CN104167003A - Method for fast registering remote-sensing image - Google Patents
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- CN104167003A CN104167003A CN201410434279.4A CN201410434279A CN104167003A CN 104167003 A CN104167003 A CN 104167003A CN 201410434279 A CN201410434279 A CN 201410434279A CN 104167003 A CN104167003 A CN 104167003A
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Abstract
The invention relates to a method for fast registering a remote-sensing image. The method comprises the following steps that S1, ORB feature points are extracted from the remote-sensing image to be registered and a reference remote-sensing image; S2, initial matching is carried out on the extracted ORB feature points, and wrongly-matched feature points are removed from the initially-matched feature points; S3, parameter solving is carried out on the remote-sensing image to be registered; S4, resampling is carried out on the remote-sensing image to be registered, and image registration is completed. The method is beneficial to improving the speed and precision of image registration.
Description
Technical field
The present invention relates to technical field of image processing, particularly a kind of method of remote sensing images more than two width of different time, different visual angles and different sensors being carried out to rapid registering.
Background technology
Image registration is an important topic in image processing techniques, and image registration has at present been widely used in every field, as remote Sensing Image Analysis, medical image analysis, image co-registration, machine vision and other field.
Image registration, as a basic problem of remote sensing image processing, is image data fusion, prerequisite and the basis of the remote sensing image integrated analysis such as Monitoring on Dynamic Change and application.For in time, monitor tested district accurately and change, the remote sensing image of acquisition need to be carried out to real-time registration.Conventional Image registration method is divided into two classes conventionally: the method for registering based on gray scale and the method for registering based on feature (as unique point, characteristic curve).Method for registering based on gray scale utilizes the similarity between gradation of image value metric image, these class methods realize simple, but speed is slow, and because remote sensing image is by obtaining in different sensors, the different situation such as visual angle and different weather, there will be the variations such as yardstick, rotation, illumination, to cause correctly registration image of such algorithm, and method based on local invariant feature is difficult for being affected.Method for registering based on feature as required between registering images the geometric relationship of key character determine registration parameter, these class methods can reduce the data volume of processing, and have certain robustness for distortion, the noise etc. of image.Therefore the quality of matching performance depends on the method for feature description and the quality of feature extraction to a great extent.
There is size scaling for image at present, rotation, the situation of translation, is mainly used SIFT extract minutiae as registration features.And that SIFT feature need to be set up the dimension of unique point vector is high, calculated amount is large, therefore cannot meet the enforcement registration of remote sensing image.
Fast angle point is based on unique point gradation of image value around, detect the pixel value that candidate feature point week makes a circle, if candidate point has the gray-scale value difference of abundant pixel and this candidate point enough large in field, think that this candidate point is a unique point, that is: around
Wherein I (x) is the gray scale of any point on circumference, the gray scale that I (p) is the center of circle,
ε d for the threshold value of gray value differences, if N is greater than given threshold value, be generally 3/4ths of circle points around, think that p is a unique point.
BRIEF descriptor is that size is in 31 × 31 block of pixels after image smoothing, chooses and obeys N(N=128,256,512) group Gaussian distribution random point pixel pair, by the right size of compared pixels, follow greatly 1, little is 0 criterion, composition binary string.
Binary detection is defined as:
Wherein,
p(
x 1),
p(
x 2) be respectively in block of pixels p
x 1with
x 2the grey scale pixel value of position.
BRIEF characterizing definition is n dimension binary string vector, that is:
ORB algorithm has good rotational invariance, with Fast angle point extract minutiae, Fast angle point is one Angular Point Extracting Method very fast, because Fast does not possess rotational invariance, therefore the angle point that ORB is ORB with centroid method adds directional information, makes feature possess rotational invariance.In addition, in order to make feature be applicable to the situation of size scaling, by setting up the method for image pyramid, can carry out feature extraction cause in the situation that image is not of uniform size.In addition, in the time setting up unique point descriptor, adopt Brief descriptor to be described, Brief descriptor is a kind of scale-of-two descriptor, can mate very fast.Therefore the image registration based on ORB feature is applicable to real-time registration.
Summary of the invention
The object of the present invention is to provide a kind of rapid registering method of remote sensing image, the method is conducive to improve speed and the precision of image registration.
