KR20170105817A - Method and apparatus for matching of digital elevation images - Google Patents

Method and apparatus for matching of digital elevation images Download PDF

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KR20170105817A
KR20170105817A KR1020160028944A KR20160028944A KR20170105817A KR 20170105817 A KR20170105817 A KR 20170105817A KR 1020160028944 A KR1020160028944 A KR 1020160028944A KR 20160028944 A KR20160028944 A KR 20160028944A KR 20170105817 A KR20170105817 A KR 20170105817A
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error
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황규영
박현규
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국방과학연구소
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Abstract

The present invention relates to a matching device of a digital elevation image, wherein the matching device is capable of matching a template digital elevation image obtained by using a reference digital elevation image and a sensor. The matching device includes a multi-image generating unit, an error calculating unit, an information amount calculating unit, a weight calculating unit, and a matching position calculating unit. The present invention can estimate a more accurate position by performing image matching using not only a template image obtained from a sensor but also a derivative image generated using the template image.

Description

[0001] METHOD AND APPARATUS FOR MATCHING OF DIGITAL ELEVATION IMAGES [0002]

The present invention relates to a method and apparatus for matching an elevation image capable of matching a reference elevation image and a template elevation image obtained using a sensor.

The Digital Elevation Model (DEM), which uses the height of the terrain as information, can be used not only for applications in geosciences such as terrain analysis and terrain changes over time, but also for estimating the position of a flying object. By measuring the digital elevation model (DEM) of the flight trajectory in the flight vehicle, it is possible to estimate the position of the flight vehicle by comparing the reference image with a given reference image. At this time, the image matching aims to find the most similar position by comparing the template image acquired from the sensor with the reference image. Although it is most common to determine the similarity between two images by cross-correlation, there are various algorithms to develop a method that improves the processing speed and is less influenced by image size and rotation.

In order to improve the accuracy of the registration, it is also possible to measure the terrain with various sensors or to generate derived data using the measured data. The data fusion algorithm is a method to effectively match the target with multiple data. It aims to overcome the disadvantages of single data and to improve the final matching performance. The key issue is how to integrate the various information, which can vary depending on the type of sensor and the measurement result.

The present invention is directed to solving the above-mentioned problems and other problems.

Yet another object of the present invention is to provide an apparatus and method for matching an elevation image capable of improving the matching accuracy between a reference image and a multiple image including at least one of a template image measured from a sensor and a derived image derived from the template image .

According to another aspect of the present invention, there is provided an apparatus for matching an elevation image, comprising: a multiple image generation unit for generating one or more derived images from a template elevation image composed of regular arrays of terrain elevations; An error calculating unit for setting the template elevation image and the one or more derivative images as a matching target image and calculating a matching error between the nth matching target image and the reference elevation image; An information amount calculation unit for calculating an information amount of the nth matching target image using an error distribution with respect to the matching error of the nth matching target image; A weight calculation unit for assigning a weight to a matching error of the n-th matching object image based on the information amount of the n-th matching object image; And m is a natural number between 1 and m, m is a total number of images set as a matching target image, and m is a matching position calculation for determining a point having a minimum error in consideration of a weight in the m matching target images as a matching point .

According to an embodiment, the information amount calculation unit may define a random variable for a region smaller than a threshold value in an error distribution with respect to a matching error of the n-th matching target image, Can be calculated.

According to one embodiment, the navigation apparatus may further include a position search unit for generating navigation information based on the matching point.

According to one embodiment, the navigation apparatus may further include an output unit that outputs the navigation information in at least one of a visual, auditory, and tactile manner.

According to an embodiment, the multiple image generation unit may generate a first derivative image using the magnitude of the gradient from the template elevation image, and generate a second derivative image using the direction of the gradient.

