CN106067172B - A method of slightly matching matches combination to the underwater topography image based on suitability analysis with essence - Google Patents
A method of slightly matching matches combination to the underwater topography image based on suitability analysis with essence Download PDFInfo
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Abstract
The present invention provides a kind of underwater topography image analyzed based on suitability method that slightly matching matches combination with essence, the underwater topography altitude data detected for sonar system, obtain its realtime graphic, the suitability in template area is analyzed first, if being suitble to matching, Different matching mode is selected by course angle departure degree again, area to be measured is matched.If course angle deviation is larger, directly using smart matching way；If course angle deviation is smaller, using by slightly to the layering and matching mode of essence.Wherein the absolute difference algorithm of gray scale is used slightly to match underwater topography image；Smart matching step is to choose the mean value and 7 invariant moments of the maximum correlation coefficient of gray level co-occurrence matrixes, Gray Level-Gradient Co-occurrence Matrix, totally 9 characteristic parameter constitutive characteristic vectors, and smart matching is carried out to image using these feature vectors.Same external interference, using same position matching algorithm when, underwater topography suitability can be accomplished well to judge, improve judgment accuracy.
Description
Technical field
The present invention relates to a kind of Digital Image Processing skill more particularly to a kind of underwater topography images based on suitability analysis
Thick matching matches the method combined with essence.
Background technology
Underwater topography suitability is to judge one piece of underwater topography if appropriate for matching the one of positioning in topographic database
Kind analysis.The complexity of underwater topography determines its characteristic parameter variation degree size, thus, the uniqueness of each piece of underwater topography
Property be determine its precision in matching position fixing process key factor.For each underwater topography, secondary navigation system has
Different ability to work, such as underwater topography secondary navigation system, because multibeam sounding system is relied on, in landform height
The big region of journey amplitude of variation, changing features amplitude is big, can show in the matching process well, and landform altitude changes width
Small region is spent, changing features amplitude is also just small, and matching effect is also relatively poor.In this case, it needs under water
The suitability of shape image does certain constraint, judges whether the real-time figure that underwater hiding-machine detects can carry out location matches.It is suitable
Research with sex chromosome mosaicism can largely save time loss of the underwater hiding-machine in the matching process of position, reduce because that cannot be registrated
And the operation time scanned for and unnecessary workload.
The present invention mainly propose a kind of new underwater topography suitability Rule of judgment for judge underwater topography if appropriate for
Matching.By analyzing the suitability between figure and reference map in real time, Combining with terrain suitability tradition criterion, design generates base
In the underwater topography suitability Rule of judgment that the thick matching and essence matching of image texture characteristic direction character parameter combine；
Invention content
It provides the purpose of the invention is to judging underwater topography if appropriate for matching and a kind of to be analyzed based on suitability
Slightly matching matches the method combined to underwater topography image with essence.
The object of the present invention is achieved like this：Include the following steps：
The first step：It is detected using sonar system and obtains underwater topography altitude data；
Second step：The altitude data of acquisition is converted to real-time figure；
Third walks：The suitability between figure and reference map, Combining with terrain suitability tradition criterion obtain in real time for analysis
Generate the underwater topography suitability Rule of judgment based on image texture characteristic direction character parameter：
{ 0.15 ∩ UNI of R/ σ >max-UNImin0.4 ∩ CON of >max-CONmin0.6 ∩ COR of >max-CORmin> 0.2 }
In formula：R is gray scale roughness；σ is gray standard deviation；UNI is 4 obtained by scheming extraction gray level co-occurrence matrixes in real time
The angular second moment in a direction, and normalized is done, UNI is obtained by comparingmax、UNImin；CON is contrast, and COR is phase
It closes, the discriminant value CON of CON and CORmax、CONminWith CORmax、CORminAcquisition methods are identical as UNI；
If meeting the formula of above-mentioned underwater topography suitability Rule of judgment, that is, regards as the region and be suitble to match, into
The 4th step of row, otherwise re-starts the first step；
4th step：Deviate situation according to actual heading angle and determines matching way：If course angle deviates smaller, the is carried out successively
Five steps and the 6th step provide by slightly arriving smart layering and matching mode；If course angle deviates larger, directly the 6th step of progress offer
The smart matching way；
5th step：Absolute difference algorithm based on underwater picture gray scale slightly matches underwater topography image；
6th step：Choose maximum correlation coefficient MCC, the Gray Level-Gradient symbiosis square by scheming extraction gray level co-occurrence matrixes in real time
The mean μ of battle array1With 7 invariant moments φ1—φ7Totally 9 characteristic parameter constitutive characteristic vectors, carry out based on image features
Essence matching；
7th step：Underwater topography algorithm simulating is surveyed, in different suitability Rule of judgment contrast simulation results.