For achieving the above object, technical scheme of the present invention is: a kind of rapid registering method of remote sensing image, comprises the following steps:
Step S1: extract ORB unique point to remote sensing images subject to registration with reference to remote sensing images respectively;
Step S2: the ORB unique point of extracting is carried out to initial matching, the unique point of initial matching is rejected to the unique point of erroneous matching;
Step S3: described remote sensing images subject to registration are carried out to parametric solution;
Step S4: described remote sensing images subject to registration are carried out to resampling, complete image registration.
Further, in step S1, respectively to remote sensing images subject to registration with set up image pyramid with reference to remote sensing images, every tomographic image pyramid is extracted to ORB unique point.
Further, in step S1, the method for extracting ORB unique point is: first carry out FAST Corner Detection, carry out Harris Corner Detection, choose the best point of top n, then suppress to verify angle point by non-maximum value, reject pseudo-edge point, so that unique point is evenly distributed.
Further, in step S2, adopt the Hamming distance characteristic point matching method based on dual threshold to carry out initial matching to the ORB unique point of extracting, it is constraint condition that the unique point of initial matching is adopted to angle point angular separation, the unique point of rejecting erroneous matching, concrete grammar is:
The angle point direction of unique point is tried to achieve by gray scale centroid method, and, by calculating the barycenter of the circular neighborhood territory pixel gray scale of angle point, the vector direction being formed by angle point and barycenter characterizes angle point direction;
The circular neighborhood square of definition angle point is:
, the barycenter of the circular neighborhood of described angle point is:
, the vector direction that angle point and barycenter form is the angle point direction of unique point:
;
Wherein, m
pqrepresent p+q rank square, I (x, y) represents the gray-scale value of pixel (x, y) in the circular neighborhood of angle point, and (x, y) represents the pixel coordinate in the circular neighborhood of angle point, m
00represent zeroth order square, m
10and m
01all represent first moment;
Δ
θ i for the angle point direction of a unique point in remote sensing images subject to registration, Δ
θ i 'for the angle point direction with reference to character pair point in remote sensing images, angle point angular separation is Δ
θ i =Δ
θ i -Δ
θ i ';
Then as follows the ORB unique point of extracting is carried out to initial matching, and rejects the unique point of erroneous matching:
A, to remote sensing images subject to registration with reference to remote sensing images extract angle point set up rBRIEF descriptor, the feature point set of establishing remote sensing images subject to registration be combined into a1, a2 ..., an1}, with reference to the feature point set of remote sensing images be combined into b1, b2 ..., bn2};
B, respectively by the descriptor of the unique point a1 of remote sensing images subject to registration with reference to all unique point b1 of remote sensing images, b2, the descriptor of bn2 compares, calculate descriptor and the b1 of a1, b2 ..., the Hamming distance of the descriptor of bn2, from b1, b2 ..., in bn2, select the shortest point of Hamming distance, and calculate the ratio of the shortest Hamming distance and time short Hamming distance, if described ratio is less than the larger threshold value of setting, point the shortest Hamming distance and a1 is left to initial match point, and calculates its angle point angular separation Δ
θ 1, otherwise give up;
C, according to the method described above, obtains the unique point a2 of remote sensing images subject to registration successively ..., an1 is at the initial match point with reference in remote sensing images, and calculates corresponding angle point angular separation Δ
θ 2..., Δ
θ n ;
D, initial match point is sorted from big to small according to the shortest Hamming distance and the ratio of time short Hamming distance, be that Feature Points Matching quality sorts from getting well to differing from, the ratio that extracts the shortest Hamming distance and time short Hamming distance is less than the initial match point of the less threshold value of setting;
E, the initial match point that steps d is extracted are obtained best angle point angular separation Δ by least square method
θ m ;
The angle point angular separation of f, all initial match points that step b and c are obtained, with best angle point angular separation Δ
θ m for constraint condition, will depart from Δ
θ m the initial match point of certain limit is rejected.
