JP2020173213A - Sonar image processing device, sonar image processing method, and program - Google Patents

Abstract

To estimate an underwater sound velocity with good accuracy and suppress a decrease in sonar image quality.SOLUTION: A sonar image processing device 10 pertaining to an embodiment comprises: a data acquisition unit 11 for acquiring a plurality of sonar data in a plurality of squint azimuths and a measured underwater sound velocity; an image processing unit 12 for visualizing the plurality of sonar data using the measured underwater sound velocity and generating a plurality of sonar images; a point image detection unit 13 for detecting a point image in each of the plurality of sonar images; a point image extraction unit 14 for extracting a point image pair, out of the point images, that are located at almost the same position throughout the plurality of sonar images; and a sound velocity estimation unit 15 for calculating a difference in the range-azimuth coordinates of the point image pair and estimating the corrected underwater sound velocity that minimizes the difference.SELECTED DRAWING: Figure 3

Description

The present invention relates to a sonar image processing apparatus, a sonar image processing method and a program.

Side-Scan Sonar (SSS) is widely known as a technique for searching the seabed. The side scan sonar emits a highly directional acoustic beam to image acoustic shadows according to the unevenness of the seafloor. Further, a synthetic aperture sonar (SAS) to which a synthetic aperture treatment is applied is known as a process for improving the resolution of the side scan sonar in the azimuth direction.

Side-scan sonars such as side-scan sonars and synthetic aperture sonars generate images of the seafloor (seafloor topography map) while scanning the search range in strips by moving the sonar. In this way, a method of collecting data by the sonar traveling linearly while continuously transmitting and receiving sound is called a strip map method (for example, Patent Document 1).

In such a sonar, in the Squint mode, the angle (skint angle) between the direction of travel of the sonar and the direction in which the acoustic beam is directed toward the acoustic irradiation area is changed, and the sound is produced in a plurality of skint directions for the same area. The beam is irradiated. This makes it possible to generate a multiplexed map based on a plurality of skinned images obtained in each of the plurality of skinned directions.

When a map generated in a plurality of skinned directions is integrated into one map, ideally, point images at the same position overlap at the same position in all skinned images. However, with sonar, the point image position may shift for each skinned image due to the measurement accuracy or uncertainty of the underwater sound velocity. This problem is more pronounced in synthetic aperture sonars with high spatial resolution and reduces the image quality of the integrated map.

Further, in the synthetic aperture sonar, if the accuracy of the underwater sound velocity used for signal processing is low, the processing gain decreases. In the case of a point image, this appears as a phenomenon in which the focus of the image becomes loose, and the image that should originally appear as a clear point appears blurred in the azimuth direction. Therefore, it is necessary to grasp the underwater sound velocity as accurately as possible not only in the integration of maps by a plurality of skinned images but also in the synthetic aperture processing of a single skinned image.

As a technique for correcting changes in the speed of sound in water, Patent Document 2 describes water temperature and water temperature in an underwater position measurement system that calculates the position of a target point based on the measurement of ultrasonic wave propagation time by the SBL (Short Base Line) method. A technique for correcting changes in sound velocity without measuring the salt density has been proposed. In Patent Document 2, sound velocity is assumed, a plurality of coordinate values of the target point are calculated by different ultrasonic propagation paths, and the assumed value of sound velocity is changed until the coordinate values match within a predetermined error range to obtain the coordinate values. The calculation is repeated, and the assumed value when the coordinate values match within the error range is used as the sound velocity value.

JP-A-2010-127771 Japanese Unexamined Patent Publication No. 09-127238

In Patent Document 2, a responder that generates an ultrasonic pulse is arranged at a target point, four receivers that receive the ultrasonic pulse are arranged in an xy plane, and two x-coordinates and y of the target point are respectively arranged. The coordinates are calculated by the SBL method, and it is not expected that this technique will be applied to a side scan type sonar.

An object of the present disclosure is to provide a sonar image processing device, a sonar image processing method and a program capable of accurately estimating the underwater sound velocity and suppressing deterioration of the image quality of the sonar image in view of the above-mentioned problems. ..

