CN113177582B - Method for associating satellite electronic information and optical image information of target position - Google Patents
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Abstract
The invention discloses a method for associating satellite electronic information and optical image information of a target position, which comprises the following steps: the method comprises the steps that target position information obtained by satellite image information and satellite electronic information is respectively equivalent to a point set, the satellite image information is used as a template point set, the satellite electronic information is used as a target point set, and local characteristics of the template point set and the target point set are respectively calculated; calculating similarity matching measures of any point pair of the template point set and the target point set by utilizing local features of the template point set and the target point set; calculating compatibility coefficients between the point pairs by using the similarity matching measure; initializing a matching probability matrix, and updating the matching probability matrix by using a support function until convergence to obtain the matching probability matrix; and (5) normalizing and normalizing the matching probability matrix V to obtain a one-to-one matching result. And a fusion basis is provided for high-level information fusion, redundancy and conflict among different source data are reduced, and the accuracy of collaborative monitoring information is improved.
Description
Technical Field
The invention belongs to the technical field of remote target monitoring methods and relates to a method for associating satellite electronic information and optical image information of a target position.
Background
The main means of remote target monitoring is satellite detection, so that the method has the advantages of high detection speed, wide range, long time, large information quantity, difficulty in attack and high positioning precision, and is effective in realization. Satellite detection mainly comprises electronic reconnaissance satellite detection and imaging remote sensing satellite detection. The main means of satellite target monitoring at present is electronic reconnaissance satellite detection, and the position and type of a target are measured and obtained by searching and intercepting electromagnetic signals emitted by the target. The electronic reconnaissance satellite can realize all-weather long-period monitoring, and the coverage area can be monitored more widely due to the high orbit of the running satellite, and the electronic reconnaissance satellite is not influenced by weather. However, the electronic reconnaissance satellite and the imaging remote sensing satellite are subjected to cooperative monitoring to complement information, so that complementary advantages are realized, and the information obtained by the electronic reconnaissance satellite is more accurate than the information obtained by a single monitoring system.
In the existing method for fusing the electronic reconnaissance satellite and the imaging remote sensing satellite, the traditional topological feature is adopted, and the noise can cause the position jitter of points in the point set, so that only local adjacent points with a certain distance are adopted to form the local topological feature. The influence of outliers on the topological features of the whole set of points can be reduced to some extent. In addition, the number of points of the local topological feature for calculating the feature is reduced, logarithmic operation is avoided when the distance coordinates are quantized, and the calculation efficiency is higher than that of the traditional topological feature. However, when position noise or outliers occur in the point set, this invariance is worse, which in turn affects the accuracy of the collaborative monitoring.
Disclosure of Invention
The invention aims to provide a method for associating satellite electronic information and optical image information of a target position, which solves the problem of lower collaborative monitoring accuracy in the prior art.
The technical scheme adopted by the invention is that the method for associating satellite electronic information and optical image information of the target position comprises the following steps:
step 1, respectively equating target position information obtained by satellite image information and satellite electronic information into a point set, taking the satellite image information as a template point set, taking the satellite electronic information as a target point set, and respectively calculating local characteristics of the template point set and the target point set;
step 2, calculating similarity matching measures of any point pair of the template point set and the target point set by utilizing local features of the template point set and the target point set;
step 3, calculating compatibility coefficients between the point pairs by using the similarity matching measure;
step 4, initializing a matching probability matrix, and updating the matching probability matrix by using a support function until convergence to obtain the matching probability matrix;
and 5, normalizing and normalizing the matching probability matrix V to obtain a one-to-one matching result.
The invention is also characterized in that:
in the step 1, the calculation modes of the local features of the template point set and the target point set are the same, and the calculation process of the local features of the template point set is as follows:
let the template point set be p= { P 1 ，p 2 ，…，p m Target point set q= { Q } 1 ，q 2 ，…，q m Calculating Euclidean distance of any point pair in point set P, using any point P i The origin is the radius of the quarter of the maximum Euclidean distance, which is the point p i Is selected to be a point p in the neighborhood e As reference point, then the point pair p of the template point set i p e Is characterized by other relative directed point pairs p in the neighborhood i p e Distance DS (p) i p e ) Angle ANG (p) i p e )：
DS(p i p e )＝{d(p i ，p e1 )，…，d(p i ，p em )} (1)；
ANG(p i p e )＝{l(p i ，p e1 )，…，l(p i ，p em )} (2)；
In the above, p em For point p i M-th neighbor point of field, d (p i ，p e ) The settings were as follows:
in the above formula, ρ ' is the Euclidean distance between two points, and min ' and max ' respectively represent p i The distance between the nearest and farthest points;
wherein +.p i p e m s Is the origin p i Reference point p e Domain centroid m s An angle therebetween.
