CN106339985B - A method of selection is inlayed line and is inlayed to aviation image from vector house data - Google Patents
A method of selection is inlayed line and is inlayed to aviation image from vector house data Download PDFInfo
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Abstract
The present invention provides a kind of from vector house data chooses and inlays the method that line inlays aviation image, which comprises the following steps: 1) according in the air strips, in the boundary line computation air strips of every image effective coverage adjacent image overlap area；2) according to adjacent air strips overlay region between the boundary line computation air strips of the effective coverage of every obtained air strips；3) existing vector house data are based on, is found in adjacent air strips overlay region between adjacent image overlap area and air strips in air strips and inlays line；4) in all air strips and line will be inlayed between air strips mutually cut, individually effectively inlay polygon for the building of each image；5) data of every image effectively inlayed in polygon are merged, formation includes the final of all single images and inlays image.The present invention initially inlays polygon using vector data building to the method that aviation image is inlayed, then with Raster Images itself come local optimum, can be shortened and inlay the polygon building time, avoids passing through the prominent object such as house, significantly reduces later period human-edited amount.
Description
Technical Field
The invention relates to the technical field of aerial image processing, in particular to a method for selecting a mosaic line from vector house data to mosaic an aerial image.
Background
The outline of the national medium-and-long-term scientific and technical development planning (2006-2020) definitely lists the development of a high-resolution earth observation system (referred to as a high-resolution special item) as one of 16 important special items. By 2020, the system is built and put into use comprehensively, and the all-weather, all-time and global-coverage earth observation capability of China is improved remarkably. However, as the resolution of remote sensing images is higher and higher, the application of remote sensing data in various industries is wider and wider, the data volume of remote sensing images covering a given area (such as Wuhan city) is increased explosively, and the GB magnitude in the early 20 th century is rapidly increased to the current TB magnitude (Chen Jie, 2012; 2008; xu Di Peak, 2009; original Jie, 2013), which means that: obtaining a high resolution image of the same ground area now requires mosaicing more remote sensing images than in the past. This puts higher demands on the rapid and intelligent mosaic of remote sensing images. The difficulty is how to make the mosaic lines avoid crossing objects that cannot be corrected by digital elevation models, such as houses (Wang et al, 2012).
However, the theory and method of remote sensing image mosaicing is relatively under development, resulting in many mapping units and departments having to hire more people or reduce the quality of mosaicing to meet the increasing remote sensing image mosaicing. For example, in wuhan city, a main urban image of 1:2000 scale is produced once, and about 10000 images need to be photographed and embedded, and a single PC runs an international well-known embedding software OrthoVista (Inpho,2014) to embed the images, so that the operation is performed for about 10 days continuously day and night, and about 6 months are required for a single technician to modify the embedding lines and embed the images (Wang et al, 2012). Such efficiency has caused the relevant departments to have to invest a lot of high-performance computers and manpower so that the inlaying work can be completed within a month. Meanwhile, the mapping industry has accumulated a large amount of very realistic vector data, such as houses and roads, in the past decades, which can serve as a priori knowledge for the production of many mapping products, such as image mosaics. Wang et al (2013) proposes a global optimal aerial image mosaic line selection method based on vector road data, so as to make the mosaic line go along the center line of the road as much as possible, thereby avoiding houses. However, few researchers have studied how to apply vector-house data to aerial image mosaicing, and how to apply it to image mosaicing.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method for selecting mosaic polygons from vector house and road data to mosaic aerial images, which constructs initial mosaic polygons using vector data, and locally optimizes the mosaic polygons generated from the vector data using raster images themselves, so as to shorten the construction time of the mosaic polygons and ensure that any final mosaic line can avoid crossing over protruding objects such as houses, thereby significantly reducing the amount of manual editing in the later period.
The specific technical scheme of the invention is a method for selecting a mosaic line from vector house data to mosaic an aerial image, which is characterized by comprising the following steps of:
1) extracting boundary lines of effective areas of each image in each flight band of the shot aerial images based on a left-turn algorithm, and calculating adjacent image overlapping areas in the flight bands according to the boundary lines of the effective areas of each image in the flight bands in each flight band;
2) combining the effective areas in each flight band to obtain the boundary line of the effective area of each flight band, and calculating the overlapping area of the adjacent flight bands between the flight bands according to the obtained boundary line of the effective area of each flight band;
3) based on the existing vector house data, finding mosaic lines in adjacent image overlapping areas in the flight band and adjacent flight band overlapping areas between the flight bands;
4) mutually cutting the mosaic lines in all the navigation bands and between the navigation bands, then connecting the cut mosaic lines end to end based on a left-turn algorithm, constructing an independent effective mosaic polygon for each image in each navigation band, finally using the data in the effective mosaic polygon of each image for mosaic images, and discarding the rest parts;
5) the data within the valid mosaic polygons of each image are merged to form a final mosaic image that encompasses all of the individual images.
