CN112819959A - Hyperspectral image and laser radar data intrinsic hyperspectral point cloud generation method - Google Patents

Hyperspectral image and laser radar data intrinsic hyperspectral point cloud generation method Download PDF

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CN112819959A
CN112819959A CN202110086571.1A CN202110086571A CN112819959A CN 112819959 A CN112819959 A CN 112819959A CN 202110086571 A CN202110086571 A CN 202110086571A CN 112819959 A CN112819959 A CN 112819959A
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谷延锋
金旭东
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Abstract

The invention relates to a method for generating a hyperspectral point cloud intrinsic to a hyperspectral image and lidar data, relates to the technical field of remote sensing image processing, and aims to solve the problems that information loss, spectrum distortion and low efficiency are easily caused by the existing hyperspectral point cloud data generation method.

Description

Hyperspectral image and laser radar data intrinsic hyperspectral point cloud generation method
Technical Field
The invention relates to the technical field of remote sensing image processing, in particular to a hyper-spectral image and lidar data intrinsic hyper-spectral point cloud generation method based on hyper-voxels.
Background
In recent years, on one hand, a hyperspectral sensor and a laser radar sensor are gradually miniaturized and integratability is continuously improved, on the other hand, load and stability of an unmanned aerial vehicle platform carrying the sensors are greatly improved, and due to changes, hyperspectral-laser radar combination and synchronous data acquisition become one of mainstream remote sensing detection schemes more and more. When a large amount of hyperspectral and laser radar data are accumulated, how to integrate the two types of data also becomes a problem which needs to be solved urgently. The hyperspectral image and the laser radar point cloud data have complementarity and isomerism, and more specifically, the hyperspectral image has rich spectral information, but the spatial information is degradation from a three-dimensional image to a two-dimensional image; the laser radar point cloud can acquire accurate three-dimensional space information, but spectral information is relatively deficient.
The existing method for fusion processing of hyperspectral images and laser radar point clouds is roughly divided into two types, one is to generate two-dimensional DSM or DEM data by the laser radar point clouds, and then match the two-dimensional DSM or DEM data with the hyperspectral images; and the other method is that the spectral information of the hyperspectral image is distributed to the laser radar point cloud. Both of the two modes are based on the characteristics extracted from the hyperspectral image and the laser radar point cloud, the problem that spectral information or spatial three-dimensional information is lost exists, and meanwhile, the spectral information is also easily influenced by environmental illumination and cannot generate satisfactory integrated data.
Disclosure of Invention
The purpose of the invention is: aiming at the problems of information loss, spectrum distortion and low efficiency of the existing hyperspectral point cloud data generation method, a hyperspectral image and lidar data intrinsic hyperspectral point cloud generation method is provided.
The technical scheme adopted by the invention to solve the technical problems is as follows:
the method for generating the hyperspectral image and the intrinsic hyperspectral point cloud of the laser radar data comprises the following steps:
the method comprises the following steps: acquiring a hyperspectral image and a laser radar point cloud, and obtaining an intrinsic mapping matrix according to the hyperspectral image and the laser radar point cloud;
step two: performing voxel segmentation on the laser radar point cloud to generate a voxel representation matrix;
step three: obtaining an incident illumination direction according to the intrinsic mapping matrix;
step four: and performing combined eigen decomposition based on the hyper-voxels according to the eigen mapping matrix, the hyper-voxel representation matrix and the incident illumination direction to generate an eigen hyperspectral point cloud.
