CN110223312A - A kind of SAR image edge detection method based on shearing wave - Google Patents

A kind of SAR image edge detection method based on shearing wave Download PDF

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CN110223312A
CN110223312A CN201910486792.0A CN201910486792A CN110223312A CN 110223312 A CN110223312 A CN 110223312A CN 201910486792 A CN201910486792 A CN 201910486792A CN 110223312 A CN110223312 A CN 110223312A
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shearing wave
edge
image
pixel
edge detection
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孙增国
赵国栋
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Shaanxi Normal University
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Shaanxi Normal University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/155—Segmentation; Edge detection involving morphological operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G06T2207/10044—Radar image

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  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention belongs to technique of image edge detection fields, and in particular to a kind of SAR image edge detection method based on shearing wave is the following steps are included: step 1: the even shearing wave pattern of construction；Step 2: Hilbert transform is carried out to the even shearing wave pattern constructed；Step 3: the optimum orientation at edge is calculated according to shearing wave coefficient；Step 4: calculate edge optimum orientation calculate edge there are a possibility that；Step 5: testing result in step 4 is subjected to binaryzation；Step 6: Morphological scale-space is carried out to binary image obtained in step 5；Step 7: the refinement at edge is carried out to the image after Morphological scale-space in step 6, obtains final image；The edge result that this method detects can remove a large amount of pseudo-edges very close to actual edge feature, the SAR image edge detection being highly suitable under very noisy interference.