The sonar image processing apparatus according to one aspect of the present invention uses a plurality of sonar data in a plurality of skinned directions, a data acquisition unit for acquiring the measured underwater sound velocity, and the measured underwater sound velocity. An image processing unit that images sonar data to generate a plurality of sonar images, a point image detection unit that detects a point image in each of the plurality of sonar images, and a plurality of the sonar images among the point images. A point image extraction unit that extracts point image pairs located at substantially the same position, and a sound velocity estimation unit that calculates the difference between the range-azimus coordinates of the point image pair and estimates the corrected underwater sound velocity that minimizes the difference. It is equipped with.

The sonar image processing method according to one aspect of the present invention acquires a plurality of sonar data in a plurality of skinted directions and a measured underwater sound velocity, and uses the measured underwater sound velocity to image a plurality of the sonar data. To generate a plurality of sonar images, detect a point image in each of the plurality of sonar images, and extract a point image pair located at substantially the same position over the plurality of the sonar images from the point images. Then, the difference between the range-azimus coordinates of the point image pair is calculated, and the corrected underwater sound velocity that minimizes the difference is estimated.

The program according to one aspect of the present invention images a plurality of the sonar data by using a process of acquiring a plurality of sonar data in a plurality of skinned directions and a measured underwater sound velocity and the measured underwater sound velocity. A process of generating a plurality of sonar images, a process of detecting a point image in each of the plurality of sonar images, and a point image of the point images located at substantially the same position over the plurality of the sonar images. The computer is made to execute the process of extracting the pair and the process of calculating the difference between the range-azimus coordinates of the point image pair and estimating the corrected underwater sound velocity that minimizes the difference.

According to the present invention, it is possible to provide a sonar image processing device, a sonar image processing method and a program capable of accurately estimating the underwater sound velocity and suppressing deterioration of the image quality of the sonar image.

It is a figure which shows the state of exploring the seabed by a side scan type sonar. FIG. 1 is a view seen from the azimuth direction. It is a figure which shows the structure of the sonar image processing apparatus which concerns on embodiment. It is a flow chart which shows the sonar image processing method which concerns on Embodiment 1. FIG. It is a figure explaining the process of detecting the point image of a skinned image, and the process of extracting a point image pair located at substantially the same position over a plurality of skinned images from the detected point images. It is a figure explaining the estimation process of the underwater sound velocity. It is a flow chart which shows the sonar image processing method which concerns on Embodiment 2. It is a flow chart which shows the sonar image processing method which concerns on Embodiment 3. It is a flow chart which shows the sonar image processing method which concerns on Embodiment 4. It is a flow chart which shows the sonar image processing method of the comparative example. In the comparative example, it is a figure which shows the relationship between the acoustic beam and the target object in three skint directions, and the state which integrated the three skint images. It is a figure explaining the problem of the comparative example. It is a figure explaining the problem of the comparative example. It is a figure explaining the problem of the comparative example.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In order to clarify the explanation, the following description and drawings have been omitted or simplified as appropriate. Further, each element described in the drawing as a functional block that performs various processes can be configured by a CPU, a memory, and other lines in terms of hardware. Further, the present invention can also realize arbitrary processing by causing a CPU (Central Processing Unit) to execute a computer program. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by hardware only, software only, or a combination thereof, and is not limited to any of them.

In addition, the programs described above can be stored and supplied to a computer using various types of non-transitory computer readable media. Non-temporary computer-readable media include various types of tangible storage media. Examples of non-temporary computer-readable media include magnetic recording media (eg, flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (eg, magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, It includes a CD-R / W and a semiconductor memory (for example, a mask ROM, a PROM (Programmable ROM), an EPROM (Erasable PROM), a flash ROM, and a RAM (Random Access Memory)). The program may also be supplied to the computer by various types of Transitory computer Readable Medium. Examples of temporary computer-readable media include electrical, optical, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire and an optical fiber, or a wireless communication path.