The invention also includes some such structure features：
1. the thick matching in the 5th step is：It is m*n by matching image, T (x, y) is that size is that known S (x, y), which is size,
The template image of M*N, in traversing image S to be matched, take with (i, j) be the upper left corner, M*N sizes subgraph, calculate itself and mould
Plate image similarity can obtain subgraph all, find the subgraph most like with template image as algorithm final output
As a result, and mean absolute difference is smaller, illustrate that subgraph is more similar to template image, therefore find minimum mean absolute difference D (i, j)
It just can determine the position of subgraph：
In formula, 1≤i≤n-M+1,1≤j≤n-N+1.
2. the 6th step specifically includes：First, the feature of 5 approximate regions of gained is slightly matched in extraction characteristic vector data library
Vector and the feature vector for calculating real-time figure；Secondly, it is acquired and the immediate approximate area in required position by Similar measure function
Domain；Again, the point on the basis of its coordinate does the traversal of each 5 pixels up and down, finds FsThe coordinate of minimum value：
Defined feature vector is c, i.e.,：
C=W (MCC, μ1,φ1,φ2,φ3,φ4,φ5,φ6,φ7)T
In formula, W is weight matrix, ωMCC、Respectively MCC,
μ1、φ1—φ7Correspondence weights；
Similar measure function FsFor：
Fs=|c-c'|
In formula, c'For the set of eigenvectors of real-time figure, c is to carry out matched reference map subregion in each search process
Set of eigenvectors enables FsThe region for obtaining minimum value is institute's refinement matching area.
Compared with prior art, the beneficial effects of the invention are as follows：1, the present invention is under round matching template, underwater topographic map
The maximum correlation coefficient of gray level co-occurrence matrixes, the mean value of Gray Level-Gradient Co-occurrence Matrix and the characteristic parameter that picture is extracted can have good
Good rotational invariance also has preferably the difference of the terrain graph resolution ratio caused by multibeam sounding system and detection range
Resistivity.2, by slightly to smart layering and matching mode, the i.e. thick matching based on underwater topography image gray scale, in conjunction with based on underwater
The essence matching way of search of terrain graph feature vector matches locating accuracy under conditions of allowable error is 3 pixels
It is more traditional to be greatly improved based on the matched TERCOM algorithmic methods of underwater topography elevation, when depletion it is few.3, in order to solve traditional water
Elevation information analysis, which is used alone, in lower Approach of Terrain Matching makes underwater topography suitability judge not accurate enough limitation, the present invention
Design is based on underwater topography image grain direction feature adaptation judgment method, and the two is effectively combined, is complemented each other, and concludes
Sum up new underwater topography suitability criterion, same external interference, using same position matching algorithm when, can be to water
Lower landform suitability accomplishes to judge well, improves judgment accuracy.And underwater terrain matching, verification are surveyed by certain lake wave
The correctness and validity of algorithm.
Description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 is underwater altitude data pcolor；
Fig. 3 is seafloor data modeling and simulating gray scale elevation map；
Fig. 4 (a) is not plus the real-time figure of processing, Fig. 4 (b) are plus scheme in real time after making an uproar and being rotated by 90 °；
Fig. 5 (a) is the thick matching that real-time figure image size is 15*15, and Fig. 5 (b) is that real-time figure image size is 17*17
Thick matching, Fig. 5 (c) are the thick matchings that real-time figure image size is 19*19, and Fig. 5 (d) is that real-time figure image size is 21*21
Thick matching；
Fig. 6 (a) is the thick matching that step-size in search is 1 pixel, and Fig. 6 (b) is thick that step-size in search is 2 pixels
Match, Fig. 6 (c) is the thick matching that step-size in search is 3 pixels, and Fig. 6 (d) is the thick matching that step-size in search is 5 pixels；
Fig. 7 is feature based parameter essence matching simulation result；
Fig. 8 (a) is terrain data under certain lake wave actual water, and Fig. 8 (b) is region to be matched, and Fig. 8 (c) is actual measurement region；
Fig. 9 is terrain graph matching result under actual water；
Figure 10 is feature vector weight table；
Figure 11 is different suitability Rule of judgment contrast simulation result tables.