Further, the present invention proposes a matching method of elevation images in order to realize the above-mentioned problems. Wherein the matching method of the elevation images is performed in a matching apparatus, the method comprising: generating one or more derived images from template elevation images consisting of a regular array of terrain elevations; Setting the template elevation image and the one or more derivative images as a matching object image, calculating a matching error between an n-th matching object image and a reference elevation image; Calculating an information amount of the nth matching target image using an error distribution with respect to a matching error of the nth matching target image; Assigning a weight to a matching error of the n-th matching object image based on the information amount of the n-th matching object image; And m is a natural number between 1 and m, m is a total number of images set as a matching target image, and determining a point where the error is minimum considering the weights in the m matching target images as a matching point do.

According to an embodiment, the step of calculating the information amount of the n-th matching target image may include: defining a random variable for a region smaller than a threshold value in an error distribution with respect to a matching error of the n-th matching target image; And calculating an information amount of the n-th matching object image using the random variable.

According to an exemplary embodiment, the method of matching the elevation image may further include generating navigation information based on the matching point.

According to an exemplary embodiment, the method of matching the elevation image may further include outputting the navigation information in at least one of a visual, auditory, and tactile manner.

According to one embodiment, the step of generating one or more derivative images may include generating a first derivative image using the magnitude of the gradient from the template elevation image; And generating a second derivative image using the direction of the gradient from the template elevation image.

The effect of the image enhancement method according to the present invention will be described as follows.

It is possible to calculate the final error by applying the weight of the same value to the template elevation image obtained from the sensor and the derived image derived therefrom, which may result in deterioration than the matching performance using one image. This can be thought of as adding an error to a well-matched image and does not contribute to the matching result. Since it is different every moment that which image is helpful for matching, giving a constant value to a plurality of matching target images can not be expected to provide optimal performance.

The present invention has the effect of significantly reducing the final registration error by solving the ambiguity of weight determination by calculating the information amount of the image to be matched and applying the weight in proportion to the amount of information and optimally extracting the information to help match .

Since the present invention performs not only the template image obtained from the sensor but also the derived image generated using the template image, the image matching can be performed more accurately. At this time, since different weights are given to each image to be subjected to image matching, optimum image matching can be performed.

The effects obtained by the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description will be.

1 is a block diagram showing a conventional image matching method.
2 is an exemplary view of a template elevation image and an image derived therefrom.
3 is a block diagram showing a matching apparatus for an elevation image for performing matching using a plurality of matching object images.
4 is a block diagram showing a multiple image matching unit;
Figures 5A, 5B, 5C and 5D illustrate examples of the distribution of the registration error.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, wherein like reference numerals are used to designate identical or similar elements, and redundant description thereof will be omitted. The suffix "module" and " part "for the components used in the following description are given or mixed in consideration of ease of specification, and do not have their own meaning or role. In the following description of the embodiments of the present invention, a detailed description of related arts will be omitted when it is determined that the gist of the embodiments disclosed herein may be blurred. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed. , ≪ / RTI > equivalents, and alternatives.

Terms including ordinals, such as first, second, etc., may be used to describe various elements, but the elements are not limited to these terms. The terms are used only for the purpose of distinguishing one component from another.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

The singular expressions include plural expressions unless the context clearly dictates otherwise.

In the present application, the terms "comprises", "having", and the like are used to specify that a feature, a number, a step, an operation, an element, a component, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

The present invention relates to registration of a reference elevation image and a template elevation image obtained using a sensor. More specifically, a method and an apparatus for performing accurate position estimation by optimally matching a plurality of derived images derived from a template elevation image and a template elevation image obtained through a sensor using an information theory (Information Theory) and a reference elevation image .

Here, the Digital Elevation Model (DEM) is a numerical model expressing a bare earth part of a real world topography information other than a building, a tree, and an artificial structure, and a Digital Surface Model (DSM) It is a model that expresses all the information of the real world such as topography, trees, buildings, artificial structures. Also, Digital Terrain Model (D TM) has the same meaning as Digital Elevation Model (DEM). The numerical elevation data is used for urban geographical planning, site selection, civil engineering and environmental fields to support national geographic information system construction project and national land development by constructing irregular terrain relief in the form of three dimensional coordinates.