Description

A kind of SAR image edge detection method based on shearing wave
Technical field
The invention belongs to technique of image edge detection fields, and in particular to a kind of image border SAR inspection based on shearing wave Survey method.
Background technique
Synthetic aperture radar (Synthetic Aperture Radar, SAR) is a kind of high-resolution coherent imaging thunder Reach, can round-the-clock, round-the-clock imaging, its imaging mechanism is extremely complex, easily multiplying property of formative coherent speckle noise.This makes Traditional edge detection mode can not be suitable for the edge detection of SAR image, and have gradually developed in recent years for gram The problem of taking the edge detection method that SAR image multiplying property coherent speckle noise influences, also exposing its weak directionality in use.
Traditional edge detection operator, such as Prewitt operator, Sobel operator, they are that differential can be used as edge Characterization, carry out edge detection by constructing the differential operator sensitive to the variation of pixel grayscale rank.Though such mode Can so detect the marginal information of image, but the bad influence of noise that can not overcome SAR image of effect, often will detect that with very Real edge false edge mixed in together.
The existing frequently-used operator in SAR image edge detection, such as exponential weighting ratio of averages (Ratio of Exponentially Weighted Averages, ROEWA) detective operators etc., these operators all have constant false alarm rate, and The classical gradient edge detective operators disadvantage sensitive to multiplicative noise is overcome to a certain extent.But scholars have found that they are deposited In weak directional problems, be only capable of obtaining when calculating the direction of marginal point [0, pi/2) angular range.
Summary of the invention
In order to solve the above-mentioned problems in the prior art, the present invention provides a kind of SAR images based on shearing wave Edge detection method.The technical problem to be solved in the present invention is achieved through the following technical solutions:
A kind of SAR image edge detection method based on shearing wave, comprising the following steps:
Step 1: the even shearing wave pattern of construction；
Step 2: Hilbert transform is carried out to the even shearing wave pattern constructed, using even symmetry shearing wave as plural number The real part of wave pattern is sheared, imaginary part of the odd symmetry shearing wave as complex shear wave obtains complicated shearing wave pattern；And using multiple Miscellaneous shearing wave pattern processes SAR image, obtains corresponding shearing wave coefficient；
Step 3: the optimum orientation at edge is calculated according to shearing wave coefficient；
Step 4: calculate edge optimum orientation calculate edge there are a possibility that；
Step 5: testing result in step 4 is subjected to binaryzation, obtains bianry image；
Step 6: Morphological scale-space is carried out to binary image obtained in step 5；
Step 7: the refinement at edge is carried out to the image after Morphological scale-space in step 6, obtains final image.
Further, the anisotropic expansion of shearing wave is defined as follows in the step 1:
Wherein a is scale parameter, and s is shear parameters, and t is translation parameters, and anisotropic expansion matrix is defined asShearing matrix is defined as
Further, the step 2 sets ψevenIt for even symmetry shearing wave, and is the real part of function, then complicated shearing wave It is defined as follows:
ψ=ψeven+iψodd:=ψeven+iH(ψeven)
Wherein ψoddIndicate that odd symmetry shearing wave, function H () indicate Hilbert transform, the transformation formula is as follows:
Secondly, constructing even symmetry shearing wave using the tensor product of Mexico's hat wavelet and Gauss wavelet, and use Xi Er Even symmetry shearing wave is converted to odd symmetry shearing wave by Bert transformation；The formula of even symmetry shearing wave and odd symmetry shearing wave is such as Under:
Wherein scale parameterShear parametersTranslation parametersAnd shear matrix definition ForAnisotropic expansion matrix is defined as
Further, it is calculated in the step 3 using odd symmetry and even symmetry shearing wave coefficient and is existed in each pixel Road optimum orientation；For scale parameter j ∈ { Jmin,...,Jmax, shear parametersAnd translation parametersEach pixelOn there are the optimum orientations at edge
It is calculate by the following formula:
Further, the edge metering in the step 4 on optimum orientation is calculate by the following formula:
Wherein f is image array,To shear matrix,For anisotropic expansion square Battle array, in order to enable following mapping relations are arranged between 0 to 1 in the edge metering probability value of each pixel:
It generates the value between one 0 to 1, and measuring does not have edge for 0 expression position, and 1 indicates there is edge certainly.
Further, binarization method is to set a threshold value in the step 5, each pixel is traversed, if pixel Value is more than or equal to threshold value and the gray value is then set to 1, the gray value is then set to 0 less than threshold value, edge in step 4 The result images of detection switch to bianry image.
Further, the step 6 first carries out the connected component labeling processing operation in bianry image, the company of bianry image Logical field mark processing operation is exactly from the width dot matrix image being made of pixel value 0 and 1, by (usually 4- neighbour adjacent to each other Connect or 8- be adjacent) the pixel set with pixel value " 1 " be marked with different digital, while counting white in different connected domains Color pixel point number, and threshold value is set, the part for assert that pixel number is less than threshold value in each connected region of image is pseudo-edge, Pixel value in these connected regions is all set to 0, to achieve the purpose that eliminate pseudo-edge, then to the figure after elimination pseudo-edge As carrying out dilation erosion operation, to remove the burr phenomena on edge.
Compared with prior art, beneficial effects of the present invention:
This programme has good effect, the close practical side of the edge result detected for the edge detection of SAR image Edge feature, and the Morphological scale-space in this programme can remove a large amount of pseudo-edges, the SAR being highly suitable under very noisy interference Image Edge-Detection.
Detailed description of the invention
Fig. 1 is this detection method schematic illustration.
Fig. 2 is present invention experiment selection figure.
Fig. 3 is that the present invention tests another selection figure.
Fig. 4 is the result figure that edge detection is carried out to experiment selection figure.
Fig. 5 is to carry out pseudo-edge result figure to Fig. 4.
