JP6146731B2 - Coordinate correction apparatus, coordinate correction program, and coordinate correction method - Google Patents

Description

  The present invention relates to a technique for correcting a change in position due to crustal movement, and more specifically, images of topographical features of two periods are imaged from the topographical amounts, and coordinate correction parameters are obtained by comparing and collating these images. The present invention relates to a coordinate correction apparatus, a coordinate correction program, and a coordinate correction method.

  Japan is known as an earthquake-prone country, and in recent years, major earthquakes such as the Tohoku-Pacific Ocean Earthquake, the Hyogoken-Nanbu Earthquake, and the Niigata-ken Chuetsu Earthquake have occurred. In addition, there are more than 100 active volcanoes in Japan, which is said to be a volcanic powerhouse, and these active volcanoes erupt intermittently, and the surrounding areas are damaged by volcanic ash and cinders.

  By the way, a reference point is used when surveying. By measuring based on this reference point, the target coordinates and altitude can be obtained. The reference point includes a triangular point that serves as a reference for plane coordinates, a standard point that serves as a reference for elevation, or an electronic reference point that is used for GPS (Global Positioning System) measurement. A value is assigned. The survey result based on the reference point can be said to be a relative position with respect to this reference value. Therefore, the reference point is constructed with a solid structure so as not to move easily, and is treated as a so-called fixed point.

  However, the crust may change greatly due to the earthquakes and volcanic activities described above, and the reference point will also move with the crustal movement. When the position of the reference point changes, “revision” is usually performed to obtain the changed reference value. According to this revision, a new reference point is obtained, and at the same time, the degree of movement of the reference point can be grasped. The Geospatial Information Authority of Japan provides a new reference value (revised value) by revising every time the position of the reference point changes due to crustal movement.

  There are not many reference points managed by the Geospatial Information Authority of Japan. For example, electronic reference points are arranged at intervals of 20 to 30 km, and the number thereof is around 1,200 points nationwide. In addition, the number of electronic reference points whose positions have changed, that is, the number of electronic reference points to be revised, is limited as long as the range of crustal deformation has occurred. On the other hand, the reference points managed by local governments, such as municipalities, are more densely arranged than the Geospatial Information Authority of Japan. Public reference point level 1 is set at an interval of about 1 km and public reference point level 2 is set at an interval of about 500 m. Furthermore, it is said that the number of first-degree triangle points, second-degree triangle points, third-degree triangle points, and fourth-degree triangle points installed based on electronic reference points exceeds 100,000 points. When crustal movements occur, a wide range of local governments may be affected, and as a result of the change of the position of many reference points, it is necessary to remeasure a huge number of reference points.

  Therefore, in order to avoid a huge amount of revision work, “coordinate correction” is actually performed. This coordinate correction is to calculate the revised value of the reference point by calculation without revising, and the movement vector (movement direction and movement distance) of the reference point before and after the crustal movement is obtained, and based on this, the movement before the crustal movement is obtained. The coordinates are corrected. A value (in this case, a movement vector) used when correcting the coordinates is referred to as a “coordinate correction parameter”. For example, the Geospatial Information Authority of Japan provides “coordinate correction parameters” of electronic reference points and software (PatchJGD) for correcting coordinates based on the “coordinate correction parameters”.

  Also, “coordinate correction” is performed on past survey results. A lot of surveying work has been carried out before the crustal movement, and a huge amount of survey results have been acquired. However, the survey results cannot be used as they are due to the change in the position of the reference point. This is because the survey target (land, building, etc.) has changed its position due to crustal movement, and the old standard value before crustal movement is used, so it does not match the current situation (after crustal movement). . However, every time crustal deformation occurs, it is not realistic from the viewpoint of cost to discard the previous surveying work and start a new survey. Therefore, “coordinate correction” is performed.

  Since the degree of change in position due to crustal movement varies depending on the location, the “coordinate correction parameter” corresponding to the location is also used when performing “coordinate correction”. For example, when the correction is performed using the “coordinate correction parameter” based on the electronic reference point, the “coordinate correction parameter” is obtained from the plurality of electronic reference points, the range surrounded by the electronic reference points is divided into approximately 1 km mesh, and a plurality of “ The value complemented by the “coordinate correction parameter” is given to each mesh.

In this way, when the “coordinate correction” is performed, the supplemented value is used, and as described above, the coordinate interval between the electronic reference points is 20 to 30 km. "Does not necessarily represent a change in local location. That is the conventional coordinate correction, had a problem that it can not properly express the change in position of the local.