Further, the specific steps of the first step are as follows:
inputting a hyperspectral image
Figure BDA0002910970420000021
And lidar point cloud
Figure BDA0002910970420000022
Wherein h isk＝[hk(λ1),hk(λ2),…,hk(λd)]T,k＝1,2,...,u，hkThe spectral characteristics of each pixel are represented by lambda, d and u, wherein lambda is the wavelength, d is the number of wave bands, and u is the total number of the hyperspectral image pixels; attribute feature p of each point clouds＝[xs,ys,zs,Is]T1,2, v, wherein (x)s,ys,zs) For coordinate information of each point cloud, IsThe intensity of the laser radar is shown, and v is the total number of the laser radar point clouds;
calculating the normal of each point in the laser radar point cloud P to obtain corresponding normal characteristics
Figure BDA0002910970420000023
Wherein n iss＝[Nx,Ny,Nz]T1,2, v denotes the projection of the normal on the x, y, z spatial coordinate axes, and the matrix a is then calculatedijEach element of (a):
Figure BDA0002910970420000024
further, from AijAnd N, calculating to obtain each element of the eigen mapping matrix:
Figure BDA0002910970420000025
according to matrix definition and
Figure BDA0002910970420000026
further obtain an eigenmapping matrix
Figure BDA0002910970420000027
Further, the second step comprises the following specific steps:
mapping the laser radar point cloud P into a weighted graph G ═ V, E, and utilizing each vertex V in ViRepresenting each point P in a lidar point cloudiAnd each vertex V in Euclidean spaceiAll the edges are connected with 50 vertexes nearest to the vertex by an edge, and the weight W (E) of each edge E ∈ E is expressed as | | Ia-IbI, where a and b are the vertex V joined by the edge eaAnd VbSerial number of (2), initialization of hyper-voxel segmentation of point clouds
Figure BDA0002910970420000028
Enabling each hyper-voxel to correspond to each vertex, traversing edges in E according to the order of the weight from small to large, judging whether two vertexes connected by E belong to two different hyper-voxels, if not, continuing to the next edge, if so, judging whether W (E) is smaller than the distance between the two hyper-voxels connected by E, if so, merging the two hyper-voxels connected by E, if not, continuing to the next edge, and obtaining a hyper-voxel set after the traversal is finished, wherein the hyper-voxel set is
Figure BDA0002910970420000029
Where t is the number of voxels in the voxel, according to
Figure BDA00029109704200000210
Calculating the hyper-voxel:
Figure BDA00029109704200000211
according to matrix definition and BijAnd then the super voxel representation matrix B is obtained.
Further, the third step comprises the following specific steps:
firstly, establishing a relational expression for each wave band lambda:
Figure BDA0002910970420000031
GL＝0
wherein the matrix G is
Figure BDA0002910970420000032
A row vector of (a);
then to
Figure BDA0002910970420000033
Matrix G ofTG, performing characteristic decomposition, arranging the characteristic values obtained by decomposition and the characteristic vectors corresponding to the characteristic values from small to large according to the characteristic values, and expressing as follows: { e1,v1}，{e2,v2And { e } and3,v3}, minimum eigenvalue e1Corresponding feature vector v1Then the direction of incident illumination L.
Further, the fourth step specifically comprises:
firstly, calculating the ith row and the jth column element of a hyperspectral image-laser radar point cloud combined eigen decomposition matrix M, wherein the ith row and the jth column element are expressed as follows:
Figure BDA0002910970420000034
then according to the matrix definition and MijObtaining a hyperspectral image-laser radar point cloud combined eigen decomposition matrix M, BjgRepresenting the jth row and jth column element of the supersubvoxel representation matrix B, Bjg，g＝1,2,...,t，
Is provided with
Figure BDA0002910970420000035
Is a set of eigen-high spectral reflectivities of hyper-voxels, the eigen-high spectral reflectance of each hyper-voxel
Figure BDA0002910970420000036
The joint eigen decomposition relationship is:
Figure BDA0002910970420000037
solved to obtain
Figure BDA0002910970420000038
Then according to
Figure BDA0002910970420000039
Obtaining the intrinsic high spectral reflectivity of all point clouds
Figure BDA00029109704200000310
Wherein r iss＝[rs(λ1),rs(λ2),...,rs(λd)]TFor the intrinsic high spectral reflectance of each point cloud, the intrinsic high spectral reflectance r of each point cloud is finally determineds＝[rs(λ1),rs(λ2),...,rs(λd)]TSpatial coordinates [ x ] of the Stack to lidar Point cloud Ps,ys,zs]TThen generating an intrinsic hyperspectral point cloud
Figure BDA00029109704200000311
Wherein
Figure BDA00029109704200000312
The attribute features of each hyperspectral point cloud.