The present invention relates to a sonar image processing device, an image processing method, and a program for searching the seabed using an acoustic beam. First, an example of searching the seabed by a side scan type sonar will be described with reference to FIGS. 1 and 2. FIG. 1 is a diagram showing a state of searching the seabed by a side scan type sonar.

The transmitter / receiver of the sonar S is mounted on an underwater vehicle (hereinafter, simply referred to as a vehicle) V such as a UUV (Unmanned Underwater Vehicle) that navigates underwater. It is also widely practiced to tow a towed body equipped with a sonar transmitter / receiver from a surface boat and perform a search while controlling the water depth. As shown in FIG. 1, the distance from the seabed to the sea surface is defined as the sea area depth. Further, the distance from the seabed to the vehicle V is defined as the vehicle seabed altitude, and the distance from the sea surface to the vehicle V is defined as the vehicle depth. The azimuth direction indicates the traveling direction of the vehicle V, that is, the moving direction of the transmitter / receiver of the sonar S.

FIG. 2 is a view of FIG. 1 from the azimuth direction. Here, it is assumed that the range direction is a direction perpendicular to the traveling direction (azimuth direction) of the vehicle V in the horizontal plane. The transmitter / receiver of the sonar S is installed so that the irradiation direction and the wave receiving direction of the acoustic beam are directed to a certain angle from the seabed. The irradiation direction of the acoustic beam is the slant range direction, and the direction in which this is projected onto the seabed is the ground range direction. The slant range direction and the ground range direction have a one-to-one correspondence with each other by multiplying and dividing the cosine of the irradiation angle of the acoustic beam. In addition, the exploration range in the range direction on the seabed of the side scan type sonar is the swath width.

In the embodiment, the angle (skin azimuth) between the direction of travel of the sonar (azimuth direction) and the direction of the acoustic beam toward the acoustic irradiation area (slant range direction) is changed in a plurality of skin azimuths with respect to the same area. An acoustic beam is emitted. A multiplexed map is generated based on a plurality of sonar images (skinned images) obtained in each of a plurality of skinned directions.

Vehicle V is equipped with a sound velocity meter and can constantly measure the speed of sound underwater even during sonar operation. As shown in FIG. 2, the speed of sound in water changes depending on the depth, and the speed of sound in water at the beagle depth can be directly measured by a sound velocity meter mounted on the vehicle V.

Here, a sonar image processing method of a comparative example will be described with reference to FIG. Here, an example of generating one integrated map by integrating maps with skint images of three skint directions (squint + α, squint0, squint−α) will be described. In the flow chart, for the processing of the skin data and skin image of (squint + α, squint 0, squint − α) of each skin orientation, the codes of (+ α), (0), and (−α) are added to the codes of each step. I'm adding.

As shown in FIG. 10, in the sonar image processing method of the comparative example, first, the sonar data of the three skint directions and the underwater sound velocity measured at the beagle depth at that time are acquired (step S101). Then, a synthetic aperture process is executed (step S102) and an imaging process is executed (step S103) for each of the three sonar data, so that a sonar image (skinned image) of each skinned direction is generated. To.

The synthetic aperture processing is a process of performing phase synthesis / integration within the synthetic aperture bandwidth in order to increase the resolution in the traveling direction (azimuth direction) when generating a sonar image. In the synthetic aperture process, the time (t) is input and the two-dimensional coordinates (R, X) in the range direction and the azimuth direction are generated. As the synthetic aperture processing, for example, a synthetic aperture processing by a general range migration algorithm can be used. Further, the synthetic aperture processing may include a sway correction process or the like by multiplying the time axis signal by the sway correction function. By performing the squint integration process for integrating the three skinted images thus obtained into one seafloor topography map (step S104), an integrated map can be obtained.

FIG. 11 shows the relationship between the acoustic beam and the point image of the target T in the three skint directions and the state in which the three skint images are integrated in the comparative example. As shown in FIG. 11, in the region where the three skinned images are superimposed (3-squint superimposed region), the point images at the same position ideally overlap at the same position in all the skinned images.

