CN106373088A - Quick mosaic method for aviation images with high tilt rate and low overlapping rate - Google Patents
Quick mosaic method for aviation images with high tilt rate and low overlapping rate Download PDFInfo
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Abstract
The invention proposes a quick mosaic method for aviation images with high tilt rate and low overlapping rate. By utilizing the method, the robustness of registration of the images with high tilt rate and low overlapping rate can be improved, the mosaic error can be reduced, and the synthesis efficiency of a wide image can be enhanced. The method is implemented by the following technical scheme of firstly, calculating a position distribution relationship of the images by utilizing imaging assistance data of the aviation images, establishing an image position relationship matrix, and calculating a reference image; secondly, calculating overlapped regions between adjacent images on adjacent paths through an adjacent image path between a shortest path search image and the reference image, and extracting significant feature points of the overlapped regions; thirdly, normalizing the feature points or the regions into a vertex, constructing a multi-scale image by utilizing different adjacent distance point sets, and performing matching by applying image matching to calculate a spatial transformation matrix; and finally, calculating corresponding coordinates of synthesized image pixels in a source image by using the spatial transformation matrix, and calculating the pixel values of the synthesized image by using bilinear interpolation, thereby obtaining a final mosaic image.
Description
Technical field
The present invention relates to a kind of image processing field is with regard to photograph cartography, computer vision, image procossing and computer
The image split-joint method of graphics is and in particular to a kind of big inclination low Duplication aerial chart based on significance key point diagram coupling
As quick joining method.
Background technology
Image mosaic (image mosaic) technology is exactly by the several images having lap it may be that different time,
The image that different visual angles obtain, is combined into the technology of a large-scale seamless high-definition picture, is by one group of mutual overlapping portion
The image sequence dividing carries out spatial match be aligned, forms, after resampling synthesis, the wide viewing angle that a width comprises each image sequence information
The technology of scene, complete, high-resolution new images.The image mosaic research of early stage is used for cartography of taking a picture, mainly always
Be to take photo by plane in a large number or satellite image integration.In order to solve the limitation of lens shooting angle, with computer technology and
The exhibition of digital image processing techniques, automatic Image Stitching technology be increasingly becoming photogrammetry, computer vision, image procossing and
The study hotspot of computer graphicss, and be widely used in survey of deep space, remote sensing image processing (multispectral classification, environmental monitoring,
Change detection, image mosaic, weather forecast, especially big image in different resolution synthesis, Earth Information emerging system (gis)), medical image
Analysis (ct and nmr Data Integration etc.), cartography map rejuvenation, computer vision target positioning, automated quality control, video pressure
Reduce the staff the fields such as code, virtual reality technology, super-resolution reconstruction.The method of image mosaic is a lot, and different algorithm steps have
Different, but process substantially is identical.In general, image mosaic mainly comprises the steps that
A) Image semantic classification.Including the basic operation of Digital Image Processing, such as denoising, edge extracting, histogram treatment etc., set up
The matching template of image and image is carried out with certain conversion, such as Fourier transformation, wavelet transformation etc. operation.
B) image registration.It is exactly using certain matching strategy, find out the template in image to be spliced or characteristic point and joining
Examine corresponding position in image, and then determine the transformation relation between two width images.
C) set up transformation model.According to the corresponding relation between template or characteristics of image, calculate in mathematical model
Each parameter value, thus set up the mathematical transformation model of two width images.
D) uniform coordinate conversion.According to the mathematics transformation model set up, image to be spliced is transformed into the seat of reference picture
In mark system, complete uniform coordinate conversion.
E) merge reconstruct.Overlapping region with stitching image is carried out merging the smooth and seamless panorama sketch obtaining splicing reconstruct
Picture.
The registration of adjacent image and splicing be panorama picture formation technology key, about image registration techniques research so far
Existing very long history, its main method has following two: the minimum method of the luminance difference based on two width images and based on spy
The method levied.Joining method using feature based template matching characteristic point.It is certain that the method allows image to be spliced to have
Tilt and deform, overcome the problem that when obtaining image, axle center must be consistent, allow there is certain aberration between adjacent image simultaneously.
The splicing of panorama sketch mainly includes following 4 steps: image pre-splicing, that is, determine the more accurate position that two width adjacent images overlap
Put, the search being characterized a little lays the foundation.The extraction of characteristic point, that is, after essentially coinciding position determination, finds spy to be matched
Levy a little.Image matrix transformation and splicing, set up the transformation matrix of image according to match point and realize the splicing of image.It is finally
The smoothing processing of image.
