CN112862677B - Acoustic image stitching method of same-platform heterologous sonar - Google Patents
Acoustic image stitching method of same-platform heterologous sonar Download PDFInfo
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Abstract
The invention discloses an acoustic image stitching method of a same-platform heterologous sonar, which comprises the steps of firstly researching an echo modeling method of an underwater same-platform different-view sonar system on an observation area, acquiring acoustic images of corresponding side-scan sonar and body-search sonar by combining a conventional acoustic imaging method, and stitching the images on the basis; introducing an image registration algorithm based on normalized mutual information into sonar image processing, and obtaining optimal transformation between two images by taking the normalized mutual information as a similarity measurement function, so as to realize image registration; and finally, carrying out image fusion on the matched sonar images by adopting a weighted average method, thereby obtaining the submarine target imaging image with higher resolution and position accuracy. The invention introduces an image registration algorithm based on normalized mutual information into the field of heterogenous sonar acoustic image stitching, and solves the problems of larger acoustic image registration error and poor subsequent image fusion effect caused by the traditional acoustic image registration method based on characteristics.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to an image stitching method.
Background
In traditional sonar system, each sonar is mutually independent, also mutually independent between each function, and the performance is limited. In recent years, a method for comprehensively performing target detection or marine topography mapping by carrying a plurality of sonar systems on a small platform such as an underwater robot, an AUV, an underwater towed body and the like is a mainstream trend of current marine detection. The side-scan sonar installed on two sides of the underwater vehicle can obtain a high-resolution submarine ground object image below the platform side, but the position information with necessary precision is often lacking because echo time delay information is not estimated; and the body search sonar installed on the head of the underwater vehicle can acquire the submarine ground object image right below the platform to obtain the high-precision measuring point position information, but the working frequency band is lower, and the imaging effect is not ideal. Therefore, the side scan sonar image and the body search sonar image have strong complementarity in resolution and position information. Therefore, image stitching is carried out on the acoustic images of the side-scan sonar and the body-search sonar which are arranged on the same platform, and a submarine target imaging image with higher resolution and higher position accuracy is obtained.
At present, most of research on sonar image stitching focuses on homologous sonar image stitching methods for processing different acoustic images of the same sonar system, for example: (1) The documents 1"Ahybrid registration approach combining SLAM and elastic matching for automatic side-scan sonar mosaic [ C ]. Oceans,2015:50-60." correct the trajectory of an underwater vehicle by SLAM algorithm (synchronous positioning and mapping algorithm), thus realizing accurate matching between multiple side-scan sonar acoustic images, but being only suitable for homologous side-scan sonar image stitching. At present, the research on heterologous sonar image stitching is relatively few, and a characteristic-based image registration method is mainly adopted, for example: (2) Document 2, "multi-beam and side-scan sonar data fusion and application thereof in seabed substrate classification [ D ]. University of martial arts, 2003." the disclosed method uses the contour features to perform image stitching on multi-beam and side-scan sonar, and depth information estimation is required during side-scan sonar mapping, so that the application range is small; (3) Document 3 "multibeam and side scan sonar image registration and fusion based on SURF algorithm, marine notification, 2016: "detect the characteristic point and thus carry on the image splice to multibeam and side scan sonar with SUFR algorithm (accelerating the robust feature to detect the matching algorithm), rely on the characteristic point to withdraw and carry on the splice of the image, but not suitable for the splice of the side scan sonar with great difference of imaging resolution and body search sonar picture, when the imaging resolution is great, can't withdraw the characteristic point and register correctly, thus make the splice effect of the image very bad.
In view of the different working frequencies of the side-scan sonar and the volume-search sonar, the larger difference in resolution of acoustic images and the different fields of view, feature points are difficult to extract and match, and a feature-based registration stitching method commonly adopted in the field of sonar image stitching is not suitable. Therefore, the image registration method based on the normalized mutual information is introduced into the field of heterogeneous sonar image stitching.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an acoustic image stitching method of a same-platform heterologous sonar, which comprises the steps of firstly researching an echo modeling method of a different-view sonar system on an underwater same-platform on an observation area, acquiring acoustic images of corresponding side-scan sonar and body-search sonar by combining a conventional acoustic imaging method, and then stitching the images on the basis; introducing an image registration algorithm based on normalized mutual information into sonar image processing, and obtaining optimal transformation between two images by taking the normalized mutual information as a similarity measurement function, so as to realize image registration; and finally, carrying out image fusion on the matched sonar images by adopting a weighted average method, thereby obtaining the submarine target imaging image with higher resolution and position accuracy. The invention introduces an image registration algorithm based on normalized mutual information into the field of heterogenous sonar acoustic image stitching, and solves the problems of larger acoustic image registration error and poor subsequent image fusion effect caused by the traditional acoustic image registration method based on characteristics.
