit is used to fuse multiple remote sensing images based on the geometric fit of the data sources.

The geospatial data fusion system can run in computing devices such as desktop computers, notebook computers, handheld computers, and cloud data centers. The geospatial data fusion system includes, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above examples are only examples of a geospatial data fusion method and system, and do not constitute a limitation to a geospatial data fusion method and system, and may include more or less than the examples. components, or a combination of certain components, or different components. For example, the geospatial data fusion system may also include input and output devices, network access devices, buses, etc.

The so-called processor can be a Central Processing Unit (CPU), or other general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or an on-site processor. Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete component gate circuits or transistor logic devices, discrete hardware components, etc. The general processor can be a microprocessor or the processor can be any conventional processor, etc. The processor is the control center of the geospatial data fusion system and uses various interfaces and lines to connect the entire Various sub-regions of the geospatial data fusion system.

The memory may be used to store the computer program and/or module, and the processor implements the process by running or executing the computer program and/or module stored in the memory and calling data stored in the memory. A variety of geospatial data fusion methods and various functions of the system. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the storage data area may store Data created based on the use of mobile phones (such as audio data, phone books, etc.), etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, Flash Card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

The invention provides a geospatial data fusion method, which acquires multiple remote sensing images, extracts the geospatial data in the multiple remote sensing images, integrates the geospatial data in the multiple remote sensing images, obtains data sources, and calculates the data sources. The geometric joining degree is used to perform data fusion on multiple remote sensing images based on the geometric joining degree of the data source. The method described can efficiently fuse geospatial data without manual adjustment of some data, greatly improving the data fusion speed. The fused image can more accurately reflect the geospatial information and surface object changes in the area, and can also improve geographical location. The quality and accuracy of spatial data greatly reduce data deviations or errors caused by multiple different remote sensing images during the fusion process. While the invention has been described in considerable detail and particularly with respect to several of the described embodiments, it is not intended to be limited to any such details or embodiments or to any particular embodiment so as to effectively encompass the intended scope of the invention. In addition, the above description of the present invention is based on embodiments foreseeable by the inventor for the purpose of providing a useful description, and those non-substantive changes to the present invention that are not yet foreseen can still represent equivalent changes of the present invention.

Claims (7)

1. A method of geospatial data fusion, the method comprising the steps of:

s100, acquiring a plurality of remote sensing images, and extracting geographic space data in the plurality of remote sensing images;

s200, integrating the geospatial data in the plurality of remote sensing images to obtain a data source;

s300, calculating the geometric degree of overlap of the data source;

s400, carrying out data fusion on a plurality of remote sensing images according to the geometric degree of the data source;

in step S300, the step of calculating the geometric degree of overlap of the data source specifically includes:

s301, initializing an integer variable k1, wherein the initial value of the variable k1 is 1, the value range of the variable k1 is [1, N1], N1 is the number of all elements in a set und { }, and turning to S302;

s302, selecting rem (un (k 1)) in a data source, namely rem (un (k 1)) as an un (k 1) Zhang Yaogan image in a plurality of remote sensing images, marking a pixel point at the upper left corner in rem (un (k 1)) as par1, marking a pixel point at the upper right corner in rem (un (k 1)) as par3, marking a pixel point at the lower left corner in rem (un (k 1)) as par4, connecting the pixel points par1 and par2 to obtain a straight line cap1, connecting the pixel points par2 and par3 to obtain a straight line cap2, connecting the pixel points par3 and par4 to obtain a straight line cap3, connecting the pixel points par4 and par1 to obtain a straight line cap4, and turning to S303;

s303, selecting a pixel point with the minimum brightness value from the current rem (und (k 1)) and marking as a soc, selecting a line with the shortest distance to the pixel point soc from the lines cap1, cap2, cap3 and cap4 and marking as a capA, selecting two lines with a perpendicular relation to the lines capA from the lines cap1, cap2, cap3 and cap4 and marking as capC1 and capC2 respectively, selecting a line with the shortest distance to the pixel point soc from the lines capC1 and capC2 and marking as a capB, and turning to S304;

s304, making a vertical line on a straight line capA through a pixel point soc to obtain a drop foot exaA, making a vertical line on a straight line capB through the pixel point soc to obtain a drop foot exaB, recording the intersection point of the straight line capA and the straight line capB as dau, sequentially connecting soc, exaA, dau, exaB to obtain a square region gro, recording all pixel points in the square region gro in the current rem (un (k 1)) as geometric pixel points, creating a blank set fut { }, sequentially adding brightness values corresponding to all geometric pixel points into the set fut { } (each pixel point corresponds to a brightness value), recording M2 as the number of all elements in the set fut { }, and recording the k2 element in the set fut { } as fut (k 2), wherein k2=1, 2, … and M2; removing all geometric pixel points in the current rem (un (k 1)), and marking the rest pixel points as first pixel points; the geometric degree of overlap geo_re (rem (un (k 1))) of the current rem (un (k 1)) is calculated by:

wherein fut _a is the element with the smallest median value in the set fut { }, soc_b is the brightness value of the pixel with the smallest brightness value in all the first pixel points, k3 is an accumulation variable, fut (k 3) is the k3 element in the set fut { }, hav is the average value of the brightness values of all the first pixel points, min { } represents the minimum value of the numbers in { }, max { } represents the maximum value of the numbers in { }, and the process goes to S305;

s305, if the value of the current variable k1 is smaller than N1, increasing the value of k1 by 1, and turning to S302; if the value of the current variable k1 is equal to or greater than N1, go to S306;

s306, creating a blank set Geo { }, adding Geo_Re (rem (und (1))), geo_Re (rem (und (2))), …, geo_Re (rem (und (N1))) to the set Geo { }, and recording the average value of all elements in the set Geo { } as GeoA.