In remote sensing technology field, can be to from the same area using the image registration techniques in image mosaic technology
Two width or multiple image are compared it is also possible to have distortion ground image using image mosaic technology by what remote sensing satellite photographed
It is spliced into more accurately complete image, as the foundation studied further.The research of image mosaic technology is most of at this stage
Be based on satellite remote sensing images, aerial mapping image, digital camera pan-shot image, its image-forming condition generally have special will
Ask, the essentially positive firing angle degree imaging of such as satellite remote sensing images, imaging platform is relatively stable;Aerial mapping image is essentially near
Like positive firing angle degree imaging, image-forming range is nearer, and the image Duplication that mapping requires is also higher;The image-forming range of pan-shot image
Relatively near it is desirable to image Duplication also higher, above-mentioned image-forming condition figure below image distortion is less, the visual angle between image overlapping region
Difference is little.But in image is scouted, due to the restriction of many shooting conditions, such as farther out, imaging angle is larger for image-forming range, imaging
Platform mobility strong, image Duplication low it is impossible to obtain high-quality image.Therefore, traditional joining method solve remote,
Many difficult points are yet suffered from, the concrete problem existing has during image mosaic problem under big inclination angle, low Duplication image-forming condition:
(1) the image mosaic problem that remote, big inclination angle image-forming condition brings
Contemporary aircraft due to remote execution task needs it is necessary under big inclination angle at a distance shooting image, at a distance
Big inclination angle shoots and inevitably causes pattern distortion.Reconnaissance flight device needs to carry out fast reserve due to task, not only schemes
As Duplication relatively low, there is also visual angle difference between the sequence image of continuous imaging, and shooting distance farther out, inclination angle larger
In the case of, the pattern distortion difference that small visual angle difference causes is also bigger.Therefore, compare and just penetrating shooting, incline greatly at a distance
The two width images shooting under oblique angle, the difference of the image overlapping region being caused due to visual angle difference is become much larger.Traditional images are spelled
The research connecing is mostly based on satellite application, joining method be applied to just penetrate or approximate orthograph picture splicing, for inclining greatly
The distortion that oblique angle causes does not consider well, it is dfficult to apply to the splicing of aerial reconnaissance image.
The key technology of splicing is image registration.The image registration techniques of early stage mainly adopt point match method, this kind of method
Speed is slow, precision is low, and usually needs manually to choose initial matching point, cannot adapt to large data volume.Image registration is figure
As the basis merged, and the amount of calculation of image registration algorithm is typically very big.Traditional can resisting is affine, yardstick, brightness become
The method for registering changed is matching process, the characteristic point of extraction or the feature of characteristic area based on local feature region or characteristic area
Vector is the statistical nature of distinguished point based surrounding neighbors pixel, coupling when by calculate characteristic vector between similarity away from
From, think when similarity distance meets threshold requirement Feature Points Matching success.Therefore, its match is successful whether depends on feature
The similarity of point regional area, but in the splicing of big tilted image, tilt to cause changing greatly of image local area, only rely on
The similarity mode of regional area is difficult to successfully.
(2) the image mosaic problem that low Duplication imaging brings
Duplication between the image being obtained due to the needs of aircraft fast reserve, imaging is relatively low, and low Duplication means figure
As overlapping region is less, the effective coverage putting forward characteristic point is little, and the characteristic point quantity of extraction is necessarily less, and traditional method characteristic point
Matching rate less, lead to obtain feature point pairs few, there is the risk that cannot resolve space conversion matrices.
(3) wide cut aerial image splicing speed issue
Image snoop requests produce Image Intelligence in time, higher to the splicing rate request of image.But the picture of aerial image leads to
Often larger, amount of images is more, consumes more time in feature extraction, coupling search, wide format images synthesis, and with
The increasing of stitching image quantity, the time of consumption dramatically increases.Therefore, how to reduce the time of feature extraction, accelerate feature
Join the speed of search, improve the efficiency of wide format images synthesis, be the difficult point solving the quick splicing of aerial image.
Content of the invention
It is an object of the invention to the image split-joint method solving at this stage does not consider remote, big inclination angle, low overlap
The aerial image distortion that rate image-forming condition causes is serious, and the local feature of overlapping region differs greatly, and characteristic point to be matched is relatively
Few problem, and the problem that aerial image picture is larger, the quantity more image mosaic time causing is longer, provide one kind to draw
Enter to be imaged assistance data and calculate effective overlapping region, by the salient region Detection and Extraction key point of overlapping region, using pass
Key point builds multiple dimensioned figure and the method based on figure coupling, improves the probability of success and the splicing of special image-forming condition hypograph splicing
Speed, realizes tilting greatly the quick joining method of low Duplication aerial image.