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
step 1: sonar echo modeling and image simulation;
selecting points at equal intervals on a designated area of the seabed surface and the seabed target surface according to a point scatterer set model, and regarding the selected points as a set of N point targets; the frequency response from the sonar transmitter to the receiver is obtained by the scattering action of N point targets:
wherein ρ is ω And k ω The density and compressibility parameters of water, ρ, respectively s And k s The density and compressibility parameters of the point target are respectively, and the distance from the sonar emitter to the point target is d 1 The distance from the receiver to the point target is d 2 ，c ω Is the sound velocity, f is the sonar signal frequency, a is the point target particle radius; the frequency domain expression of the scattered echo signal received by the R-th receive array sensor of the receiver is:
wherein P (f) is the Fourier transform of the sonar signals emitted in the scene;
after the back wave of each receiving array sensor is obtained, matched filtering and wave beam forming are carried out, and finally, an acoustic image of the side scan sonar and the body search sonar is obtained;
step 2: performing sonar image registration based on the normalized mutual information;
the side scan sonar acoustic image is represented as an image A, the volume search sonar acoustic image is represented as an image B, and the normalized mutual information quantity of the image A and the image B is defined as:
wherein H (A, B) represents the joint entropy of the image A and the image B, and H (A) and H (B) represent the independent entropy of the image A and the image B respectively;
registering image a and image B based on the normalized mutual information:
taking a volume search sonar acoustic image B as a reference image, a side scan sonar acoustic image A as an image to be registered, performing spatial transformation on the image A, obtaining normalized mutual information of the transformed image and the image B, changing parameters in a matrix for performing spatial transformation on the image A to enable the normalized mutual information of the transformed image and the image B to be maximum, performing spatial transformation when the normalized mutual information is maximum to obtain optimal spatial transformation, and performing optimal spatial transformation on the image A to obtain a registered heterologous sonar image;
step 3: carrying out image fusion on the registered images by adopting a weighted average method;
the pixel value weighted fusion process of the registered side scan sonar acoustic image A and the body search sonar acoustic image B is expressed as follows:
F(x,y)＝ω 1 A(x,y)+ω 2 B(x,y) (4)
wherein x, y are corresponding pixel coordinates, F is a fused image, F (x, y) represents pixel values of the image F at coordinates (x, y), and A (x, y) and B (x, y) represent pixel values of the images A and B at coordinates (x, y), respectively; omega 1 ,ω 2 Weighting coefficients, ω, for image a and image B, respectively 1 +ω 2 =1, weighting coefficient ω 1 ,ω 2 Calculated from formula (5):
wherein Corr is the correlation coefficient of the image A and the image B, and the expression is:
wherein,for the pixel mean value of image a, +.>The average value of pixels of the image B is that X and Y are the total number of pixels in the transverse and longitudinal directions respectively;
f (x, y) is the final spliced image.
The beneficial effects of the invention are as follows:
1. compared with the prior art, the method is only suitable for splicing images of the homologous side-scan sonar, is suitable for splicing the acoustic images of the heterologous sonar with different visual angles, different working frequencies and different acoustic imaging results on the same platform,
2. the method only utilizes the amplitude information of the side scan sonar echo signals, does not need to carry out depth information estimation, and has wider application range.
3. According to the method, an image registration algorithm based on normalized mutual information is introduced into sonar image processing, so that negative effects caused by feature extraction under the condition of large imaging resolution difference are avoided, registration accuracy is improved, and an image splicing effect is better.
4. The method adopts a weighted average fusion method to the registered images, is simple and visual, has small calculated amount and is suitable for real-time processing.
Drawings
FIG. 1 is a schematic flow chart of the method of the invention.
Fig. 2 is a flowchart of image registration based on normalized mutual information for the method of the present invention.
FIG. 3 is a schematic diagram of a side scan sonar and a volume search sonar position mounted on the same platform.
Fig. 4 is a schematic view of an underwater scene object constructed according to an embodiment of the present invention, wherein fig. a is a three-dimensional view of a submarine object and fig. b is a top view of the submarine object.
Fig. 5 is an acoustic array shape and a schematic diagram of a 64-element side scan sonar and a 16×16-element body search sonar according to an embodiment of the present invention, where fig. a is a side scan sonar array shape and fig. b is a body search sonar array shape.
Fig. 6 is a graph of emission signal spectrum of a side-scan sonar and a body-search sonar according to an embodiment of the present invention, where (a) is the spectrum of emission signal of the side-scan sonar and (b) is the spectrum of emission signal of the body-search sonar.