Further, in step S3, the method for described remote sensing images subject to registration being carried out to parametric solution is:
If the pixel with reference to remote sensing images is
f(
x,
y), the pixel of image subject to registration is
g(
x ',
y '), the coordinate of putting on hypothetical reference remote sensing images for (
x i ,
y i ), the coordinate of putting on the remote sensing images subject to registration of answering in contrast for (
x i ',
y i '), (
x i ,
y i ) and (
x i ',
y i ') between affined transformation be expressed as:
In formula,
sfor scale factor,
θfor the anglec of rotation, Δ
xand Δ
ybe respectively two translation of axes amounts;
After obtaining m unique point, obtain optimal mapping matrix according to RANSAC algorithm, determine registration parameter
s,
θ, Δ
x, Δ
y;
According to registration parameter, remote sensing images subject to registration are carried out to resampling, complete image registration.
The invention has the beneficial effects as follows for there being the situation such as different deformation, illumination between remote sensing image, for reflection monitoring section dynamic change in time, a kind of rapid registering method of remote sensing image has been proposed, the method can effectively be rejected and mismatch a little, ensure the precision of image registration, there is very strong practicality and wide application prospect.
Brief description of the drawings
Fig. 1 is the realization flow figure of the embodiment of the present invention.
Fig. 2 is the reference remote sensing images in the embodiment of the present invention.
Fig. 3 is the remote sensing images subject to registration in the embodiment of the present invention.
Fig. 4 is the initial characteristics point match map in the embodiment of the present invention.
Fig. 5 is the Feature Points Matching figure after a rejecting that mismatches in the embodiment of the present invention.
Fig. 6 is in the embodiment of the present invention after remote sensing image registration subject to registration and with reference to the figure after remote sensing image fusion.
Embodiment
Below in conjunction with drawings and the specific embodiments, the present invention is described in further detail.
The rapid registering method of remote sensing image of the present invention, as shown in Figure 1, comprises the following steps:
Step S1: set up image pyramid to remote sensing images subject to registration (Fig. 3) with reference to remote sensing images (Fig. 2) respectively, every tomographic image pyramid is extracted to ORB unique point.The method of extracting ORB unique point is: first carry out FAST Corner Detection, because the angle point number that FAST extracts is too many, and comprise marginal point and pseudo-angle point, Harris Corner Detection Algorithm is a stable Corner Detection device, carry out Harris Corner Detection, choose the best point of top n, then suppress to verify angle point by non-maximum value, reject pseudo-edge point, so that unique point is evenly distributed.
Step S2: in order to obtain as far as possible many correct unique points of coupling, adopt the Hamming distance characteristic point matching method based on dual threshold to carry out initial matching to the ORB unique point of extracting, it is constraint condition that the unique point of initial matching is adopted to angle point angular separation, rejects the unique point of erroneous matching.Concrete grammar is:
The angle point direction of unique point is tried to achieve by gray scale centroid method, and, by calculating the barycenter of the circular neighborhood territory pixel gray scale of angle point, the vector direction being formed by angle point and barycenter characterizes angle point direction;
The circular neighborhood square of definition angle point is:
, the barycenter of the circular neighborhood of described angle point is:
, the vector direction that angle point and barycenter form is the angle point direction of unique point:
;
Wherein, m
pqrepresent p+q rank square, p, q are respectively a coefficient, and I (x, y) represents the gray-scale value of pixel (x, y) in the circular neighborhood of angle point, and (x, y) represents the pixel coordinate in the circular neighborhood of angle point, m
00represent zeroth order square, m
10and m
01all represent first moment;
Δ
θ i for the angle point direction of a unique point in remote sensing images subject to registration, Δ
θ i 'for the angle point direction with reference to character pair point in remote sensing images, angle point angular separation is Δ
θ i =Δ
θ i -Δ
θ i ';
Then as follows the ORB unique point of extracting is carried out to initial matching, and rejects the unique point of erroneous matching:
A, to remote sensing images subject to registration with reference to remote sensing images extract angle point set up rBRIEF descriptor, the feature point set of establishing remote sensing images subject to registration be combined into a1, a2 ..., an1}, with reference to the feature point set of remote sensing images be combined into b1, b2 ..., bn2};
B, adopt Brute-Force algorithm, respectively by the descriptor of the unique point a1 of remote sensing images subject to registration with reference to all unique point b1 of remote sensing images, b2, the descriptor of bn2 compares, calculate descriptor and the b1 of a1, b2, the Hamming distance of the descriptor of bn2, from b1, b2, in bn2, select the shortest point of Hamming distance, and calculate the ratio of the shortest Hamming distance and time short Hamming distance, setting larger threshold value is 0.8, if described ratio is less than the larger threshold value of setting, point the shortest Hamming distance and a1 are left to initial match point, and calculate its angle point angular separation Δ
θ 1, otherwise give up,
C, according to the method described above, obtains the unique point a2 of remote sensing images subject to registration successively ..., an1, at the initial match point with reference in remote sensing images, as shown in Figure 4, and calculates corresponding angle point angular separation Δ
θ 2..., Δ
θ n ;
D, initial match point is sorted from big to small according to the shortest Hamming distance and the ratio of time short Hamming distance, be that Feature Points Matching quality sorts from getting well to differing from, setting less threshold value is 0.5, and the ratio that extracts the shortest Hamming distance and time short Hamming distance is less than the initial match point of the less threshold value of setting;
E, the initial match point that steps d is extracted are obtained best angle point angular separation Δ by least square method
θ m ;
The angle point angular separation of f, all initial match points that step b and c are obtained, with best angle point angular separation Δ
θ m for constraint condition, will depart from Δ
θ m the initial match point of certain limit is rejected, as shown in Figure 5.
