CN108171193B - Polarized SAR (synthetic aperture radar) ship target detection method based on super-pixel local information measurement - Google Patents

Polarized SAR (synthetic aperture radar) ship target detection method based on super-pixel local information measurement Download PDF

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CN108171193B
CN108171193B CN201810013991.5A CN201810013991A CN108171193B CN 108171193 B CN108171193 B CN 108171193B CN 201810013991 A CN201810013991 A CN 201810013991A CN 108171193 B CN108171193 B CN 108171193B
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CN108171193A (en
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王英华
吕翠文
何敬鲁
刘宏伟
王宁
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Xidian University
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Abstract

The invention discloses a polarized SAR (synthetic aperture radar) ship target detection method based on superpixel local information measurement, which mainly solves the problem of low target detection rate in a complex scene, and has the scheme that: 1. performing superpixel segmentation on an original image to obtain superpixel segmentation results under different scales; 2. calculating three super-pixel-level-based difference metrics by utilizing a sliding window model for the segmented result; 3. converting the super-pixel level based dissimilarity measure into a pixel level based dissimilarity measure; 4. mapping the difference measurement vector of the pixel level into a difference measurement value by utilizing kernel fisher judgment to obtain the difference measurement value of each pixel point; 5. and classifying the difference metric value of each pixel point by using a linear SVM classifier, determining the category of each pixel point, and performing automatic target detection. The method improves the target detection performance in a complex scene, realizes the automatic detection process, and can be used for subsequent ship target identification, identification and classification.

Description

Polarized SAR (synthetic aperture radar) ship target detection method based on super-pixel local information measurement
Technical Field
The invention belongs to the technical field of radar target detection, and mainly relates to a polarized SAR ship target detection method which can be used for subsequent ship target identification, identification and classification.
Background
The synthetic aperture radar SAR utilizes a microwave remote sensing technology, is not influenced by weather and day and night, has all-weather and all-day working capability, and has the characteristics of multiple frequency bands, multiple polarization, variable visual angle, penetrability and the like. At present, the SAR is widely applied to the fields of military reconnaissance, geological survey, topographic mapping and charting, disaster prediction, marine application, scientific research and the like, and has wide research and application prospects. Due to the remarkable advantage of being able to obtain complete polarization information, polarization SAR is rapidly becoming one of the important directions for SAR development. The ship target detection based on the polarized SAR image is an important application field of the polarized SAR.
To date, many methods have been proposed to achieve target detection using polarized SAR data, such as polarized whitening filters, polarized notch filters, and reflective symmetric filters. In addition, some polarization parameters and discrimination features are proposed to enhance the difference between the potential target and the local clutter. Polarization entropy and polarizability have also been used for ship target detection, for example. Recently, a ship target detection method based on the scattering mechanism distribution characteristics of the superpixel and the local scattering mechanism difference of the regression kernel is also proposed. These methods, while enhancing ship and sea surface contrast to some extent, also have some potential to be affected by complex sea state and signal-to-clutter ratio changes.
Since the existing detection methods are basically unsupervised detection methods, the determination of the detection threshold is an important task. There are three main ways to determine the detection threshold. The first approach is to apply a constant false alarm rate to the polarization statistics to calculate a detection threshold, which depends largely on the accuracy of clutter statistical modeling and parameter estimation. The second method, based on sensitivity analysis of certain parameters, selects detection thresholds empirically, which is inconvenient for different polarization systems. The third method is to determine the detection threshold by using a clustering method, but the clustering process of this method must be performed in a local area, and the detection threshold determined by this method may cause a lot of false alarms to be detected subsequently when no ship exists.
The above conventional method has the following two main disadvantages: firstly, the ship is easily influenced by complex sea conditions and signal-to-clutter ratio changes, so that the contrast between ships and sea clutter is reduced; secondly, it is difficult to obtain an accurate detection threshold, which brings great inconvenience to the polarized target detection system.
Disclosure of Invention
The invention aims to provide a polarized SAR ship target detection method based on superpixel local information measurement aiming at the defects of the existing polarized SAR ship target detection method, so as to enhance the contrast of ships and sea clutter, realize automatic target detection and improve the detection performance under complex conditions.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
(1) respectively carrying out multi-scale superpixel segmentation on a given polarized SAR image I to obtain superpixel segmentation results of the polarized SAR image under 4 scales: s1,S2,S3,S4；
(2) For the result S after super pixel segmentationkAnd respectively calculating three super-pixel-level-based difference metrics by using a super-pixel sliding window model: likelihood ratio metric
Figure BDA0001541279360000021
Riemann distance metric
Figure BDA0001541279360000022
Scatter component similarity metric
Figure BDA0001541279360000023
Wherein k 1., 4, represents 4 scales,
Figure BDA0001541279360000024
Figure BDA0001541279360000025
representing the current scale segmentation result SkJ is 1, Mi，MiRepresenting by super-pixels
Figure BDA0001541279360000026
The number of superpixels on the sliding window boundary which is the center;
(3) converting the three super-pixel-level-based disparity metrics in (2) into pixel-level-based disparity metric vectors
Figure BDA0001541279360000027
Obtaining a difference measurement vector D of each pixel point after fusion under different segmentation scaless：
Figure BDA0001541279360000028
Wherein the content of the first and second substances,
Figure BDA0001541279360000029
super-pixel segmentation result S for each pixel pointkThe difference measurement vector s is 1, the other words, and P is the number of all pixel points in the polarized SAR image I;
(4) selecting a Gaussian kernel function, and fusing the difference measurement vector D obtained in the step (3) under different segmentation scales by using kernel fisher discriminant analysis algorithm KFDAsMapping into the final difference metric Dis of each pixel points；
(5) Utilizing a support vector machine classifier SVM to carry out final difference metric Dis of each pixel point mapped in the step (4)sAnd classifying, outputting the category of each pixel point, and realizing automatic target detection.