Specific implementation mode
Present invention is further described in detail with specific implementation mode below in conjunction with the accompanying drawings.
The present invention is used for the real-time positioning of underwater hiding-machine.For the underwater topography altitude data that sonar system detects, obtain
Take its realtime graphic.Under round matching template, the suitability in template area is analyzed first, if being suitble to matching, then is passed through
Course angle departure degree selects Different matching mode, is matched to area to be measured.If course angle deviation is larger, directly using essence
With mode；If course angle deviation is smaller, use is more quickly by slightly to the layering and matching mode of essence.Wherein use the exhausted of gray scale
Difference algorithm slightly matches underwater topography image；Smart matching step be choose gray level co-occurrence matrixes maximum correlation coefficient,
The mean value and 7 invariant moments of Gray Level-Gradient Co-occurrence Matrix, totally 9 characteristic parameter constitutive characteristic vectors, use these feature vectors
Smart matching is carried out to image.Same external interference, using same position matching algorithm when, underwater topography suitability can be done
To judging well, judgment accuracy is improved.
Specifically, in conjunction with Fig. 1, the present invention includes the following steps：
(1) underwater topography altitude data is obtained；
Underwater altitude data pcolor is as shown in Figure 2.Fig. 3 is a certain water depth number obtained by multibeam echosounding sonar
According to being converted into gray level image, referred to as reference map.
(2) altitude data is converted to real-time figure image；
Real-time figure is that underwater hiding-machine carries bathymetric data that multibeam sounding system obtains in real time under circular shuttering, conversion
At gray level image, as shown in Figure 4.
(3) real-time figure suitability condition value is analyzed；The suitability between figure and reference map, Combining with terrain are adapted in real time for analysis
Property tradition criterion, design generate the underwater topography suitability Rule of judgment based on image texture characteristic direction character parameter；
If pixel along the x-axis direction is N altogetherx, pixel along the y-axis direction is N altogethery, G is matrix number of grey levels, is denoted as：
Lx=1,2 ..., Nx}
Ly=1,2 ..., Ny}
G=1,2 ..., Ng}
First defining gray level co-occurrence matrixes is：
Pc=p (i, j, d, θ)
PcFor newly-generated gray level co-occurrence matrixes, p (i, j, d, θ) is matrix PcIn the i-th row, jth row element；I, j is original
Gray level in matrix；The value of distances of the d between gray level, d is usually 1, is used for the coarse journey of post analysis image texture
Degree, for open grain, the value on newly-generated gray level co-occurrence matrixes leading diagonal is generally large, and is distributed along leading diagonal, right
It is generally little in the value on close grain, newly-generated gray level co-occurrence matrixes leading diagonal, and it is distributed in leading diagonal both sides；θ is
Value direction, if without particular/special requirement, the usual values of θ are 0 °, 45 °, 90 °, 135 °.
Be starting with X-axis, start in the counterclockwise direction, with 0 °, 45 °, 90 °, 135 ° of four directions, to the element of matrix into
Row definition：
P (i, j, d, 0 °)=π { (k, l) (m, n) ∈ (Lx*Ly)*(Lx*Ly)|K-m=0 ,s |l-n|=d；F (k, l)=i, f
(m, n)=j }
P (i, j, d, 45 °)=π { (k, l) (m, n) ∈ (Lx*Ly)*(Lx*Ly)|(k-m=d, l-n=d) or (k-m=-d,
L-n=-d)；F (k, l)=i, f (m, n)=j }
P (i, j, d, 90 °)=π { (k, l) (m, n) ∈ (Lx*Ly)*(Lx*Ly)||k-m|=d, l-n=0；F (k, l)=i,
F (m, n)=j }
P (i, j, d, 135 °)=π { (k, l) (m, n) ∈ (Lx*Ly)*(Lx*Ly)|(k-m=d, l-n=-d) or (k-m=-
D, l-n=d)；F (k, l)=i, f (m, n)=j }
In above formula, π { x } indicates element number contained in set x, the i-th row, jth column element in newly-generated matrix
The member that a gray value is i, another gray value is j along the pixel that the directions θ, neighbor distance are d is expressed as in original matrix
Element is to quantity.