スキント方位squint０のときのレンジ方向の座標Ｒ_０は、式（２）のように表される。

スキント方位squint−αのときのレンジ方向の座標Ｒ₋は、式（３）のように表される。

図１２に示すように、実際の水中音速では、３つのスキント画像における座標Ｒ_＋、Ｒ_０、Ｒ₋は一致する。 In FIG. 12, the coordinates R ₊ , R ₀ , and R _{− in} the range direction of each skint direction squint + α, squint 0, and squint − α in the case of the actual underwater sound velocity c are shown.
The coordinates R _{+ in} the range direction when the skint direction squint + α is expressed as in Eq. (1).

The coordinates R _{0 in} the range direction when the skinned direction squint ₀ is expressed as in Eq. (2).

The coordinates R _{− in} the range direction when the skint direction squint − α is expressed as in Eq. (3).

As shown in FIG. 12, at the actual underwater speed of sound, the coordinates R ₊ , R ₀ , and R ₋ in the three skinted images match.

However, as shown in FIG. 2, the speed of sound in water changes depending on the depth, and the speed of sound in water other than the operating depth cannot be directly measured by the sound velocity meter mounted on the vehicle V. Further, in the sonar, the measured underwater sound velocity may differ from the actual underwater sound velocity due to the measurement accuracy or uncertainty of the underwater sound velocity.

If greater than the actual water sound speed c underwater sound velocity c _m as measured in FIG. 13 _{(c m>} c), it is less than the actual water sound speed c underwater sound velocity c _m as measured in FIG. 14 (c _m The coordinates R _{m +} , R _m0 , and R _{m- in} the range direction of each skint direction squint + α, squint0, and squint−α in <c) are shown.

As shown in FIGS. 13 and 14, if the measured underwater sound velocity _cm is different from the actual underwater sound velocity c, the point image position of the target object T shifts for each skinned image. This problem is more pronounced in synthetic aperture sonars with high spatial resolution and reduces the image quality of the integrated map.

Prior to the operation of the sonar, the water temperature can be measured at a typical point by a BT (Bathy Thermograph) or the like, and the underwater sound velocity can be calculated from the relationship between the depth and the sound velocity based on the measurement result. However, it is not possible to measure the area directly under the vehicle V, and the sound velocity distribution in the entire sea area also changes with the passage of time.

In order to fundamentally solve this situation, it is necessary to simultaneously operate a sensor capable of directly measuring the sound velocity distribution directly under the vehicle V during operation. However, the realization of this method is not practical due to various costs and operational restrictions. In view of such problems, the present inventor has devised the following embodiments.

FIG. 3 is a diagram showing a configuration of the sonar image processing device 10 according to the embodiment. As shown in FIG. 1, the sonar image processing device 10 uses a plurality of data acquisition units 11 for acquiring a plurality of sonar data and measured underwater sound velocities in a plurality of skinned directions, and a plurality of measured underwater sound velocities. An image processing unit 12 that images sonar data to generate a plurality of sonar images, a point image detection unit 13 that detects a point image in each of the plurality of sonar images, and a plurality of sonar images among the detected point images. A point image extraction unit 14 that extracts point image pairs located at substantially the same position, and a sound velocity estimation unit that estimates the corrected underwater sound velocity that minimizes the difference by calculating the difference between the range-azimus coordinates of the point image pair. It is provided with 15. This makes it possible to accurately estimate the speed of sound underwater and suppress deterioration in the image quality of the sonar image. Hereinafter, specific embodiments will be described.

Embodiment 1.
FIG. 4 is a flow chart showing a sonar image processing method according to the first embodiment. Here, as in the comparative example, an example in which maps based on skint images of three skint directions (squint + α, squint0, squint−α) are integrated to generate one integrated map will be described.

As shown in FIG. 4, in the sonar image processing method of the first embodiment, first, the data acquisition unit 11 acquires the sonar data of the three skint directions and the underwater sound velocity measured at the beagle depth at that time. (Step S1). Then, for each of the three sonar data, the image processing unit 12 executes a synthetic aperture process (step S2) and an imaging process (step S3) to generate a sonar image. Let the sonar image of each skinned direction be the skinned image.