The invention has the beneficial effects that:
according to the method, a physical model of hyperspectral imaging is utilized, spectral information of a hyperspectral image and three-dimensional space xi information of a laser radar point cloud are combined from the angle of signals, the spectrum uncertainty of the hyperspectral image caused by the degradation of the spectral information caused by illumination is eliminated, the speed of an algorithm is greatly increased by a mode that a voxel faces an object, the intrinsic hyperspectral point cloud integrating spectral information and spatial three-dimensional information is finally generated, and information loss and spectrum distortion are greatly avoided.
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FIG. 1 is a flow chart of the present application;
FIG. 2 is an input hyperspectral image;
FIG. 3 is an input lidar point cloud;
FIG. 4 is a schematic diagram of the super voxel segmentation results generated by the present invention;
FIG. 5 is a schematic diagram of an intrinsic hyperspectral point cloud generated by the present invention;
FIG. 6 is a schematic diagram of the error comparison between the hyperspectral point cloud generated by the present invention and other methods.
Detailed Description
It should be noted that, in the present invention, the embodiments disclosed in the present application may be combined with each other without conflict.
The first embodiment is as follows: specifically describing the present embodiment with reference to fig. 1, the method for generating a hyperspectral image and lidar data-intrinsic hyperspectral point cloud according to the present embodiment includes the following steps:
the method comprises the following steps: acquiring a hyperspectral image and a laser radar point cloud, and obtaining an intrinsic mapping matrix according to the hyperspectral image and the laser radar point cloud;
step two: performing voxel segmentation on the laser radar point cloud to generate a voxel representation matrix;
step three: obtaining an incident illumination direction according to the intrinsic mapping matrix;
step four: and performing combined eigen decomposition based on the hyper-voxels according to the eigen mapping matrix, the hyper-voxel representation matrix and the incident illumination direction to generate an eigen hyperspectral point cloud.
Step 1: inputting a hyperspectral image
Figure BDA0002910970420000041
And lidar point cloud
Figure BDA0002910970420000042
Wherein h isk＝[hk(λ1),hk(λ2),…,hk(λd)]TK is 1, 2., u is the spectral feature of each pixel, λ is the wavelength, d is the number of bands, and u is the number of hyperspectral image pixels; p is a radical ofk＝[xk,yk,zk,Ik]T K 1,2,.., v, inner (x)k,yk,zk) Is the coordinate information of each point cloud, IkIs the intensity of the lidar and v is the number of lidar point clouds. Firstly, calculating the normal of each point in the point cloud P of the laser radar to obtain corresponding normal characteristics
Figure BDA0002910970420000043
Wherein n isk＝[Nx,Ny,Nz]TK is 1,2, v is the projection of the normal on the x, y, z spatial coordinate axes. Computing hyperspectral-lidar mapping matrices
Figure BDA0002910970420000044
Figure BDA0002910970420000045
Further, the eigenmapping matrix can be calculated from a and N:
Figure BDA0002910970420000046
step 2: mapping the lidar point cloud to a weighted graph G ═ V, E, such that each vertex V in V isiRepresenting individual points P in a lidar point cloudiAnd each vertex is connected with 50 vertexes nearest to the vertex in the Euclidean space by an edge, and the weight W (E) of each edge E belonging to the E is calculated as Ia-IbI, where a and b are the numbers of the two vertices that the edge e joins. Supervoxel segmentation of initialized point clouds
Figure BDA0002910970420000051
