CN114399451A - Synthetic aperture radar image ship target cooperative enhancement method and device - Google Patents
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Abstract
The invention discloses a synthetic aperture radar image ship target cooperative enhancement method and a synthetic aperture radar image ship target cooperative enhancement device, wherein the method comprises the following steps: inputting an original Synthetic Aperture Radar (SAR) image, and acquiring a slice set of all SAR image slices containing a ship target in the SAR image to obtain superpixels in the slice set; calculating local gray contrast of all superpixels in the slice set based on the slice set, and calculating a superpixel histogram density value of local gray according to histogram information; calculating to obtain cross correlation characteristics based on the density value of the super-pixel histogram; and performing feature fusion based on the cross correlation features to obtain and output an image enhancement result. According to the method, the weak targets in different SAR image slices are synergistically enhanced and the strong sea clutter is inhibited by developing the correlation based on density characteristics, so that the subsequent target detection performance is favorably improved.
Description
Technical Field
The invention relates to the technical field of Synthetic Aperture Radar (SAR) image processing, in particular to a synthetic aperture radar image ship target cooperative enhancement method and a synthetic aperture radar image ship target cooperative enhancement device.
Background
A Synthetic Aperture Radar (SAR) belongs to an active imaging sensor and can provide high-resolution images of targets such as sea surface ships and the like. Compared with passive sensors such as optical sensors, infrared sensors and the like, the SAR image is not affected by illumination and weather, and has all-weather and all-time monitoring capability. The method has important application in the aspects of military civil use such as military sea defense, sustainable fishery and the like for enhancing, segmenting and detecting the ship target in the SAR image.
In recent years, a number of expert scholars have proposed various methods of enhancing targets in SAR images. However, existing target enhancement methods ignore the correlation between different image slices resulting from segmentation in a single SAR image, resulting in limited target enhancement effects.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the invention aims to overcome the defects of the prior art and provides a synthetic aperture radar image ship target cooperative enhancement method. According to the method, the weak targets in different SAR image slices are synergistically enhanced and the strong sea clutter is inhibited by developing the correlation based on density characteristics, so that the subsequent target detection performance is favorably improved.
The invention also aims to provide a synthetic aperture radar image ship target cooperative enhancement device.
In order to achieve the above object, the present invention provides a synthetic aperture radar image ship target cooperative enhancement method, including the following steps:
inputting an original Synthetic Aperture Radar (SAR) image, and acquiring a slice set of all SAR image slices containing a ship target in the SAR image to obtain superpixels in the slice set; calculating local gray contrast of all superpixels in the slice set based on the slice set, and calculating a superpixel histogram density value of the local gray according to histogram information; calculating to obtain cross correlation characteristics based on the density value of the super pixel histogram; and performing feature fusion based on the cross correlation features to obtain and output an image enhancement result.
The synthetic aperture radar image ship target cooperative enhancement method provided by the embodiment of the invention can be used for increasing the contrast between a ship target and a sea clutter in a sea surface SAR image and enhancing the detection performance of the ship target.
In addition, the synthetic aperture radar image ship target cooperative enhancement method according to the above embodiment of the present invention may further have the following additional technical features:
further, the local gray contrast is calculated by the following formula:
wherein the content of the first and second substances,
for local gray contrast, m represents the index of the super-pixel,
representing the number of superpixels, δ, in the nth SAR image slicen,mRepresents the mean gray value of the mth superpixel in the nth SAR image slice,
an index set representing the contiguous superpixel of the mth superpixel in the nth SAR image slice, p represents
Of (1).
Further, the calculating the super-pixel histogram density value of the local gray according to the histogram information includes:
preset of
Representing the nth SAR image slice
The density value of the super-pixel histogram is calculated by the following formula:
wherein the content of the first and second substances,
indicates falling in
In each of the histograms centered
K denotes the respective number of histogram divisions, K denotes the index of the bin in the histogram,
denotes the truncation distance, pn,m,qFor the super-pixel histogram density value, ndi (·) is when the value in the parenthesis is less than zero, ndi (·) outputs 1, otherwise 0 is output, q denotes the index of the SAR image slice, and q is 1,2, …, N.
Further, the cross-correlation characteristic is calculated by the following formula:
wherein the content of the first and second substances,
for cross-correlation features, phiqIs rhon,m,qThe weight of (c).
Further onSaid phiqCalculated from the following formula:
φq＝1/[1+κln(1+εq)]
wherein κ -50 is a constant, and ε is fTA[θ(intra)]And fCA[θ(intra)]Area under the curve of the intersection.