Fig. 7 is an acoustic image of a side-scan sonar and a volume-search sonar according to an embodiment of the present invention, where fig. (a) is a volume-search sonar imaging diagram and fig. (b) is a side-scan sonar imaging diagram.
Fig. 8 is a result of registering a side scan sonar image and a volume search sonar image based on normalized mutual information according to an embodiment of the present invention.
Fig. 9 is a result of image fusion of registered images by a weighted average method, that is, a final image stitching result according to an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
As shown in FIG. 2, two sonar systems with different visual angles, different working frequencies and different acoustic imaging results are installed on the same underwater platform, namely, a body search sonar and a side scan sonar, and the advantages of the two images can be integrated after the acoustic images of the two sonars are spliced, so that a submarine target imaging image with higher resolution and higher position accuracy is obtained.
As shown in fig. 1, the method for splicing acoustic images of the same-platform heterologous sonar comprises the following steps:
step 1: sonar echo modeling and image simulation;
selecting points at equal intervals on a designated area of the seabed surface and the seabed target surface according to a point scatterer set model, and regarding the selected points as a set of N point targets; the frequency response from the sonar transmitter to the receiver is obtained by the scattering action of N point targets:
wherein ρ is ω And k ω The density and the water respectivelyCompressibility parameter ρ s And k s The density and compressibility parameters of the point target are respectively, and the distance from the sonar emitter to the point target is d 1 The distance from the receiver to the point target is d 2 ，c ω Is the sound velocity, f is the sonar signal frequency, a is the point target particle radius; the frequency domain expression of the scattered echo signal received by the R-th receive array sensor of the receiver is:
wherein P (f) is the Fourier transform of the sonar signals emitted in the scene;
after the back wave of each receiving array sensor is obtained, matched filtering and wave beam forming are carried out, and finally, an acoustic image of the side scan sonar and the body search sonar is obtained;
step 2: performing sonar image registration based on the normalized mutual information;
the side scan sonar acoustic image is represented as an image A, the volume search sonar acoustic image is represented as an image B, and the normalized mutual information quantity of the image A and the image B is defined as:
wherein, H (A, B) represents the joint entropy of the image A and the image B, H (A), H (B) represent the independent entropy of the image A and the image B respectively, when the similarity of the two images is higher or the overlapping part is larger, the joint entropy is smaller, namely the normalized mutual information is larger, and the registering effect is better; the image registration based on the normalized mutual information is the searching process of the optimal space transformation, and the normalized mutual information quantity of the image A and the image B is maximized after the transformation;
registering image a and image B based on the normalized mutual information:
taking a volume search sonar acoustic image B as a reference image, a side scan sonar acoustic image A as an image to be registered, performing spatial transformation on the image A, obtaining normalized mutual information of the transformed image and the image B, changing parameters in a matrix for performing spatial transformation on the image A to enable the normalized mutual information of the transformed image and the image B to be maximum, performing spatial transformation when the normalized mutual information is maximum to obtain optimal spatial transformation, and performing optimal spatial transformation on the image A to obtain a registered heterologous sonar image;
step 3: carrying out image fusion on the registered images by adopting a weighted average method;
the pixel value weighted fusion process of the registered side scan sonar acoustic image A and the body search sonar acoustic image B is expressed as follows:
F(x,y)＝ω 1 A(x,y)+ω 2 B(x,y) (4)
wherein x, y are corresponding pixel coordinates, F is a fused image, F (x, y) represents pixel values of the image F at coordinates (x, y), and A (x, y) and B (x, y) represent pixel values of the images A and B at coordinates (x, y), respectively; omega 1 ,ω 2 Weighting coefficients, ω, for image a and image B, respectively 1 +ω 2 =1, weighting coefficient ω 1 ,ω 2 Calculated from formula (5):
wherein Corr is the correlation coefficient of the image A and the image B, and the expression is:
wherein,for the pixel mean value of image a, +.>The average value of pixels of the image B is that X and Y are the total number of pixels in the transverse and longitudinal directions respectively;
f (x, y) is the final spliced image.
Specific examples:
fig. 4 shows a schematic view of a constructed underwater scene target, and fig. 5 shows an acoustic array shape and a schematic view of a 64-element side scan sonar and a 16 x 16 element body search sonar, wherein the side scan sonar has a length of 0.63m and a body search sonar diameter of 0.6m. FIG. 6 is a spectrum of emitted signals of a side scan sonar versus a volume search sonar. The side scan sonar emission signal is an LFM signal with the center frequency of 75kHz, the bandwidth of 7kHz and the pulse time width of 2ms, and the body search sonar emission signal is an LFM signal with the center frequency of 50kHz, the bandwidth of 5kHz and the pulse time width of 2 ms.