In order to obtain above-mentioned technique effect, the technical solution used in the present invention is: a kind of low Duplication aerial chart of inclination greatly
The quick joining method of picture is it is characterised in that comprise the steps:
Read the sequence aerial image of sensor front end collected by camera and the imaging assistance data of each image first, according to one-tenth
As assistance data calculates the position distribution relation of each image, set up image positional relationship matrix;According to image positional relationship matrix
Calculate the corresponding image in center of all image overlay areas, and the reference map as composite diagram coordinate system;Then pass through the shortest
Routing algorithm searches for the adjacent map path between image and reference map, calculates the position between adjacent image on neighborhood paths and closes
System, calculates the overlapping region between adjacent image according to position relationship and Duplication, extracts overlapping region in two width images respectively
Significant characteristics point and region, set up significant characteristics set of regions, the characteristic point that concentrate characteristic area or region normalization
For summit, set up the non-directed graph of different scale with the point set of different adjacencies;Carry out feature in two width images with figure feature again
The figure coupling of point, obtains matching double points collection, one by one registering adjacent two width images, calculates all neighbor map two-by-two on neighborhood paths
Space conversion matrices between picture, transmit, using neighborhood paths, the space conversion matrices calculating between image and reference map;With
Space conversion matrices calculate respective coordinates in source images for the composograph pixel and calculate composograph using bilinear interpolation
Pixel value, obtain final stitching image.
The present invention has the advantages that compared to prior art.
The present invention utilizes the imaging assistance data of aerial image to calculate the position distribution relation between image, decreases image
The number of times of registration two-by-two;By calculating suitable image as the reference map of the frame of reference, reduce image and reference coordinate
Average viewing angle difference between system, thus reducing the averaged deformation error of composite diagram, overcomes the figure that low Duplication imaging brings
As Bonding Problem.
The present invention adopts Shortest Path Searching to calculate the adjacent map path between image and reference map, by extracting image
Significance key point sets up the multiple dimensioned figure of key point, decreases the time of feature extraction, accelerates the speed of characteristic matching search
Degree, improves the efficiency of wide format images synthesis, reduces wide cut aerial image splicing speed difficulty.
The present invention is directed to the problem that prior art exists, and the imaging assistance data introducing aerial image shortens big image mosaic
Time, using the Stable distritation characteristic when multi-angle is imaged for the significance key point, reduce the spelling tilting greatly low Duplication image
Connect error;Introduce the robustness that figure matching technique improves image registration.By salient region Detection and Extraction key point, solve figure
The few problem of the characteristic point that causes as low Duplication;Build large-scale structure feature by using key point and based on figure coupling
Method, solves the problems, such as that the local feature that remote, big inclination angle imaging the causes coupling that differs greatly is more difficult;It is imaged by introducing
Assistance data calculates image overlapping region to reduce the feature extraction and matching time, solves the quick Bonding Problem of aerial image.Make
Calculate the space conversion matrices between adjacent image on adjacent map path one by one with figure matching process, transmit meter using neighborhood paths
Space conversion matrices between nomogram picture and reference map;Finally calculate composograph pixel in source images using space conversion matrices
In respective coordinates and using bilinear interpolation calculate composograph pixel value, obtain final stitching image, solve long distance
The image mosaic problem brought from, big inclination angle image-forming condition.
Brief description
In order to be more clearly understood that the present invention, now pass through embodiment of the present invention, referring concurrently to accompanying drawing, to describe this
Bright, wherein:
Fig. 1 is the image mosaic flow chart of the present invention low Duplication aerial image of big inclination.
Fig. 2 is that the present invention calculates image positional relationship matrix schematic diagram.
Fig. 3 is the space conversion matrices flow chart that the present invention calculates between image and reference map.
Fig. 4 is the schematic diagram that the present invention describes graph structure feature.