Further, the targets and clutter in the nth slice are preset
Obeying two different Gamma distributions, then fTA[θ(intra)]And fCA[θ(intra)]Calculated from the following formula:
where TA denotes a target region, CA denotes a clutter region, ξ and μ denote a shape parameter and a scale parameter of a Gamma distribution, and Γ (·) denotes a Gamma function.
Further, preset 0<θ*<+ ∞ represents the fTA[θ(intra)]And fCA[θ(intra)]Then the epsilon is calculated by the following formula:
where γ (,) represents the incomplete Gamma function.
Further, the image enhancement result is calculated by the following formula:
wherein N is 1,2, …, N,
is the image enhancement result.
Further, the method further comprises, for ρn,m,qAnd (3) carrying out normalization:
in order to achieve the above object, another aspect of the present invention provides a synthetic aperture radar image ship target cooperative enhancing apparatus, including:
the system comprises a pixel acquisition module, a data acquisition module and a data processing module, wherein the pixel acquisition module is used for inputting an original Synthetic Aperture Radar (SAR) image and acquiring a slice set of all SAR image slices containing a ship target in the SAR image so as to obtain superpixels in the slice set;
the first calculation module is used for calculating the local gray contrast of all the superpixels in the slice set based on the slice set and calculating the superpixel histogram density value of the local gray according to histogram information;
the second calculation module is used for calculating to obtain cross correlation characteristics based on the density value of the super-pixel histogram;
and the fusion output module is used for carrying out feature fusion based on the cross correlation features to obtain and output an image enhancement result.
The synthetic aperture radar image ship target cooperative enhancement device provided by the embodiment of the invention can be used for increasing the contrast between a ship target and a sea clutter in a sea surface SAR image and enhancing the detection performance of the ship target.
The invention has the beneficial effects that:
the existing method for enhancing the ship target in the SAR image has the disadvantages that only the internal characteristics of a single SAR image slice are developed, and the similarity between different sea surface SAR image slices is not considered in a combined manner, for example, the ship target shows the sparsity of an airspace in different sea surface SAR image slices. This lack of consideration of image slice correlation results in lower performance of existing target enhancement methods. The invention provides a density-characteristic SAR image ship target enhancement method based on the low density characteristic of ship targets in different sea surface SAR image slices, obviously enhances the ship targets by developing the similarity of targets/clutters among different slices in density, and is expected to improve the quick response capability of China to the sea surface ship targets.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a synthetic aperture radar image ship target cooperative enhancement method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the effect of a synthetic aperture radar image ship target cooperative enhancement method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a synthetic aperture radar image ship target cooperative enhancement device according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method and the device for cooperative enhancement of synthetic aperture radar image ship targets provided by the embodiment of the invention are described below with reference to the accompanying drawings, and first, the method for cooperative enhancement of synthetic aperture radar image ship targets provided by the embodiment of the invention is described with reference to the accompanying drawings.
Fig. 1 is a flowchart of a synthetic aperture radar image ship target cooperative enhancement method according to an embodiment of the present invention.
As shown in fig. 1, the synthetic aperture radar image ship target cooperative enhancement method includes the following steps:
step S1, inputting an original synthetic aperture radar SAR image, and acquiring a slice set of all SAR image slices containing a ship target in the SAR image to obtain superpixels in the slice set.
Specifically, an original synthetic aperture radar SAR image is input first, and the following is taken as an implementation manner:
an original SAR image with the size of M multiplied by N;
the SAR image slice size Mc multiplied by Nc meets the condition that Mc is more than or equal to 256 and is less than M, and Nc is more than or equal to 256 and is less than N;
and the scale factor alpha epsilon (0,1) is used for calculating the superpixel histogram density characteristic.
Preferably, all SAR image slices containing the ship target in the original SAR image are obtained by adopting a weighted sparse optimization method, and all obtained slice sets containing the target are recorded as
Wherein, InThe nth slice containing the target is shown, N is 1,2, …, N represents the number of SAR images containing the ship target slice in the original SAR image.
Preferably, a simple linear iterative clustering algorithm is adopted to obtain the slice set
Is used as a super pixel.
And step S2, calculating the local gray contrast of all the superpixels in the slice set based on the slice set, and calculating the density value of the superpixel histogram of the local gray according to the histogram information.
Specifically, calculating
Local gray contrast of all super pixels in the image
Where m denotes the index of the super-pixel,
representing the number of superpixels, δ, in the nth SAR image slicen,mRepresents the mean gray value of the mth superpixel in the nth SAR image slice,
an index set representing the contiguous superpixel of the mth superpixel in the nth SAR image slice, p represents
Of (1). Order to
Representing all of the nth SAR image slice
A collection of (a).