  Furthermore, in order to perform the conventional “coordinate correction”, it is necessary to have a “signpost” that clearly shows before and after the change, such as a reference point. There was also a problem that it was not possible.

  The applicant of the present application proposes a technique in Patent Document 1 that can grasp the topographic change in two periods even when there is no such “sign”.

Japanese Patent No. 4545219

  However, Patent Document 1 grasps a change in topography in two periods, and does not propose a technique for performing “coordinate correction”. The subject of the present invention is to solve the above problem, that is, it is corrected with a “coordinate correction parameter” that appropriately represents a change in the local position, and in addition, any “signpost” that clearly shows the change before and after the change is arbitrary. An object of the present invention is to provide a coordinate correction apparatus, a coordinate correction program, and a coordinate correction method capable of obtaining a “coordinate correction parameter” at a place.

  The invention of the present application focuses on the point of comparing two-dimensional terrain models based on three-dimensional spatial information, such as DSM and DEM, after imaging the amount of terrain. It is an invention made based on this.

  The coordinate correction apparatus of the present invention comprises coordinate input means, terrain model storage means, parameter calculation means, and coordinate correction means. The contents of each means are as follows. The coordinate input means is for inputting arbitrary coordinates. The terrain model storage means stores a pre-change terrain model that represents the terrain before the crustal deformation and includes a plurality of meshes, and a post-change terrain model that represents the terrain after the crustal change and includes a plurality of meshes. The parameter calculation means obtains a coordinate correction parameter for correcting a change in position based on the pre-change terrain model and the post-change terrain model. The coordinate correction means obtains the coordinates after the position change due to crustal movement based on the coordinates input by the coordinate input means and the coordinate correction parameters. The parameter calculating means calculates the terrain amount for each mesh constituting the pre-change terrain model, calculates the terrain amount for each mesh constituting the post-change terrain model, and sets pixels for image creation based on the mesh. Form. Furthermore, by assigning pixel information according to the amount of topography to the pixels, a pre-change image representing the topography before the crustal deformation and a post-change image representing the topography after the crustal deformation are created. The movement vector of the terrain is calculated by collating the images, and the coordinate correction parameter is obtained based on the movement vector.

  In the coordinate correction apparatus of the present invention, the parameter calculation means may be as follows. The parameter calculation means selects one of the pre-fluctuation image and the post-fluctuation image as a search image, selects the other as a search target image, and sets a search region that is a partial range of the search image. Further, by extracting a pixel group composed of a combination of two or more pixels from the pixels constituting the search area, and detecting a combination of pixels to be matched with the pixel group from the search target image, a movement vector of the search area And this movement vector is used as a coordinate correction parameter for a correction area (configured based on the search area).

  The coordinate correction program of the present invention has a function of causing a computer to execute a terrain model reading process, a parameter calculation process, and a coordinate correction process. The contents of each process are as follows. The terrain model reading process is to read a pre-change terrain model that represents the terrain before the crustal movement and includes a plurality of meshes, and a post-change terrain model that represents the terrain after the crustal movement and includes a plurality of meshes. The parameter calculation process is to obtain a coordinate correction parameter for correcting a change in position based on the pre-change terrain model and the post-change terrain model. The coordinate correction process is to obtain coordinates after a position change due to crustal movement in arbitrary coordinates based on the coordinate correction parameter. The parameter calculation process calculates the terrain amount for each mesh constituting the pre-change terrain model, calculates the terrain amount for each mesh constituting the post-change terrain model, and sets pixel information according to the terrain amount, This pixel information is given to the pixel configured based on the mesh. As a result, a pre-change image representing the terrain before the crustal deformation and a post-change image representing the terrain after the crustal movement are created, and the terrain movement vector is calculated by comparing the pre-change image and the post-change image. The coordinate correction parameter is obtained based on this movement vector.

  In the coordinate correction program of the present invention, the parameter calculation process may be as follows. In the parameter calculation process, one of the pre-fluctuation image and the post-fluctuation image is selected as a search image, the other is selected as a search target image, and a search region that is a partial range of the search image is set. Further, by extracting a pixel group composed of a combination of two or more pixels from the pixels constituting the search area, and detecting a combination of pixels to be matched with the pixel group from the search target image, a movement vector of the search area And this movement vector is used as a coordinate correction parameter for a correction area (configured based on the search area).