Elevation refers to the vertical distance from the level reference plane, which is the horizontal plane that is the reference of the elevation, to any point on the ground. In Korea, the mean sea level of Incheon Bay is defined as the National Geodetic Vertical Datum (NGVD), and the altitude is calculated based on this level. However, the level reference plane is inconvenient to use as a virtual plane. Therefore, the average sea level is measured, and a mark is placed near it. The accurate elevation of the sea level is measured, and the level origin is used as a level origin. The reference sign of the level origin is called a vertical datum point.

An elevation image refers to an image captured using an image sensor (for example, a radar, a camera, an infrared camera, or the like) arranged so as to face the ground surface in a flight or satellite.

The reference elevation image refers to the elevation image that is the reference of matching and refers to the elevation image stored in memory or server.

The template elevation image is an image taken using an image sensor placed on a flight body to estimate the position of the flight body.

The reference elevation image and the template elevation image are images generated by a digital elevation model (DEM), and can include a height value of each point distributed on the ground surface as a numerical value.

The derived image is an image derived from the template elevation image, and means an image in which various image processing is performed.

The template elevation image and the derivative image are to be matched to the reference elevation image and are referred to as a " matching object image ".

In computer vision, when a scene or object is photographed at different times or points of view, the image is obtained in different coordinate systems. That is, the matching target image to be matched with the reference elevation image has different coordinates.

Image registration is a processing technique that transforms these different images into one coordinate system. Through matching, we can see how the images obtained through different measurement methods correspond.

In order to perform the matching of the images, a point having the smallest error between the reference elevation image and the template image measured from the sensor is found. In this case, the difference can be measured in various ways. For example, there are an average of absolute values of the differences of image pixel values, a mean square of the differences, and a reciprocal of cross correlation. The present invention does not specify the method of measuring this difference in any particular way.

FIG. 1 is a block diagram illustrating a conventional image matching method. FIG. 1 illustrates a process of calculating a matching position by matching a template elevation image measured by a sensor with a reference elevation image.

Matching is performed by matching the template elevation image 102 to the reference elevation image 101 with the existing matching device 0. Conventionally, a template elevation image acquired from an image sensor or the like is referred to as a reference elevation image

The moving body acquires a plurality of template elevation images while moving, and estimates the position of the flying body by matching the plurality of template elevation images with the reference elevation image. When matching is performed using a plurality of matching target images to be matched, how to combine the matching results of the matching target images becomes a problem.

In particular, according to the present invention, one or more derivative images are generated using one template elevation image in order to enhance accuracy of position estimation. In this case, since there are a plurality of matching target images corresponding to the same region, there is a problem of how the matching results of the respective matching target images are combined based on what criteria.

When a matching point is calculated using a plurality of matching target images including a template elevation image and a derived image derived from the template elevation image, there is a problem in how to combine the respective matching results because there are a plurality of matching target images do. In order to calculate the final matching point, the final matching point can be calculated by calculating the error for each image and assigning a weight to each calculated error. However, since the information of the matching object image varies depending on the characteristics of the object to be measured, it is necessary to determine the weight optimally for efficient and high-performance matching.

In the present invention, the information theory is used to optimally determine the weights necessary to fuse the errors generated in the plurality of images to be matched with the reference elevation image. That is, a larger weight is assigned to a template elevation image having a larger amount of information to calculate a registration error. The information of the image is obtained through the matching probability between the template elevation image and the reference elevation image.

By calculating the error between the reference elevation image and the template elevation image and obtaining the histogram, the distribution of the error can be confirmed. If the position of the minimum error is not unique, the matching error is increased because there are several matching points. On the other hand, if the position of the minimum error is unique, the matching error is likely to be very small. That is, in the present invention, the probability that the matching target image and the reference elevation image are matched is calculated, the entropy is calculated from the probability, and the weight is determined.