Further, let
Representing the nth SAR image slice
Of the histogram information of, wherein
Indicates falling in
In each of the histograms centered
K denotes the respective number of histogram divisions, and K denotes the index of the grid in the histogram. Next, each of the nth SAR image slices is calculated
Superpixel histogram density value ρ in the qth (q ═ 1,2, … … N) SAR image slicen,m,q：
Wherein the content of the first and second substances,
indicating the truncation distance, ndi (·) means that when the value in the parenthesis is less than zero, ndi (·) outputs 1, otherwise 0 is output, q denotes the index of the SAR image slice, and q is 1,2, …, N. Then p is pairedn,m,qAnd (3) carrying out normalization:
and step S3, calculating to obtain cross correlation characteristics based on the density value of the super-pixel histogram.
Specifically, the method develops the similarity of different SAR image slices on density characteristics and calculates cross-correlation characteristics
Wherein phi isqDenotes ρn,m,qThe weight of (c). Phi is aqThe calculation of (c) is as follows. Assuming target and clutter in the nth slice
Two different Gamma distributions were obeyed:
where TA denotes a target region, CA denotes a clutter region, ξ and μ denote a shape parameter and a scale parameter of a Gamma distribution, Γ (.) denotes a Gamma function, { ξTA,μTA,ξCA,μCAMay be obtained by Otsu algorithm. Let 0<θ*<+ ∞ denotes fTA[θ(intra)]And fCA[θ(intra)]The intersection of the curves, calculate fTA[θ(intra)]And fCA[θ(intra)]Area under the cross-point curve ε:
where γ (,) represents the incomplete Gamma function. The weights are calculated as follows:
φq＝1[1+κln(1+εq)]
wherein κ ═ 50 is a constant.
And step S4, performing feature fusion based on the cross correlation features, and obtaining and outputting an image enhancement result.
Specifically, according to the cross correlation characteristics, the SAR image characteristic fusion is carried out. Obtaining the enhancement result of the super-pixel in each SAR image slice
The output result is then:
n＝1,2,…,N，
further, fig. 2 is a diagram illustrating a technical effect of the embodiment of the present invention, as shown in fig. 2, in which a first behavior SAR image slice is shown; a second line of image effects presented by existing methods; the third is the image effect presented by the present invention.
According to the synthetic aperture radar image ship target cooperative enhancement method disclosed by the embodiment of the invention, an original synthetic aperture radar SAR image is input, and a slice set of all SAR image slices containing a ship target in the SAR image is obtained so as to obtain superpixels in the slice set; calculating local gray contrast of all superpixels in the slice set based on the slice set, and calculating a superpixel histogram density value of local gray according to histogram information; calculating to obtain cross correlation characteristics based on the density value of the super-pixel histogram; and performing feature fusion based on the cross correlation features to obtain and output an image enhancement result. According to the method, the weak targets in different SAR image slices are synergistically enhanced and the strong sea clutter is inhibited by developing the correlation based on density characteristics, so that the subsequent target detection performance is favorably improved.
In order to implement the above embodiment, as shown in fig. 3, a synthetic aperture radar image ship target cooperative enhancement apparatus 10 is further provided in this embodiment, where the apparatus 10 includes: a pixel acquisition module 100, a first calculation module 200, a second calculation module 300, and a fusion output module 400.
The system comprises a pixel acquisition module 100, a data processing module and a data processing module, wherein the pixel acquisition module 100 is used for inputting an original Synthetic Aperture Radar (SAR) image and acquiring a slice set of all SAR image slices containing a ship target in the SAR image so as to obtain superpixels in the slice set;
the first calculation module 200 is configured to calculate local gray level contrast of all superpixels in a slice set based on the slice set, and calculate a superpixel histogram density value of a local gray level according to histogram information;
a second calculating module 300, configured to calculate a cross correlation feature based on the density value of the super-pixel histogram;
and a fusion output module 400, configured to perform feature fusion based on the cross correlation features, obtain an image enhancement result, and output the image enhancement result.
According to the synthetic aperture radar image ship target cooperative enhancement device disclosed by the embodiment of the invention, the original synthetic aperture radar SAR image is input, and the slice set of all SAR image slices containing the ship target in the SAR image is obtained so as to obtain the superpixels in the slice set; calculating local gray contrast of all superpixels in the slice set based on the slice set, and calculating a superpixel histogram density value of local gray according to histogram information; calculating to obtain cross correlation characteristics based on the density value of the super-pixel histogram; and performing feature fusion based on the cross correlation features to obtain and output an image enhancement result. According to the method, the weak targets in different SAR image slices are synergistically enhanced and the strong sea clutter is inhibited by developing the correlation based on density characteristics, so that the subsequent target detection performance is favorably improved.