The invention has the following advantages:
1. because the difference measurement of three super-pixel levels is calculated and the difference of the super-pixel levels is converted into the difference measurement of the pixel levels, compared with the traditional polarimetric SAR image ship target detection method, the method not only reduces the influence of coherent speckles, but also enhances the contrast of ships and sea clutter;
2. the invention adopts a supervision and classification method to carry out automatic target detection, thereby ensuring good detection performance under complex conditions.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a measured polarimetric SAR image used in experiment 1 of the present invention;
FIG. 3 is a measured polarimetric SAR image used in experiment 2 of the present invention;
FIG. 4 is a detection result image of the R5 region and the R5 region in FIG. 2 under each detection algorithm;
fig. 5 is a detection result image of the R6 region and the R6 region in fig. 3 under each detection algorithm.
Detailed Description
The embodiments and effects of the present invention will be further described in detail with reference to the accompanying drawings:
referring to fig. 1, the implementation steps of the present invention include the following:
step 1, respectively carrying out multi-scale superpixel segmentation on a given polarized SAR image I.
The superpixel segmentation algorithm comprises the following steps: the method comprises the following steps of performing super-pixel segmentation on a polarized SAR image I by using the improved SLIC algorithm according to different scales according to the SLIC algorithm, the Turbo pixel algorithm, the Normalized-cuts algorithm, the Y.Wang and the like, wherein the improved SLIC algorithm is provided for the polarized SAR image I, and the method is realized as follows:
when the scale is 6, performing superpixel segmentation on the given polarized SAR image I by using an improved SLIC algorithm to obtain a segmented result S1；
When the scale is 9, performing superpixel segmentation on the given polarized SAR image I by using an improved SLIC algorithm to obtain a segmented result S2；
When the scale is 12, performing superpixel segmentation on the given polarized SAR image I by using an improved SLIC algorithm to obtain a segmented result S3；
When the scale is 15, the given polarized SAR image I is superpixed by using a modified SLIC algorithmDividing to obtain divided result S4。
Step 2, dividing the result S of the super pixelkThree superpixel-level-based dissimilarity measures are respectively calculated by using a superpixel sliding window model, wherein k is 1.
The three superpixel-level-based dissimilarity metrics include a likelihood ratio metric
Figure BDA0001541279360000031
Riemann distance metric
Figure BDA0001541279360000032
Scatter component similarity metric
Figure BDA0001541279360000033
Wherein
Figure BDA0001541279360000034
Figure BDA0001541279360000035
Representing the current scale segmentation result SkJ is 1, Mi，MiRepresenting by super-pixels
Figure BDA0001541279360000041
The number of superpixels on the sliding window boundary which is the center;
the three superpixel-based disparity metrics are calculated as follows:
2a) segmenting the result S at each scalekNext, a likelihood ratio metric based on the superpixel level is calculated
Figure BDA0001541279360000042
2a1) At the super-pixel segmentation result SkIn the method, a superpixel sliding window model is used for calculating the superpixel in the center of a sliding window
Figure BDA0001541279360000043
And superpixels on sliding window boundary
Figure BDA0001541279360000044
Likelihood ratio statistics
Figure BDA0001541279360000045
Figure BDA0001541279360000046
Where L (-) is a likelihood function, N is a view, NiRepresenting a superpixel
Figure BDA0001541279360000047
Number of pixels in, NjRepresenting a superpixel
Figure BDA0001541279360000048
Number of pixels in (1), parameter
Figure BDA0001541279360000049
Figure BDA00015412793600000410
And
Figure BDA00015412793600000411
is an intermediate variable, estimated by the following formula:
Figure BDA00015412793600000412
wherein the content of the first and second substances,
Figure BDA00015412793600000413
representing center superpixel of sliding window
Figure BDA00015412793600000414
The first ofiA coherence matrix of the individual pixels of the image,
Figure BDA00015412793600000415
representing superpixels on sliding window boundaries
Figure BDA00015412793600000416
The first ofjA coherence matrix of the individual pixels of the image,
Figure BDA00015412793600000417
to represent
Figure BDA00015412793600000418
And
Figure BDA00015412793600000419
the coherence matrix of the ith pixel in (a);
2a2) detecting statistics from likelihood ratios calculated in step 2a1)
Figure BDA00015412793600000420
Calculating center superpixel of sliding window
Figure BDA00015412793600000421
And superpixels on sliding window boundary
Figure BDA00015412793600000422
Likelihood ratio metric of
Figure BDA00015412793600000423
Figure BDA00015412793600000424
Wherein, lnQcIs a constant parameter with the value of-150;
2b) segmenting the result S at each scalekNext, a super-pixel level based Riemann distance metric is calculated
Figure BDA00015412793600000425
2b1) Respectively calculating the center superpixel of the sliding window by using the following formula
Figure BDA00015412793600000426
Equivalent coherence matrix
Figure BDA00015412793600000427
And superpixels at the sliding window boundary
Figure BDA00015412793600000428
Equivalent coherence matrix
Figure BDA00015412793600000429
Figure BDA00015412793600000430