Specific embodiment
Refering to Fig. 1.According to the present invention, read the sequence aerial image of sensor front end collected by camera and every width figure first
The imaging assistance data of picture, calculates the position distribution relation of each image, sets up image positional relationship square according to imaging assistance data
Battle array;According to the corresponding image in center of image positional relationship matrix calculus all images overlay area, and as composite diagram coordinate
The reference map of system;Then the adjacent map path between image and reference map is searched for by shortest path first, calculate neighborhood paths
Position relationship between upper adjacent image, calculates the overlapping region between adjacent image according to position relationship and Duplication, respectively
Extract significant characteristics point and the region of overlapping region in two width images, set up significant characteristics set of regions, by characteristic area collection
In characteristic point or region be normalized to summit, set up the non-directed graph of different scale with the point set of different adjacencies;Again with figure
Feature carries out the figure coupling of characteristic point in two width images, obtains matching double points collection, registering adjacent two width images, calculate neighbour one by one
Connect the space conversion matrices between all adjacent images two-by-two on path, transmitted using neighborhood paths calculate image and reference map it
Between space conversion matrices;Calculate respective coordinates in source images for the composograph pixel with space conversion matrices and using double
Linear interpolation calculates the pixel value of composograph, obtains final stitching image.
Refering to Fig. 2.With camera primary optical axis and ground intersection point p as picture centre, set up c-xyz and represent with aircraft center
C is expressed as the northeast sky coordinate system of initial point, according to the spatial relationship of image i imaging, calculates figure using assistance data during imaging
As the geographical coordinate at the center of i, then calculate the position relationship matrix of image according to assistance data, by each picture centre coordinate meter
The position distribution relation of nomogram picture, sets up position relationship matrix, and wherein, o-xyz is earth axes, θ,Middle expression image i becomes
As the angle of pitch and the azimuth of phase owner optical axis cp, when (x, y, h) represents image i imaging, aircraft center is in ground o-xyz
Position in coordinate system.
Refering to Fig. 3.In calculating the space conversion matrix between each image and reference map, calculate adjacent on neighborhood paths first
Position relationship between image;Then, the overlapping region between adjacent image is calculated according to position relationship and Duplication;Then divide
Indescribably take significant characteristics point and the region of overlapping region in two width images, set up significant characteristics set of regions;By characteristic area
The characteristic point concentrated or region are normalized to summit, set up the non-directed graph of different scale, structure using the point set of different adjacencies
Build the multiple dimensioned figure of significant characteristics;Then the architectural feature of figure is described;Reuse figure feature and carry out characteristic point in two width images
Figure coupling, obtain matching double points collection;Calculate the space conversion matrices between adjacent two width images followed by matching double points;?
Calculate the space conversion matrices between all adjacent images two-by-two on neighborhood paths afterwards, calculated by the transmission of space reflection
Space conversion matrices between image and reference map.
Refering to Fig. 4.In the architectural feature of description figure, with characteristic point p point in image and d closest adjacent feature point
k1,k2,…kd-1,kdBetween line constitute the non-directed graph of diagram p, the up time between the side forming using the Neighbor Points of p and p
Pin angle theta1,θ2,…θd-1,θdThe architectural feature of description figure.
In the quick splicing of the low Duplication aerial image of big inclination,
(1) s1: read sequence image and assistance data
Read n width sequence image { in, n=1,2 ..., n and image imaging assistance data
Assume image center and aircraft center superposition.
(2) s2: calculate image positional relationship matrix r
S21: according to image inSpatial relationship during imaging, using assistance data menCalculate image inGeographical coordinate
S22: calculate the position relationship matrix of n width image, wherein, θn,Represent in the northeast with aircraft center c as initial point
In its c-xyz coordinate system, the n-th width image is imaged the angle of pitch and the azimuth of phase owner optical axis cp, xn,yn,hnIt is illustrated respectively in
Aircraft center position coordinates during the n-th width image imaging in the o-xyz coordinate system of ground.
S221: initially set up the position relationship matrix r of a n × n, and be initialized as 0,
S222: the longitudinal center's distance calculating between image is dh=d × h/2, wherein image picture elements resolution are d, and image is big
Little wherein w is picture traverse for w × h, and h is picture altitude.
S223: according to the y of imagenN width image is sorted by coordinate from small to large, then according to distance interval dhIt is divided into l
Individual set, i.e. cl={ in, l=1,2 ... l.
S224: the index of n width image is mapped to position relationship matrix r
It is first depending in same a line that image index is mapped to position relationship matrix by image latitude y-coordinate: to image in, n=
1,2 ..., n, if in∈cl, then indexed the l row that n is mapped to position relationship matrix r, when multiple image belongs to same
Set clWhen, multiple image is both mapped in l row, specific algorithm is as follows.
For the multiple image in l row, it is ranked up according to image longitude x coordinate, and is mapped to each row of l row
In: corresponding image collection c in the l row to position relational matrix rl, according to the x of imagenCoordinate is sorted from small to large,
And from left to right map in the l row of matrix.