Fig. 6 is the result figure that expansive working is carried out to Fig. 5.
Fig. 7 is the result figure that Refinement operation is carried out to Fig. 6.
Fig. 8 is to the result figure for testing another selection figure progress edge detection.
Fig. 9 is to carry out pseudo-edge result figure to Fig. 8.
Figure 10 is the result figure to Fig. 9 expansive working.
Figure 11 is the result figure that Refinement operation is carried out to Figure 10.
Specific embodiment
Further detailed description is done to the present invention combined with specific embodiments below, but embodiments of the present invention are not limited to This.
The Main way at edge that may be present is determined by odd symmetry shearing wave greatest coefficient in SAR image, also It is the absolute value of its odd symmetry shearing wave coefficient to be maximized for each of two-dimensional surface point, and select relevant shear wave Direction is as Main way, then, is calculated based on this direction using even symmetry shearing wave coefficient and odd symmetry shearing wave coefficient A possibility that there are roads in the pixel.
As shown in Figure 1, a kind of SAR image edge detection method based on shearing wave, comprising the following steps:
Step 1: the even shearing wave pattern of construction；
Step 2: Hilbert transform is carried out to the even shearing wave pattern constructed, using even symmetry shearing wave as plural number The real part of wave pattern is sheared, imaginary part of the odd symmetry shearing wave as complex shear wave obtains complicated shearing wave pattern；And using multiple Miscellaneous shearing wave pattern processes SAR image, obtains corresponding shearing wave coefficient；
Step 3: the optimum orientation at edge is calculated according to shearing wave coefficient；
Step 4: calculate edge optimum orientation calculate edge there are a possibility that；
Step 5: testing result in step 4 is subjected to binaryzation, obtains bianry image；
Step 6: Morphological scale-space is carried out to binary image obtained in step 5；
Step 7: edge is carried out to the image after Morphological scale-space in step 6 using the bwmorph function in MATLAB Refinement, obtain final image.
Further, the anisotropic expansion of shearing wave is defined as follows in step 1:
Wherein a is scale parameter, and s is shear parameters, and t is translation parameters, and anisotropic expansion matrix is defined asShearing matrix is defined as
Further, the real part for guaranteeing generating function be even symmetry (such as cosine) and imaginary part be odd symmetry (such as just String), even symmetry function is converted to odd symmetry function using Hilbert transform by this programme, and step 2 sets ψevenIt is cut for even symmetry Wave is cut, and is the real part of function, then complicated shearing wave is defined as follows:
ψ=ψeven+iψodd:=ψeven+iH(ψeven)
Wherein ψoddIndicate that odd symmetry shearing wave, function H () indicate Hilbert transform, the transformation formula is as follows:
Secondly, constructing even symmetry shearing wave using the tensor product of Mexico's hat wavelet and Gauss wavelet, and use Xi Er Even symmetry shearing wave is converted to odd symmetry shearing wave by Bert transformation；The formula of even symmetry shearing wave and odd symmetry shearing wave is such as Under:
Wherein scale parameterShear parametersTranslation parametersAnd shear matrix definition ForAnisotropic expansion matrix is defined as
Further, road present on each pixel is calculated in step 3 using odd symmetry and even symmetry shearing wave coefficient The optimum orientation on road；For scale parameter j ∈ { Jmin,...,Jmax, shear parametersAnd translation parametersEach pixelOn there are the optimum orientations at edge
It is calculate by the following formula:
Further, the edge metering in step 4 on optimum orientation is calculate by the following formula:
Wherein f is image array,To shear matrix,For anisotropic expansion Matrix, the ε in the measure equation of position be level off to 0 minimum and ε > 0, to ensure that denominator will not be 0, in order to enable each Following mapping relations are arranged between 0 to 1 in the edge metering probability value of pixel:
It generates the value between one 0 to 1, and for indicating " edge " of some position measured, measure indicates to be somebody's turn to do for 0 Position does not have edge, and 1 indicates there is edge certainly.
Further, binarization method is to set a threshold value in step 5, each pixel is traversed, if pixel value is big The gray value is then set to 1 in being equal to threshold value, the gray value is then set to 0 less than threshold value, edge detection in step 4 Result images switch to bianry image.
Further, step 6 first carries out the connected component labeling processing operation in bianry image, the connected domain of bianry image Label processing operation be exactly from the width dot matrix image being made of pixel value 0 and 1, by it is adjacent to each other (usually 4- it is adjacent or 8- is adjacent) the pixel set with pixel value " 1 " be marked with different digital, while counting white picture in different connected domains Vegetarian refreshments number, and threshold value is set, the part for assert that pixel number is less than threshold value in each connected region of image is pseudo-edge, by this Pixel value in a little connected regions is all set to 0, to achieve the purpose that eliminate pseudo-edge, then to eliminate the image after pseudo-edge into Row dilation erosion operation, to remove the burr phenomena on edge.
In order to verify method of the invention to the edge detection effect of SAR image, such as Fig. 2 and shown in Fig. 3 is selected below Two width SAR images are tested, and are detected first to Fig. 2, the fourth officer effect picture generated during will test: edge detection knot Fruit figure, removal pseudo-edge result figure, dilation erosion result figure, final result figure, individually extract, as shown in Figures 4 to 7. And same treatment is also carried out to Fig. 3, extracts fourth officer effect picture, as shown in Figs. 8 to 11.
It can be seen that method of the invention has good effect, inspection to the edge detection of SAR image in from Fig. 4 to Figure 11 The edge result measured is very close to actual edge feature.And the Morphological scale-space in the method for the present invention can remove largely Pseudo-edge, the SAR Image Edge-Detection being highly suitable under very noisy interference.
The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be said that Specific implementation of the invention is only limited to these instructions.For those of ordinary skill in the art to which the present invention belongs, exist Under the premise of not departing from present inventive concept, a number of simple deductions or replacements can also be made, all shall be regarded as belonging to of the invention Protection scope.

