CN108389191B - Method for detecting target shadow region in SAR image - Google Patents

Method for detecting target shadow region in SAR image Download PDF

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CN108389191B
CN108389191B CN201810133186.6A CN201810133186A CN108389191B CN 108389191 B CN108389191 B CN 108389191B CN 201810133186 A CN201810133186 A CN 201810133186A CN 108389191 B CN108389191 B CN 108389191B
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张月婷
丁赤飚
雷斌
仇晓兰
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Institute of Electronics of CAS
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    • G06T2207/20048—Transform domain processing
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Abstract

A method for detecting a target shadow region in an SAR image comprises the following steps: selecting a target data area; reading initial parameters and calculating a target height vector; phase approximate compensation processing; CFAR detection; calculating an optimal detection result; solving shadow feature enhanced image G (x, y) shadow detection is carried out on image G (x, y) by using a CFAR detection method. The method combines the shadow boundary enhancement process with the shadow detection problem, utilizes the complex data of the image, considers the phase information, combines the phase compensation and the CFAR method, establishes the SAR image target area detection method, is a method established by considering the SAR image shadow specificity problem, overcomes the defect that the traditional method can not effectively detect the shadow area when the target height is high and the image resolution is high, and effectively realizes the detection of the target shadow area in the SAR image. The method provides a support for SAR image target detection and identification and target refinement information extraction.

Description

Method for detecting target shadow region in SAR image
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a method for detecting a target shadow area in a Synthetic Aperture Radar (SAR) image.
Background
In recent years, SAR systems are developed at a rapid speed, and SAR data acquisition capacity is effectively improved. Due to the characteristics of all-time, all-weather and penetrability, the SAR becomes one of the important means of remote sensing observation. For a typical target, efficient extraction of target information from massive SAR data is one of the key problems in effective utilization of SAR images. The establishment of an efficient and reliable target detection and identification technology has important significance for improving the application level of the SAR image and effectively exerting the remote sensing observation task of the SAR. For a typical target, the outline of the target is one of important information used in the automatic detection and recognition of the SAR image.
The contour information of the target shadow region contains the information of the contour of the target shape, and on the SAR image, the extraction of the shadow region is greatly helpful for extracting the information of the target shape. The SAR image is different from an optical image, the optical image is similar to the visual law of human eyes and reflects the characteristics of a target in visible light, the color and the contour of the target are similar to those observed by the human eyes, and the image has good connectivity; the SAR image reflects electromagnetic scattering of a target, the target usually appears to be composed of discontinuous scattering centers in the SAR image, usually, a scattering area of the target itself corresponds to some discrete points, lines and other features, the contour of the target is difficult to be directly extracted from the area of the target itself, particularly, some artificial targets with smooth surfaces, the area of the target itself appears weak scattering features, and at the moment, the contour feature of a shadow area is one of effective ways for extracting the contour information of the target. In summary, the detection of the target shadow region in the SAR image plays an important role in the detection and identification of the target in the SAR image.
The shadow of the target in the SAR image corresponds to an echo-free region in the radar echo. The problem to be solved in the detection of the shadow area of the SAR image is as follows: according to a given SAR image, the influence of the target and background scattering is distinguished, and a region corresponding to the shadow generated by the target is given. The existing SAR image detection method mainly utilizes a CFAR algorithm, the method utilizes a probability distribution function of a target image pixel value to solve a threshold value for distinguishing shadow weak scattering areas and target strong scattering areas, and a binarization detection result is given according to the threshold value. The method effectively plays a role in detecting problems in the SAR image.
The CFAR method can play a certain role in the detection of the shadow area, but the extraction effect of the shadow area corresponding to the target part with higher target height and a slender structure is not ideal. In fact, the problem of detecting the shadow region in the SAR image can also provide a more effective method, in the SAR image, a special fuzzy phenomenon exists on the target shadow boundary, and if the shadow boundary characteristic enhancement and the shadow region extraction are combined, a more effective shadow region extraction method can be established.
Aiming at the research of a target shadow region detection method in an SAR image, the traditional research starts from an SAR image amplitude image, and a common CFAR method for detecting the target shadow region is utilized by a conventional optical image. The main drawbacks of these studies are: the specificity of the boundary ambiguity presented by the shaded region in the SAR image is not taken into account. For an SAR image with very high target height and high resolution, a conventional CFAR detection method can only detect a partial shadow region, and for a target above a certain height, especially for a shadow region locally generated by the target with a similar elongated structure and at the certain height, the extraction result of the shadow region will have a deviation from the actual shadow region, which affects the extraction of the target shape information.