Later, to Pc=p (i, j, d, θ) gray level co-occurrence matrixes carry out normalization：
In formula, R is regularization parameter, is to make to be calculated by gray level co-occurrence matrixes to gray level co-occurrence matrixes progress normalization
Characteristic value have more higher leveled texture divide example.The R=2N as d=1, θ=0 °y(Nx- 1), the R=as d=1, θ=45 °
2(Ny-1)(Nx- 1), the R=2N as d=1, θ=90 °x(Ny- 1), the R=2 (N as d=1, θ=45 °x-1)(Ny-1)。
The gray level co-occurrence matrixes of obtained four direction can not also be used directly, need to calculate separately textural characteristics
Value, the characteristic value being used in combination carry out the analysis of textural characteristics.
Since the real-time figure that the present invention studies is obtained for real-time detection in underwater hiding-machine traveling process, so turning
When being melted into after real-time figure compared with reference map, certain angle rotation is had on direction, therefore, is studying above-mentioned gray scale symbiosis square
It is main with having rotational invariance and the relatively small characteristic parameter of calculation amount when battle array, specific respectively angular second moment
(UNI), contrast (CON), related (COR), entropy (ENT) and entropy (SENT), poor entropy (DE), mutual information measurement (IMC), most
Big related coefficient (MCC), totally 8 kinds.
Angular second moment (UNI)：
UNI=∑s ∑ { p (i, j) }2
Angular second moment mainly reflects whether intensity profile is uniform in image, also referred to as energy.For open grain, angular second moment
It is worth larger, for close grain, angular second moment is smaller.
Contrast (CON)：
Formula Zhong |i-j|=n.
The meaning of contrast is the readability of image, that is, the readability of texture.Contrast is bigger, then texture ditch
Line is more apparent, and degree is deeper, and image effect is better.For open grain, contrast value is smaller, for close grain, contrast value compared with
Greatly.
Related (COR)：
In formula, μxIt is pxMean value, σxIt is pxMean square deviation, μyIt is pyMean value, σyIt is pyMean square deviation.
Relevant effect is the similar journey weighed on the direction that each element in four direction gray level co-occurrence matrixes is expert at
Degree.For example, piece image has the texture in vertical direction, then the gray level co-occurrence matrixes for θ=90 ° that the image is calculated
Correlation values generally can be bigger than the correlation of the gray level co-occurrence matrixes on θ=0 °, θ=45 °, the direction of θ=135 ° 3.
Entropy (ENT)：
Entropy indicates a kind of measurement of the amount of image information.For the image of not texture, gray level co-occurrence matrixes are almost
For null matrix, so entropy also levels off to zero；If image is covered with close grain, the element value approximation phase in gray level co-occurrence matrixes
Deng then the entropy of the image is also maximum；If there was only less texture in image, the element value difference of gray level co-occurrence matrixes compared with
Greatly, then the entropy of diagram picture is smaller accordingly.
With entropy (SENT)：
Poor entropy (DE)：
Mutual information measures (IMC)：
f12=(HXY-HXY1)/max { HX, HY }
f13=(1-exp { -2 (HXY2-HXY) })1/2
In formula, HX is pxEntropy, HY is pyEntropy, HXY is the entropy of p (i, j).
Maximum correlation coefficient (MCC)：
MCC=(the maximum second order characteristic value of Q)1/2
In formula：
Due to analyzing texture eigenvalue in image transform processes by calculating the gray level co-occurrence matrixes that gradation of image is worth to
In can have a certain difference, picture characteristics cannot be characterized completely, so on the basis of to obtaining gray level co-occurrence matrixes before, into
One step calculates the Gray Level-Gradient Co-occurrence Matrix of image, because Gray Level-Gradient space is not only able to the good each pixel of description
With the spatial relationship between the pixel adjacent domains pixel, each pixel gray level and gradient inside image are also described simultaneously
The regularity of distribution, the gray value information of image is combined with gradient information, the main side for considering grey scale pixel value and image
The statistical distribution of edge Grad combines grey level histogram and edge gradient histogram.