Furthermore, in the step 3), the method for finding mosaic lines in the overlapping regions of adjacent images in the navigation band extracts mosaic lines between all adjacent images one by one, when finding mosaic lines in the overlapping regions of adjacent navigation bands between the navigation bands, the adjacent navigation bands are used as adjacent images, the mosaic lines between the adjacent navigation bands are extracted by the same method as the method for finding mosaic lines in the overlapping regions of the adjacent images, two adjacent images are set as m-degree overlapped images, namely, the two images are overlapped with m-2 other images except the images, m is more than or equal to 2, and the method for extracting mosaic lines between the two adjacent images comprises the following steps:
(1) extracting intermediate lines of the space between two adjacent image house spaces in an overlapping area and skeleton lines of the overlapping area based on a constrained Delaunay triangulation algorithm, and then combining the intermediate lines of the space between the vector house spaces in the overlapping area and the skeleton lines of the overlapping area to form a candidate mosaic line library;
(2) giving a passing cost to the candidate inlaid line according to the effective width of the candidate inlaid line, and searching the candidate inlaid line with the lowest cost from the starting point to the terminal point based on the Dijkstra algorithm;
(3) and (3) optimizing the candidate mosaic line extracted in the step (2) with the lowest cost in an overlapping area by using m-degree overlapped image data to form a final mosaic line, so that any section of the final mosaic line can be ensured to be capable of avoiding crossing a salient object which exists in the image data and is not contained in the vector house data.
Furthermore, in the step (2), a passing cost is given to the candidate mosaic line according to the effective width of the candidate mosaic line, and a specific method for finding the candidate mosaic line with the lowest cost from the starting point to the end point based on the Dijkstra algorithm is to set liFor the candidate mosaic line in the overlap region of the ith two adjacent images, the candidate mosaic line liEffective width of (l) vadW (l)i) Calculated as follows:
wherein d (l)i) Representative and candidate inlaid wireiMinimum distance between adjacent houses, projD (h)i1,hi2) The sum of the projection differences of two adjacent houses is calculated according to the following formula (II):
projD(hi1,hi2)＝tanθj1*hi1(li)+tanθi2*hi2(li) (II)
wherein, thetai1And thetai2For two adjacent images respectively in the candidate mosaic lineiThe lower view angles of two adjacent houses; h isi1(li)、hi2(li) As a result of the height of the two houses,
candidate inlaid wireiPassing cost Tcost (l)i) Calculated as follows:
Tcost(li)＝κ(li)×len(li) (III)
wherein, len (l)i) Indicating candidate inlaid wire liLength of (d); kappa (l)i) Indicating candidate inlaid wire liContribution of the width of (c) to the cost, κ (l)i) Calculated as follows (IV):
wherein c is a constant of 0.1, minW is a minimum width of a middle line of the empty space between houses in the overlapping area,
let SL represent all candidate mosaic line sets composed of the middle line of the space between houses and the skeleton line of the overlap area, CS represent the path connecting several candidate mosaic lines in SL and passing the mosaic line starting point and end point, the mosaic line starting point and end point are two intersection points of the adjacent video effective area, and the passing cost f (CS) of any path CS in CS is calculated according to the following formula (V):
based on Dijkstra algorithm, the candidate inlaid line ms with the lowest cost in CS can be found, and the passing cost is min [ f (CS) ], and CS belongs to CS and SL.