  The coordinate correction method of the present invention includes a parameter calculation step and a coordinate correction step. The contents of each process are as follows. The parameter calculation process is a coordinate that corrects the position change based on the pre-change terrain model that represents the terrain before the crustal deformation and consists of multiple meshes and the post-change terrain model that represents the terrain after the crustal deformation and consists of multiple meshes. This is a step of obtaining a correction parameter. The coordinate correction step is a step of obtaining coordinates after position change due to crustal movement in arbitrary coordinates based on the coordinate correction parameters. The parameter calculation step calculates the terrain amount for each mesh constituting the pre-change terrain model, calculates the terrain amount for each mesh constituting the post-change terrain model, and sets pixel information according to the terrain amount, This pixel information is given to the pixel configured based on the mesh. As a result, a pre-change image representing the terrain before the crustal deformation and a post-change image representing the terrain after the crustal movement are created, and the terrain movement vector is calculated by comparing the pre-change image and the post-change image. The coordinate correction parameter is obtained based on this movement vector.

  In the coordinate correction method of the present invention, the parameter calculation step may be as follows. In the parameter calculation step, one of the pre-fluctuation image and the post-fluctuation image is selected as a search image, the other is selected as a search target image, and a search area that is a partial range of the search image is set. Further, by extracting a pixel group composed of a combination of two or more pixels from the pixels constituting the search area, and detecting a combination of pixels to be matched with the pixel group from the search target image, a movement vector of the search area And this movement vector is used as a coordinate correction parameter for a correction area (configured based on the search area).

The coordinate correction device, coordinate correction program, and coordinate correction method of the present invention have the following effects.
(1) Since no coordinate correction is performed in accordance with the terrain change of the place without using a method of complementing the change of the reference point in the distance, it is possible to obtain a coordinate correction value that appropriately represents the local position change.
(2) Since the coordinate correction parameter can be obtained at any place even without a “marker” such as a reference point, it is extremely versatile.
(3) Since correction of the reference point is not required, coordinate correction can be performed at low cost.

Explanatory drawing which shows the measurement condition by an aviation laser. The model figure explaining "surface". The model figure explaining "the ground surface". The block diagram which shows the structure of a coordinate correction apparatus. The flowchart which shows the process sequence implemented with a parameter calculation means. Explanatory drawing which shows the search area | region in a search image. (A) is explanatory drawing which shows the to-be-searched area moved to the upper left from the equivalent range of the search area in an image to be searched, (b) is the to-be-searched area moved to the upper right from the equivalent area of the search area in the to-be-searched image. (C) is an explanatory diagram showing the search area moved from the corresponding range of the search area in the search image to the lower left, and (d) is moved from the equivalent range of the search area in the search image to the lower right. Explanatory drawing which shows the to-be-searched area | region. Explanatory drawing which shows the pixel group extracted in the search area | region. (A) is the model figure which showed the image of the search area | region, (b) is the model figure which showed the image of the to-be-searched area | region. (A) is explanatory drawing which modeled the surface of the topography before crustal deformation, (b) is explanatory drawing which modeled the surface of the topography after crustal deformation. The flowchart which shows the process sequence after performing determination of a collation grade.

  An example of an embodiment of a coordinate correction apparatus, a coordinate correction program, and a coordinate correction method of the present invention will be described with reference to the drawings.

1. Overall Outline The present invention obtains coordinate correction parameters for a somewhat wide range (hereinafter referred to as “target region”) such as a municipality or a prefecture, and performs coordinate correction by this. Since a wide area is processed at a time, three-dimensional spatial information representing terrain is used. Therefore, first, three-dimensional spatial information will be described.

  The three-dimensional spatial information is information composed of points, lines, surfaces, or combinations thereof having plane coordinate values and height information. Further, the plane coordinate value is represented by latitude and longitude or X and Y coordinates, and the height means a vertical distance from a predetermined reference horizontal plane such as an altitude. This three-dimensional spatial information can be created by various means. For example, it can be created based on a set of two stereo aerial photographs (satellite photographs), created by aerial laser measurement or satellite radar measurement, or can be created by surveying the site directly.

  FIG. 1 is an explanatory diagram showing a measurement situation by an aviation laser. As shown in this figure, the aviation laser measurement is performed by flying the aircraft 2 over the terrain 1 to be measured and receiving the reflection of the laser 3 irradiated on the terrain 1 during the flight. According to this method, three-dimensional spatial information composed of many point groups can be acquired by one measurement.