If：Gray Level-Gradient Co-occurrence Matrix is H, and element is H (i, j)；Gray matrix F after normalization, element F
(m,n)；Gradient matrix G after normalization, element are G (m, n).It is F (m, n) to define Gray Level-Gradient Co-occurrence Matrix H (i, j)
With the points that gray scale in G (m, n) is total pixel that i is j with gradient.For example, H (5,2)=1 expressions is pixel ash in image
The total points for the pixel that degree is 5, gradient is 2 are 1.
The present invention calculates the Grad in former gray level image using Laplace operator：
G (m, n)=4f (m, n)-f (m+1, n)-f (m-1, n)-f (m, n+1)-f (m, n-1)
The gradient matrix that above formula is calculated is normalized：
G (m, n)=INT (g (m, n) * Gmax/gmax)+1
In formula, INT indicates to carry out rounding to each element in G (m, n)；GmaxIndicate maximum after being normalized to gradient matrix
Grad, G of the present inventionmaxValue is 16；gmaxIndicate the maximum of gradients in former gradient matrix.
Similarly, gray scale normalization processing is carried out to original image：
F (m, n)=INT (f (m, n) * Fmax/fmax)+1
In formula, FmaxIndicate maximum gradation value after being normalized to gray matrix, F of the present inventionmaxValue is 16；fmaxIndicate former
Gray scale maximum value in gray matrix.
To the gray level image matrix F (m, n) and gradient image matrix G (m, n) statistics F (m, n) after being normalized
The point logarithm of the pixel of=i and G (m, n)=j, as newly-generated Gray Level-Gradient Co-occurrence Matrix H (i, j) is in the i-th row, jth
The element value h of rowij。
H (F (m, n), G (m, n))=H (F (m, n), G (m, n))+1
Further obtain the probability of Gray Level-Gradient Co-occurrence Matrix：
By calculating the characteristic value of newly-generated Gray Level-Gradient Co-occurrence Matrix, characteristic value is analyzed, original image is obtained
Texture features.
For the present invention when studying underwater topography image feature, preferably to analyze characteristics of image, reduction need not
Ensure that picture characteristics well embodies while the calculation amount wanted again, therefore selects energy, gray average, gradient mean value, correlation, mixes
Entropy, difference square, unfavourable balance are closed away from 7 characteristic parameters.
Energy：
Gray average：
Gradient mean value：
Correlation：
The entropy of mixing：
Difference square：
Unfavourable balance divides square：
The method for indicating stochastic variable distribution situation using square, extracts the moment characteristics of gray level image.In the case of being located at continuously
Image function is f (x, y), then can define image p+q ranks geometric moment (also known as standard square) and be：
And it defines p+q rank centre-to-centre spacing and is：
In formula,m00It is the zeroth order geometric moment of image f (x, y), i.e., all pictures in image
The summation of element value, so m00Can regard as image quality,WithThe barycenter for indicating image, then can be by image
The heart is away from μpqTo reflect distribution situation of the gray scale of image relative to gray scale barycenter.
For discrete digital picture, defining image p+q rank geometric moments is：
Defining p+q rank centre-to-centre spacing is：
In formula, N and M indicate the height and width of image respectively.
Under normal circumstances, 0~3 rank centre-to-centre spacing of image is indicated by the geometric moment of image：
In image processing process, in order to eliminate the influence that image scaled variation generates, generally require in definition normalization
The heart away from for：
Centre-to-centre spacing is unrelated with the starting point of image, can solve translation invariance；And it normalizes centre-to-centre spacing and eliminates image ratio
It is influenced caused by example, there is translation and scale invariance, but cannot effectively solve rotational invariance.Utilize second order and three ranks
7 invariant moments derived from normalization central moment, can solve these problems, be shown below, they have translation, rotation and ruler
Spend invariance.