Further, the method for optimizing the candidate mosaic line with the lowest cost in the overlapping region in step (3) is to first determine the optimization processing range of the candidate mosaic line with the lowest cost, and optimize the candidate mosaic line with the lowest cost within the optimization distance bufdis (ms) from the candidate mosaic line with the lowest cost according to the following formula (VI):
let the mosaic line in the optimization be rs, which is calculated by the cost Tcost (rs) as follows:
where D denotes the search range defined by equation (VI) above, Tcost (u, v) denotes the cost of the mosaic line rs in the optimization through the pixel (u, v), Tcost (u, v) being the difference between the maximum and minimum values of the pixel (u, v) in the overlap region of the m images, calculated as equation (VIII) below:
Tcost(u,v)＝max[Lj(u,v)]-min[Lj(u,v)],j＝1,…,m (VIII)
in the formula, Lj(u, v) refers to the luminance value of the jth image in pixel (u, v), which is calculated from the three bands of red, green and blue of the image according to the following formula (IX):
wherein,andrefers to the pixel value of the pixel (u, v) in the red, green and blue bands of the jth image,
based on the Dijkstra algorithm of the grid, the inlaid line with the lowest passing cost in the rs can be found, and the found inlaid line is the final inlaid line.
Due to the adoption of the technical scheme, the invention has the following advantages: 1) the method can remarkably improve the mosaic efficiency by mapping the big data by using the history. Experiments show that 80-90% of operation time can be saved compared with the current Dijkstra algorithm-based mosaic line selection method with the best effect. This is because the nodes of the vector data are usually much smaller than the raster data pixels, and although the mosaic lines need to be optimized later on based on the raster data, the optimization range is less than 1/10, and thus the efficiency is significantly higher. 2) The mosaic quality can be improved significantly by mapping the big data, traversing fewer houses. Mosaic experiments based on three groups of different building densities showed that: compared with the mosaic line selection method based on the vector road, the method can pass through 10-40% less houses; compared with the mosaic line selection method based on Dijkstra algorithm which is generally recognized to be the best in effect, the method can pass through 1-15% of houses less. The method for inlaying the aerial image is particularly suitable for inlaying the aerial image in an urban built area with rich house data.
Drawings
FIG. 1 is a schematic view of a process for embedding a plurality of aerial images in a plurality of aerial bands based on vector house data according to the method for embedding aerial images of the present invention;
FIG. 2 is a flowchart illustrating a method for extracting a mosaic line between two images according to the method for mosaicing aerial images of the present invention;
FIG. 3 is a schematic diagram of the effective width of a candidate mosaic line passing through the space between two houses in the method for mosaicing an aerial image according to the present invention;
fig. 4 is a schematic diagram of the optimization processing range of the lowest candidate mosaic line in the method of the present invention, and the gray area is the optimization processing range of the mosaic line.
Detailed Description
The technical scheme of the invention is further described in the following with the accompanying drawings of the specification.
As shown in fig. 1, a method for selecting a mosaic line from vector house data to mosaic an aerial image is characterized by comprising the following steps:
1) based on a left-turn algorithm, a boundary line of an effective area is extracted from each image in each navigation band of the shot aerial image, and the effective area refers to an area with a non-null value in the image. In each navigation band, calculating an adjacent image overlapping area in the navigation band according to the boundary line of each image effective area in the navigation band;
2) combining the effective areas in each flight band to obtain the boundary line of the effective area of each flight band, and calculating the overlapping area of the adjacent flight bands between the flight bands according to the obtained boundary line of the effective area of each flight band;
3) based on the existing vector house data, finding mosaic lines in adjacent image overlapping areas in the flight band and adjacent flight band overlapping areas between the flight bands;
as shown in fig. 2, the method for finding mosaic lines in the adjacent image overlapping regions in the navigation band extracts mosaic lines between all adjacent images one by one, when finding mosaic lines in the adjacent navigation band overlapping regions between the navigation bands, the adjacent navigation bands are used as adjacent images, the mosaic lines between the adjacent navigation bands are extracted by the same method as the method for finding mosaic lines in the adjacent image overlapping regions, two adjacent images are set as m-degree overlapped images, namely, the two images are overlapped with m-2 other images except the two images, wherein m is more than or equal to 2, and the method for extracting mosaic lines between the two adjacent images comprises the following steps:
(1) extracting intermediate lines of the space between two adjacent image house spaces in an overlapping area and skeleton lines of the overlapping area based on a constrained Delaunay triangulation algorithm, and then combining the intermediate lines of the space between the vector house spaces in the overlapping area and the skeleton lines of the overlapping area to form a candidate mosaic line library;
(2) giving a passing cost to the candidate inlaid line according to the effective width of the candidate inlaid line, and searching the candidate inlaid line with the lowest cost from the starting point to the terminal point based on the Dijkstra algorithm;
(3) and (3) optimizing the candidate mosaic line extracted in the step (2) with the lowest cost in an overlapping area by using m-degree overlapped image data to form a final mosaic line, so that any section of the final mosaic line can be ensured to be prevented from crossing salient objects which exist in the image data and are not contained in the vector house data, wherein the salient objects comprise houses, temporary construction stacking objects and the like.