Such that each hyper-voxel corresponds to each vertex itself. Traversing the edges in the E according to the order of the weight values from small to large, judging whether two vertexes connected by the E belong to two different hyper-voxels, and if not, continuing to obtain the next edge; if w (e) is less than the distance between those two superpixels, then the e-connected superpixels are merged, or else the next edge is continued. The set of hyper-voxels obtained after the traversal is finished is
Figure BDA0002910970420000052
Where t is the number of voxels. According to
Figure BDA0002910970420000053
Computing a voxel representation matrix
Figure BDA0002910970420000054
Comprises the following steps:
Figure BDA0002910970420000055
and step 3: if two pixels H of the hyperspectral imageaAnd HbAnd (3) establishing a relational expression for each wave band lambda when the corresponding points on the laser radar point cloud all belong to the same voxel:
Figure BDA0002910970420000056
then, a series of equations for L obtained by the above formula are collated to obtain a system of equations for L:
GL＝0
where each row of the matrix G corresponds to an equation
Figure BDA0002910970420000057
In (1)
Figure BDA0002910970420000058
Part is one
Figure BDA0002910970420000059
The row vector of (2). To pair
Figure BDA00029109704200000510
Matrix GTG(GL＝0，GT*G*L＝G T0 ═ 0, L is the matrix GTG eigenvector), arranging the eigenvalue and eigenvector obtained by decomposition from the following to large according to the eigenvalue: { e1,v1}，{e2,v2And { e } and3,v3}. The illumination direction L is equal to the minimum eigenvalue e1Corresponding feature vector v1。
And 4, step 4: computing a hyperspectral image-lidar point cloud combined eigen decomposition matrix
Figure BDA00029109704200000511
So that
Figure BDA00029109704200000512
Is provided with
Figure BDA00029109704200000513
Is the intrinsic hyperspectral reflectivity of each of the voxels, wherein
Figure BDA00029109704200000514
From this, the following joint eigen decomposition relation can be obtained:
Figure BDA00029109704200000515
solved to obtain
Figure BDA00029109704200000516
Thus, the intrinsic high spectral reflectance of each point cloud
Figure BDA00029109704200000517
Is calculated as
Figure BDA00029109704200000518
And finally generating an intrinsic hyperspectral point cloud:
Figure BDA00029109704200000519
wherein
Figure BDA0002910970420000061
Wherein A represents a matrix of u x v,
Figure BDA0002910970420000062
aij represents an element of matrix A at ith row and jth column;
the experiment designed by the invention comprises the following steps:
the data used in the experiment are data of Houston university, and FIG. 2 is an input hyperspectral image; FIG. 3 is an input lidar point cloud; FIG. 4 is a super voxel segmentation result generated by the present invention; FIG. 5 is an intrinsic hyperspectral point cloud generated by the present invention; FIG. 6 is an error comparison of the hyperspectral point cloud generated by the present invention with other methods. It can be seen from fig. 6 that the error of the intrinsic hyperspectral point cloud generated by the invention is minimum.
It should be noted that the detailed description is only for explaining and explaining the technical solution of the present invention, and the scope of protection of the claims is not limited thereby. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.

Claims (5)

1. The method for generating the hyperspectral image and the intrinsic hyperspectral point cloud of the laser radar data is characterized by comprising the following steps of:
the method comprises the following steps: acquiring a hyperspectral image and a laser radar point cloud, and obtaining an intrinsic mapping matrix according to the hyperspectral image and the laser radar point cloud;
step two: performing voxel segmentation on the laser radar point cloud to generate a voxel representation matrix;
step three: obtaining an incident illumination direction according to the intrinsic mapping matrix;
step four: and performing combined eigen decomposition based on the hyper-voxels according to the eigen mapping matrix, the hyper-voxel representation matrix and the incident illumination direction to generate an eigen hyperspectral point cloud.