  The three-dimensional spatial information created on the basis of stereo aerial photographs and aerial laser measurements usually represents a “surface”. Here, as shown in FIG. 2, the surface means the upper surface of a feature that stands on the ground, such as a green object such as a forest or farmland, or a building. On the other hand, the “ground surface” means a surface after removing green objects or buildings, that is, the ground as shown in FIG. Here, a “surface model” represents the “surface” with three-dimensional spatial information, and a “ground model” represents the “surface” with three-dimensional spatial information. Note that DSM is known as a representative surface model, and DEM is also known as a typical surface model.

  In order to create the ground surface model, processing for removing green objects and buildings from the surface layer model, so-called filtering processing is performed. The filtering process is a well-known technique, and recognizes and excludes objects that meet a predetermined condition as green objects or buildings. This process is generally executed by a computer using general-purpose software.

  Next, an outline of the present invention will be described. In the present invention, a “terrain model” created based on a surface layer model or a ground surface model (or a surface layer model or a ground surface model itself) is used. The “terrain model” includes a large number of grids and a mesh divided by the grids. In other words, the “terrain model” includes a plurality of meshes. In addition, since the present invention performs coordinate correction by crustal movement, a terrain model before the occurrence of crustal movement and a terrain model after the occurrence are used. For convenience, the terrain model before the occurrence of crustal deformation is referred to as “pre-change terrain model”, and the terrain model after crustal deformation is referred to as “post-change terrain model”.

  In order to perform coordinate correction, the “pre-change terrain model” and the “post-change terrain model” are compared. At this time, both terrain models are imaged and compared. In order to image the terrain model, the terrain amount is calculated for each mesh, and pixel information corresponding to the terrain amount is given. By comparing the image of the pre-change terrain model with the image of the post-change terrain model, the amount and direction of movement of the terrain (hereinafter referred to as “movement vector”) are obtained, and coordinate correction is performed according to the movement vector.

  Hereinafter, each element will be described in detail. Here, the technical contents of the present invention will be described using an example of a coordinate correction apparatus, and the contents specific to the coordinate correction program and the coordinate correction method will be described later.

2. Coordinate correction device (input means)
FIG. 4 is a block diagram showing the configuration of the coordinate correction apparatus 4. As shown in this figure, the coordinate correction device 4 includes an input means 5. The input means 5 inputs coordinates before correction, and can be a computer. The uncorrected coordinates input here are stored in the uncorrected coordinate storage means. Note that the coordinates input here may be only plane coordinates, only height (elevation), or a combination of plane coordinates and height.

(Terrain model storage means)
The terrain model storage means 6 shown in FIG. 4 stores a pre-change terrain model and a post-change terrain model, and is a storage medium such as a computer hard disk or a CD-ROM. That is, the pre-change terrain model and the post-change terrain model are formed in a data format that can be processed by a computer. The coordinate correction device 4 can be configured separately for the pre-change terrain model and for the pre-change terrain model, or can be configured as an integral unit. Further, as shown in FIG. 4, the data registration means 7 is provided, and the pre-change terrain model and the post-change terrain model can be stored in a hard disk or the like of the computer.

  As described above, the terrain model is composed of a plurality of meshes. The mesh is formed by being divided into, for example, orthogonal grids, and each mesh has a representative point. Since the laser measurement point is usually random data, geometric calculation is often performed to give a height to the representative point of the mesh. As this calculation method, in addition to a method by TIN (Triangulated Irregular Network) for obtaining height by an irregular triangle network formed from random data, a method by nearest neighbor method (Nearest Neibor) employing the nearest laser measurement point 4, Various conventionally used methods such as an inverse distance weighting method (IWD), a Kriging method, and an averaging method can be employed. In addition, the grid which comprises a mesh does not necessarily need to orthogonally cross, and can be made into the grid by arbitrary crossing angles. Further, the pre-change terrain model and post-change terrain model used here may be created for the present invention, but of course, if there is a ready-made one, it can also be used.

(Parameter calculation means)
The parameter calculation means 8 shown in FIG. 4 is to cause a computer to process using software, and obtains coordinate correction parameters based on the pre-change terrain model and the post-change terrain model. FIG. 5 is a flowchart showing a processing procedure performed by the parameter calculation means 8, and will be described below with reference to this figure.