Next, the point image detection unit 13 detects the point images in these three skinned images (step S4). In the case of measured water sound speed c _m in FIG. 5, the squint orientation squint + α, squint0, squint- α range direction coordinates of the point image of the target T in the squint image _{R m +, R m0, R} m- is shown Has been done.

スキント方位squint０のときのレンジ方向の座標Ｒ_０は、式（５）のように表される。

スキント方位squint−αのときのレンジ方向の座標Ｒ₋は、式（６）のように表される。

図５に示す例では、測定水中音速ｃ_ｍが実際の水中音速ｃよりも小さく、３つのスキント画像における目標物Ｔの点像が一致していない。 The coordinates R _{+ in} the range direction when the skint direction squint + α is expressed as in Eq. (4).

The coordinates R _{0 in} the range direction when the skinned direction squint ₀ is expressed as in the equation (5).

The coordinates R _{− in} the range direction when the skint direction squint − α is expressed as in Eq. (6).

In the example shown in FIG. 5, the measured underwater sound velocity _cm is smaller than the actual underwater sound velocity c, and the point images of the target objects T in the three skinned images do not match.

Therefore, in step S5, among the detected point images, a point image pair located at substantially the same position over two or more sonar images is extracted for use in correcting the underwater sound velocity (step S5). ). The point image pair extraction may be performed, for example, by the point image extraction unit 14 executing an image processing program, or by the operator manually designating the point image pair. As the point image pair, for example, the same point scatterer echo captured over a plurality of skinned images can be assumed. Here, it is assumed that as the point image pair, a point image pair having the skinted directions squint + α and squint0 and a point image pair having the skinned directions squint−α and squint0 are extracted.

Then, the sound velocity estimation unit 15 calculates the difference between the range-azimus coordinates of the extracted point image pair, and executes the sound velocity estimation process for estimating the corrected underwater sound velocity that minimizes the difference (step S6). The coordinates of the point image of the target object T in each skinned image are shown in FIG. As shown in FIG. 6, the coordinates of the point image in the skint direction squint + α are [rm ₊ , x _{m +} ], the coordinates of the point image in the skint direction squint ₀ are [R _m0 , X ₀ ], and the coordinates of the point image in the skint direction squint−α. Let the coordinates be [ _rm- , x _m- ].

スキント方位squint−αのときの、ソーナーＳの送受信器のアジマス方向の位置Ｘ₋は、式（８）で表される。

The position X ₊ of the sonar S transmitter / receiver in the azimuth direction when the skinned azimuth squint + α is expressed by the equation (7).

The position X _{− in} the azimuth direction of the transmitter / receiver of the sonar S when the skin azimuth squint − α is expressed by the equation (8).

In the sound velocity estimation process, the following operations are executed. First, the range-azimuth coordinates of the point image determined in step S5 are calculated using the measured underwater sound velocity _cm . Then, assuming the speed of sound underwater, the coordinates of the point image of each skint direction at this time are calculated. The assumed water sound speed and c _c.

When the water sound speed _{c c,} the coordinates of the point image squint orientation squint0 _[R c0, _{X 0]} is expressed by the equation (9).

When the water sound speed _{c c,} the coordinates of the point image squint orientation _{squint-α [r c-, x} c-] is expressed by the equation (10).

The coordinates [rc ₊ , x _{c +} ] of the point image of the skint direction squint + α at the speed of sound c _c underwater are expressed by the equation (11).

Then, the difference between the range-azimus coordinates of the extracted point image pair is calculated, and the corrected underwater sound velocity that minimizes the difference is estimated.
The difference between the range-azimuth coordinates of the point image pair is expressed by the following evaluation function.