2. The hyperspectral image and lidar data intrinsic hyperspectral point cloud generation method according to claim 1, characterized in that the specific steps of the first step are:
inputting a hyperspectral image
Figure FDA0002910970410000011
And lidar point cloud
Figure FDA0002910970410000012
Wherein h isk＝[hk(λ1),hk(λ2),…,hk(λd)]T,k＝1,2,...,u，hkThe spectral characteristics of each pixel are represented by lambda, d and u, wherein lambda is the wavelength, d is the number of wave bands, and u is the total number of the hyperspectral image pixels; attribute feature p of each point clouds＝[xs,ys,zs,Is]T1,2, v, wherein (x)s,ys,zs) For coordinate information of each point cloud, IsThe intensity of the laser radar is shown, and v is the total number of the laser radar point clouds;
calculating the normal of each point in the laser radar point cloud P to obtain corresponding normal characteristics
Figure FDA0002910970410000013
Wherein n iss＝[Nx,Ny,Nz]T1,2, v denotes the projection of the normal on the x, y, z spatial coordinate axes, and the matrix a is then calculatedijEach element of (a):
Figure FDA0002910970410000014
further, from AijAnd N is calculated to obtainEach element of the eigenmap matrix:
Figure FDA0002910970410000015
according to matrix definition and
Figure FDA0002910970410000016
further obtain an eigenmapping matrix
Figure FDA0002910970410000017
3. The hyperspectral image and lidar data intrinsic hyperspectral point cloud generation method according to claim 2, characterized in that the specific steps of the second step are:
mapping the laser radar point cloud P into a weighted graph G ═ V, E, and utilizing each vertex V in ViRepresenting each point P in a lidar point cloudiAnd each vertex V in Euclidean spaceiAll the edges are connected with 50 vertexes nearest to the vertex by an edge, and the weight W (E) of each edge E ∈ E is expressed as | | Ia-IbI, where a and b are the vertex V joined by the edge eaAnd VbSerial number of (2), initialization of hyper-voxel segmentation of point clouds
Figure FDA0002910970410000018
Enabling each hyper-voxel to correspond to each vertex, traversing edges in E according to the order of the weight from small to large, judging whether two vertexes connected by E belong to two different hyper-voxels, if not, continuing to the next edge, if so, judging whether W (E) is smaller than the distance between the two hyper-voxels connected by E, if so, merging the two hyper-voxels connected by E, if not, continuing to the next edge, and obtaining a hyper-voxel set after the traversal is finished, wherein the hyper-voxel set is
Figure FDA0002910970410000021
Where t is the number of voxels in the voxel, according to
Figure FDA0002910970410000022
Calculating the hyper-voxel:
Figure FDA0002910970410000023
according to matrix definition and BijAnd then the super voxel representation matrix B is obtained.
4. The hyperspectral image and lidar data intrinsic hyperspectral point cloud generation method according to claim 3, characterized in that the specific steps of the third step are:
firstly, establishing a relational expression for each wave band lambda:
Figure FDA0002910970410000024
GL＝0
wherein the matrix G is
Figure FDA0002910970410000025
A row vector of (a);
then to
Figure FDA0002910970410000026
Matrix G ofTG, performing characteristic decomposition, arranging the characteristic values obtained by decomposition and the characteristic vectors corresponding to the characteristic values from small to large according to the characteristic values, and expressing as follows: { e1,v1}，{e2,v2And { e } and3,v3}, minimum eigenvalue e1Corresponding feature vector v1Then the direction of incident illumination L.
5. The hyperspectral image and lidar data intrinsic hyperspectral point cloud generation method according to claim 4, characterized in that the specific steps of the fourth step are:
firstly, calculating the ith row and the jth column element of a hyperspectral image-laser radar point cloud combined eigen decomposition matrix M, wherein the ith row and the jth column element are expressed as follows:
Figure FDA0002910970410000027
then according to the matrix definition and MijObtaining a hyperspectral image-laser radar point cloud combined eigen decomposition matrix M, BjgRepresenting the jth row and jth column element of the supersubvoxel representation matrix B, Bjg，g＝1,2,...,t，
Is provided with
Figure FDA0002910970410000028
Is a set of eigen-high spectral reflectivities of hyper-voxels, the eigen-high spectral reflectance of each hyper-voxel
Figure FDA0002910970410000029
The joint eigen decomposition relationship is:
Figure FDA00029109704100000210
solved to obtain
Figure FDA00029109704100000211
Then according to
Figure FDA00029109704100000212
Obtaining the intrinsic high spectral reflectivity of all point clouds
Figure FDA0002910970410000031
Wherein r iss＝[rs(λ1),rs(λ2),...,rs(λd)]TFor the intrinsic high spectral reflectance of each point cloud, the intrinsic high spectral reflectance r of each point cloud is finally determineds＝[rs(λ1),rs(λ2),...,rs(λd)]TSpatial coordinates [ x ] of the Stack to lidar Point cloud Ps,ys,zs]TThen generating an intrinsic hyperspectral point cloud
Figure FDA0002910970410000032
Wherein
Figure FDA0002910970410000033
The attribute features of each hyperspectral point cloud.
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