It should be noted that the foregoing explanation of the embodiment of the synthetic aperture radar image ship target cooperative enhancement method is also applicable to the synthetic aperture radar image ship target cooperative enhancement apparatus of the embodiment, and details are not repeated here.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A synthetic aperture radar image ship target cooperative enhancement method is characterized by comprising the following steps:
inputting an original Synthetic Aperture Radar (SAR) image, and acquiring a slice set of all SAR image slices containing a ship target in the SAR image to obtain superpixels in the slice set;
calculating local gray contrast of all superpixels in the slice set based on the slice set, and calculating a superpixel histogram density value of the local gray according to histogram information;
calculating to obtain cross correlation characteristics based on the density value of the super pixel histogram;
and performing feature fusion based on the cross correlation features to obtain and output an image enhancement result.
2. The method of claim 1, wherein the local gray-scale contrast is calculated by the following formula:
wherein the content of the first and second substances,
for local gray contrast, m denotes the index of the super-pixel, m is 1,2, …,
representing the number of superpixels, δ, in the nth SAR image slicen,mRepresents the mean gray value of the mth superpixel in the nth SAR image slice,
an index set representing the contiguous superpixel of the mth superpixel in the nth SAR image slice, p represents
Of (1).
3. The method of claim 2, wherein the calculating the super-pixel histogram density value of the local gray from the histogram information comprises:
preset of
Representing the nth SAR image slice
The density value of the super-pixel histogram is calculated by the following formula:
wherein the content of the first and second substances,
indicates falling in
In each of the histograms centered
K denotes the respective number of histogram divisions, K denotes the index of the bin in the histogram,
denotes the truncation distance, pn,m,qFor the super-pixel histogram density value, ndi (·) is when the value in the parenthesis is less than zero, ndi (·) outputs 1, otherwise 0 is output, q denotes the index of the SAR image slice, and q is 1,2, …, N.
4. The method of claim 3, wherein the cross-correlation characteristic is calculated by the following formula:
wherein the content of the first and second substances,
for cross-correlation features, phiqIs rhon,m,qThe weight of (c).
5. The method of claim 4 wherein phi isqCalculated from the following formula:
φq＝1/[1+κln(1+εq)]
wherein κ -50 is a constant, and ε is fTA[θ(intra)]And fCA[θ(intra)]Area under the curve of the intersection.
6. The method of claim 5, wherein the targets and clutter in the nth slice are preset
Obeying two different Gamma distributions, then fTA[θ(intra)]And fCA[θ(intra)]Calculated from the following formula:
where TA denotes a target region, CA denotes a clutter region, ξ and μ denote a shape parameter and a scale parameter of a Gamma distribution, and Γ (·) denotes a Gamma function.
7. Method according to claim 6, characterized in that 0 is preset<θ*<+ ∞ represents the fTA[θ(intra)]And fCA[θ(intra)]Then the epsilon is calculated by the following formula:
where γ (,) represents the incomplete Gamma function.
8. The method of claim 1, wherein the image enhancement result is calculated by the following formula:
wherein N is 1,2, …, N, m is 1,2, …,
is the image enhancement result.
9. The method of claim 4, further comprising, for pn,m,qAnd (3) carrying out normalization:
10. a synthetic aperture radar image ship target cooperative enhancement device is characterized by comprising:
the system comprises a pixel acquisition module, a data acquisition module and a data processing module, wherein the pixel acquisition module is used for inputting an original Synthetic Aperture Radar (SAR) image and acquiring a slice set of all SAR image slices containing a ship target in the SAR image so as to obtain superpixels in the slice set;
the first calculation module is used for calculating the local gray contrast of all the superpixels in the slice set based on the slice set and calculating the superpixel histogram density value of the local gray according to histogram information;
the second calculation module is used for calculating to obtain cross correlation characteristics based on the density value of the super-pixel histogram;
and the fusion output module is used for carrying out feature fusion based on the cross correlation features to obtain and output an image enhancement result.
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| WO2016101279A1 (en) * | 2014-12-26 | 2016-06-30 | 中国海洋大学 | Quick detecting method for synthetic aperture radar image of ship target |
| CN113362293A (en) * | 2021-05-27 | 2021-09-07 | 西安理工大学 | SAR image ship target rapid detection method based on significance |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2016101279A1 (en) * | 2014-12-26 | 2016-06-30 | 中国海洋大学 | Quick detecting method for synthetic aperture radar image of ship target |
| CN113362293A (en) * | 2021-05-27 | 2021-09-07 | 西安理工大学 | SAR image ship target rapid detection method based on significance |
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| Title |
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