In step 2, the pairs of points (P i ，p e ) Pairs of points in the target point set Q (Q j ，q h ) Similarity match measure C of (2) ie，jh The method comprises the following steps:
C ie，jh ＝(1-α)·(1-β) (5)；
in the step 3, the formula for calculating the compatibility coefficient between the point pairs by using the similarity matching measure is as follows:
the step 4 specifically comprises the following steps:
step 4.1, according to the template point set P and the number N of the target point set Q p 、N q Obtaining an initial matching probability matrix v (0) The method comprises the following steps:
step 4.2, using the tag compatibility and the current probability estimate as support function S (t) (i，j)：
S (t) (i，j)＝∑ e≠i ∑ h≠j T ij (e，h)v (t) (e，h) (10)；
In the above, v (t) (e, h) is the target point p e And mark point q h Initial probability of match;
step 4.3, utilizing support function S (t) (i, j) updating the matching probability matrix:
step 4.4, iterating the steps 4.2-4.3 until the matching probability matrix converges to obtain a matching probability matrix V:
δ(t)＝||V (t) -V (t-1) ||＜ε (12)；
wherein V is (t) Is the matching probability matrix at the t-th iteration, V (t-1) Is a matching probability matrix at the t-1 th iteration, and I are the sum of absolute values of all elements in the matrix.
The beneficial effects of the invention are as follows:
the invention relates to a satellite electronic information and optical image information association method of a target position, which takes two target groups of satellite electronic information and optical image information as point sets in space, uses local characteristics of characteristic point sets to describe the characteristics of the target point sets and template point sets, associates the two with probability relaxation marking algorithm, and alternately and iteratively estimates the matching relationship and deformation relationship between the template point sets and the target point sets, thereby realizing target association of the two information, providing fusion basis for high-level information fusion, reducing redundancy and conflict between different source data and improving the accuracy of collaborative monitoring information.
Drawings
FIG. 1 is a flow chart of a method of correlating satellite electronic information with optical image information for a target location in accordance with the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
A method for correlating satellite electronic information and optical image information of a target location, as shown in fig. 1, comprising the steps of:
step 1, respectively equating target position information obtained by satellite image information and satellite electronic information into a point set, taking the satellite image information as a template point set, taking the satellite electronic information as a target point set, and respectively calculating local characteristics of the template point set and the target point set;
the local features of the template point set and the target point set are calculated in the same mode, and the local features of the template point set are calculated as follows:
let the template point set be p= { P 1 ,p 2 ,…,p m Target point set q= { Q } 1 ，q 2 ，…，q m Calculating Euclidean distance of any point pair in point set P, using any point P i The origin is the radius of the quarter of the maximum Euclidean distance, which is the point p i Is a neighborhood of (1) and centroid point m in the neighborhood s Optionally a point p in the neighborhood e As reference point, then the point pair p of the template point set i p e Is characterized by other relative directed point pairs p in the neighborhood i p e Distance DS (p) i p e ) Angle ANG (p) i p e )：
DS(p i p e )＝{d(p i ，p e1 )，…，d(p i ，p em )} (1)；
ANG(p i p e )＝{l(p i ，p e1 )，…，l(p i ，p em )} (2)；
In the above, p em For point p i M-th neighbor point of field, d (p i ，p e ) The settings were as follows:
in the above formula, ρ ' is the Euclidean distance between two points, and min ' and max ' respectively represent p i The distance between the nearest and farthest points;
wherein +.p i p e m s Is the origin p i Reference point p e Domain centroid m s An angle therebetween.
Step 2, calculating similarity matching measures of any point pair of the template point set and the target point set by utilizing local features of the template point set and the target point set;
specifically, the pairs (P i ，p e ) Pairs of points in the target point set Q (Q j ，q h ) Similarity match measure C of (2) ie，jh The method comprises the following steps:
C ie，jh ＝(1-α)·(1-β) (5)；
C ie，jh the smaller the description point p e Relative to p i Is matched with Q in the point set Q h In relation to q j The more similar the point-to-topology features.
In step 3, in the token-relaxation iterative algorithm, the compatibility coefficient is generally used to evaluate the matching accuracy, and based on the local features, the formula for calculating the compatibility coefficient between the point pairs by using the similarity matching measure is as follows:
T ij when (e, h) =1Description Point pair (p) i ，q j ) And point pair (p) e ，q h ) The greater the compatibility of coexistence.
