CN111127474B - Airborne LiDAR point cloud assisted orthophoto mosaic line automatic selection method and system - Google Patents
Airborne LiDAR point cloud assisted orthophoto mosaic line automatic selection method and system Download PDFInfo
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Abstract
The invention provides an airborne LiDAR point cloud assisted orthophoto mosaic line automatic selection method and system, which comprises data preparation, wherein the data preparation comprises the steps of inputting two orthophotos with a certain overlapping area and point cloud data corresponding to the overlapping area, as well as the starting point position S and the end point position E of a mosaic line in the overlapping area and the size of a point cloud grid; obtaining an initial mosaic line through dynamic programming based on point cloud data, wherein the method comprises the steps of carrying out regular division on the point cloud according to plane coordinates to obtain regular voxels, then finding out the optimal mosaic line from S to E through the dynamic programming by taking the voxels as units to serve as the initial mosaic line, and the nodes of the initial mosaic line are the centers of the voxels; the mosaic line optimization based on the image information comprises the step of optimizing by utilizing the image information on the basis of an initial mosaic line obtained through point cloud dynamic planning to obtain an optimized smooth mosaic line. The invention greatly reduces the problem of inconsistent vision at two sides of the image mosaic line, so that the mosaic line successfully avoids the high-range abrupt change area of a house.
Description
Technical Field
The invention belongs to the field of surveying and mapping science and technology, and relates to a point cloud assisted orthophoto mosaic line automatic selection method and system, which are mainly applied to large-range splicing of an orthophoto image in aerial photogrammetry and large-range splicing of an orthophoto image in an airborne LiDAR system.
Background
Due to the fact that the ground surface fluctuation and the image imaging angles are different, imaging positions of ground objects such as vegetation and houses on the corrected orthographic images on different images have differences, namely image parallax, and the difference can be quantified to a certain degree by constructing a cost matrix based on information such as gray scale, gradient and texture of the images, the minimum cost is taken as a standard, a final mosaic line is obtained by utilizing a dynamic programming algorithm, and obstacle ground objects such as houses and vegetation can be bypassed to a certain degree. The mosaic line obtained in the process has the minimum global cost value, but the local visual inconsistency of the mosaic line cannot be guaranteed to be small. Therefore, the foreground area of the image in the overlapping area can be segmented in advance, and the foreground area is set to be the area where the mosaic lines cannot pass, so that the final mosaic lines are lower in local cost. Foreground segmentation can be achieved in two ways: firstly, setting a threshold value T, wherein the area with the cost matrix value larger than the threshold value T is a foreground area, and the threshold value T can be obtained in an iterative self-adaptive mode to meet the communication between a starting point and an end point of a mosaic line; and secondly, an object-based method comprises the steps of firstly segmenting an image in an overlapping area to obtain an object, constructing a superpixel cost matrix based on the object, and setting a threshold value I, wherein the threshold value I can be obtained in an iterative self-adaptive mode, so that the superpixel cost meets the condition that a superpixel where a starting point is located is communicated with a superpixel where an end point is located. The automatic embedding method only depending on the image is difficult to completely realize the avoidance of the obstacle ground objects such as buildings, vegetation and the like, so that the automatic selection of the embedding lines is assisted by introducing data such as house, road vector information, DSM (digital surface model), point cloud and the like, and the effect of avoiding the obstacle ground objects in the automatic embedding process can be improved.
Disclosure of Invention
The invention aims to provide a technical scheme for automatically selecting a point cloud auxiliary mosaic line, so as to simplify the selection of the point cloud auxiliary mosaic line and improve the edge connecting precision of large-range orthoimage generation.
The technical scheme of the invention provides an airborne LiDAR point cloud assisted orthophoto mosaic line automatic selection method, which comprises the following steps:
step one, data preparation, which comprises inputting two orthoimages with certain overlapping areas and point cloud data corresponding to the overlapping areas, as well as the starting position S and the end position E of a mosaic line in the overlapping areas and the size of a point cloud grid;
step two, obtaining an initial mosaic line through dynamic planning based on point cloud data, wherein the method comprises the steps of carrying out rule division on the point cloud according to plane coordinates to obtain rule voxels, then finding out the optimal mosaic line from S to E through dynamic planning by taking the voxels as units to serve as the initial mosaic line, and the nodes of the initial mosaic line are the centers of the voxels;
and step three, mosaic line optimization based on image information, which comprises the step of optimizing by utilizing the image information on the basis of the initial mosaic line obtained by point cloud dynamic planning to obtain the optimized smooth mosaic line.