Then, the underwater sound velocity at which the difference between the coordinates of the point images between the plurality of skinned images is minimized is calculated by the following equation (13), and this is used as the corrected underwater sound velocity. The underwater sound velocity C _c , which minimizes F (C _c ) in equation (13), can be obtained numerically. Further, for example, a plurality of underwater sound velocities are assumed, and among the assumed underwater sound velocities, the value at which the difference in the coordinates of the point images between the plurality of skinned images is the smallest may be used as the corrected underwater sound velocity.

The image processing unit 12 reimages the sonar data of each skinned direction obtained in step S1 by using the corrected underwater sound velocity thus obtained (step S7). That is, the image processing unit 12 plays the role of a reimaging processing unit that reimages a plurality of sonar data using the corrected underwater sound velocity to generate a plurality of reimaged sonar images. As a result, the underwater speed of sound can be grasped as accurately as possible in a single skinned image.

Then, in the first embodiment, the image processing unit 12 integrates the reimaged skin image and performs the Squint integration process (step S8). Therefore, the image processing unit 12 also serves as an integrated processing unit that integrates a plurality of reimaged sonar images. The reimaging processing unit and the integrated processing unit may be provided separately from the image processing unit 12.

As described above, according to the embodiment, a more accurate corrected underwater sound velocity can be obtained. By using the corrected underwater sound velocity for the imaging process and the Squint integration process, it is possible to improve the misalignment of the target T on the seafloor topography map after integration, and it is possible to obtain a clearer integrated map.

Embodiment 2.
FIG. 7 is a flow chart showing the sonar image processing method according to the second embodiment. In the second embodiment, instead of the reimaging process (step S7) of the first embodiment, the processes of steps S10 to S12 described below are performed. In FIG. 7, the same processes as those in FIG. 4 are designated by the same reference numerals.

In the embodiment, after the sound velocity estimation process (step S6), the corrected sonar data in the three skinted directions and the corrected underwater sound velocity are acquired again (step S10). Then, using this corrected underwater sound velocity, the image processing unit 12 executes a synthetic aperture process (step S11) and an imaging process (step S12) for each of the three corrected sonar data. As a result, a corrected sonar image is obtained. Then, in step S8, the corrected sonar images of each skinned orientation are integrated to generate an integrated map.

As described above, in the second embodiment, the misalignment of the point image in the integrated map can be improved and a clearer integrated map can be obtained, as in the first embodiment. Further, in the second embodiment, since the synthetic aperture processing is performed using the corrected underwater sound velocity to generate the corrected sonar image, it is possible to improve the focus of the point image.

Embodiment 3.
FIG. 8 is a flow chart showing the sonar image processing method according to the third embodiment. In the third embodiment, a process (step S20) of determining whether or not the corrected underwater sound velocity has converged is performed between the steps S6 and S7 of the first embodiment.

As described above, in step S6, a plurality of underwater sound velocities are assumed, and the value that minimizes the difference in the coordinates of the point images between the plurality of skinned images among the assumed underwater sound velocities is defined as the corrected underwater sound velocity. In step S20, it is determined whether or not the corrected underwater sound velocity has converged. As the determination of convergence, for example, the threshold value can be determined by using the difference in the coordinates of the point image between the plurality of skinned images, the variation before and after the corrected underwater sound velocity that is repeatedly acquired, and the like.

When the corrected underwater sound velocity has converged (step S20, YES), the obtained corrected underwater sound velocity is used to reimage the sonar data of each skinned direction obtained in step S1 (step S7). On the other hand, when the corrected underwater sound velocity does not converge (step S20), the process returns to step S1 and the sonar data in the three skinted directions and the corrected underwater sound velocity used for determining the convergence are acquired again. Then, using the corrected underwater sound velocity used for determining the convergence, a new estimation of the corrected underwater sound velocity based on the sonar data is executed. After that, the processes of steps S1 to S6 are repeated until the corrected underwater sound velocity converges in step S20.

By performing the convergence test on the sound velocity estimation result in this way, the accuracy of the corrected underwater sound velocity can be further improved. This makes it possible to further improve the focus of the point image in each skinned image and the misalignment of the point image in the integrated map.