  First, the pre-change terrain model and post-change terrain model are read from the terrain model storage means 6, and the terrain quantity is calculated for each mesh (A1, A2 in FIG. 5). The amount of terrain is a so-called index representing the feature of the terrain, and is calculated based on the representative points of the mesh, original random data, and the like.

  Examples of the terrain amount include an altitude value, a Laplacian value, a ground opening value, an underground opening value, an inclination amount, or a combination thereof. Here, the Laplacian value is generally used for drawing a Laplacian diagram that represents the rate of change in inclination. This Laplacian diagram has a feature that it is positive in a depressed terrain, negative in a protruding terrain, and has an absolute value where the change in the terrain is large.

  The ground opening value and the underground opening value are generally for drawing an opening diagram. Among the opening diagrams, the ground opening diagram represents the extent of the sky that can be seen within a certain distance from the point of interest, and the ground opening value increases as the point protrudes from the surroundings. A large terrain opening value is shown at the ridge, a small ground opening value is shown at the depression and valley bottom, and protruding peaks and ridges are emphasized. On the other hand, in the opening map, the underground opening map shows the size of the basement within a certain distance when looking down from the ground surface, contrary to the ground opening map. The underground opening value shows a large value, for example, a large underground opening value at a depression or a valley bottom, a small underground opening value at a mountain top or a ridge, and a characteristic that a depression or valley is emphasized. is there.

  The tilt amount is generally used for drawing a tilt amount diagram. The inclination amount map indicates the degree of inclination of the terrain. The larger the inclination is, the larger the inclination value is. In contrast, the gentler inclination is, the smaller the inclination value is.

  Next, pixels (pixels) for image creation are formed, and pixel information is assigned to the pixels to create an image (B1, B2 in FIG. 5). This pixel is formed on the basis of a mesh. For example, one pixel is one pixel, four meshes are one pixel, and nine meshes are one pixel. Can be formed.

  Pixel information given to a pixel represents brightness and color (hue, saturation, and brightness), and color models such as RGB, CMYK, and NCS can be used. Pixel information is given according to the terrain amount of the mesh. For example, red, orange, yellow, green, and blue are set in the order in which the terrain amount shows a large value. The altitude value can be arbitrarily set, for example, the altitude value can be a color and the slope amount can be a combination of brightness.

  In this way, an image after crustal movement is created. That is, based on the pre-change terrain model, an image representing the terrain before the crustal movement (hereinafter referred to as “pre-change image”) is created, and based on the post-change terrain model, an image representing the terrain after the crustal movement ( Hereinafter, the “post-change image” is created. When the pre-change image and post-change image are created, one is selected as the “search image” and the other is selected as the “searched image”. Here, the case where the pre-change image is the search image and the post-change image is the search image will be described. However, the post-change image may be the search image and the pre-change image may be the search image.

  When a search image is selected, a “search area (window)” is set in the search image (C1 in FIG. 5). FIG. 6 is an explanatory diagram showing the search area 11 in the search image. Since the search image (and the image to be searched) is usually created for a wide range, it is more efficient to execute the subsequent processing in a predetermined partial range, that is, “search area 11” as shown in FIG. It becomes the target. The search area is composed of a plurality of pixels, but the number of pixels can be arbitrarily set.

  When the search area 11 is set, a “search area 12” corresponding to the search area 11 is set in the search image (C2 in FIG. 5). FIG. 7 is an explanatory diagram showing the search area 12 in the search image. In this figure, the broken line portion at the center of the search image is a range corresponding to the search region 11, and FIG. 6A shows the search region 12 moved from the corresponding range of the search region 11 to the upper left. b) shows the search area 12 moved to the upper right, (c) shows the search area 12 moved to the lower left, and (d) shows the search area 12 moved to the lower right. In this way, the search target region 12 is set by moving little by little around the range corresponding to the search region 11. Of these, the search target region 12 that most matches the image of the search region 11 is extracted, and the movement vector of the search region 11 is obtained. Note that it is possible to arbitrarily design the range around the search area 11 as the search target area 12.