4) Mutually cutting the mosaic lines in all the navigation bands and between the navigation bands, then connecting the cut mosaic lines end to end based on a left-turn algorithm, constructing an independent effective mosaic polygon for each image in each navigation band, finally using the data in the effective mosaic polygon of each image for mosaic images, and discarding the rest parts;
5) the data within the valid mosaic polygons of each image are merged to form a final mosaic image that encompasses all of the individual images.
In the step (2), the candidate mosaic lines are given a passing cost according to the effective width of the candidate mosaic lines, and the specific method for searching the candidate mosaic line with the lowest cost from the starting point to the end point based on the Dijkstra algorithm is that l is setiFor the candidate mosaic line in the overlap region of the ith two adjacent images, the candidate mosaic line liEffective width of (l) vadW (l)i) Calculated as follows:
wherein d (l)i) Representative and candidate inlaid wireiMinimum distance between adjacent houses, shown in FIG. 3, projD (h)i1,hi2) The sum of the projection differences of two adjacent houses is calculated according to the following formula (II):
projD(hi1,hi2)＝tanθi1*hi1(li)+tanθi2*hi2(li) (II)
wherein, thetai1And thetai2For two adjacent images respectively in the candidate mosaic lineiThe lower view angles of two adjacent houses; h isi1(li)、hi2(li) As a result of the height of the two houses,
candidate inlaid wireiPassing cost Tcost (l)i) Calculated as follows:
Tcost(li)＝κ(li)×len(li) (III)
wherein, len (l)i) Indicating candidate inlaid wire liLength of (d); kappa (l)i) Indicating candidate inlaid wire liContribution of the width of (c) to the cost, κ (l)i) Calculated as follows (IV):
in order to ensure that the passing cost of the skeleton line of the overlapping area is far higher than the passing cost of the minimum width of the middle line of the space between houses in the overlapping area, so that the lowest-cost mosaic line can preferentially track the middle line of the space between houses, c is set to be constant 0.1, minW is the minimum width of the middle line of the space between houses in the overlapping area, SL is set to represent all candidate mosaic line sets consisting of the middle line of the space between houses and the skeleton line of the overlapping area, CS is set to represent a path connecting the candidate mosaic lines in a plurality of SLs and passing through the start point and the end point of the mosaic line, the start point and the end point of the mosaic line are two intersection points of adjacent image effective areas, and if the number of the intersection points exceeds two, the two intersection points with the farthest distance are selected in the prior art. The passing cost f (CS) of any path CS in CS is calculated as follows:
based on Dijkstra algorithm, the candidate inlaid line ms with the lowest cost in CS can be found, and the passing cost is min [ f (CS) ], and CS belongs to CS and SL.
The method for performing optimization processing on the candidate mosaic line with the lowest cost in the overlapping region in step (3) is to determine the optimization processing range of the candidate mosaic line with the lowest cost, and as shown in fig. 4, optimize the candidate mosaic line with the lowest cost within the optimization distance bufdis (ms) according to the following formula (VI):
that is, when the effective width of the candidate mosaic line with the lowest cost is between 10M (meter) and 60M (meter), it is considered that the two houses are likely to be roads, and the range of the optimization process using the image data is defined as 5M (meter), otherwise, when the effective width of the candidate mosaic line segment is less than 10M (meter) or more than 60M (meter), it is considered that other buildings or salient objects are highly likely to exist between the two houses, and the range of the optimization process using the image data is defined as 50M (meter).
Let the mosaic line in the optimization be rs, which is calculated by the cost Tcost (rs) as follows:
where D denotes the search range defined by the above formula (VI), Tcost (u, v) denotes the cost of the mosaic line rs in the optimization through the pixel (u, v), and Tcost (u, v) is the difference between the maximum and minimum values of the pixel (u, v) in the overlapping region of the m images. The initial value of the mosaic line in the optimization is rs, which can be set as the sum of the cost of the candidate mosaic line ms with the lowest cost in the CS passing through all pixels, and any other path which is positioned in the search range D and passes through the starting point and the end point of the mosaic line can also be set, wherein the starting point and the end point of the mosaic line are respectively two intersection points of the adjacent image effective areas. The cost is calculated by the following formula (VIII):
Tcost(u,v)＝max[Lj(u,v)]-min[Lj(u,v)],j＝1,…,m (VIII)
in the formula, Lj(u, v) refers to the luminance value of the jth image in pixel (u, v), which is calculated from the three bands of red, green and blue of the image according to the following formula (IX):
wherein,andrefers to the pixel value of the pixel (u, v) in the red, green and blue bands of the jth image, and the weights of the red, green and blue bands are set to 0.3, 0.59 and 0.11 according to the prior art.