φ1=η20+η02
φ3=(η30-3η12)2+3(η21-η03)2
φ4=(η30+η12)2+(η21+η03)3
φ5=(η30+3η12)(η30+η12)[(η30+η12)2-3(η21+η03)2]+(3η21-η03)(η21+η03)
[3(η30+η12)2-(η21+η03)2]
φ6=(η20-η02)[(η30+η12)2-(η21+η03)2]+4η11(η30+η12)(η21+η03)
φ7=(3 η21-η03)(η30+η12)[(η30+η12)2-3(η21+η03)2]+(3η12-η30)(η21+η03)
[3(η30+η12)2-(η21+η03)2]
The present invention extracts underwater topography image 7 invariant moments feature, is primarily directed to underwater hiding-machine and carries multi-beam survey
The image caused by sounding system resolution ratio difference that the underwater picture that deep system is detected during traveling will appear
Scaling, the image caused by direction of travel difference rotation, and not bending moment have for the noise of underwater picture it is certain
Anti-interference.
Analysis obtains suitability Rule of judgment：
{ 0.15 ∩ UNI of R/ σ >max-UNImin0.4 ∩ CON of >max-CONmin0.6 ∩ COR of >max-CORmin> 0.2 }
In formula, R is gray scale roughness；σ is gray standard deviation；UNI is 4 obtained by scheming extraction gray level co-occurrence matrixes in real time
The angular second moment in a direction, and normalized is done, UNI is obtained by comparingmax、UNImin；CON is contrast, and COR is phase
It closes, differentiates that value-acquiring method is identical as UNI.I.e. if meeting above formula, you can it regards as the region and is suitble to match, on the contrary, or
It is unsatisfactory for obtaining new real-time figure from new, and re-starts suitability analysis.
(4) situation is deviateed according to actual heading angle and determines matching way.If course angle deviation is larger, (6) institute is directly used
State smart matching way；If course angle deviate it is smaller, using described in (5) and (6) by slightly to smart layering and matching mode；
(5) the absolute difference algorithm based on underwater picture gray scale slightly matches underwater topography image；
MAD algorithm (Mean Absolute Differences) is that one kind being widely used in images match
In matching algorithm.
If S (x, y) be size be m*n by matching image, T (x, y) be size be M*N template image, i.e., by
With finding template image in image.In traversing image S to be matched, take with (i, j) be the upper left corner, M*N sizes subgraph, calculate
Itself and template image similarity can obtain subgraph all, find the subgraph most like with template image as algorithm most
Output result eventually.Obviously, mean absolute difference is smaller, illustrates that subgraph is more similar to template image, therefore only needs to find minimum
Mean absolute difference is assured that the position of subgraph.
In formula, 1≤i≤n-M+1,1≤j≤n-N+1.
It randomly selects underwater topography at one and is converted to real-time figure, noise, 15 ° of rotation to image plus 40dB, analysis is not
With influence of the real-time figure to matching accuracy of size, the results are shown in Figure 5.In Figure 5, blue solid lines are to be matched actual bit
It sets, red dotted line is to detect to obtain terrain graph to underwater hiding-machine by algorithm to calculate highest 5 positions of its possibility.By scheming
5 it can be found that figure is too small in real time, i.e., latent device moves ahead apart from too short, is affected to matching accuracy；Figure is too big in real time, and makes
Time waste is calculated at unnecessary latent device traveling time and algorithm.It can be obtained by the comparison of 4 subgraphs, for this water
The underwater topography feature in domain, in the size for considering region of search, i.e. reference map information contained and pixel number, design 19*19 is big
Small real-time figure is matched, and not only can guarantee matched validity, but also is avoided that time loss is long.
It randomly selects underwater topography and is converted to real-time figure, noise, 15 ° of rotation to image plus 40dB, analysis difference are searched
Influences of the Suo Buchang to matching accuracy, the results are shown in Figure 6.In Fig. 6, blue solid lines are to be matched physical location, red empty
Line is to detect to obtain terrain graph to underwater hiding-machine by algorithm to calculate highest 5 positions of its possibility.It can by Fig. 6 results
See, matching area size is identical, picture noise is identical, rotation angle under the same conditions, different step-size in search to scheming in real time
Matching will produce certain influence, step-size in search is longer, the physical location of images match is deviateed it is bigger, but in physical location
Near zone is included in multiple similar area positions of the algorithm to scheming supposition in real time, the essence of feature based vector after being
Matching provides strong support.