2. The geospatial data fusion method of claim 1 wherein in step S100, the remote sensing image is a remote sensing digital image, the remote sensing digital image is stored in digital form, the basic unit of the remote sensing digital image is a pixel, and the pixel has a corresponding brightness value.

3. The method of claim 1, wherein in step S100, the step of obtaining a plurality of remote sensing images and extracting geospatial data in the plurality of remote sensing images is specifically: and acquiring a plurality of remote sensing images through remote sensing monitoring, taking the brightness value of the pixel point in the remote sensing images and the space coordinate of the pixel point in the remote sensing images as the geographic space data in the remote sensing images, and sequentially extracting and storing the geographic space data of each remote sensing image in the plurality of remote sensing images.

4. The geospatial data fusion method according to claim 1 wherein in step S200, the step of integrating geospatial data in a plurality of remote sensing images to obtain a data source is specifically:

s201, representing an ith remote sensing image in a plurality of remote sensing images by rem (i), recording the number of the plurality of remote sensing images as N (namely, the specific number of the plurality of remote sensing images is N), initializing an integer variable j1, wherein the initial value of the variable j1 is 1, the value range of the variable j1 is [1, N ], traversing j1 from j1 = 1, creating a blank set lan { } and turning to S202;

s202, recording the number of all pixel points in the current rem (j 1) as M _j1 Expressed as alr (j)Luminance value of j-th pixel in current rem (j 1), j=1, 2, …, M _j1 Representing the average value of the brightness values of all pixel points in the current rem (j 1) by using tha (j 1), adding the value of the current tha (j 1) into a set lan { }, and turning to S203;

s203, if the value of the current j1 is smaller than N, the value of the current j1 is increased by 1, and the process goes to S202; if the value of current j1 is equal to or greater than N, go to S204;

s204, representing the ith element in the set lan { } by lan (i), i=1, 2, …, N, recording the largest median element in the set lan { } as lan (M1), recording the smallest median element in the set lan { } as lan (M2), creating a blank set mis { }, adding all the elements remained after removing the elements lan (M1) and lan (M2) from the set lan { } to the set mis { }, recording tow=mis_a/(lan (M1) -lan (M2)), where mis_a represents the sum of all the elements in the set mis { }; resetting the value of the variable j1 to 1, creating a blank set und { }, and turning to S205;

s205, if the value of the current lan (j 1) is larger than the value of the round dup (tow), adding the value of the current variable j1 into the set und; if the value of the current lan (j 1) is less than or equal to the value of the round dup (tow), then go to S206; wherein, the round dup (top) is a value obtained by rounding up the top value;

s206, if the value of the current j1 is smaller than N, the value of the current j1 is increased by 1, and the process goes to S205; if the value of current j1 is equal to or greater than N, go to S207;

s207, the number of all elements in the set und { } is denoted by N1, and the i1 st element in the set und { } is denoted by und (i 1), i1=1, 2, …, N1, and rem (und (1)), rem (und (2)), …, rem (und (N1)) are sequentially stored as a data source.

5. The geospatial data fusion method according to claim 1 wherein in step S400, the method for data fusion of a plurality of remote sensing images according to the geometric degree of overlap of the data sources is specifically as follows:

s401, initializing an integer variable j2, wherein the initial value of the variable j2 is 1, the value range of the variable j2 is [1, N ], N is the number of a plurality of remote sensing images, traversing the variable j2 from j2 = 1, and turning to S402;

s402, recording the pixel point with the maximum brightness value in the current rem (j 2) as pag (j 2), marking the critical pixel point with the brightness value larger than cla in rem (j 2) as a second pixel point, and turning to S403; wherein cla=geoa×pag (j 2), and critical pixel points in rem (j 2) are defined as: a pixel having a distance less than T from the edge of rem (j 2) (i.e., a critical pixel is a pixel having a distance less than T from the edge of rem (j 2)); t is the distance between [3,50] pixel points;

s403, if the value of the current variable j2 is smaller than N, the value of the variable j2 is increased by 1 and the process goes to S402.

6. The geospatial data fusion method of claim 1 wherein in step S400, data fusion is performed on a plurality of remote sensing images according to geometric degree of overlap of data sources, further comprising: filtering and smoothing the second pixel point in the plurality of remote sensing images by using a neighborhood mean value method, and carrying out data fusion on all the remote sensing images subjected to filtering and smoothing pretreatment; the data fusion method is any one of pixel-level image fusion, feature-level image fusion and decision-level image fusion.

7. A geospatial data fusion system, the geospatial data fusion system comprising: a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of a geospatial data fusion method according to any of claims 1-6 when the computer program is executed, the geospatial data fusion system running in a computing device of a desktop, notebook, palm or cloud data center.