Figure BDA0001541279360000051
Wherein p represents the center superpixel of the sliding window
Figure BDA0001541279360000052
Pixel of (2), NiTo represent
Figure BDA0001541279360000053
The number of pixels in (1) | · non-woven phosphorFDenotes the F norm, TpTo represent
Figure BDA0001541279360000054
Q represents a superpixel at the boundary of the sliding window
Figure BDA0001541279360000055
Pixel of (2), NjTo represent
Figure BDA0001541279360000056
Number of pixels in, TqTo represent
Figure BDA0001541279360000057
A coherence matrix of the pixel q in (a);
2b2) at the super-pixel segmentation result SkIn the method, a superpixel sliding window model is used for calculating the superpixel in the center of a sliding window
Figure BDA0001541279360000058
And superpixels on sliding window boundary
Figure BDA0001541279360000059
Riemann distance statistic
Figure BDA00015412793600000510
Figure BDA00015412793600000511
Wherein tr (-) represents the trace of the matrix;
2b3) from the Riemann distance statistic obtained in step 2b2)
Figure BDA00015412793600000512
Calculating center superpixel of sliding window
Figure BDA00015412793600000513
And superpixels on sliding window boundary
Figure BDA00015412793600000514
Riemann distance measurement
Figure BDA00015412793600000515
Figure BDA00015412793600000516
Wherein h is a parameter having a value of 0.5;
2c) segmenting the result S at each scalekNext, a scatter component similarity metric based on the superpixel level is calculated
Figure BDA00015412793600000517
2c1) Calculating center superpixel of sliding window
Figure BDA00015412793600000518
Scattered power vector ki：
Figure BDA00015412793600000519
Where T represents the transpose of the matrix,
Figure BDA00015412793600000520
respectively obtained by the following formulas:
Figure BDA00015412793600000521
representing center superpixel of sliding window
Figure BDA00015412793600000522
Coherence matrix
Figure BDA00015412793600000523
The surface of (a) scatters the power,
Figure BDA00015412793600000524
representing center superpixel of sliding window
Figure BDA00015412793600000525
Coherence matrix
Figure BDA00015412793600000526
The secondary scattered power of (a) is,
Figure BDA00015412793600000527
representing center superpixel of sliding window
Figure BDA00015412793600000528
Coherence matrix
Figure BDA00015412793600000529
The volume of (a) is used to scatter power,
wherein, | - | represents the absolute value, β1Is a center superpixel of a sliding window
Figure BDA00015412793600000530
Coherence matrix
Figure BDA00015412793600000531
Surface scattering parameter, alpha, obtained by a polarized target decomposition algorithm1Is a center superpixel of a sliding window
Figure BDA00015412793600000532
Coherence matrix
Figure BDA00015412793600000533
The secondary scattering parameters obtained by the polarized target decomposition algorithm,
Figure BDA0001541279360000061
is a center superpixel of a sliding window
Figure BDA0001541279360000062
Coherence matrix
Figure BDA0001541279360000063
The surface scattering decomposition coefficient obtained by the polarized target decomposition algorithm,
Figure BDA0001541279360000064
is a center superpixel of a sliding window
Figure BDA0001541279360000065
Coherence matrix
Figure BDA0001541279360000066
The secondary scattering decomposition coefficient obtained by the polarized target decomposition algorithm,
Figure BDA0001541279360000067
is a center superpixel of a sliding window
Figure BDA0001541279360000068
Coherence matrix
Figure BDA0001541279360000069
Obtaining a volume scattering decomposition coefficient through a polarized target decomposition algorithm;
2c2) computing sliding window boundary superpixels
Figure BDA00015412793600000610
Scattered power vector kj：
Figure BDA00015412793600000611
Wherein the content of the first and second substances,
Figure BDA00015412793600000612
respectively obtained by the following formulas:
Figure BDA00015412793600000613
representing sliding window boundary superpixels
Figure BDA00015412793600000614
Coherence matrix
Figure BDA00015412793600000615
The surface of (a) scatters the power,
Figure BDA00015412793600000616
representing sliding window boundary superpixels
Figure BDA00015412793600000617
Coherence matrix
Figure BDA00015412793600000618
The secondary scattered power of (a) is,
Figure BDA00015412793600000619
representing sliding window boundary superpixels
Figure BDA00015412793600000620
Coherence matrix
Figure BDA00015412793600000621
The volume of (a) is used to scatter power,
wherein, beta2Is a sliding window boundary superpixel
Figure BDA00015412793600000622
Coherence matrix
Figure BDA00015412793600000623
Surface scattering parameter, alpha, obtained by a polarized target decomposition algorithm2Is a sliding window boundary superpixel
Figure BDA00015412793600000624
Coherence matrix
Figure BDA00015412793600000625
The secondary scattering parameters obtained by the polarized target decomposition algorithm,
Figure BDA00015412793600000626
superpixels for sliding window boundaries
Figure BDA00015412793600000627
Coherence matrix
Figure BDA00015412793600000628
The surface scattering decomposition coefficient obtained by the polarized target decomposition algorithm,
Figure BDA00015412793600000629
superpixels for sliding window boundaries
Figure BDA00015412793600000630
Coherence matrix
Figure BDA00015412793600000631
The secondary scattering decomposition coefficient obtained by the polarized target decomposition algorithm,
Figure BDA00015412793600000632
superpixels for sliding window boundaries
Figure BDA00015412793600000633
Coherence matrix
Figure BDA00015412793600000634