And, in the second step, defining the original point cloud as
The divided point cloud voxels are
The objective function of the dynamic programming problem is:
wherein the content of the first and second substances,
refers to the starting voxel
To the voxel
Corresponding to the voxel
To the voxel
The optimal path of (2);
refers to a voxel
Beta is a harmonic coefficient,
is a slave voxel
To a certain neighboring voxel
Is determined by the local cost function of the path of (1).
Moreover, the values of β are as follows,
furthermore, a local cost function
The definition is that,
wherein, the lambda is a coefficient,
and
are respectively voxels
And volume element
Average normalized elevation of.
Furthermore, in step three, the definition is based on point clouds
The obtained initial inlaid wire is
In the range of the orthographic image overlapping region
Expanding the fold line to obtain a buffer region defined as
Wherein, n is the symbol of region intersection,
to inlay the wire
After expansion to obtainR is the expansion radius;
in mosaic line optimization based on image information, the optimization function of chon is adopted to calculate the mosaic line passing cost of a certain position according to the image information of two orthoimages at the position p
And reducing the size of the interior of the buffer
Obtaining a local cost function cost (p),
where β is the reduction factor.
The invention also provides an airborne LiDAR point cloud assisted orthophoto mosaic line automatic selection system, which comprises the following modules:
the first module is used for data preparation and comprises the steps of inputting two orthoimages with certain overlapping areas and point cloud data corresponding to the overlapping areas, as well as the starting position S and the end position E of a mosaic line in the overlapping areas and the size of a point cloud grid;
the second module is used for obtaining an initial mosaic line through dynamic planning based on the point cloud data, and comprises the steps of carrying out rule division on the point cloud according to plane coordinates to obtain rule voxels, then finding out the optimal mosaic line from S to E through dynamic planning by taking the voxels as units to serve as the initial mosaic line, wherein the nodes are the centers of the voxels;
and the third module is used for mosaic line optimization based on image information, and comprises the step of optimizing by using the image information on the basis of the initial mosaic line obtained by point cloud dynamic planning to obtain the optimized smooth mosaic line.
Furthermore, in the second module, the original point cloud is defined as
The divided point cloud voxels are
The objective function of the dynamic programming problem is:
wherein, the first and the second end of the pipe are connected with each other,
refers to the starting voxel
To the voxel
Corresponding to the voxel
To the voxel
The optimal path of (2);
refers to a voxel
Beta is a harmonic coefficient,
is a slave voxel
To a certain neighboring voxel
Is determined by the local cost function of the path of (1).
Moreover, the value of β is as follows,
furthermore, a local cost function
The definition is that,
wherein, the lambda is a coefficient,
and
are respectively voxels
And volume element
Average normalized elevation of.
Furthermore, in a third module, the definition is based on a point cloud
The obtained initial inlaid wire is
In the range of the orthographic image overlapping region
Expanding the fold line to obtain a buffer region defined as
Wherein n is region intersectionThe number of the symbols is such that,
to inlay the wire
In the region obtained after expansion, R is the expansion radius;
in mosaic line optimization based on image information, the optimization function of chon is adopted to calculate the mosaic line passing cost of a certain position according to the image information of two orthoimages at the position p
And reducing the size of the interior of the buffer
Obtaining a local cost function cost (p),
where β is the reduction factor.
The method greatly reduces the problem of inconsistent vision at two sides of the image mosaic line, so that the mosaic line successfully avoids the high-range abrupt change area of a house. Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention utilizes intermediate result dense point clouds generated by photogrammetry or registered LIDAR point clouds to assist automatic selection of mosaic lines.
2. The invention provides a technical scheme for obtaining an initial path on a point cloud, and the refinement of an initial mosaic line is assisted by arranging a buffer area on an image, so that the efficiency and the precision are high.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a diagram illustrating a topological relationship between images according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the embodiments of the present invention are not limited thereto.