Disclosure of Invention
Aiming at the problems, the invention aims to solve the problem that a shadow area under a high-resolution observation condition with a certain height cannot be detected by only using a CFAR method, and provides a method for detecting the shadow boundary of a high-resolution SAR image by combining a shadow boundary characteristic enhancement process with a detection process by using a plurality of images of a target instead of an amplitude image. By the method, the shadow detection effect of the traditional SAR image can be improved, and particularly the shadow region detection effect of a target with higher height can be improved.
In order to achieve the purpose, the invention provides a method for detecting a target shadow region in a high-resolution SAR image, which combines a shadow boundary enhancement process with a shadow region detection process, utilizes a CFAR (computational fluid dynamics and autoregressive) method to detect images with enhanced shadow features under different parameters for multiple times to find an optimal detection result, and establishes the method for detecting the shadow region of the SAR image.
The method for detecting the target shadow region in the SAR image comprises the following steps:
selecting a target data area, wherein in the step, plural data of the SAR image to be detected containing a target and a surrounding area thereof are selected, the plural data need to correspond to a slant range plural image, and are marked as I (x, y), and the size is as follows: m is multiplied by N, wherein M corresponds to a distance direction and corresponds to an x direction; n corresponds to the azimuth direction and corresponds to the y direction;
reading initial parameters and calculating target height vectors, wherein the reading of the initial parameters comprises reading radar observation and imaging main parameters corresponding to images, and the calculation of the target height vectors comprises settingSetting the minimum interval of the height parameters to be delta h; estimating an initial altitude interval [ H ] of a targetmin，Hmax]The interval represents the height range of the target to be detected and is calculated and given through imaging conditions;
phase approximation compensation processing, which comprises the step of carrying out phase approximation compensation processing on K subintervals of the initial height interval one by one;
CFAR detection, comprising using a sliding window based CFAR detection method on image Iij(x, y) detecting a shadow region;
calculating an optimal detection result;
the shadow feature enhanced image G (x, y) is solved.
The shadow detection is performed on image G (x, y) using the CFAR detection method.
Preferably, in the target data area selecting step, the image data area should be greater than three times the target area.
Preferably, the radar observation and imaging main parameters include: distance-wise pixel spacing size srAngle of incidence theta, radar flying height HRRadar observation azimuth bandwidth BaSlope distance value R corresponding to image area0Speed of radar movement VrAnd a wavelength lambda corresponding to the radar operating frequency.
Preferably, in the target height vector calculation step, H is performed without considering the calculation loadminTaking 0 or giving according to target prior knowledge; hmaxInitial values are given according to the imaging geometry by: hmax＝sr×M/cosθ。
Preferably, in the target height vector calculating step, the initial height interval is divided into K sub-intervals, and the K is calculated by:
K＝(Hmax-Hmin)/2
then the K sub-intervals are:
Li＝[lis，lie]，i＝1，2，…，K，
wherein:
Figure BDA0001575292890000031
on this basis, for the ith subinterval, the corresponding height vector is calculated: hi＝[hi1，hi2，…，hiP]Wherein:
hij＝lis+(j-1)×Δh，j＝1，2，…，P
Figure BDA0001575292890000041
Figure BDA0001575292890000042
representing a ceiling operation.
Preferably, the phase approximation compensation processing step includes: and sequentially calculating the following K sub-intervals of the initial height interval:
Figure BDA0001575292890000043
Iij(x，y)＝IFFTy{FFTy[I(x，y)]×Fij(x，fη)}，j＝1，2，…，P
wherein, FFTy，IFFTyRespectively representing fast Fourier transform and inverse fast Fourier transform in the y direction; f. ofηIs an azimuth frequency domain vector corresponding to-Ba2 to BaA vector of length N at equal intervals of/2.
Preferably, the CFAR detection comprises:
(1) for | Iij(x, y) | performs detection of a shadow region, where: l | represents the amplitude calculation, and the obtained result is l'ij(x, y), the result is a binary image, and the constant false alarm rate is set to be;
(2) convolution processing to obtain I ″)ij(x, y), calculating:
I″ij(x，y)＝I′ij(x，y)*A(x，y)
wherein a (x, y) represents a matrix of size 3x3 with an element of 1; denotes a convolution operation;
(3) calculate I ″)ijThe number of non-zero elements of (x, y) corresponds to the size S of the shadow regionij；
(4) Repeating (1) - (3) for all i and j to obtain all SijWherein i ═ 1, 2, …, K; j is 1, 2, …, P.