Obtaining a volume scattering decomposition coefficient through a polarized target decomposition algorithm;
2c3) at the super-pixel segmentation result SkIn the method, a superpixel sliding window model is used for calculating the superpixel in the center of a sliding window
Figure BDA00015412793600000635
And sliding window boundary superpixel
Figure BDA00015412793600000636
Scattering power vector similarity parameter r ofij：
Figure BDA00015412793600000637
Wherein the superscript H is a conjugate transpose, | · |. non-woven phosphor2Is the norm of L2, |, represents the absolute value;
2c4) according to the scattering power vector similarity parameter r obtained in the step 2c3)ijCalculating the center superpixel of the sliding window
Figure BDA00015412793600000638
And sliding window boundary superpixel
Figure BDA00015412793600000639
Scatter component similarity measure of
Figure BDA00015412793600000640
The value is calculated by the following formula:
Figure BDA0001541279360000071
step 3, converting the three types of difference metrics based on the super-pixel level in the step (2) into difference metric vectors based on the pixel level
Figure BDA0001541279360000072
Obtaining a difference measurement vector D of each pixel point after fusion under different segmentation scaless；
3a) Performing superpixel on the center of the sliding window by using a K-means clustering algorithm
Figure BDA0001541279360000073
And sliding window boundary superpixel
Figure BDA0001541279360000074
Likelihood ratio metric of
Figure BDA0001541279360000075
j＝1,...,MiClustering into two categories, using the mean of likelihood ratio metrics in the majority of categories as the center superpixel of the sliding window
Figure BDA0001541279360000076
Final likelihood ratio metric
Figure BDA0001541279360000077
Figure BDA0001541279360000078
Wherein the content of the first and second substances,
Figure BDA0001541279360000079
representing a value of a superpixel likelihood ratio metric, c, in a current majority class1＝1,...,ML，MLRepresenting likelihood ratio metric in current clusterThe number of superpixels of the majority class;
3b) performing superpixel on the center of the sliding window by using a K-means clustering algorithm
Figure BDA00015412793600000710
And sliding window boundary superpixel
Figure BDA00015412793600000711
Riemann distance measurement
Figure BDA00015412793600000712
Clustering into two classes, and taking the mean of Riemann distance measures in the majority of classes as the center superpixel of the sliding window
Figure BDA00015412793600000713
Final Riemann distance metric
Figure BDA00015412793600000714
Figure BDA00015412793600000715
Wherein the content of the first and second substances,
Figure BDA00015412793600000716
representing a super-pixel Riemann distance metric, c, in a current majority class2＝1,...,MR，MRRepresenting the number of the superpixels of most types of the Riemann distance metric value in the current cluster;
3c) performing superpixel on the center of the sliding window by using a K-means clustering algorithm
Figure BDA00015412793600000717
And sliding window boundary superpixel
Figure BDA00015412793600000718
Scatter component similarity measure of
Figure BDA00015412793600000719
Cluster toIn both categories, the mean of the scatter component similarity measures in the majority of classes is taken as the sliding window center superpixel
Figure BDA00015412793600000720
Final scatter component similarity measure
Figure BDA00015412793600000721
Figure BDA00015412793600000722
Wherein the content of the first and second substances,
Figure BDA00015412793600000723
representing a similarity measure, c, of the superpixel scatter components in the current majority class3＝1,...,MC，MCRepresenting the number of superpixels of a majority of classes of the similarity measurement of the scattering components in the current cluster;
3d) center superpixel of sliding window
Figure BDA00015412793600000724
Final likelihood ratio metric
Figure BDA00015412793600000725
Riemann distance metric
Figure BDA00015412793600000726
Scatter component similarity metric
Figure BDA0001541279360000081
Constituting a vector as a center superpixel of the sliding window
Figure BDA0001541279360000082
Of a vector of dissimilarity measures
Figure BDA0001541279360000083
Figure BDA0001541279360000084
3e) Center superpixel of sliding window
Figure BDA0001541279360000085
Of a vector of dissimilarity measures
Figure BDA0001541279360000086
Is distributed to the pixel inside to obtain the difference metric vector of the pixel p
Figure BDA0001541279360000087
Wherein
Figure BDA0001541279360000088
Representing a superpixel
Figure BDA0001541279360000089
A pixel of (1);
3f) repeating the steps 3a) to 3e) through a sliding window model to obtain a difference measurement vector of the pixel point in the superpixel at the center of each sliding window
Figure BDA00015412793600000810
s 1, P represents the number of pixel points in the polarized SAR image I;
3g) segmenting the different superpixel segmentation results SkAveraging the difference metric of each pixel to obtain a final difference metric vector of each pixel s:
Figure BDA00015412793600000811
step 4, selecting a Gaussian kernel function, and fusing the difference measurement vector D obtained in the step 3 under different segmentation scales by using a kernel fisher discriminant analysis algorithm KFDAsMapping into the final difference metric Dis of each pixel points。
Step 5, utilizing a linear support vector machine classifier SVM to carry out final treatment on each pixel point mapped in the step (4)Difference metric DissAnd classifying, outputting the category of each pixel point, and realizing automatic target detection.