(3) s3: calculate suitable figure as the reference map i of the frame of referencec
Position relationship r according to imageijWith image inLatitude coordinates (xn,yn), calculate suitable reference map ic.Consider
Position relationship matrix r represents the spatial relationship of image, when the barycenter of chosen position relational matrix is as reference map, other images
Average viewing angle difference and reference map between is less, and the anamorphose of splicing synthesis is less, circular
get_row_index(rij≠ 0) represent and meet rijCorresponding element line index in matrix r, get_colum_ when ≠ 0
index(rij≠ 0) represent and meet rijColumn index in matrix r for corresponding element when ≠ 0.Then reference map isCorresponding
Image, that is,
(4) s4: calculate image inWith reference map icBetween adjacent map path
Calculate image inWith reference map icBetween adjacent shortest path, the image that it passes through is followed successively byWill
The index of these images is as image inTo reference map icBetween transition matrix bang path ln, that is,
ln=(n0,n1,n2,…,nm,c),nm∈{1,2,…,n}
Wherein m represents path lnLength, index n0Correspondence image in, index c correspondence benchmark image ic, index nmCorresponding figure
Picture and index nm+1Corresponding image is adjacent.
(5) s5: calculate image inWith reference map icBetween space conversion matrix hn.
1) s51: calculate path lnOn adjacent imageWithBetween position relationship
Represent y direction Shang You overlapping region between the corresponding image of adjacent lines in position relationship matrix r, remember the position between them
Relation is right_left or left_right, represents x direction Shang You overlapping region, remember him between the corresponding image of adjacent column
Between position relationship be top_bottom or bottom_top, computation rule is as follows: assumes imageWithIn r
In corresponding element be respectivelyWith
If i2=i1+ 1, represent imageWithBetween position relationship be
If i1=i2+ 1, represent imageWithBetween position relationship be
If j2=j1+ 1, represent imageWithBetween position relationship be
If j1=j2+ 1, represent imageWithBetween position relationship be
2) s52: calculate lnUpper adjacent imageWithBetween overlay chart pictureWith
FoundationWith adjacent imageBetween position relationshipWith Duplication ρ, calculate the weight in image
Folded region, computational methods are as follows:
IfThen
IfThen
IfThen
IfThen
3) s53: extract the significant characteristics set of regions of overlay chart picture
Respectively to overlapping imageWithThe significant characteristics set of regions of extraction as followsWith
S531: using wave filter f to overlapping area imageCarry out convolution and extract feature fi,
fi=o*f, i ∈ o
S532: to each point i in the o of overlapping region, calculate the significance measure s of i(i)
Wherein n (i) is 8 Neighbourhood set of point i, dijFeature difference for point i and point j
d(i,j)=| fi-fj|2
Wherein e(i,j)Represent the irrelevance of human eye retina, the distance dependent with block i and j, wherein c (f, e) are oxyopia
Degree.
C (f, e)=1/t (f, e)
In formula, t (f, e) is contrast threshold
F is the spatial frequency of image, and α is spatial frequency constant, and e is Eccentricity, e2It is the half-resolution degree of eccentricity, t0It is
Minimum contrast threshold value, empirical valueα=0.106, e2=2.3.
S533: calculate the significant characteristics s of each point i(i)Afterwards, do following threshold process:
Work as s(i)< threshold, then s(i)=0.
Work as s(i)> threshold, then s(i)=s(i).
Significant characteristics point s by connection(i)Merge, unconnected significance feature is rejected, and obtains salient region collection a
={ s(p), p=1,2 ..., p.
Respectively to overlapping regionWithThe step carrying out s441 to s443, extracts salient region collection
4) s54: in the multiple dimensioned in figure building significant characteristics,
Respectively to overlapping imageWithThe salient region collection of middle extraction Do following process, build the multiple dimensioned figure of significant characteristics
S541: take the salient region s in marked feature set of regions a of overlapping region o of image i(p)∈ a, p=1,2 ..., p
Center as figure summit c(p).
mpRepresent characteristic area s(p)The number at midpoint,Represent coordinate in image i for the pixel i.Obtain overlap
Region o vertex set c={ c(p), p=1,2 ..., p }.
S542: build multiple dimensioned neighbour using vertex set c and scheme g.
S5421: calculate summit c using k nearest neighbor algorithm(p)The kd of ∈ cthNeighbor vertices set(k-1) dthNeighbor vertices
SetThen summit c(p)DkNeighbour's point setFor
Neighbour's point setIn have d fixing point.