(6) maximum correlation coefficient (MCC) of gray level co-occurrence matrixes, the mean value (μ of Gray Level-Gradient Co-occurrence Matrix are chosen1) and 7
A not bending moment (φ1—φ7), totally 9 characteristic parameter constitutive characteristic vectors, carry out the essence matching based on image features；
Specific practice is：First, the feature vector of 5 approximate regions of gained is slightly matched simultaneously in extraction characteristic vector data library
Calculate the feature vector of real-time figure；Secondly, it is acquired and the immediate approximate region in required position by Similar measure function；Again
Secondary, the point on the basis of its coordinate does the traversal of each 5 pixels (i.e. the regions 11*11) up and down, finds FsThe seat of minimum value
Mark, as latent device position, as shown in Figure 7.
To improve efficiency, time loss is reduced, doing the maximum inscribed circles of 19*19 squares to reference map, (round diameter is
The square length of side) step through to template, extraction image features vector establishes underwater topography image characteristic vector data
Library.
Because each characteristic parameter order of magnitude difference is excessive, so to set feature weight before feature vector, make its vector
Equal weight between value, in order to avoid influence matching accuracy because a certain characteristic parameter is excessive.
Defined feature vector is c, i.e.,：
C=W (MCC, μ1,φ1,φ2,φ3,φ4,φ5,φ6,φ7)T
In formula, W is weight matrix, ωMCC、Respectively MCC,
μ1、φ1—φ7Correspondence weights.
It is as follows to design Similar measure function：
Fs=|c-c'|
In formula, c'For the set of eigenvectors of real-time figure, c is to carry out matched reference map subregion in each search process
Set of eigenvectors.Obviously, F is enabledsThe region for obtaining minimum value is exactly required matching area.Weights setting is as shown in Figure 10, finally
Carry out smart matching area using the threshold value of such as Figure 10.
(7) underwater topography algorithm simulating is surveyed, different suitability Rule of judgment contrast simulation results are compared.
Algorithm simulating experiment is carried out based on terrain data under certain lake actual water, as shown in Fig. 8 (a), for convenience of computer
Operation intercepts 220*220 pixels sized images as region to be matched, as shown in Fig. 8 (b), real-time water-depth measurement on it
Data are obtained by adding 40dB-50dB noises at random in true depth of water sequence, as shown in Fig. 8 (c).5 ° -75 ° of Random-Rotation
Under the conditions of, by the thick matching based on gradation of image that step-length is 5 pixels, local 11*11 pixels traversal based on figure
As the essence matching of feature vector distance positions underwater hiding-machine, as a result under conditions of allowable error is 3 pixels
As shown in Figure 8.Different suitability Rule of judgment comparing results are as shown in figure 11.
Compared with prior art, the beneficial effects of the invention are as follows：
1, under round matching template, maximum correlation coefficient, the ash of the gray level co-occurrence matrixes that underwater topography image is extracted
Totally 9 characteristic parameters can have good rotational invariance, maximum anti-noise energy to the mean value and 7 invariant moments of degree-gradient co-occurrence matrix
Power is 40dB, also has preferable resistance to the difference of the terrain graph resolution ratio caused by multibeam sounding system and detection range
Ability.
2, tradition is simple but computationally intensive based on the matched TERCOM algorithmic methods of underwater topography elevation, time-consuming for calculating,
And matching correct localization is not high；Present invention design is by slightly to smart layering and matching mode, i.e. global search step-length is 5 pixels
Absolute difference algorithm based on underwater topography image gray scale slightly matches underwater topography image, as shown in Figure 5 and Figure 6.In conjunction with
The regions 11*11 local search step-length is 1 pixel, chooses maximum correlation coefficient (MCC), the gray scale-ladder of gray level co-occurrence matrixes
Spend the mean value (μ of co-occurrence matrix1) and 7 invariant moments (φ1—φ7), the essence matching of totally 9 characteristic parameter constitutive characteristic vectors is searched
Rope mode, as shown in Figure 7.Under conditions of allowable error is 3 pixels, matching locating accuracy reaches 91%, than tradition
TERCOM algorithmic match correct localizations improve more than 10 a percentage points, and depletion few 30% when calculating；And pass through certain lake wave actual measurement
Underwater terrain matching, the correctness and validity of verification algorithm, as shown in Figure 8 and Figure 9.