The effects of the present invention can be further illustrated by the following experimental data:
experiment 1:
1.1) experimental scenario:
the data used in this experiment was a C-band RadarSat-2 fully polarized dataset with a resolution of 12 m × 8 m, i.e., range direction × azimuth direction, obtained in tokyo bay on 8/4/2010, and an angle of incidence of 35 degrees, as shown in fig. 2.
The training data is the R4 region image in fig. 2, the test data is the R5 region image in fig. 2 as shown in fig. 4(a), there are a total of 38 potential targets in fig. 4(a), the strong targets are circled by rectangles, and the weak targets are circled by circles.
1.2) experimental parameters:
the penalty coefficient V of the linear support vector machine SVM is 50, and the kernel parameter g in the kernel fisher discriminant analysis algorithm KFDA is 2.
1.3) contents of the experiment:
FIG. 4(a) was tested according to the present invention, and the results are shown in FIG. 4 (b);
the experiment of fig. 4(a) was performed using a conventional polarization whitening filter detector PWF, and the result is shown in fig. 4 (c);
fig. 4(a) was subjected to an experiment using a conventional polarization notch filter PNF, and the result is shown in fig. 4 (d);
the experiment of FIG. 4(a) was performed using a conventional reflection symmetric filter RSF, and the result is shown in FIG. 4 (e);
fig. 4(a) was tested with the existing significance-based detector SD-LSMDRK, and the results are shown in fig. 4 (f);
fig. 4(a) was tested with a prior art detector SPD based on the distribution characteristics of the superpixel scattering mechanism, and the results are shown in fig. 4 (g).
As can be seen from fig. 4(b) -4(g), the method of the present invention detected all targets and had few false alarms, which demonstrates the effectiveness of the method for suppressing sea clutter. For the traditional methods PWF, PNF and RSF, more false alarms exist; the PWF detects fewer target pixels than the method of the present invention, the PNF does not detect target 3 in fig. 4(a), and the RSF does not detect targets 1-4 in fig. 4 (a); although the SD-LSMDRK and the SPD have similar detection performance to the method, the method can automatically determine the threshold value, and the two methods cannot automatically determine the threshold value.
The results of the above test 1 are shown in table 1:
TABLE 1 test results of different methods
Different methods The invention PWF PNF RSF SD-LSMDRK SPD
Ntd 38 38 37 34 38 38
Nfa 6 >6 12 >6 5 12
N in Table 1tdIndicating the number of detected objects, NfaIndicating the number of false objects.
As can be seen from Table 1, the present invention detects all the targets, and the number of the false targets is also small, so that compared with the conventional method, the present invention improves the detection performance of the algorithm to a certain extent.
Experiment 2:
2.1) experimental scenario:
the data used in this experiment was a C-band RadarSat-2 fully polarized dataset with a resolution of 12 meters × 8 meters, i.e., distance direction × azimuth direction, obtained at 23 days 6/2011 in chinese staving harbor at an angle of incidence of 30 degrees. As shown in fig. 3.
The training data is the R7 region image in fig. 3, the test data is the R6 region image in fig. 3 as shown in fig. 5(a), a total of 46 potential targets are shown in fig. 5(a), the strong targets are circled by rectangles, and the weak targets are circled by circles.
2.2) experimental parameters:
the penalty coefficient V of the linear support vector machine SVM is 50, and the kernel parameter g in the kernel fisher discriminant analysis algorithm KFDA is 2.
2.3) contents of the experiment:
FIG. 5(a) was tested according to the present invention, and the results are shown in FIG. 5 (b);
the experiment of fig. 5(a) was performed using a conventional polarization whitening filter detector PWF, and the result is shown in fig. 5 (c);
fig. 5(a) was subjected to an experiment using a conventional polarization notch filter PNF, and the result is shown in fig. 5 (d);
the experiment of FIG. 5(a) with the conventional reflection symmetric filter RSF shows the result of FIG. 5 (e);
FIG. 5(a) was tested with the existing significance-based detector SD-LSMDRK, and the results are shown in FIG. 5 (f);
fig. 5(a) was tested with a prior art detector SPD based on the distribution characteristics of the superpixel scattering mechanism, and the results are shown in fig. 5 (g).
As can be seen from fig. 5(b) -5(g), the method of the present invention detected all targets and had few false alarms, which demonstrates the effectiveness of the method for suppressing sea clutter. And more false alarms exist for the traditional methods PWF, PNF, RSF, SD-LSMDRK and SPD. The number of target pixels detected by PWF, PNF, RSF, SD-LSMDRK and SPD is less than that of the target pixels detected by the method, and the detection performance of the algorithm is improved to a certain extent.