The invention proposes that the left and right images corresponding to the overlapping area can be represented as L, R, and the auxiliary information as F. The image can be used for extracting characteristic information such as gradient, texture, gray level and the like, and auxiliary information such as point cloud comprises high-precision elevation and position information and reliable attribute information. The cost matrix obtained by quantifying the difference of gradient, texture and gray scale between the left and right images can show the inconsistency of the left and right images caused by the projection difference to a certain extent. The auxiliary information, such as point clouds, can be used to assist in quantifying the disparity between the left and right images using the elevation information and the attribute information.
Cost(p)＝k(p)·D L,R (p)
Where p represents position, k (p) represents the contribution of the corresponding position assistance information to the cost quantification, D L,R And (p) represents the contribution of the quantization of the corresponding position by the characteristic difference of the left and right image gradients, textures, gray scales and the like to cost quantization.
Referring to fig. 2, the automatic selection of the mosaic line is to determine a starting point S and an end point E, and obtain an optimal mosaic line based on the minimum quantization cost.
The technical scheme provided by the embodiment of the invention is a point cloud assisted orthophoto mosaic line automatic selection method, as shown in figure 1, firstly, an initial mosaic line capable of bypassing an obstacle ground object is obtained through dynamic planning by utilizing classification information (ground points and non-ground points) and elevation information of point cloud, then a cost matrix is constructed through the characteristics of similarity, gradient and the like of images in an overlapping area, and the mosaic line is optimized in a certain buffer area around the initial mosaic line by taking the minimum quantization cost as a standard, so that a final mosaic line is obtained. The method implementation flow of the embodiment comprises the following steps:
step 1, data preparation
The input data of the method comprises two orthographic images with certain overlapping areas and point cloud data corresponding to the overlapping areas, wherein the point cloud data is acquired through airborne LiDAR, and the airborne point cloud data is classified into two types of ground points and non-ground points through a filtering algorithm. The input parameters of the method comprise the positions of a starting point and an end point of a mosaic line in an overlapping area (respectively defined as S and E) and the size d of a point cloud grid;
step 2, obtaining an initial mosaic line through point cloud data
The method comprises the steps of firstly, regularly dividing point clouds according to plane coordinates of the point clouds to obtain regular voxels, then, taking the voxels as units, finding out the optimal mosaic line from point positions S to E through dynamic planning, wherein the mosaic line is an initial mosaic line, and nodes of the mosaic line are the centers of the voxels.
Defining an original point cloud as
The divided point cloud voxels are
The objective function of the dynamic programming problem is:
wherein the content of the first and second substances,
refers to the starting voxel
To the voxel
Corresponding to the voxel
To the voxel
The optimal path of (2);
refers to a voxel
The method searches eight planar neighborhoods of the voxels; β is a harmonic coefficient whose value is set to:
then the slave voxel
To a certain neighboring voxel
Local cost function of the path of
Is defined as:
wherein λ is a coefficient and can be set to any positive number;
and
are respectively voxels
And volume element
Average normalized elevation of, e.g.
Is defined as:
where N (-) is represented as the number of points in a certain point cloud voxel, H min Is the minimum elevation, H, in the point cloud max As the maximum elevation in the point cloud data, H (P) i ) Refers to point P i The elevation of (a).
Step 3, mosaic line optimization based on image information
And optimizing by using image information on the basis of the initial mosaic line obtained by point cloud dynamic planning so as to obtain a smoother mosaic line.
Defining is based on point clouds
The obtained initial inlaid wire is
This is a polygonal line structure of the object plane in the range of the orthographic image overlapping region
This fold line is expanded to obtain a buffer zone defined as:
wherein, n is the symbol of region intersection,
to inlay the wire
The region obtained after the expansion, R is the expansion radius, and is generally set to 5 to 20 pixels according to the image resolution. In mosaic line optimization based on image information, the method adopts chon optimization function to calculate mosaic line passing cost of a certain position according to image information of two orthoimages at the position p
And reducing the interior of the buffer
Obtaining a local cost function cost (p):
wherein beta is a reduction coefficient, in the method, the value is a number which is greater than 0 and less than 1, and the expression of chon cost is as follows:
where (x, y) is the normalized correlation coefficient in a 5 × 5 image window centered on position p, and (x, y) is the coordinate of p
Wherein f and g respectively represent two ortho images being mosaicked, f (i, j) represents the gray scale on the f image corresponding to the coordinates (i, j),
representing the average of the f image gray levels in a 5 x 5 window centered on the coordinate (x, y), g (i, j) representing the gray level on the g image corresponding to the coordinate (i, j),
the g image gray level average value in a 5 × 5 window centered on the coordinates (x, y) is shown.