Preferably for | IijThe method for detecting the shadow area by (x, y) | comprises the following steps: (i) counting the probability distribution function of clutter in the surrounding area of each pixel in the target area by using a sliding window, and enabling the size of the sliding window W to be as follows: min (M, N)/3, wherein min represents the minimum value, and the probability distribution function f (x, y) of a W coverage area with a certain pixel (x, y) as the center is obtained through statistical calculation;
(ii) solving a threshold value T (x, y) corresponding to shadow detection corresponding to the position of the pixel (x, y), namely: f (x, y) is the corresponding pixel intensity value when C is equal to f;
(iii) traverse | Iij(x, y) | all pixels, solving a matrix: t (x, y) of size M N;
(iv) obtaining a result after CFAR detection:
Figure BDA0001575292890000051
preferably, the step of calculating the optimal detection result includes:
(1) calculating the optimal shadow size S of each parameter intervaliThe calculation method comprises the following steps:
Figure BDA0001575292890000052
(2) calculating the size of an optimal shadow area corresponding to the initial height interval:
Figure BDA0001575292890000053
let the height corresponding to the maximum shadow region be hIJThe processed image is I ″)IJ(x，y)。
Preferably, the calculation method for solving the shadow feature enhanced image G (x, y) is as follows:
Figure BDA0001575292890000054
compared with the prior art, the invention has the advantages that: the traditional SAR image detection method utilizes the amplitude characteristics of an image and the idea of region detection in a conventional optical image, so that the detection process has an undesirable effect on the shadow region detection of the SAR image with high height and high resolution, the shadow region corresponding to the target cannot be effectively detected, and the accuracy of inversion of the target height and the geometric profile is reduced. The invention overcomes the defects and establishes a shadow region detection method in the high-resolution SAR image.
The method takes the special characteristics of the target shadow area in the SAR image into consideration, applies the shadow boundary enhancement process to the shadow area detection process, establishes the target shadow area detection method by utilizing the phase compensation and CFAR detection methods, and provides a means for accurately extracting the target shadow area in the SAR image.
Drawings
FIG. 1 is a schematic flow diagram of the detection method of the present invention;
FIG. 2 is a schematic diagram of data selection in the detection method of the present invention;
FIG. 3 is a SAR image of a target region in an embodiment of the invention;
FIG. 4 is a graph showing the results of detection directly using the CFAR method;
FIG. 5 is a graph showing the results of detection using the method of the present invention.
Detailed Description
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
As shown in fig. 1, the method for detecting a target shadow region in a high-resolution SAR image provided by the present invention includes the following steps:
the first step is as follows: and selecting a target data area. The specific method comprises the following steps:
selecting complex data of SAR images to be detected, wherein the complex data comprises a target and a surrounding area of the target, and the complex data needs to correspond to a slant range complex image. As shown in fig. 2 (vertical direction corresponds to x, horizontal direction corresponds to y), the area where the target is located is manually selected, and is marked as I (x, y), and the size is: m is multiplied by N, wherein M corresponds to a distance direction and corresponds to an x direction; n corresponds to the azimuth direction and y corresponds to the y direction. It is required that the image data area should be more than three times the target area.
The second step is that: initial parameter reading and target height vector calculation. The specific method comprises the following steps:
(1) initial parameter reading: reading radar observation and imaging main parameters corresponding to the image, comprising: the distance-wise pixel spacing is sr(ii) a An incident angle theta; hRIs the radar flying height; b isaObserving azimuth bandwidth for the radar; r0The corresponding slope distance value of the image area is obtained; vrIs the radar motion speed. λ is the wavelength corresponding to the radar operating frequency.
(2) Calculating a target height vector: setting the minimum interval of the height parameters as delta h; estimating an initial altitude interval [ H ] of a targetmin，Hmax]And the interval represents the height range of the target to be detected and is calculated and given through imaging conditions. The specific method comprises the following steps: h without considering the computational burdenminCan take 0; can also be given according to the prior knowledge of the target; hmaxInitial values are given according to the imaging geometry by:
Hmax＝sr×M/cosθ
dividing the initial height interval into K subintervals, wherein the K calculation method comprises the following steps:
K＝(Hmax-Hmin)/2
then the K sub-intervals are:
Li＝[lis，lie]，i＝1，2，…，K，
wherein:
Figure BDA0001575292890000061
on this basis, for the ith subinterval, the corresponding height vector is calculated: hi＝[hi1，hi2，…，hiP]Wherein:
hij＝lis+(j-1)×Δh，j＝1，2，…，P
Figure BDA0001575292890000062
Figure BDA0001575292890000071
representing a ceiling operation.