The results of the above experiment 2 are shown in table 2:
TABLE 2 test results of different methods
Different methods The invention PWF PNF RSF SD-LSMDRK SPD
Ntd 46 45 43 42 45 42
Nfa 3 >3 >3 >3 5 4
As can be seen from table 2, only the present invention detects all the targets, and the number of the false targets is also the least, so compared with the conventional method, the present invention improves the detection performance of the algorithm to a certain extent.
In conclusion, the polarized SAR ship target detection method based on the superpixel local information measurement enhances the contrast ratio of ships and sea clutter, inhibits the influence of coherent spots, increases the difference between the ships and the sea clutter, and utilizes supervision and classification to perform automatic target detection, thereby improving the performance of a ship detection algorithm.

Claims (5)

1. A polarized SAR ship target detection method based on super-pixel local information measurement comprises the following steps:
(1) respectively carrying out multi-scale superpixel segmentation on a given polarized SAR image I to obtain superpixel segmentation results of the polarized SAR image under 4 scales: s1,S2,S3,S4；
(2) For the result S after super pixel segmentationkAnd respectively calculating three super-pixel-level-based difference metrics by using a super-pixel sliding window model: likelihood ratio metric
Figure FDA0003176095960000011
Riemann distance metric
Figure FDA0003176095960000012
Scatter component similarity metric
Figure FDA0003176095960000013
Wherein k 1., 4, represents 4 scales,
Figure FDA0003176095960000014
Figure FDA0003176095960000015
representing the current scale segmentation result SkJ is 1, Mi，MiRepresenting by super-pixels
Figure FDA0003176095960000016
The number of superpixels on the sliding window boundary which is the center; wherein solving likelihood ratio metrics based on superpixel level
Figure FDA0003176095960000017
The method comprises the following steps:
2a1) at the super-pixel segmentation result SkIn the method, a superpixel sliding window model is used for calculating the superpixel in the center of a sliding window
Figure FDA0003176095960000018
And superpixels on sliding window boundary
Figure FDA0003176095960000019
Likelihood ratio statistics
Figure FDA00031760959600000110
Figure FDA00031760959600000111
Where L (-) is a likelihood function, N is a view, NiRepresenting a super imageVegetable extract
Figure FDA00031760959600000112
Number of pixels in, NjRepresenting a superpixel
Figure FDA00031760959600000113
Number of pixels in (1), parameter
Figure FDA00031760959600000114
And
Figure FDA00031760959600000115
is an intermediate variable, estimated by the following formula:
Figure FDA00031760959600000116
wherein the content of the first and second substances,
Figure FDA00031760959600000117
representing center superpixel of sliding window
Figure FDA00031760959600000118
The first ofiA coherence matrix of the individual pixels of the image,
Figure FDA00031760959600000119
representing superpixels on sliding window boundaries
Figure FDA00031760959600000120
The first ofjA coherence matrix of the individual pixels of the image,
Figure FDA00031760959600000121
to represent
Figure FDA00031760959600000122
And
Figure FDA00031760959600000123
the coherence matrix of the ith pixel in (a);
2a2) detecting statistics from likelihood ratios calculated in step 2a1)
Figure FDA00031760959600000124
Calculating center superpixel of sliding window
Figure FDA00031760959600000125
And superpixels on sliding window boundary
Figure FDA00031760959600000126
Likelihood ratio metric of
Figure FDA00031760959600000127
Figure FDA00031760959600000128
Wherein, lnQcIs a constant parameter with the value of-150;
(3) converting the three super-pixel-level-based disparity metrics in (2) into pixel-level-based disparity metric vectors
Figure FDA0003176095960000021
Obtaining a difference measurement vector D of each pixel point after fusion under different segmentation scaless：
Figure FDA0003176095960000022
Wherein the content of the first and second substances,
Figure FDA0003176095960000023
super-pixel segmentation result S for each pixel pointkThe vector of dissimilarity measures of (1),.., wherein P is the number of all pixel points in the polarized SAR image I;
(4) selecting a Gaussian kernel function, and fusing the difference measurement vector D obtained in the step (3) under different segmentation scales by using kernel fisher discriminant analysis algorithm KFDAsMapping into the final difference metric Dis of each pixel points；
(5) Utilizing a support vector machine classifier SVM to carry out final difference metric Dis of each pixel point mapped in the step (4)sAnd classifying, outputting the category of each pixel point, and realizing automatic target detection.