Claims (7)

1. a kind of SAR image edge detection method based on shearing wave, it is characterised in that: the following steps are included:
Step 1: the even shearing wave pattern of construction；
Step 2: Hilbert transform is carried out to the even shearing wave pattern constructed, using even symmetry shearing wave as complex shear The real part of wave pattern, imaginary part of the odd symmetry shearing wave as complex shear wave obtain complicated shearing wave pattern；And use complexity Shearing wave pattern processes SAR image, obtains corresponding shearing wave coefficient；
Step 3: the optimum orientation at edge is calculated according to shearing wave coefficient；
Step 4: calculate edge optimum orientation calculate edge there are a possibility that；
Step 5: testing result in step 4 is subjected to binaryzation, obtains bianry image；
Step 6: Morphological scale-space is carried out to binary image obtained in step 5；
Step 7: the refinement at edge is carried out to the image after Morphological scale-space in step 6, obtains final image.
2. a kind of SAR image edge detection method based on shearing wave according to claim 1, it is characterised in that: described The anisotropic expansion of shearing wave is defined as follows in step 1:
Wherein a is scale parameter, and s is shear parameters, and t is translation parameters, and anisotropic expansion matrix is defined asShearing matrix is defined as
3. a kind of SAR image edge detection method based on shearing wave according to claim 1, it is characterised in that: described Step 2 sets ψevenIt for even symmetry shearing wave, and is the real part of function, then complicated shearing wave is defined as follows:
ψ=ψeven+iψodd:=ψeven+iH(ψeven)
Wherein ψoddIndicate that odd symmetry shearing wave, function H () indicate Hilbert transform, the transformation formula is as follows:
Secondly, constructing even symmetry shearing wave using the tensor product of Mexico's hat wavelet and Gauss wavelet, and use Hilbert Even symmetry shearing wave is converted to odd symmetry shearing wave by transformation；The formula of even symmetry shearing wave and odd symmetry shearing wave is as follows:
Wherein scale parameterShear parametersTranslation parametersAnd it shears matrix to be defined asAnisotropic expansion matrix is defined as
4. a kind of SAR image edge detection method based on shearing wave according to claim 1, it is characterised in that: described The optimum orientation of road present on each pixel is calculated in step 3 using odd symmetry and even symmetry shearing wave coefficient；For Scale parameter j ∈ { Jmin,...,Jmax, shear parametersAnd translation parametersEach pixelOn there are the optimum orientations at edge
It is calculate by the following formula:
5. a kind of SAR image edge detection method based on shearing wave according to claim 1, it is characterised in that: described Edge metering in step 4 on optimum orientation is calculate by the following formula:
Wherein f is image array,To shear matrix,For anisotropic expansion matrix, In order to enable following mapping relations are arranged between 0 to 1 in the edge metering probability value of each pixel:
It generates the value between one 0 to 1, and measuring does not have edge for 0 expression position, and 1 indicates there is edge certainly.
6. a kind of SAR image edge detection method based on shearing wave according to claim 1, it is characterised in that: described Binarization method is in step 5, sets a threshold value, traverses each pixel, by the point if pixel value is more than or equal to threshold value Gray value is set to 1, and the gray value is then set to 0 less than threshold value, the result images of edge detection in step 4 are switched to two-value Image.
7. a kind of a kind of SAR image edge detection method based on shearing wave described in claim 1, it is characterised in that: described Step 6 first carries out the connected component labeling processing operation in bianry image, the connected component labeling processing operation of bianry image be exactly from In the width dot matrix image be made of pixel value 0 and 1, (usually 4- is adjacent or 8- is adjacent) adjacent to each other had into pixel value The pixel set of " 1 " is marked with different digital, while counting white pixel point number in different connected domains, and threshold is arranged Value, the part for assert that pixel number is less than threshold value in each connected region of image is pseudo-edge, by the picture in these connected regions Plain value is all set to 0, to achieve the purpose that eliminate pseudo-edge, then carries out dilation erosion operation to the image after elimination pseudo-edge, To remove the burr phenomena on edge.
CN201910486792.0A 2019-06-05 2019-06-05 A kind of SAR image edge detection method based on shearing wave Pending CN110223312A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111160153A (en) * 2019-12-17 2020-05-15 华南理工大学 Road surface drainage monitoring and evaluating method and system based on image processing
CN111768360A (en) * 2020-05-07 2020-10-13 陕西师范大学 Shore line detection method based on shear wave processing high-resolution three-SAR image
CN115100696A (en) * 2022-08-29 2022-09-23 山东圣点世纪科技有限公司 Connected domain rapid marking and extracting method and system in palm vein recognition