The third step: and (5) phase approximate compensation processing.
Phase approximation compensation processing is performed successively for the K parameter intervals.
The specific method comprises the following steps: and aiming at K parameter intervals, sequentially calculating:
Figure BDA0001575292890000072
Iij(x，y)＝IFFTy{FFTy[I(x，y)]*Fij(x，fη)}，j＝1，2，…，P
wherein, FFTyRepresenting fast Fourier transform, IFFT, in the y-directionyRepresenting the inverse fast fourier transform in the y-direction; f. ofηIs an azimuth frequency domain vector corresponding to-Ba2 to BaA vector of length N at equal intervals of/2.
The fourth step: and (5) CFAR detection. The specific method is that the CFAR detection method based on the sliding window is utilized to detect the image Iij(x, y) the detection of the shaded area is performed.
(1) For | Iij(x, y) | performs detection of a shadow region, where: and | represents the amplitude operation. The obtained result is I'ij(x, y), the result being a binarized image. And setting the constant false alarm rate as C. The specific method comprises the following steps:
(i) and counting the probability distribution function of the clutter in the region around each pixel of the target region by using a sliding window. Let the sliding window W be: min (M, N)/3, wherein min represents the minimum value, and the probability distribution function f (x, y) of a W coverage area with a certain pixel (x, y) as the center is obtained through statistical calculation;
(ii) and solving a threshold value T (x, y) corresponding to shadow detection corresponding to the position of the pixel (x, y). Namely: f (x, y) is the corresponding pixel intensity value when C is equal to f;
(iii) traverse | Iij(x, y) | all pixels, solving a matrix: t (x, y) with size of M N.
(iv) Obtaining a result after CFAR detection:
Figure BDA0001575292890000073
(2) convolution processing to obtain I ″)ij(x, y). And (3) calculating:
I″ij(x，y)＝I′ij(x，y)*A(x，y)
wherein a (x, y) represents a matrix of size 3x3 with an element of 1; denotes convolution operation.
(3) Calculate I ″)ijThe number of non-zero elements of (x, y) corresponds to the size S of the shadow regionij。
(4) Repeating (1) - (3) in the fourth step for all i and j to obtain all Sij。i＝1，2，…，K；j＝1，2，…，P
The fifth step: and calculating an optimal detection result.
(1) Calculating the optimal shadow size S of each parameter intervali. The calculation method comprises the following steps:
Figure BDA0001575292890000081
(2) calculating the size of an optimal shadow area corresponding to the initial height interval:
Figure BDA0001575292890000082
let the height corresponding to the maximum shadow region be hIJThe processed image is I ″)IJ(x，y)。
And a sixth step: solving the shadow feature enhanced image G (x, y) by the following method:
Figure BDA0001575292890000083
the seventh step: and (5) carrying out shadow detection on the image G (x, y) by using a CFAR detection method, wherein the result is the final result. The specific method of detection is shown in step (1) of the fourth step.
Fig. 3 shows an SAR image of a target area in an embodiment, the azimuth pixel interval is 0.085m, the incident angle is 63 degrees, the radar flying height is 1498.2m, and the radar operating wavelength is: 0.0178m, the flying speed of the radar is 79.6m/s, the corresponding slant distance of a target area is 3300m, and the bandwidth of the observation azimuth of the radar is as follows: 939.7Hz, and the constant false alarm rate is set to 3 percent.
FIG. 4 is a graph showing the results of detection using the CFAR method directly; figure 5 shows a graph of the results given with the method of the invention. It can be seen that the detection effect of fig. 5 is better, and the validity of the method is verified.