Based on the Dijkstra algorithm of the grid, the inlaid line with the lowest passing cost in the rs can be found, and the found inlaid line is the final inlaid line.
The efficiency and effect of the method for selecting mosaic lines from vector house data to mosaic aerial images are tested by utilizing three groups of aerial orthographic image data, and compared with the method based on vector roads and Dijkstra algorithm. The parameters of the three sets of image data are shown in table 1. The precision comparison of the method of the invention and the method based on the vector road and the Dijkstra algorithm is shown in tables 2-4.
TABLE 1 three sets of aerial image correlation parameters
As can be seen from Table 1, the three aerial images are respectively from three regions with different building densities, including urban areas, suburban areas and rural areas, and the data accuracy of the vector house is from 0.5M to 5M.
Table 2 three methods of comparison based on 36 urban images
*Note that: the Dijkstra algorithm does not rely on vector data. The candidate mosaic line is a straight line between the intersections of the two image overlapping regions, and therefore the calculation time approaches 0 s.
Table 3 three methods of comparison based on 6 suburb images
TABLE 4 comparison of three methods based on 110 rural images
As can be seen from tables 2-4, the method for selecting mosaic polygons from vector house and road data to mosaic aerial images provided by the invention has obvious advantages compared with the vector road-based method and the Dijkstra algorithm-based mosaic line selection method: 1. compared with the mosaic line selection method based on Dijkstra algorithm which is recognized to have the best effect at present, 80-90% of operation time can be saved. 2. The invention can obviously improve the inlaying quality by mapping big data and pass through less houses. Compared with the mosaic line selection method based on the vector road, the method can pass through 10-40% less houses; compared with the mosaic line selection method based on Dijkstra algorithm which is generally recognized to be the best in effect, the method can pass through 1-15% of houses less. Therefore, the method has great advantage for inlaying the urban aerial image with rich house data and can avoid traversing almost all houses. 3. The maximum memory required by the vector house-based method is similar to that of the vector road-based method, but is significantly smaller than that of the Dijkstra-based method (only about 26-30% of the latter).
The above embodiments are only for illustrating the present invention, and the connection and structure of the components may be changed, and on the basis of the technical solution of the present invention, the improvement and equivalent transformation of the connection and structure of the individual components according to the principle of the present invention should not be excluded from the scope of the present invention.
Claims (2)
1. A method for selecting a mosaic line from vector house data to mosaic an aerial image is characterized by comprising the following steps:
1) extracting boundary lines of effective areas of each image in each flight band of the shot aerial images based on a left-turn algorithm, and calculating adjacent image overlapping areas in the flight bands according to the boundary lines of the effective areas of each image in the flight bands in each flight band;
2) combining the effective areas in each flight band to obtain the boundary line of the effective area of each flight band, and calculating the overlapping area of the adjacent flight bands between the flight bands according to the obtained boundary line of the effective area of each flight band;
3) based on the existing vector house data, finding mosaic lines in adjacent image overlapping areas in the flight band and adjacent flight band overlapping areas between the flight bands;
4) mutually cutting the mosaic lines in all the navigation bands and between the navigation bands, then connecting the cut mosaic lines end to end based on a left-turn algorithm, constructing an independent effective mosaic polygon for each image in each navigation band, finally using the data in the effective mosaic polygon of each image for mosaic images, and discarding the rest parts;
5) merging the data in the effective mosaic polygon of each image to form a final mosaic image which contains all the single images;
the method for finding mosaic lines in the adjacent image overlapping regions in the aerial zones in the step 3) is to extract mosaic lines between all adjacent images one by one, when finding mosaic lines in the adjacent aerial zone overlapping regions between the aerial zones, the adjacent aerial zones are used as adjacent images, the mosaic lines between the adjacent aerial zones are extracted by the same method as the method for finding mosaic lines in the adjacent image overlapping regions, two adjacent images are set to be m-degree overlapped images, namely, the two images are overlapped with m-2 other images except the two images, and m is more than or equal to 2, and the method for extracting the mosaic lines between the two adjacent images comprises the following steps:
(1) extracting intermediate lines of the space between two adjacent image house spaces in an overlapping area and skeleton lines of the overlapping area based on a constrained Delaunay triangulation algorithm, and then combining the intermediate lines of the space between the vector house spaces in the overlapping area and the skeleton lines of the overlapping area to form a candidate mosaic line library;
(2) giving a passing cost to the candidate inlaid line according to the effective width of the candidate inlaid line, and searching the candidate inlaid line with the lowest cost from the starting point to the terminal point based on the Dijkstra algorithm;
(3) optimizing the candidate mosaic line extracted in the step (2) with the lowest cost in an overlapping area by using m-degree overlapped image data to form a final mosaic line, and ensuring that any section of the final mosaic line can avoid crossing a salient object which exists in the image data and is not contained in the vector house data;
said step (2)The specific method for finding the candidate mosaic line with the lowest cost from the starting point to the end point based on the Dijkstra algorithm comprises the following steps of setting liFor the candidate mosaic line in the overlap region of the ith two adjacent images, the candidate mosaic line liEffective width of (l) vadW (l)i) Calculated as follows:
wherein d (l)i) Representative and candidate inlaid wireiMinimum distance between adjacent houses, projD (h)i1，hi2) The sum of the projection differences of two adjacent houses is calculated according to the following formula (II):
projD(hi1，hi2)＝tanθi1*hi1(li)+tanθi2*hi2(li) (II)
wherein, thetai1And thetai2For two adjacent images respectively in the candidate mosaic lineiThe lower view angles of two adjacent houses; h isi1(li)、hi2(li) As a result of the height of the two houses,
candidate inlaid wireiPassing cost Tcost (l)i) Calculated as follows:
Tcost(li)＝κ(li)×len(li) (III)
wherein, len (l)i) Indicating candidate inlaid wire liLength of (d); kappa (l)i) Indicating candidate inlaid wire liContribution of the width of (c) to the cost, κ (l)i) Calculated as follows (IV):
wherein c is a constant of 0.1, minW is the minimum width of the middle line of the room space in the overlap region, SL is a set of all candidate mosaic lines consisting of the middle line of the room space and the skeleton line of the overlap region, CS is a path connecting the candidate mosaic lines in the SLs and passing through the start point and the end point of the mosaic line, the start point and the end point of the mosaic line are two intersection points of adjacent video effective regions, and the passing cost f (CS) of any path CS in CS is calculated according to the following formula (V):
f(cs)＝ΣTcost(li)，li∈CS∈SL (V)
based on Dijkstra algorithm, the candidate inlaid line ms with the lowest cost in CS can be found, and the passing cost is min [ f (CS) ], and CS belongs to CS and SL.
2. The method of claim 1 for selecting a mosaic line from vector house data for mosaicing an aerial image, wherein: the method for optimizing the candidate mosaic line with the lowest cost in the overlapping region in the step (3) is that the optimization processing range of the candidate mosaic line with the lowest cost is determined firstly, and the candidate mosaic line with the lowest cost is optimized within the optimization distance BufDis (ms) according to the following formula (VI):
let the mosaic line in the optimization be rs, which is calculated by the cost Tcost (rs) as follows:
Tcost(rs)＝ΣTcost(u，v)，(u，v)∈rs∈D (VII)
where D denotes the search range defined by equation (VI) above, Tcost (u, v) denotes the cost of the mosaic line rs in the optimization through the pixel (u, v), Tcost (u, v) being the difference between the maximum and minimum values of the pixel (u, v) in the overlap region of the m images, calculated as equation (VIII) below:
Tcost(u，v)＝max[Lj(u，v)]-min[Lj(u，v)]，j＝1，...，m (VIII)
in the formula, Lj(u, v) refers to the luminance value of the jth image in pixel (u, v), which is calculated from the three bands of red, green and blue of the image according to the following formula (IX):
wherein,andrefers to the pixel value of the pixel (u, v) in the red, green and blue bands of the jth image,
based on the Dijkstra algorithm of the grid, the inlaid line with the lowest passing cost in the rs can be found, and the found inlaid line is the final inlaid line.
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