The present invention relates to a method and apparatus for optimally calculating weights and applying them to final matching when matching a template height image measured from a sensor with a reference elevation image using a plurality of matching target images to improve matching performance It is about.

When a template elevation image is generated by measuring a surface elevation (or altitude) using a radar or other sensor, there may be a measurement error at the time of elevation measurement. Therefore, a derivative image derived from a template elevation image So that the matching of the images can be performed. At this time, the derived image can improve the matching performance by expressing the characteristics of the image more prominently.

For example, the derived image may include the gradient information of the template elevation image.

2 shows an example of a first template elevation image 104, a first derivative image 105 including size information of a differential value, and a second derivative image 106 including direction information of a differential value .

In such a method, a gradient value at each position can be obtained from a reference elevation image, and an absolute value image 105 of a gradient and a direction value image 106 of a gradient can be generated. Since the input image is a template elevation image generated by the elevation model, the gradient can be calculated through a gradient filter, and an example of calculating the gradient is shown in Equation (1).

Figure pat00001

The magnitude of the gradient is expressed by the altitude difference from the adjacent coordinates in the specific coordinates of the image, and the direction of the gradient is between 0 and 360 degrees. Therefore, a new image can be generated by calculating the gradient using the template elevation image measured from the sensor, and a plurality of matching target images can be constructed using the image derived in this manner. In this case, it is possible to perform image matching using not only the obtained template elevation image but also derived images using the slope information.

FIG. 3 is a block diagram showing an apparatus 300 for matching an image of an elevation image that performs matching using a plurality of images to be matched.

The apparatus 300 for aligning the elevation images according to the present invention includes a multiplex image generator 320 of a template image and a multiplex image generator 310 of a reference image as compared with the conventional matching apparatus shown in FIG. .

The multiple image generation unit 320 of the template image generates one or more derivative images from the template elevation image 102 obtained from the image sensor. The template image multi-image generating unit 320 outputs one template height image and one or more derived images derived therefrom to the multiple image matching unit 330. The template elevation image obtained at different times according to the flow of time and the derivative image corresponding thereto are output to the multiple image matching unit 330. [

 The multiple image generating unit 310 of the reference image may generate a plurality of reference images using the reference elevation image 101. [ The multiple image generating unit 310 of the reference image may generate a predetermined number of reference images corresponding to the number of matching target images output from the multiple image generating unit 320 of the template image. For example, if four matching images are generated by the multiplex image generator 320 of the template image at a specific time, four reference images may be generated in the multiplex image generator 310 of the reference image.

The multiple image matching unit 330 performs final image matching using images output from the multiple image generating unit 310 of the reference image and the multiple image generating unit 320 of the template image.

The multiple image matching unit 330 improves the matching performance using data fusion. When data fusion is used to improve the matching performance, the most important part is how to assign the weight to the matching result of each image.

Basically, matching is the process of finding the position with the smallest error between two images, but multiple errors occur in the matching of multiple images and how to combine them is an important problem. Since the matching performance is different for each image, the total error should be calculated by adding the weights according to the importance of the errors generated. In order to optimally determine this weight, the present invention uses information theory.

4, the multiple image matching unit 330 includes an error calculation unit 331 for calculating errors of multiple images, an error distribution calculation unit 332 for calculating a distribution of the generated errors, A weight calculation unit 334 for calculating a weight using the amount of information, and a matching position calculation unit 335 for determining a final matching position using a weight.

According to information theory, information is expressed as entropy, which is a concept such as uncertainty. In other words, the higher the probability that an event occurs for a random variable, the smaller the entropy is because the uncertainty becomes smaller, and the lower the probability of occurrence of the event, the greater the uncertainty becomes. That is, the smaller the probability that an event occurs, the greater the amount of information.