S5422: utilizeBuild summit c(p)Neighbour's yardstick be dkRadiation diagram
S5423: define yardstick set d={ dk, k=1,2,3 ..., k }, wherein dk=[(k-1) d, kd].Take different chis respectively
Degree dk∈ d, repeat step s4521 to s4522, build summit c(p)Multiple dimensioned neighbour figure
S5424: all summit c in opposite vertexes collection c(p)∈ c, p=1,2,3 ..., p are s4521 to s4523 and process, and build summit
The neighbour figure g={ g of collection c(p), p=1,2,3 ..., p }.
Respectively to overlapping imageWithIt is step s451 to s452 to process, obtain the multiple dimensioned figure of significant characteristics
5) s55: the architectural feature of description figure
Respectively to overlapping imageWithSignificant characteristics build figureWithDo following method description, obtain
Architectural feature to figureWith
S551: utilize point c(p)DkNeighbour's figure of yardstickCalculate by point c(p)With summit kd-1The side connecting and point c(p)With summit kdThe angle on side connecting isWhereinSo that feature has rotational invariance, d is pressed from both sides
AnglePoint c is formed after being sorted from small to large(p)Characteristic vector.
S552: to point c(p)Multiple dimensioned neighbour's figureDo s461 process, obtain point c(p)Multiple dimensioned
Graph structure feature
S553: to all summit c(p)∈ c, p=1,2 ..., the neighbour figure g of p(p)∈ g is s461 to s462 and processes, and obtains notable
Architectural feature f={ the f of the figure g of property feature(p), p=1,2 ..., p.
Respectively to overlapping imageWithSignificant characteristics build figureWithIt is s461 to s463 to process,
Obtain the graph structure feature of significant characteristicsWith
6) s56: characteristic matching
S561: utilize feature point setSet up kd-
Tree search tree;
S5611: calculate d dimensional feature collectionIn there is maximum variance
Intrinsic dimensionality k*, as the dimension of left and right subtree division,
WhereinRepresentKthiThe variance of dimensional feature component.
S5612: by the kth of all of point in set*Dimensional feature componentBe ranked up, take medianAs
k*Divide value in dimension
S5613: utilize k*WithSet up root node feature set is divided, by kth*Dimensional feature componentCharacteristic point as right subtree, by kth*Dimensional feature component
Characteristic point as left subtree.
S5614: respectively s4711, s4712, s4713 recursive operation is carried out to the point set in left subtree, right subtree, until left and right
Tree is all regardless of subdivided.
S562: find characteristic point in kd-tree search tree using bbf methodNearest neighbor pointWith secondary
Neighbor Points
S5621: for pointRoot node from kd-tree search tree starts search and compares, with node in search treeCompare, ifThen select sub- rightchild tree search to the right,
Otherwise select to search for left subtree leftchild, and by not selected subtree subtree={ rightchild or then
Leftchild } and its root node withBetween characteristic distanceIt is saved in minimum
In Priority Queues q, queue q is according to the order arrangement from small to large apart from d.
The execution s5621 operation of s5622: recurrence, until searching for leaf node, then calculates pointPreserve with leafy node
Characteristic pointThe distance between
The possible more than one of characteristic point that wherein leaf node preserves, preserves wherein two minimum characteristic points of distance As nearest neighbor point and time Neighbor Points, and each self-corresponding two distancesWith
S5623: select queue q in corresponding subtree subtree=q of minimum range (0), execution s5621 and s5622 operate into
Row retrospective search, when the new nearest neighbor point calculatingDistance less than original nearest neighbor distance dis ' when, will be original
Nearest neighbor point replace with time Neighbor Points and beNearest neighbor distance originally replaces with time nearest neighbor distance
Dis "=dis ', new nearest neighbor point is replaced original nearest neighbor point isNew nearest neighbor distance is replaced
Nearest neighbor distance originally is dis '=dis " ';When the new secondary Neighbor Points calculatingDistance than original secondary neighbour away from
From dis, " hour, new secondary Neighbor Points are replaced original secondary Neighbor Points isNew secondary nearest neighbor distance is replaced
Secondary nearest neighbor distance originally is dis "=ds " ".Subtree q (0) recalled in queue is deleted after the completion of lookup.
S5624: repeated execution of steps s5623, until it is sky that backtracking step is more than threshold value or queue q, finally gives a little
Nearest neighbor pointWith secondary Neighbor Points
S563: judge when satisfaction
When it is believed that pointFor pointMatch point, note matching double points be
S564: rightIn all characteristic pointsRepeat step s472 and s473, obtain
To two width adjacent imagesWithThe matching double points set of image
7) s57: using matching double points setCalculate adjacent image using stochastic sampling concordance (rancac) methodWithBetween space conversion matrices
S571: from matching double points collectionIn randomly draw four points to collection Calculate four points between transformation matrix h, and calculate the interior point set of transformation matrices h.