1. Sonar echo modeling and image simulation;
according to the method of step 1, the echo of each receiving array sensor is obtained, then matched filtering and wave beam forming are carried out, and finally, the acoustic images of the side-scan sonar and the body search sonar are respectively obtained, as shown in fig. 7, the resolution ratio of the acoustic image of the side-scan sonar is higher, but the target position is inaccurate; the object position in the volume search sonar acoustic image is accurate but the resolution is lower.
2. Performing sonar image registration based on the normalized mutual information;
the flow chart of image registration is shown in fig. 3, and for the simulation image in fig. 7, the volume search sonar image is used as a reference image, the side scan sonar image is used as an image to be registered, and the optimal spatial transformation of the right side and left side scan sonar images in registration can be calculated to be T respectively 1 、T 2 ：
The sonar image registration result based on the normalized mutual information is shown in fig. 8.
3. Carrying out image fusion on the registered images by adopting a weighted average method;
the final image fusion result is shown in fig. 9, and it can be seen that the resolution of the final sonar image and the accuracy of the target position are both well improved.
According to the invention, an image registration algorithm based on normalized mutual information is introduced into sonar image processing, and the normalized mutual information is used as a similarity measurement function to obtain the optimal transformation between two images, so that the image registration is realized, and the problem that more characteristic points are mismatched due to low sonar imaging quality and overlarge heterogeneous sonar imaging resolution difference when the characteristic points are used for registration in the past is avoided, so that the image splicing result is influenced. And then carrying out image fusion on the registered images by adopting a weighted average method, and determining a weighting coefficient by calculating the correlation coefficient of the two images to finally obtain an image splicing result. The weighted average fusion is characterized by simplicity, intuition, small calculated amount and suitability for real-time processing.
Claims (1)
1. The method for splicing the acoustic images of the same-platform heterologous sonar is characterized by comprising the following steps of:
step 1: sonar echo modeling and image simulation;
selecting points at equal intervals on a designated area of the seabed surface and the seabed target surface according to a point scatterer set model, and regarding the selected points as a set of N point targets; the frequency response from the sonar transmitter to the receiver is obtained by the scattering action of N point targets:
wherein ρ is ω And k ω The density and compressibility parameters of water, ρ, respectively s And k s The density and compressibility parameters of the point target are respectively, and the distance from the sonar emitter to the point target is d 1 The distance from the receiver to the point target is d 2 ，c ω Is the sound velocity, f is the sonar signal frequency, a is the point target particle radius; the frequency domain expression of the scattered echo signal received by the R-th receive array sensor of the receiver is:
wherein P (f) is the Fourier transform of the sonar signals emitted in the scene;
after the back wave of each receiving array sensor is obtained, matched filtering and wave beam forming are carried out, and finally, an acoustic image of the side scan sonar and the body search sonar is obtained;
step 2: performing sonar image registration based on the normalized mutual information;
the side scan sonar acoustic image is represented as an image A, the volume search sonar acoustic image is represented as an image B, and the normalized mutual information quantity of the image A and the image B is defined as:
wherein H (A, B) represents the joint entropy of the image A and the image B, and H (A) and H (B) represent the independent entropy of the image A and the image B respectively;
registering image a and image B based on the normalized mutual information:
taking a volume search sonar acoustic image B as a reference image, a side scan sonar acoustic image A as an image to be registered, performing spatial transformation on the image A, obtaining normalized mutual information of the transformed image and the image B, changing parameters in a matrix for performing spatial transformation on the image A to enable the normalized mutual information of the transformed image and the image B to be maximum, performing spatial transformation when the normalized mutual information is maximum to obtain optimal spatial transformation, and performing optimal spatial transformation on the image A to obtain a registered heterologous sonar image;
step 3: carrying out image fusion on the registered images by adopting a weighted average method;
the pixel value weighted fusion process of the registered side scan sonar acoustic image A and the body search sonar acoustic image B is expressed as follows:
F(x，y)＝ω 1 A(x，y)+ω 2 B(x，y) (4)
wherein x, y are corresponding pixel coordinates, F is a fused image, F (x, y) represents pixel values of the image F at coordinates (x, y), and A (x, y) and B (x, y) represent pixel values of the images A and B at coordinates (x, y), respectively; omega 1 ，ω 2 Weighting coefficients, ω, for image a and image B, respectively 1 +ω 2 =1, weighting coefficient ω 1 ，ω 2 Calculated from formula (5):
wherein Corr is the correlation coefficient of the image A and the image B, and the expression is:
wherein,for the pixel mean value of image a, +.>The average value of pixels of the image B is that X and Y are the total number of pixels in the transverse and longitudinal directions respectively;
f (x, y) is the final spliced image.
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