The amount of information on a single random variable is calculated as in Equation (2) below.

Figure pat00002

Where p denotes the probability of occurrence of an event, and H denotes entropy.

The distribution of the matching error is used to apply this theory to weight determination. The matching error can be calculated while moving the template image in the direction of the scan line, and the point with the smallest error is determined as the matching point.

The error calculator 331 compares the reference elevation image and the matching object image to calculate an error at each position. For example, when there are a total of n matching images, an error is calculated for each of the n matching images (n is a natural number).

The error distribution calculation unit 332 calculates an error distribution and generates a histogram of errors of all the matching target images. That is, n histograms of n matching target images are generated. As shown in Figs. 5A to 5D, it can be seen that the error distribution in the region where the error is close to zero varies depending on the image.

The information amount calculation unit 333 calculates the amount of information in the image using the error distribution, i.e., the histogram. The information amount calculation unit 333 calculates the probability that the matching target image is accurately matched to the reference elevation image through the error distribution, and calculates entropy as shown in Equation (2) using the log function of the probability.

The region with zero error corresponds to the position where matching is performed in each image. The smaller the region near zero, the higher the matching accuracy. The more the region near zero, the lower the matching accuracy.

Using this characteristic, the information amount calculation unit 333 defines a region in which a matching error is close to 0 using a specific threshold value, and defines the region as a random variable in a ratio of the entire error distribution. That is, we define a random variable for the matching accuracy. Accordingly, an n-th random variable corresponding to the n-th matching target image is defined, and a total of n random variables are generated.

The information amount calculation unit 333 calculates the entropy as shown in Equation (2) using the random variable, and calculates the information amount using the calculated entropy. If there is a low error of similar level, the entropy is low and the amount of information is small, while if there is a low error of similar level, the entropy is high and the information amount becomes large.

The weight calculation unit 334 calculates a weight for each position in the matching target image using the calculated amount of information. A large amount of information means that an optimal matching point can be found with a high probability. Therefore, different weights are given according to the amount of information. The larger the amount of information, the larger the weight is given, and the smaller the amount of information, the smaller the weight can be given.

The weight calculation unit 334 assigns weights to the n-th matching object image using the n-th random variable corresponding to the n-th matching object image. As a result, each of the images to be matched has a unique weight.

The matching position calculation unit 335 calculates the final matching point based on the weight given to each matching target image. For example, a first weight may be given to the first matching target image, and a second weight may be given to the second matching target image. The matching error of the first matching object image is multiplied by the first weight and the matching error of the second matching object is multiplied by the second weight. The matching position calculation unit 335 determines a point at which the error in consideration of the weight is minimum as the final matching point.

If the position of the minimum error is not unique, the matching error is increased because there are several matching points. However, the apparatus 300 for matching the elevation image according to the present invention calculates the final matching point based on the weight given to the matching point (or given to the matching target image). There is an effect that the final matching error can be remarkably reduced by solving the ambiguity of the weight determination by calculating the information amount of the image and applying the weight in proportion to the amount of information and extracting the information that helps the matching.

Although not shown in the drawing, the matching apparatus 300 of the elevation image may further include a position search unit and an output unit.

The position searching section searches for the position of the matching device 300 based on the final matching point determined by the matching position calculating section 335. The position search unit can perform a role of navigation based on the best matching point formed in real time. That is, the position searching unit generates the navigation information based on the matching position output from the matching position calculating unit 335. [

The output unit may include at least one of a display unit, an audio output unit, a haptrip module, and a light output unit for generating an output related to a visual, auditory or tactile sense. The display unit may have a mutual layer structure with the touch sensor or may be integrally formed to realize a touch screen. The touch screen may function as a user input unit for providing an input interface between the matching device 300 of the elevation image and the user and may provide an output interface between the matching device 300 of the elevation image and the user.

