CN110222631B - Data block parallel based cut space arrangement SAR image target identification method - Google Patents
Data block parallel based cut space arrangement SAR image target identification method Download PDFInfo
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Abstract
The invention belongs to the field of radar image processing, and particularly relates to a data block parallel based cut space arrangement SAR image target identification method, which is used for overcoming the limitations that an original LLTSA cannot process a large data set and the matrix order and the characteristic number are the same. The method comprises the steps of searching a blocking point with the minimum loss of target information amount in a target and noise distribution graph by analyzing pixel point distribution of an original data set, carrying out data blocking on each type of single-type original data according to the blocking point, and carrying out LLTSA on the blocked data respectively; by the method, when the dimension reduction is performed on the LLTSA, the content of the original information does not contain impurities to the maximum extent, the information of the target is completely reserved, and the dimension reduction of the subsequent steps can be set to a small value, so that the influence of speckle noise is effectively reduced; meanwhile, each type of single-type original data can be subjected to a data partitioning to target identification process in parallel, and the identification speed is greatly increased.
Description
Technical Field
The invention belongs to the field of Radar image processing, and particularly relates to a Synthetic Aperture Radar (SAR) image target identification method, in particular to a data block parallel-based SAR image target identification method based on spatial permutation.
Background
Feature extraction is the most important operation link in the target recognition of the SAR image, and in recent years, a plurality of classical feature extraction algorithms, such as a principal component analysis algorithm and a linear discriminant analysis, and the like, are successfully applied to the SAR image feature extraction stage. But due to a unique imaging mechanism of the SAR image, the SAR image is very sensitive to a target azimuth angle, a pitch angle and environmental noise; that is, in the case of a small change in the target pose, global features such as the contour of the SAR image, the target silhouette, and the like become extremely unstable; therefore, in a real SAR target recognition scenario, the global feature extraction method faces a significant challenge.
Manifold learning, which is a new feature mining technology, has also been applied to the field of pattern recognition such as computer vision, radar high-resolution range profile, and the like; the method can keep the inherent local structure of the high-dimensional data and can excavate the potential low-dimensional structure in the original high-dimensional data, thereby attracting the research of a plurality of scholars. The Linear Local Tangent Space Alignment (LLTSA) algorithm is one of the representatives of the Local manifold learning algorithm, and has been successfully applied to radar high-resolution range profile identification because it can extract the Local features of the target; however, facing the problem of high-dimensional SAR image recognition, the biggest disadvantages of LLTSA are: in a feature decomposition part in the algorithm, the order number of the matrix must be the same as the number of samples, so that the processing of data sets with higher data dimensions is difficult due to the larger number of samples.
Disclosure of Invention
The invention aims to provide a SAR image target identification method of Data Block Parallel Linear indexing Space Alignment (DBPLLTSA) aiming at the problems of SAR image feature extraction; the method effectively overcomes the limitations that the original LLTSA can not process a large data set and the matrix order number is the same as the characteristic number, introduces the parallel thought, and greatly improves the operation efficiency; meanwhile, inherent local features of the original SAR image are excavated by utilizing linear local tangent space arrangement, so that the major challenge of a global feature extraction method is relieved, and the target identification performance of the SAR image is improved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a data block parallel based cut space arrangement SAR image target recognition method comprises the following steps:
step 1: vectorizing each original image of each type of target to form single type of original data, and forming an original data set A (A) by all the single type of original data1,A2,...,Am,...,AMIn which AmSingle-class original data representing an mth class target;
step 2: analyzing the pixel point distribution of the original data set A to obtain a target and noise distribution map; searching block points with the minimum target information loss in the target and noise distribution map, namely a target distribution maximum area and a noise purest area;
and step 3: partitioning data, and calculating a dimension reduction result of each block by using the LLTSA, specifically:
the single-class original data A of the mth class targetmCarrying out data blocking according to the blocking points obtained in the step 2 to obtain a data block A'm：A′m＝{Am,1,Am,2,Am,3In which Am,2Representing a target area, Am,1、Am,3All represent noise regions;
and 4, step 4: to Am,1、Am,2、Am,3Respectively executing LLTSA to obtain dimension-reduced data blocks A ″m＝{A′m,1,A′m,2,A′m,3}；
And 4, step 4: for data block A ″)m＝{A′m,1,A′m,2,A′m,3Performing linear fusion to obtain a judgment feature set;
and 5: and finishing target identification aiming at the discrimination feature set by adopting a classifier.
Further, in step 2, the search process of the block point is as follows:
for single type original data A of m type targetmPerforming pixel point analysis to search the left boundary L of the target regionmAnd the right boundary Rm(ii) a Searching all single-type original data in the original data set A to perform pixel point analysis to obtain a left boundary set and a right boundary set, taking the minimum value in the left boundary set as a left block point, and taking the maximum value in the right boundary set as a right block point.
Further, in the step 4, Am,1、Am,2、Am,3The dimensionality of the dimension reduction is a, b and c respectively, wherein b is larger than a and c.
The invention has the beneficial effects that:
the invention provides a cut space arrangement SAR image target identification method based on data blocking parallel, which comprises the steps of searching a blocking point with minimum target information loss in a target and noise distribution diagram by analyzing pixel point distribution of an original data set A, carrying out data blocking on each type of single original data according to the blocking point, and carrying out LLTSA on the blocked data respectively; by the method, when the dimension reduction is performed on the LLTSA, the content of the original information does not contain impurities to the maximum extent, the information of the target is completely reserved, some regions with less target distribution possibly carry certain noise, but the noise region can be a pure noise region, and the dimension reduction of the subsequent steps can be set to a small value, so that the influence of speckle noise is effectively reduced; meanwhile, each type of single-type original data can be subjected to a data partitioning to target identification process in parallel, and the identification speed is greatly increased.