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107895354A (en) * 2017-11-29 2018-04-10 陕西师范大学 A kind of SAR image method for reducing speckle of high score three based on non-lower sampling Shearlet conversion

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107895354A (en) * 2017-11-29 2018-04-10 陕西师范大学 A kind of SAR image method for reducing speckle of high score three based on non-lower sampling Shearlet conversion

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
GEORG HEYGSTER等: "Shearlet-Based Edge Detection:Flame Fronts and Tidal Flats", 《APPLICATION OF DIGITAL IMAGE PROCESSING XXXVIII》 *
PAN_JINQUAN: "Matlab形态学图像处理：二值图像分割 标记连通区域和重心位置删除连通区域", 《HTTPS://BLOG.CSDN.NET/GUYUEALIAN/ARTICLE/DETAILS/71440949》 *
RAFAEL REISENHOFER: "The complex shearlet transform and applications to image quality assessment", 《TECHNISCHE UNIVERSITÄT BERLIN MASTER’S THESIS》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111160153A (en) * 2019-12-17 2020-05-15 华南理工大学 Road surface drainage monitoring and evaluating method and system based on image processing
CN111160153B (en) * 2019-12-17 2023-03-28 华南理工大学 Road surface drainage monitoring and evaluating method and system based on image processing
CN111768360A (en) * 2020-05-07 2020-10-13 陕西师范大学 Shore line detection method based on shear wave processing high-resolution three-SAR image
CN115100696A (en) * 2022-08-29 2022-09-23 山东圣点世纪科技有限公司 Connected domain rapid marking and extracting method and system in palm vein recognition

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Application publication date: 20190910