Step S3: described remote sensing images subject to registration are carried out to parametric solution.
Consider remote sensing images subject to registration and with reference to there being the conversion such as rotation, size between remote sensing images, determine that the transformation matrix between image is H, H is expressed as:
Adopt RANSAC algorithm to obtain optimized transformation parameters, process is as follows:
1) randomly draw m sample the unique point centering of coupling, obtain transformation matrix H by this m sample, obtain unique point in remote sensing images subject to registration at the same place with reference in remote sensing image according to transformation matrix H again, obtain the same place being obtained by transformation matrix H and the distance of being mated the match point drawing by Hamming distance, the point that distance is less than to threshold value is as interior point again;
2) above-mentioned steps is repeated k time to the maximum point set of counting out in selecting to comprise;
3) recalculate transformation matrix H with the concentrated sample of point of choosing, thereby obtain meeting the optimal mapping model of most of match points.
If the pixel with reference to remote sensing images is
f(
x,
y), the pixel of image subject to registration is
g(
x ',
y '), the coordinate of putting on hypothetical reference remote sensing images for (
x i ,
y i ), the coordinate of putting on the remote sensing images subject to registration of answering in contrast for (
x i ',
y i '), (
x i ,
y i ) and (
x i ',
y i ') between affined transformation be expressed as:
In formula,
sfor scale factor,
θfor the anglec of rotation, Δ
xand Δ
ybe respectively two translation of axes amounts;
Obtaining m(m>=4) after individual unique point, obtain optimal mapping matrix according to RANSAC algorithm, determine registration parameter
s,
θ, Δ
x, Δ
y.
Step S4: adopt bilinear interpolation to carry out resampling according to registration parameter to described remote sensing images subject to registration, complete image registration.
Remote sensing images subject to registration are carried out to the conversion such as convergent-divergent, rotation with the transformation matrix H obtaining, and adopt bilinear interpolation to carry out resampling, two width images merge by the mode of 0.5 × reference picture+0.5 × image subject to registration, as shown in Figure 6, complete image registration.
Be more than preferred embodiment of the present invention, all changes of doing according to technical solution of the present invention, when the function producing does not exceed the scope of technical solution of the present invention, all belong to protection scope of the present invention.
Claims (5)
1. a rapid registering method for remote sensing image, is characterized in that, comprises the following steps:
Step S1: extract ORB unique point to remote sensing images subject to registration with reference to remote sensing images respectively;
Step S2: the ORB unique point of extracting is carried out to initial matching, the unique point of initial matching is rejected to the unique point of erroneous matching;
Step S3: described remote sensing images subject to registration are carried out to parametric solution;
Step S4: described remote sensing images subject to registration are carried out to resampling, complete image registration.
2. the rapid registering method of a kind of remote sensing image according to claim 1, is characterized in that, in step S1, respectively to remote sensing images subject to registration with set up image pyramid with reference to remote sensing images, every tomographic image pyramid is extracted to ORB unique point.