The output unit may output the current position of the matching device 300 based on the position information searched by the position search unit. For example, when the reference elevation image is composed of a topographic map, the reference elevation image may be outputted and a graphic object may be displayed at a position corresponding to the current position.

It is possible to guide a more accurate position as the plurality of images are matched optimally according to the weights.

The present invention can be embodied as computer-readable codes on a medium on which a program is recorded. The computer readable medium includes all kinds of recording devices in which data that can be read by a computer system is stored. Examples of the computer readable medium include a hard disk drive (HDD), a solid state disk (SSD), a silicon disk drive (SDD), a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, , And may also be implemented in the form of a carrier wave (e.g., transmission over the Internet). The computer may also include a wake detector (100). Accordingly, the above description should not be construed in a limiting sense in all respects and should be considered illustrative. The scope of the present invention should be determined by rational interpretation of the appended claims, and all changes within the scope of equivalents of the present invention are included in the scope of the present invention.

300: matching device, 331: error calculating section,
332: error distribution calculation unit, 333: information amount calculation unit,
334: Weight calculation unit, 335: Registration position calculation unit

Claims (10)

A multiple image generation unit for generating one or more derived images from a template elevation image composed of a regular array of terrain elevations;
An error calculating unit for setting the template elevation image and the one or more derivative images as a matching target image and calculating a matching error between the nth matching target image and the reference elevation image;
An information amount calculation unit for calculating an information amount of the nth matching target image using an error distribution with respect to the matching error of the nth matching target image;
A weight calculation unit for assigning a weight to a matching error of the n-th matching object image based on the information amount of the n-th matching object image; And
Wherein m is a natural number between 1 and m, m is a total number of images set as a matching target image, and m is a matching position calculating unit for determining a point having a minimum error in consideration of a weight in the m matching target images as a matching point And the matching device of the elevation image. The method according to claim 1,
The information-
Wherein a probability variable for a region smaller than a threshold value is defined in an error distribution with respect to a matching error of the nth matching target image and an information amount of the nth matching target image is calculated using the random variable, / RTI > The method according to claim 1,
Further comprising a position search unit for generating navigation information based on the matching point. The method of claim 3,
Further comprising an output unit for outputting the navigation information in at least one of a visual, auditory, and tactile manner. The method according to claim 1,
Wherein the multiple image generation unit generates a first derivative image using the magnitude of the gradient from the template elevation image and generates a second derivative image using the direction of the gradient. Generating one or more derived images from a template elevation image comprising a regular array of terrain elevations;
Setting the template elevation image and the one or more derivative images as a matching object image, calculating a matching error between an n-th matching object image and a reference elevation image;
Calculating an information amount of the nth matching target image using an error distribution with respect to a matching error of the nth matching target image;
Assigning a weight to a matching error of the n-th matching object image based on the information amount of the n-th matching object image; And
Wherein the n is a natural number between 1 and m, m is a total number of images set as a matching target image, and determining a point having a minimum error considering the weight in the m matching target images as a matching point Matching method of elevation image. The method according to claim 6,
The step of calculating the information amount of the n-th matching object image may include:
Defining a random variable for a region smaller than a threshold value in an error distribution with respect to a matching error of the nth matching target image; And
And calculating an information amount of the n-th matching target image using the random variable. The method according to claim 6,
And generating navigation information based on the matching point. 9. The method of claim 8,
Further comprising the step of outputting the navigation information in at least one of a visual, auditory, and tactile manner. The method according to claim 6,
Wherein generating the one or more derivative images comprises:
Generating a first derivative image using the magnitude of the gradient from the template elevation image; And
And generating a second derivative image using the direction of the gradient from the template elevation image.
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CN117333688A (en) * 2023-12-01 2024-01-02 西安现代控制技术研究所 High-precision terrain matching method based on multidimensional gradient characteristics

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CN112530012A (en) * 2020-12-24 2021-03-19 网易（杭州）网络有限公司 Virtual earth surface processing method and device and electronic device
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