2. The method of claim 1, wherein step (1) performs multi-scale superpixel segmentation as follows;
the superpixel segmentation algorithm comprises the following steps: the method comprises the following steps that according to different scales, an SLIC algorithm, a Turbo pixel algorithm, a Normalized-cuts algorithm and an improved SLIC algorithm for the polarized SAR image, which is proposed by Y.Wang, the improved SLIC algorithm is used for carrying out superpixel segmentation on the polarized SAR image I, and the method is realized as follows:
when the scale is 6, performing superpixel segmentation on the given polarized SAR image I by using an improved SLIC algorithm to obtain a segmented result S1；
When the scale is 9, performing superpixel segmentation on the given polarized SAR image I by using an improved SLIC algorithm to obtain a segmented result S2；
When the scale is 12, performing superpixel segmentation on the given polarized SAR image I by using an improved SLIC algorithm to obtain a segmented result S3；
When the scale is 15, performing superpixel segmentation on the given polarized SAR image I by using an improved SLIC algorithm to obtain a segmented result S4。
3. The method of claim 1, wherein step 2) finds a super-pixel level based Riemann distance metric
Figure FDA0003176095960000024
The method comprises the following steps of;
2b1) respectively calculating the center superpixel of the sliding window by using the following formula
Figure FDA0003176095960000031
Equivalent coherence matrix
Figure FDA0003176095960000032
And superpixels at the sliding window boundary
Figure FDA0003176095960000033
Equivalent coherence matrix
Figure FDA0003176095960000034
Figure FDA0003176095960000035
Figure FDA0003176095960000036
Wherein p represents the center superpixel of the sliding window
Figure FDA0003176095960000037
Pixel of (2), NiTo represent
Figure FDA0003176095960000038
The number of pixels in (1) | · non-woven phosphorFDenotes the F norm, TpTo represent
Figure FDA0003176095960000039
Q represents a superpixel at the boundary of the sliding window
Figure FDA00031760959600000310
Pixel of (2), NjTo represent
Figure FDA00031760959600000311
Number of pixels in, TqTo represent
Figure FDA00031760959600000312
A coherence matrix of the pixel q in (a);
2b2) at the super-pixel segmentation result SkIn the method, a superpixel sliding window model is used for calculating the superpixel in the center of a sliding window
Figure FDA00031760959600000313
And superpixels on sliding window boundary
Figure FDA00031760959600000314
Riemann distance statistic
Figure FDA00031760959600000315
Figure FDA00031760959600000316
Wherein tr (-) represents the trace of the matrix;
2b3) from the Riemann distance statistic obtained in step 2b2)
Figure FDA00031760959600000317
Calculating center superpixel of sliding window
Figure FDA00031760959600000318
And superpixels on sliding window boundary
Figure FDA00031760959600000319
Riemann distance measurement
Figure FDA00031760959600000320
Figure FDA00031760959600000321
Where h is a parameter with a value of 0.5.
4. The method of claim 1, wherein step 2) evaluates a scatter component similarity metric based on superpixel levels
Figure FDA00031760959600000322
The method comprises the following steps of;
2c1) calculating center superpixel of sliding window
Figure FDA00031760959600000323
Scattered power vector ki：
Figure FDA00031760959600000324
Where T represents the transpose of the matrix,
Figure FDA00031760959600000325
respectively obtained by the following formulas:
Figure FDA0003176095960000041
representing center superpixel of sliding window
Figure FDA0003176095960000042
Coherence matrix
Figure FDA0003176095960000043
The surface of (a) scatters the power,
Figure FDA0003176095960000044
representing center superpixel of sliding window
Figure FDA0003176095960000045
Coherence matrix
Figure FDA0003176095960000046
The secondary scattered power of (a) is,
Figure FDA0003176095960000047
representing center superpixel of sliding window
Figure FDA0003176095960000048
Coherence matrix
Figure FDA0003176095960000049
The volume of (a) is used to scatter power,
wherein, | - | represents the absolute value, β1Is a center superpixel of a sliding window
Figure FDA00031760959600000410
Coherence matrix
Figure FDA00031760959600000411
Surface scattering parameter, alpha, obtained by a polarized target decomposition algorithm1Is a center superpixel of a sliding window
Figure FDA00031760959600000412
Coherence matrix
Figure FDA00031760959600000413
The secondary scattering parameters obtained by the polarized target decomposition algorithm,
Figure FDA00031760959600000414
is a center superpixel of a sliding window
Figure FDA00031760959600000415
Coherence momentMatrix of
Figure FDA00031760959600000416
The surface scattering decomposition coefficient obtained by the polarized target decomposition algorithm,
Figure FDA00031760959600000417
is a center superpixel of a sliding window
Figure FDA00031760959600000418
Coherence matrix
Figure FDA00031760959600000419
The secondary scattering decomposition coefficient obtained by the polarized target decomposition algorithm,
Figure FDA00031760959600000420
is a center superpixel of a sliding window
Figure FDA00031760959600000421
Coherence matrix
Figure FDA00031760959600000422
Obtaining a volume scattering decomposition coefficient through a polarized target decomposition algorithm;
2c2) computing sliding window boundary superpixels
Figure FDA00031760959600000423
Scattered power vector kj：
Figure FDA00031760959600000424
Wherein the content of the first and second substances,
Figure FDA00031760959600000425
respectively obtained by the following formulas:
Figure FDA00031760959600000426
representing sliding window boundary superpixels
Figure FDA00031760959600000427
Coherence matrix
Figure FDA00031760959600000428
The surface of (a) scatters the power,
Figure FDA00031760959600000429
representing sliding window boundary superpixels
Figure FDA00031760959600000430
Coherence matrix
Figure FDA00031760959600000431
The secondary scattered power of (a) is,
Figure FDA00031760959600000432
representing sliding window boundary superpixels
Figure FDA00031760959600000433
Coherence matrix
Figure FDA00031760959600000434
The volume of (a) is used to scatter power,