Embodiment 4.
FIG. 9 is a flow chart showing the sonar image processing method according to the fourth embodiment. FIG. 9 shows an example of generating one integrated map by integrating maps with skint images of five skint directions (squint + β, squint + α, squint0, squint−α, squint−β). The processing for the five skinned images is the same as that in the first embodiment. In this way, the same effect can be obtained even when the number of skinned directions is increased.

As described above, according to the embodiment, the focus and misalignment of the point image of the map generated in two or more plurality of skinned directions are corrected, and the deterioration of the image quality of the integrated map based on this map is suppressed. It becomes possible.

The present invention is not limited to the above embodiment, and can be appropriately modified without departing from the spirit. For example, the processing of the second embodiment or the third embodiment may be applied to the five skinned images of the fourth embodiment. Further, any of the processes of the first, second, and third embodiments may be applied to more than five skinned images.

10 Sonner image processing device 11 Data acquisition unit 12 Image processing unit 13 Point image detection unit 14 Point image extraction unit 15 Sound velocity estimation unit S Sonner V Vehicle T Target

Claims (10)

A data acquisition unit that acquires multiple sonar data in multiple skinned directions and the measured underwater sound velocity,
An image processing unit that uses the measured underwater sound velocity to image a plurality of the sonar data to generate a plurality of sonar images.
A point image detection unit that detects a point image in each of the plurality of sonar images,
Among the point images, a point image extraction unit that extracts point image pairs located at substantially the same position over a plurality of the sonar images, and a point image extraction unit.
A sound velocity estimation unit that calculates the difference between the range-azimuth coordinates of the point image pair and estimates the corrected underwater sound velocity that minimizes the difference.
To prepare
Sonar image processing equipment. A convergence test unit for determining whether or not the corrected underwater sound velocity has converged is further provided.
The sonar image processing apparatus according to claim 1. When the corrected underwater sound velocity does not converge, the data acquisition unit again acquires a plurality of sonar data in a plurality of skinned directions and the corrected underwater sound velocity used for determining the convergence, and the correction used for determining the convergence. Using the underwater sound velocity, a new corrected underwater sound velocity estimation based on the sonar data is performed.
The sonar image processing apparatus according to claim 2. Further provided is a reimaging processing unit that reimages a plurality of the sonar data using the corrected underwater sound velocity to generate a plurality of reimaged sonar images.
The sonar image processing apparatus according to any one of claims 1 to 3. Further provided with an integrated processing unit that integrates the plurality of the reimaged sonar images.
The sonar image processing apparatus according to claim 4. The data acquisition unit acquires a plurality of corrected sonar data in a plurality of skinned directions and the corrected underwater sound velocity.
The image processing unit uses the corrected underwater sound velocity to image a plurality of the corrected sonar data to generate a plurality of corrected sonar images.
The sonar image processing apparatus according to claim 1. Further provided with an integrated processing unit that integrates the plurality of the corrected sonar images.
The sonar image processing apparatus according to claim 6. The image processing unit performs synthetic aperture processing when generating a plurality of the sonar images.
The sonar image processing apparatus according to any one of claims 1 to 6. Acquire multiple sonar data in multiple skinned directions and measured underwater sound velocity,
Using the measured underwater sound velocity, a plurality of the sonar data are imaged to generate a plurality of sonar images.
A point image in each of the plurality of sonar images is detected,
From the point images, a point image pair located at substantially the same position over the plurality of sonar images is extracted.
The difference between the range-azimuth coordinates of the point image pair is calculated, and the corrected underwater sound velocity that minimizes the difference is estimated.
Sonar image processing method. Processing to acquire multiple sonar data in multiple skinted directions and measured underwater sound velocity,
Using the measured underwater sound velocity, a process of imaging a plurality of the sonar data to generate a plurality of sonar images, and
Processing to detect a point image in each of the plurality of sonar images, and
Among the point images, a process of extracting point image pairs located at substantially the same position over a plurality of the sonar images, and
A process of calculating the difference between the range-azimuth coordinates of the point image pair and estimating the corrected underwater sound velocity that minimizes the difference.
Let the computer run
program.

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