Solve overdetermined equation using method of least square, obtain transformation matrix h
S572: execution step s571n time, in record, the interior point set of the point maximum single sample of number is as concordance matching double points
Collection
S573: using concordance matching double points collectionCalculate the space conversion matrices between matching double points
8) s58: calculate image inWith reference map icBetween transition matrix
To path ln=(n0,n1,n2,…,nm, c) on adjacent two-by-two imageWithExecution s51 to s57 is processed, respectively
Calculate the transformation matrix between all adjacent imagesThen image inWith reference map icBetween
Transition matrix hnFor
WhereinFor unit matrix.
To each image inDo the process of step s4, s5 respectively, calculate image inWith reference map icBetween space conversion matrices hn, n=
1,2,…,n.
(6) s6: image synthesizes
S61: calculate each image inFour angles of mapping area in the composite image coordinate mapn={ (tlxn,tlyn),
(trxn,tryn),(brxn,bryn),(blxn,blyn), wherein tlxn=c, tlyn=f
Wherein wnFor the width of ground n width image, hnHeight for the n-th figure.
S62: according to all of mapping area { mapn, calculate the size of composograph
Lx=min { tlxn,blxn}
Ty=min { tlyn,tryn}
Rx=max { trxn,brxn}
By=max { blyn,bryn}
Then the size of composograph is
Width=rx-lx
Height=by-ty
S63: calculate hnInverse matrix hinvn
S64: calculate mapping area map in composograph using bilinear interpolationnIn pixel value
For mapping area mapnIn pixel (x, y) ∈ mapn, corresponding artwork inMidpoint is
Xint=floor (x '), x "=x '-xint, "=y '-yint, wherein floor () represent for yint=floor (y '), y
Round downwards, then in composograph, the corresponding pixel value of pixel (x, y) is
F (x, y)=x " * y " * i (xint, yint)+(1-x ") * y " * i (xint+1, yint)+x " * (1-y ") * i (xint, yint
+1)+(1-x″)*(1-y″)*i(xint+1,yint+1).
Claims (9)
1. a kind of quick joining method of the low Duplication aerial image of big inclination is it is characterised in that comprise the steps:
Read the sequence aerial image of sensor front end collected by camera and the imaging assistance data of each image first, according to one-tenth
As assistance data calculates the position distribution relation of each image, set up image positional relationship matrix;According to image positional relationship matrix
Calculate the corresponding image in center of all image overlay areas, and the reference map as composite diagram coordinate system;Then pass through the shortest
Routing algorithm searches for the adjacent map path between image and reference map, calculates the position between adjacent image on neighborhood paths and closes
System, calculates the overlapping region between adjacent image according to position relationship and Duplication, extracts overlapping region in two width images respectively
Significant characteristics point and region, set up significant characteristics set of regions, the characteristic point that concentrate characteristic area or region normalization
For summit, set up the non-directed graph of different scale with the point set of different adjacencies;Carry out feature in two width images with figure feature again
The figure coupling of point, obtains matching double points collection, one by one registering adjacent two width images, calculates all neighbor map two-by-two on neighborhood paths
Space conversion matrices between picture, transmit, using neighborhood paths, the space conversion matrices calculating between image and reference map;With
Space conversion matrices calculate respective coordinates in source images for the composograph pixel and calculate composograph using bilinear interpolation
Pixel value, obtain final stitching image.
2. as claimed in claim 1 big tilt low Duplication aerial image quick joining method it is characterised in that: with camera
Primary optical axis is the center p of image with the focus on ground, sets up c-xyz and represents in the northeast being expressed as initial point with aircraft center c
Its coordinate system, according to the spatial relationship of image i imaging, calculates the geographical seat at the center of image i using assistance data during imaging
Mark, is then calculated the position distribution relation of image, sets up position relationship matrix by each picture centre coordinate, and wherein, o-xyz is ground
Areal coordinate system, θ,Represent that image i is imaged the angle of pitch and the azimuth of phase owner optical axis cp, (x, y, h) represents image i imaging
When position in the o-xyz coordinate system of ground for the aircraft center.
3. as claimed in claim 2 big tilt low Duplication aerial image quick joining method it is characterised in that: with image
Middle characteristic point p point and d closest adjacent feature point k1, k2... kd-1, kdBetween line composition p point non-directed graph, make
Angle theta clockwise between the side being formed with Neighbor Points and the p of p point1, θ2... θd-1, θdThe architectural feature of description figure.