3, the conventional adaptation judgment method based on underwater topography depth displacement has certain limitation, that is, is used alone high
It is not accurate enough that journey information analysis makes underwater topography suitability judge.Conventional adaptation Rule of judgment provide it is suitable matched as a result,
But it is used for matched eigentransformation unobvious, is unfavorable for matching；Present invention design is special based on underwater topography image grain direction
Suitability judgment method is levied, and the two is effectively combined, is complemented each other, induction and conclusion goes out new underwater topography suitability and differentiates item
Part, same external interference, using same position matching algorithm when, underwater topography suitability can be accomplished well to judge,
Judgment accuracy is improved, as shown in figure 11.
Claims (1)
1. slightly matching matches the method combined to a kind of underwater topography image based on suitability analysis with essence, it is characterised in that：Packet
Include following steps：
The first step：It is detected using sonar system and obtains underwater topography altitude data；
Second step：The altitude data of acquisition is converted to real-time figure；
Third walks：The suitability between figure and reference map, Combining with terrain suitability tradition criterion obtain generation in real time for analysis
Underwater topography suitability Rule of judgment based on image texture characteristic direction character parameter：
{ 0.15 ∩ UNI of R/ σ >max-UNImin0.4 ∩ CON of >max-CONmin0.6 ∩ COR of >max-CORmin> 0.2 }
In formula：R is gray scale roughness；σ is gray standard deviation；UNI is 4 sides obtained by scheming extraction gray level co-occurrence matrixes in real time
To angular second moment, and do normalized, obtain UNI by comparingmax、UNImin；CON is contrast, and COR is correlation, CON
With the discriminant value CON of CORmax、CONminWith CORmax、CORminAcquisition methods are identical as UNI；
If meeting the formula of above-mentioned underwater topography suitability Rule of judgment, that is, regard as the region and be suitble to match, carries out the
Four steps, otherwise re-start the first step；
4th step：Deviate situation according to actual heading angle and determines matching way：If course angle deviation is smaller, the 5th step is carried out successively
With the offer of the 6th step by slightly to the layering and matching mode of essence；If course angle deviates the institute larger, directly the 6th step of progress provides
State smart matching way；
5th step：Absolute difference algorithm based on underwater picture gray scale slightly matches underwater topography image：Specially：
It is m*n is template image that size is M*N by matching image, T (x, y) that known S (x, y), which is size, is waited in traversal
With in image S, take with (i, j) be the upper left corner, M*N sizes subgraph, calculate itself and template image similarity, can be taken all
It obtains in subgraph, finds the subgraph most like with template image as algorithm final output, and mean absolute difference is smaller, explanation
Subgraph is more similar to template image, therefore finds the position that minimum mean absolute difference D (i, j) just can determine subgraph：
In formula, 1≤i≤n-M+1,1≤j≤n-N+1；
6th step：It chooses by scheming to extract the maximum correlation coefficient MCC of gray level co-occurrence matrixes, Gray Level-Gradient Co-occurrence Matrix in real time
Mean μ1With 7 invariant moments φ1—φ7Totally 9 characteristic parameter constitutive characteristic vectors, carry out the essence based on image features
Match；
It specifically includes：First, the feature vector of 5 approximate regions of gained is slightly matched in extraction characteristic vector data library and calculates reality
When figure feature vector；Secondly, it is acquired and the immediate approximate region in required position by Similar measure function；Again, with it
Point on the basis of coordinate does the traversal of each 5 pixels up and down, finds FsThe coordinate of minimum value：
Defined feature vector is c, i.e.,：
C=W (MCC, μ1,φ1,φ2,φ3,φ4,φ5,φ6,φ7)T
In formula, W is weight matrix, ωMCC、Respectively MCC, μ1、
φ1—φ7Correspondence weights；
Similar measure function FsFor：
Fs=|c-c'|
In formula, c'For the set of eigenvectors of real-time figure, c is the feature that matched reference map subregion is carried out in each search process
Vector set enables FsThe region for obtaining minimum value is institute's refinement matching area；
7th step：Underwater topography algorithm simulating is surveyed, in different suitability Rule of judgment contrast simulation results.
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