In practice, pass cost
The calculation of (D) can be found in Chon J, Kim H, Lin C S.Seam-line decimination for image mosaicking:A technique minimizing the maximum local mismatch and the global cost[J].ISPRS Journal of Photogrammetry&Remote Sensing,2010,65(1):86-92.
The invention solves the cost minimum path corresponding to the cost function cost (p) by adopting the same dynamic programming algorithm, thereby obtaining the final mosaic line
For reference, the dynamic programming-based optimal path detection algorithm step principle involved herein is described as follows:
(1) the dynamic programming algorithm is represented as DP (O, Cost, S, E), where O represents the left and right image overlap region, Cost represents the quantization Cost matrix, S represents the starting point, and E represents the ending point.
(2) The initialization variable dist [ p ] - ∞, Father [ p ] - ∞, and found [ p ] - ∞wherep ∈ O denotes a node, dist [ p ] denotes the cost of the starting point S to each node p in the matrix, the initialization is set to infinity, Father [ p ] denotes the parent node of each node p, the initialization is undefined, and found [ p ] records whether the node p is searched for, and the initialization is unsearched (i.e., unset).
(3) And constructing a node queue Q (V, dist [ V ]) to represent the nodes of the mosaic line path from the starting point S to the point V, wherein the dist [ V ] represents the corresponding cost value of the mosaic line path. The node queue Q is initialized to an empty set, and adds the starting point S, and sets dist [ p ] ═ 0 and Visited [ S ] ═ Visited. At this point, the Father [ S ] is still undefined, and there is only one combination in queue Q, i.e., (S, 0).
(4) Execution cycle (5) - (8)
(5) And extracting a combination (U, dist [ U ]) from all the node combinations in the Q, wherein the search record corresponding to the node U is Visited [ U ] ═ unvisited, and the cost value dist [ U ] corresponding to the node U is the minimum in all the nodes with the unvisited search record values.
(6) Setting visual [ U ] as visual, traversing all nodes in the eight-neighborhood of U, and executing (7) when dist [ V ] > dist [ U ] + cost (U) aiming at a certain domain node V.
(7) Setting dist [ V ] = dist [ U ] + cost (U), setting facility [ V ] ═ U, and adding a combination (V, dist [ V ]) in the queue Q, when V ═ E, it is stated that an optimal path from the starting point S to the end point E has been found by the dynamic programming algorithm, and at this time, a loop is skipped.
(8) And for the terminal point E, obtaining the previous node of the node E in the optimal mosaic line path through the Father [ E ], and analogizing in turn, finding out the previous node in the path again, and finally finding out the node sequence corresponding to the minimum cost path so as to obtain the optimal mosaic line.
In specific implementation, the invention can realize automatic operation flow by adopting a software mode and can also realize automatic operation flow by adopting a system mode.
The embodiment of the invention also provides an airborne LiDAR point cloud assisted orthophoto mosaic line automatic selection system, which comprises the following modules:
the first module is used for data preparation and comprises the steps of inputting two orthoimages with certain overlapping areas and point cloud data corresponding to the overlapping areas, as well as the starting position S and the end position E of a mosaic line in the overlapping areas and the size of a point cloud grid;
the second module is used for obtaining an initial mosaic line through dynamic planning based on the point cloud data, and comprises the steps of carrying out rule division on the point cloud according to plane coordinates to obtain rule voxels, then finding out the optimal mosaic line from S to E through dynamic planning by taking the voxels as units to serve as the initial mosaic line, wherein the nodes are the centers of the voxels;
and the third module is used for mosaic line optimization based on image information, and comprises the step of optimizing by using the image information on the basis of the initial mosaic line obtained by point cloud dynamic planning to obtain the optimized smooth mosaic line.
In specific implementation, the implementation of each module may refer to the corresponding steps of the method, which is not described in detail herein.
Although the preferred embodiments of the present invention have been described with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are only illustrative and not restrictive, and those skilled in the art can make many modifications without departing from the spirit and scope of the invention as claimed.