4. as claimed in claim 1 big tilt low Duplication aerial image quick joining method it is characterised in that: inclining greatly
Tiltedly in the quick splicing of low Duplication aerial image, read n width sequence image { in, n=1,2 ..., n } and the imaging of image auxiliary
Help dataAccording to image inSpatial relationship during imaging, using assistance data menCalculate figure
As inGeographical coordinate
Calculate the position relationship matrix r of n width image, wherein, θn、Represent in the northeast sky with aircraft center c as initial point
In c-xyz coordinate system, the n-th width image is imaged the angle of pitch and the azimuth of phase owner optical axis cp, xn, yn, hnIt is illustrated respectively in ground
Aircraft center position coordinates during the n-th width image imaging in the o-xyz coordinate system of face, n represents sequence image quantity to be spliced.
5. as claimed in claim 4 big tilt low Duplication aerial image quick joining method it is characterised in that: set up one
The position relationship matrix r of individual n × n, and it is initialized as 0, the longitudinal center's distance calculating between image is dh=d × h/2, its
In, image picture elements resolution is d, and image size is w × h, and wherein, w is image pixel width, h image pixel height.According to figure
The latitude y of picturenN width image is sorted by coordinate from small to large, then according to distance interval dhIt is divided into l set cl=
in, l=1,2 ... l, to image in, n=1,2 ..., n, if in∈cl, then indexed n and be mapped to relational matrix r's
L row, when multiple image belongs to same set clWhen, multiple image is both mapped in l row;For many in l row
Width image, it is ranked up according to image longitude x coordinate, and is mapped in each row of l row: the l row to relational matrix r
In corresponding image collection cl, according to the x of imagenCoordinate is sorted from small to large, and from left to right maps to the l of matrix
In row.
6. as claimed in claim 5 big tilt low Duplication aerial image quick joining method it is characterised in that: calculate figure
As inWith reference map icBetween adjacent shortest path, the image of process is followed successively byRope by these images
Draw as image inTo reference map icBetween transition matrix bang path ln, ln=(n0, n1, n2..., nm, c), nm∈ 1,
2 ..., n }, wherein, m represents path lnLength, index noCorrespondence image in, index c correspondence benchmark image ic, index nmCorresponding
Image with index nm+1Corresponding image is adjacent.
7. as claimed in claim 1 big tilt low Duplication aerial image quick joining method it is characterised in that: extract
During the significant characteristics set of regions of overlay chart picture, respectively to overlapping imageWithThe significance of extraction as follows special
Levy set of regionsWithUsing wave filter f to overlapping area imageCarry out convolution and extract feature
fi, fi=o*f, i ∈ o, then to each point i in the o of overlapping region, calculates the significance measure s of i(i), s(i)=
∑I ≠ j, j ∈ n (i)C (f, e(i, j))d(i, j);Wherein, n (i) is 8 Neighbourhood set of point i, dijFeature difference d for point i and point j(i, j)
=| fi-fj|2, e(i, j)Represent the irrelevance of human eye retina, the distance dependent with block i and j, c (f, e) is visual acuity, e
It is Eccentricity.
8. as claimed in claim 1 big tilt low Duplication aerial image quick joining method it is characterised in that: build
The multiple dimensioned in figure of significant characteristics, respectively to overlapping imageWithThe salient region collection of middle extractionDo following process, build notable
The multiple dimensioned figure of property featureTake the salient region s in marked feature set of regions a of overlapping region o of image i(p)The center of ∈ a, p=1,2 ..., p is as the summit c of figure(p),?
To overlapping region o vertex set c={ c(p), p=1,2 ..., p }, build multiple dimensioned neighbour using vertex set c and scheme g, mpRepresent special
Levy region s(p)The number at midpoint,Represent coordinate in image i for the pixel i.
9. as claimed in claim 1 big tilt low Duplication aerial image quick joining method it is characterised in that: figure
In the feature description joined, respectively to overlapping imageWithSignificant characteristics build figureWithDo as follows
Process, obtain the architectural feature of figureWithTake point c(p)With summit kd-1The side connecting and point c(p)With summit kdConnect
The angle on side isAnd after being sorted from small to large, form point c(p)Characteristic vector?
Arrive point c(p)Multiple dimensioned graph structure featureTo all summit c(p)∈ c, p=1,
2 ..., p neighbour figure g(p)∈ g process, obtains the architectural feature f={ f of the figure g of significant characteristics(p), p=1,2 ..., p.
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