In conclusion, the invention provides a data block parallel based cut space arrangement SAR image target identification method, which can greatly improve the target identification speed while ensuring the algorithm identification rate.
Drawings
FIG. 1 is a schematic flow chart of a data block parallel based cut space arrangement SAR image target recognition method of the present invention.
Fig. 2 shows a DBPLLTSA data blocking policy according to an embodiment of the present invention.
Fig. 3 is a three-dimensional view of the target and noise distributions in an embodiment of the present invention.
Detailed Description
Hereinafter, a detailed description will be given of an embodiment of the present disclosure in order to better embody the technical points of the present disclosure.
The present embodiment provides an SAR image recognition method based on data block parallel linear local tangent space arrangement, the flow of which is shown in fig. 1, and the method specifically includes the following steps:
step 1: vectorizing each original image of each type of target to form single type of original data, and forming an original data set A (A) by all the single type of original data1,A2,...,Am,...,AMIn which AmSingle-class original data representing an mth class target;
step 2: analyzing the distribution of the pixel points of the original data set A to obtain a target and noise distribution map, as shown in FIG. 3; searching block points with the minimum target information loss in the target and noise distribution map, namely a target distribution maximum area and a noise purest area;
and step 3: partitioning data, and calculating a dimension reduction result of each block by using the LLTSA, specifically:
the single-class original data A of the mth class targetmCarrying out data blocking according to the blocking points obtained in the step 2 to obtain a data block A'm：A′m＝{Am,1,Am,2,Am,3In which Am,2Representing a target area, Am,1、Am,3All represent noise regions;
and 4, step 4: to Am,1、Am,2、Am,3Respectively executing LLTSA to obtain dimension-reduced data blocks A ″m＝{A′m,1,A′m,2,A′m,3}；
And 4, step 4: for data block A ″)m＝{A′m,1,A′m,2,A′m,3Performing linear fusion to obtain a judgment feature set;
and 5: and verifying the calculated identification precision through a K-Nearest Neighbor (KNN) classifier and a Logistic Regression (LR) classifier, and finishing target identification and operation index recording.
In this example, the experimental data set is shown in table 1; the verification method uses an MSTAR standard data set and ten different types of ground targets: BMP2, BRDM _2, BTR70, BTR60, T72, 2S1, D7, T62, ZIL131, ZSU23_4, using the a-J alphabet respectively; the image size uniformly adopts 128 × 128 pixels, the parameter a is 10, the parameter b is 30, and the parameter c is 10; in step 2, the target region is [5001,11385 ]]I.e. the two cut points are: x is the number of1Is 5001, x2Is 11385.
TABLE 1
Through simulation tests, the performance comparison of the BPLLTSA and LLTSA algorithms is shown in Table 2, and the table shows that the running speed of the invention is improved by about 2.8 times under the condition of ensuring that the recognition rate is not changed, and the effect is obviously better than that of the LLTSA algorithm.
TABLE 2
| Algorithm | Single sheet identification time(s) | KNN recognition rate (%) | LR recognition rate (%) |
| LLTSA | 2.37s | 85.65 | 84.78 |
| DBPLLTSA | 0.84s | 86.56 | 84.82 |
While the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.
Claims (3)
1. A data block parallel based cut space arrangement SAR image target recognition method comprises the following steps:
step 1: vectorizing each original image of each type of target to form single type of original data, and forming an original data set A (A) by all the single type of original data1,A2,...,Am,...,AMIn which AmSingle-class original data representing an mth class target;
step 2: analyzing the pixel point distribution of the original data set A to obtain a target and noise distribution map; searching block points with the minimum target information loss in the target and noise distribution map, namely a target distribution maximum area and a noise purest area;
and step 3: partitioning data, and calculating a dimension reduction result of each block by using the LLTSA, specifically:
the single-class original data A of the mth class targetmCarrying out data blocking according to the blocking points obtained in the step 2 to obtain a data block A'm：A′m＝{Am,1,Am,2,Am,3In which Am,2Representing a target area, Am,1、Am,3All represent noise regions;
and 4, step 4: to Am,1、Am,2、Am,3Respectively executing LLTSA to obtain dimension-reduced data blocks A ″m＝{A′m,1,A′m,2,A′m,3}；
And 4, step 4: for data block A ″)m＝{A′m,1,A′m,2,A′m,3Performing linear fusion to obtain a judgment feature set;
and 5: and finishing target identification aiming at the discrimination feature set by adopting a classifier.
2. The method for identifying the SAR image target based on the data block parallel cutting space arrangement is characterized in that in the step 2, the search process of the block points is as follows:
for single type original data A of m type targetmPerforming pixel point analysis to search the left boundary L of the target regionmAnd the right boundary Rm(ii) a Searching all single-type original data in the original data set A to perform pixel point analysis to obtain a left boundary set and a right boundary set, and takingAnd taking the minimum value in the left boundary set as a left block point, and taking the maximum value in the right boundary set as a right block point.
3. The method for identifying the SAR image target based on the data block parallel cutting space arrangement is characterized in that in the step 4, Am,1、Am,2、Am,3The dimensionality of the dimension reduction is a, b and c respectively, wherein b is larger than a and c.
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