Claims (6)
1. An airborne LiDAR point cloud assisted orthophoto mosaic line automatic selection method is characterized by comprising the following steps:
step one, data preparation, which comprises inputting two orthoimages with certain overlapping areas and point cloud data corresponding to the overlapping areas, as well as the starting position S and the end position E of a mosaic line in the overlapping areas and the size of a point cloud grid;
step two, acquiring initial mosaic lines through dynamic planning based on point cloud data, wherein the method comprises the steps of carrying out regular division on the point cloud according to plane coordinates to obtain regular voxels, then finding out the optimal mosaic lines from S to E through the dynamic planning by taking the voxels as units to serve as the initial mosaic lines, and the nodes of the initial mosaic lines are the centers of the voxels;
in the implementation process, the original point cloud is defined as
The divided point cloud voxels are
The objective function of the dynamic programming problem is:
wherein the content of the first and second substances,
refers to the starting voxel
To the voxel
Corresponding to the voxel
To the voxel
The optimal path of (2);
refers to a voxel
Beta is a harmonic coefficient,
is a slave voxel
To a certain neighboring voxel
A local cost function of the path of (a);
the value of beta is as follows,
and step three, mosaic line optimization based on image information, which comprises the step of optimizing by utilizing the image information on the basis of the initial mosaic line obtained by point cloud dynamic planning to obtain the optimized smooth mosaic line.
2. The method of claim 1, wherein the method comprises the steps of: local cost function
The definition is that,
wherein, the lambda is a coefficient,
and
are respectively voxels
And volume element
Average normalized elevation of.
3. The method of claim 1, wherein the method comprises the steps of: in step three, the definition is based on point cloud
The obtained initial inlaid wire is
In the range of the orthographic image overlapping region
Expanding the fold line to obtain a buffer region defined as
In the formulaN is a symbol for region intersection,
to inlay the wire
In the region obtained after expansion, R is the expansion radius;
in mosaic line optimization based on image information, the optimization function of chon is adopted to calculate the mosaic line passing cost of a certain position according to the image information of two orthoimages at the position p
And reducing the size of the interior of the buffer
Obtaining a local cost function cost (p),
where β' is the reduction factor.
4. The utility model provides an airborne LiDAR point cloud assisted orthophoto mosaic line automatic selection system which characterized in that includes the following module:
the first module is used for data preparation and comprises the steps of inputting two orthoimages with certain overlapping areas and point cloud data corresponding to the overlapping areas, as well as the starting position S and the end position E of a mosaic line in the overlapping areas and the size of a point cloud grid;
the second module is used for obtaining an initial mosaic line through dynamic planning based on the point cloud data, and comprises the steps of carrying out rule division on the point cloud according to plane coordinates to obtain rule voxels, then finding out the optimal mosaic line from S to E through dynamic planning by taking the voxels as units to serve as the initial mosaic line, wherein the nodes are the centers of the voxels;
in the implementation process, the original point cloud is defined as
The divided point cloud voxels are
The objective function of the dynamic programming problem is:
wherein the content of the first and second substances,
refers to the starting voxel
To the voxel
Corresponding to the voxel
To the voxel
The optimal path of (2);
refers to a voxel
Beta is a harmonic coefficient,
is a slave voxel
To a certain neighboring voxel
A local cost function of the path of (a);
the value of beta is as follows,
and the third module is used for mosaic line optimization based on image information, and comprises the step of optimizing by using the image information on the basis of the initial mosaic line obtained by point cloud dynamic planning to obtain the optimized smooth mosaic line.
5. The automatic orthographic image mosaic line selection system as recited in claim 4, wherein said automatic orthographic image mosaic line selection system comprises: local cost function
The definition is that,
wherein, the lambda is a coefficient,
and
are respectively voxels
And volume element
Average normalized elevation of.
6. The automatic orthographic image mosaic line selection system as claimed in claim 4 or 5 and as claimed in any one of the preceding claims, wherein: in a third module, the definition is based on point clouds
The obtained initial inlaid wire is
In the range of the orthographic image overlapping region
Expanding the fold line to obtain a buffer region defined as
Wherein, n is the symbol of region intersection,
to inlay the wire
In the region obtained after expansion, R is the expansion radius;
in mosaic line optimization based on image information, the optimization function of chon is adopted to calculate the mosaic line passing cost of a certain position according to the image information of two orthoimages at the position p
And reducing the size of the interior of the buffer
Obtaining a local cost function cost (p),
where β' is the reduction factor.
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