Step 4, initializing a matching probability matrix, and updating the matching probability matrix by using a support function until convergence to obtain the matching probability matrix;
step 4.1, according to the template point set P and the number N of the target point set Q p 、N q Obtaining an initial matching probability matrix v (0) The method comprises the following steps:
step 4.2, using the tag compatibility and the current probability estimate as support function S (t) (i，j)：
S (t) (i，j)＝∑ e≠i ∑ h≠j T ij (e，h)v (t) (e，h) (10)；
In the above, v (t) (e, h) is the target point p e And mark point q h Initial probability of match;
step 4.3, utilizing support function S (t) (i, j) updating the matching probability matrix:
step 4.4, iterating the steps 4.2-4.3 until the matching probability matrix converges to obtain a matching probability matrix V:
δ(t)＝||V (t) -V (t-1) ||＜ε (12)；
wherein V is (t) Is the matching probability matrix at the t-th iteration, V (t-1) The probability matrix is matched in the t-1 th iteration, and I are the sum of absolute values of all elements in the matrix; the threshold epsilon takes a checked value of 0.001.
And 5, normalizing and normalizing the matching probability matrix V to obtain a one-to-one matching result.
Through the method, the satellite electronic information and the optical image information association method of the target position treat two target groups of the satellite electronic information and the optical image information as point sets in space, the local features of the feature point sets are used for describing the features of the target point sets and the template point sets, the two point sets are associated through a probability relaxation marking algorithm, and the matching relationship and the deformation relationship between the template point sets and the target point sets are alternately and iteratively estimated, so that the target association of the two information is realized, fusion basis is provided for high-level information fusion, redundancy and conflict between different source data are reduced, and the accuracy of collaborative monitoring information is improved.
Claims (1)
1. A method for correlating satellite electronic information with optical image information for a target location, comprising the steps of:
step 1, respectively and equivalently obtaining target position information of satellite image information and satellite electronic information into a point set, taking the satellite image information as a template point set, taking the satellite electronic information as the target point set, and respectively calculating local characteristics of the template point set and the target point set;
step 2, calculating similarity matching measures of any point pair of the template point set and the target point set by utilizing the local features of the template point set and the target point set;
step 3, calculating compatibility coefficients between the point pairs by using the similarity matching measure;
step 4, initializing a matching probability matrix, and updating the matching probability matrix by using a support function until convergence to obtain the matching probability matrix;
step 5, normalizing and normalizing the matching probability matrix V to obtain a one-to-one matching result;
in the step 1, the calculation modes of the local features of the template point set and the target point set are the same, and the calculation process of the local features of the template point set is as follows:
let the template point set be p= { P 1 ，p 2 ，…，p m Target point set q= { Q } 1 ，q 2 ，…，q m Calculating Euclidean distance of any point pair in point set P, using any point P i The origin is the radius of the quarter of the maximum Euclidean distance, which is the point p i Is selected to be a point p in the neighborhood e As reference point, then the point pair p of the template point set i p e Is characterized by other relative directed point pairs p in the neighborhood i p e Distance DS (p) i p e ) Angle ANG (p) i p e )：
DS(p i p e )＝{d(p i ，p e1 )，…，d(p i ，p em )} (1)；
ANG(p i p e )＝{l(p i ，p e1 )，…，l(p i ，p em )} (2)；
In the above, p em For point p i M-th neighbor point of field, d (p i ，p e ) The settings were as follows:
in the above formula, ρ ' is the Euclidean distance between two points, and min ' and max ' respectively represent p i The distance between the nearest and farthest points;
wherein +.p i p e m s Is the origin p i Reference point p e Domain centroid m s An angle therebetween;
in step 2, the pairs of points (P i ，p e ) Pairs of points in the target point set Q (Q j ，q h ) Similarity match measure C of (2) ie，jh The method comprises the following steps:
C ie，jh ＝(1-α)·(1-β) (5)；
in the step 3, the formula for calculating the compatibility coefficient between the point pairs by using the similarity matching measure is as follows:
the step 4 specifically comprises the following steps:
step 4.1, according to the template point set P and the number N of the target point set Q p 、N q Obtaining an initial matching probability matrix v (0) The method comprises the following steps:
step 4.2, using the tag compatibility and the current probability estimate as support function S (t) (i，j)：
S (t) (i，j)＝∑ e≠i ∑ h≠j T ij (e，h)v (t) (e，h) (10)；
In the above, v (t) (e, h) is the target point p e And mark point q h Initial probability of match;
step 4.3, utilizing support function S (t) (i, j) updating the matching probability matrix:
step 4.4, iterating the steps 4.2-4.3 until the matching probability matrix converges to obtain a matching probability matrix y:
δ(t)＝||V (t) -V (t-1) ||＜ε (12)；
wherein V is (t) Is the match at the t-th iterationMatching probability matrix, V (t-1) Is a matching probability matrix at the t-1 th iteration, and I are the sum of absolute values of all elements in the matrix.
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