wherein, beta2Is a sliding window boundary superpixel
Figure FDA00031760959600000435
Coherence matrix
Figure FDA00031760959600000436
Surface scattering parameter, alpha, obtained by a polarized target decomposition algorithm2Is a sliding window boundary superpixel
Figure FDA00031760959600000437
Coherence matrix
Figure FDA00031760959600000438
The secondary scattering parameters obtained by the polarized target decomposition algorithm,
Figure FDA00031760959600000439
superpixels for sliding window boundaries
Figure FDA00031760959600000440
Coherence matrix
Figure FDA00031760959600000441
The surface scattering decomposition coefficient obtained by the polarized target decomposition algorithm,
Figure FDA00031760959600000442
superpixels for sliding window boundaries
Figure FDA00031760959600000443
Coherence matrix
Figure FDA00031760959600000444
The secondary scattering decomposition coefficient obtained by the polarized target decomposition algorithm,
Figure FDA00031760959600000445
superpixels for sliding window boundaries
Figure FDA00031760959600000446
Coherence matrix
Figure FDA00031760959600000447
Obtaining a volume scattering decomposition coefficient through a polarized target decomposition algorithm;
2c3) at the super-pixel segmentation result SkIn (1), calculating a sliding window by using a superpixel sliding window modelCenter super pixel
Figure FDA00031760959600000448
And sliding window boundary superpixel
Figure FDA00031760959600000449
Scattering power vector similarity parameter r ofij：
Figure FDA0003176095960000051
Wherein the superscript H is a conjugate transpose, | · |. non-woven phosphor2Is the norm of L2, |, represents the absolute value;
2c4) according to the scattering power vector similarity parameter r obtained in the step 2c3)ijCalculating the center superpixel of the sliding window
Figure FDA0003176095960000052
And sliding window boundary superpixel
Figure FDA0003176095960000053
Scatter component similarity measure of
Figure FDA0003176095960000054
The value is calculated by the following formula:
Figure FDA0003176095960000055
5. the method of claim 1, step (3) measuring the likelihood ratio
Figure FDA0003176095960000056
Riemann distance metric
Figure FDA0003176095960000057
Scattering componentVolume similarity measure
Figure FDA0003176095960000058
The three super-pixel level-based difference metrics are converted into pixel level-based difference metric vectors
Figure FDA0003176095960000059
The method comprises the following steps of;
3a) performing superpixel on the center of the sliding window by using a K-means clustering algorithm
Figure FDA00031760959600000510
And sliding window boundary superpixel
Figure FDA00031760959600000511
Likelihood ratio metric of
Figure FDA00031760959600000512
Clustering into two categories, using the mean of likelihood ratio metrics in the majority of categories as the center superpixel of the sliding window
Figure FDA00031760959600000513
Final likelihood ratio metric
Figure FDA00031760959600000514
Figure FDA00031760959600000515
Wherein the content of the first and second substances,
Figure FDA00031760959600000516
representing a value of a superpixel likelihood ratio metric, c, in a current majority class1＝1,...,ML，MLRepresenting the number of the superpixels of most types of the likelihood ratio metric value in the current cluster;
3b) sliding window by using K mean value clustering algorithmCenter super pixel
Figure FDA00031760959600000517
And sliding window boundary superpixel
Figure FDA00031760959600000518
Riemann distance measurement
Figure FDA00031760959600000519
Clustering into two classes, and taking the mean of Riemann distance measures in the majority of classes as the center superpixel of the sliding window
Figure FDA00031760959600000520
Final Riemann distance metric
Figure FDA00031760959600000521
Figure FDA00031760959600000522
Wherein the content of the first and second substances,
Figure FDA00031760959600000523
representing a super-pixel Riemann distance metric, c, in a current majority class2＝1,...,MR，MRRepresenting the number of the superpixels of most types of the Riemann distance metric value in the current cluster;
3c) performing superpixel on the center of the sliding window by using a K-means clustering algorithm
Figure FDA0003176095960000061
And sliding window boundary superpixel
Figure FDA0003176095960000062
Scatter component similarity measure of
Figure FDA0003176095960000063
Clustering into two categories, taking the mean of the similarity measures of the scattering components in the majority of categories as the center superpixel of the sliding window
Figure FDA0003176095960000064
Final scatter component similarity measure
Figure FDA0003176095960000065
Figure FDA0003176095960000066
Wherein the content of the first and second substances,
Figure FDA0003176095960000067
representing a similarity measure, c, of the superpixel scatter components in the current majority class3＝1,...,MC，MCRepresenting the number of superpixels of a majority of classes of the similarity measurement of the scattering components in the current cluster;
3d) center superpixel of sliding window
Figure FDA0003176095960000068
Final likelihood ratio metric
Figure FDA0003176095960000069
Riemann distance metric
Figure FDA00031760959600000610
Scatter component similarity metric
Figure FDA00031760959600000611
Constituting a vector as a center superpixel of the sliding window
Figure FDA00031760959600000612
Of a vector of dissimilarity measures
Figure FDA00031760959600000613
Figure FDA00031760959600000614
3e) Center superpixel of sliding window
Figure FDA00031760959600000615
Of a vector of dissimilarity measures
Figure FDA00031760959600000616
Is distributed to the pixel inside to obtain the difference metric vector of the pixel p
Figure FDA00031760959600000617
Wherein
Figure FDA00031760959600000618
Representing a superpixel
Figure FDA00031760959600000619
A pixel of (1);
3f) repeating the steps 3a) to 3e) through a sliding window model to obtain a difference measurement vector of the pixel point in the superpixel at the center of each sliding window
Figure FDA00031760959600000620
P represents the number of pixel points in the polarized SAR image I.
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