In summary, the method combines the shadow boundary enhancement process with the shadow detection problem, utilizes the complex data of the image, considers the phase information, combines the phase compensation and the CFAR method, establishes the SAR image target area detection method, is established by considering the SAR image shadow specificity problem, overcomes the defect that the traditional method can not effectively detect the shadow area when the target height is high and the image resolution is high, and effectively realizes the detection of the target shadow area in the SAR image. The method provides a support for SAR image target detection and identification and target refinement information extraction.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1. A method for detecting a target shadow region in an SAR image comprises the following steps:
selecting a target data area, wherein in the step, plural data of the SAR image to be detected containing a target and a surrounding area thereof are selected, the plural data need to correspond to a slant range plural image, and are marked as I (x, y), and the size is as follows: m is multiplied by N, wherein M corresponds to a distance direction and corresponds to an x direction; n corresponds to the azimuth direction and corresponds to the y direction;
reading initial parameters and calculating a target height vector, wherein the reading of the initial parameters comprises reading radar observation and imaging main parameters corresponding to an image, and the radar observation and imaging main parameters comprise: distance-wise pixel spacing size srAngle of incidence theta, radar flying height HRRadar observation azimuth bandwidth BaSlope distance value R corresponding to image area0Speed of radar movement VrAnd wavelength lambda corresponding to the radar working frequency; calculating the target height vector comprises setting the minimum interval of the height parameters to be delta h; estimating an initial altitude interval [ H ] of a targetmin，Hmax]The interval represents the height range of the target to be detected and is calculated and given through imaging conditions;
phase approximation compensation processing, which comprises the step of carrying out phase approximation compensation processing on K subintervals of the initial height interval one by one;
CFAR detection, comprising using a sliding window based CFAR detection method on image Iij(x, y) detecting a shadow region;
calculating an optimal detection result:
(1) calculating the optimal shadow size S of each parameter intervaliThe calculation method comprises the following steps:
Figure FDA0002770783330000011
(2) calculating the size of an optimal shadow area corresponding to the initial height interval:
Figure FDA0002770783330000012
let the height corresponding to the maximum shadow region be hIJTreatment ofThe image is IIJ(x，y)；
Solving shadow feature enhanced image G(x，y)；
Carrying out shadow detection on the image G (x, y) by using a CFAR detection method;
h in the standard height vector calculation step without considering the calculation loadminTaking 0 or giving according to target prior knowledge; hmaxInitial values are given according to the imaging geometry by: hmax＝sr×M/cosθ；
The CFAR detection includes:
(1) for | Iij(x, y) | performs detection of a shadow region, where: l | represents the amplitude calculation, and the obtained result is l'ij(x, y), wherein the result is a binary image, and the constant false alarm rate is set to be C;
(2) convolution processing to obtain I ″)ij(x, y), calculating:
I″ij(x，y)＝I′ij(x，y)*A(x，y)
wherein a (x, y) represents a matrix of size 3 × 3 with elements of 1; denotes a convolution operation;
(3) calculate I ″)ijThe number of non-zero elements of (x, y) corresponds to the size S of the shadow regionij；
(4) Repeating (1) - (3) for all i and j to obtain all SijWherein i ═ 1, 2, …, K; j ═ 1, 2, …, P;
for | IijThe method for detecting the shadow area by (x, y) | comprises the following steps:
(i) counting the probability distribution function of clutter in the surrounding area of each pixel in the target area by using a sliding window, and enabling the size of the sliding window W to be as follows: min (M, N)/3, wherein min represents the minimum value, and the probability distribution function f (x, y) of a W coverage area with a certain pixel (x, y) as the center is obtained through statistical calculation;
(ii) solving a threshold value T (x, y) corresponding to shadow detection corresponding to the position of the pixel (x, y), namely: f (x, y) is the corresponding pixel intensity value when C is equal to f;
(iii) traverse | Iij(x, y) | all pixels, solving a matrix: t (x, y) of sizeM×N；
(iv) Obtaining a result after CFAR detection:
Figure FDA0002770783330000021
2. the detection method according to claim 1, wherein in the target data area selection step, the image data area should be more than three times the target area.
3. The detection method according to claim 1, wherein in the target height vector calculation step, the initial height interval is divided into K sub-intervals, and K is calculated by:
K＝(Hmax-Hmin)/2
then the K sub-intervals are:
Li＝[lis，lie]，i＝1，2，…，K，
wherein:
Figure FDA0002770783330000022
on this basis, for the ith subinterval, the corresponding height vector is calculated: hi＝[hi1，hi2，…，hiP]Wherein:
hij＝lis+(j-1)×Δh，j＝1，2，…，P
Figure FDA0002770783330000031
Figure FDA0002770783330000032
representing a ceiling operation.
4. The detection method of claim 1, wherein the phase approximation compensation processing step comprises: and sequentially calculating the following K sub-intervals of the initial height interval:
Figure FDA0002770783330000033
Iij(x，y)＝IFFTy{FFTy[I(x，y)]×Fij(x，fη)}，j＝1，2，…，P
wherein, FFTy，IFFTyRespectively representing fast Fourier transform and inverse fast Fourier transform in the y direction; f. ofηIs an azimuth frequency domain vector corresponding to-Ba2 to BaA vector of length N at equal intervals of/2.
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