3. the rapid registering method of a kind of remote sensing image according to claim 1, it is characterized in that, in step S1, the method of extracting ORB unique point is: first carry out FAST Corner Detection, carry out Harris Corner Detection, choose the best point of top n, then suppress to verify angle point by non-maximum value, reject pseudo-edge point, so that unique point is evenly distributed.
4. the rapid registering method of a kind of remote sensing image according to claim 1, it is characterized in that, in step S2, adopt the Hamming distance characteristic point matching method based on dual threshold to carry out initial matching to the ORB unique point of extracting, it is constraint condition that the unique point of initial matching is adopted to angle point angular separation, the unique point of rejecting erroneous matching, concrete grammar is:
The angle point direction of unique point is tried to achieve by gray scale centroid method, and, by calculating the barycenter of the circular neighborhood territory pixel gray scale of angle point, the vector direction being formed by angle point and barycenter characterizes angle point direction;
The circular neighborhood square of definition angle point is:
, the barycenter of the circular neighborhood of described angle point is:
, the vector direction that angle point and barycenter form is the angle point direction of unique point:
;
Wherein, m
pqrepresent p+q rank square, I (x, y) represents the gray-scale value of pixel (x, y) in the circular neighborhood of angle point, and (x, y) represents the pixel coordinate in the circular neighborhood of angle point, m
00represent zeroth order square, m
10and m
01all represent first moment;
Δ
θ i for the angle point direction of a unique point in remote sensing images subject to registration, Δ
θ i 'for the angle point direction with reference to character pair point in remote sensing images, angle point angular separation is Δ
θ i =Δ
θ i -Δ
θ i ';
Then as follows the ORB unique point of extracting is carried out to initial matching, and rejects the unique point of erroneous matching:
A, to remote sensing images subject to registration with reference to remote sensing images extract angle point set up rBRIEF descriptor, the feature point set of establishing remote sensing images subject to registration be combined into a1, a2 ..., an1}, with reference to the feature point set of remote sensing images be combined into b1, b2 ..., bn2};
B, respectively by the descriptor of the unique point a1 of remote sensing images subject to registration with reference to all unique point b1 of remote sensing images, b2, the descriptor of bn2 compares, calculate descriptor and the b1 of a1, b2 ..., the Hamming distance of the descriptor of bn2, from b1, b2 ..., in bn2, select the shortest point of Hamming distance, and calculate the ratio of the shortest Hamming distance and time short Hamming distance, if described ratio is less than the larger threshold value of setting, point the shortest Hamming distance and a1 is left to initial match point, and calculates its angle point angular separation Δ
θ 1, otherwise give up;
C, according to the method described above, obtains the unique point a2 of remote sensing images subject to registration successively ..., an1 is at the initial match point with reference in remote sensing images, and calculates corresponding angle point angular separation Δ
θ 2..., Δ
θ n ;
D, initial match point is sorted from big to small according to the shortest Hamming distance and the ratio of time short Hamming distance, be that Feature Points Matching quality sorts from getting well to differing from, the ratio that extracts the shortest Hamming distance and time short Hamming distance is less than the initial match point of the less threshold value of setting;
E, the initial match point that steps d is extracted are obtained best angle point angular separation Δ by least square method
θ m ;
The angle point angular separation of f, all initial match points that step b and c are obtained, with best angle point angular separation Δ
θ m for constraint condition, will depart from Δ
θ m the initial match point of certain limit is rejected.
5. the rapid registering method of a kind of remote sensing image according to claim 1, is characterized in that, in step S3, the method for described remote sensing images subject to registration being carried out to parametric solution is:
If the pixel with reference to remote sensing images is
f(
x,
y), the pixel of image subject to registration is
g(
x ',
y '), the coordinate of putting on hypothetical reference remote sensing images for (
x i ,
y i ), the coordinate of putting on the remote sensing images subject to registration of answering in contrast for (
x i ',
y i '), (
x i ,
y i ) and (
x i ',
y i ') between affined transformation be expressed as:
In formula,
sfor scale factor,
θfor the anglec of rotation, Δ
xand Δ
ybe respectively two translation of axes amounts;
After obtaining m unique point, obtain optimal mapping matrix according to RANSAC algorithm, determine registration parameter
s,
θ, Δ
x, Δ
y;
According to registration parameter, remote sensing images subject to registration are carried out to resampling, complete image registration.
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