  Next, a pixel group (pixel set) is extracted from the search area 11 (D in FIG. 5). FIG. 8 is an explanatory diagram showing the pixel group 13 extracted in the search area 11. The pixel group 13 includes two or more pixels. For example, the pixel group 13 may be a collection of pixels that meet a predetermined condition, such as a collection of pixels whose pixel information is within a predetermined range. Alternatively, the pixel group 13 can be extracted under various conditions, such as determining the boundary of the pixel group 13 according to the difference (residual) of the pixel information between the compared pixels. Further, only one type of pixel group 13 can be extracted from one search area 11, or two or more types of pixel groups 13 can be extracted.

A similar image is collated (matched) in the search area 12 using the extracted pixel group 13 as a template. Here, the reason for matching by the pixel group 13 will be described. FIG. 9A is a model diagram showing an image of the search area 11, and FIG. 9B is a model diagram showing an image of the search area 12. Focusing on one pixel V ₀ in FIG. 9A and searching for a corresponding pixel in FIG. 9B, pixel V ₁ , pixel V ₂ , and pixel V having the same luminance (brightness) are obtained. ₃ is matched. That is, after the movement of the pixel V ₀ , three patterns can be considered and cannot be specified as any one. On the other hand, by focusing on the pixel group 13 consisting of four pixels including the pixel V ₁ of FIG. 9 (a) (a range surrounded by the broken line in the drawing), the image corresponding thereto among shown in FIG. 9 (b) When searching, the pixel group 13 surrounded by the broken line in FIG. 9B can be specified. In this way, if the pixel group 13 is used, two images can be easily compared.

  When the pixel group 13 is extracted, a similar image is collated in the search area 12 using the pixel group 13 as a template (E in FIG. 5). Specifically, based on the pixel information included in the pixel group 13, a combination having the same pixel information arrangement is searched from the image of the search target area 12. At this time, if a plurality of types of pixel groups 13 are extracted, the search process is repeated for the number of types.

  It is not always the case that the same image as the pixel group 13 exists in the search area 12. Therefore, a certain degree of redundancy can be given to the degree of collation. For example, a threshold is provided for the residual of the compared pixel information, and if it is within this threshold, it is determined as an equivalent image (pixel), or if the number (or ratio) of different pixels is equal to or less than a predetermined threshold Differences in images can be allowed under various conditions such as determination as a pixel group 13 or a combination thereof.

  In other words, as shown in FIG. 10 (a), the surface of the terrain before the crustal movement is captured, and a partial surface (shaded portion) is extracted from the surface, as shown in FIG. 10 (b). In this way, a similar surface is searched from the surface of the topography before the crustal movement. In other words, terrain surface matching is performed before and after the change.

  The matching process by the pixel group 13 is repeatedly performed for the set number of search areas 12, and when this is performed, the optimal search area 12 is specified (F in FIG. 5). The optimal search target area 12 is selected from the search target areas 12 having an image (pixel information) arrangement that most closely matches the pixel group 13 (for example, by the sum of absolute values of pixel information residuals).

  When the optimum search area 12 is specified, a movement vector (movement amount and movement direction) is calculated (G in FIG. 5). By comparing the position of the search area 11 and the optimal position of the search target area 12, a planar movement vector can be easily calculated. In addition, since the pre-change terrain model and the post-change terrain model are composed of three-dimensional spatial information, the search area 11 and the optimum search area 12 have height (elevation) information. In addition to a typical movement vector, a three-dimensional movement vector can also be obtained. Note that if a plurality of types of pixel groups 13 are extracted, different movement vectors may be calculated for the number of types. In this case, the average value of different movement vectors can be used, or a median value can be set as appropriate. However, one representative movement vector is determined based on the calculated movement vector anyway. .

  After the matching process by the pixel group 13 (before specifying the optimum search area), it is possible to determine the degree of matching (H in FIG. 11). FIG. 11 is a flowchart showing a processing procedure after determining the degree of matching. The search area 12 having the most collated image (pixel information) arrangement is selected by the matching processing by the pixel group 13, but the degree of collation may not be uniform, and in some cases, the degree of collation is extremely high. There may be low cases. Here, it is determined which of the next steps is to be performed in accordance with the degree of collation. The degree of collation is, for example, the number or ratio of pixels in which the residual of pixel information between compared pixels is equal to or greater than a threshold (hereinafter referred to as “error pixel”), or the absolute value of the pixel information residual in all pixels. It can be determined by the sum total. Hereinafter, the subsequent processing procedure will be described in the case where the degree of matching is determined by the number of error pixels (FIG. 11).

  If the number of extracted error pixels is larger than the preset upper limit threshold value (arrow a), it is considered that the terrain is greatly deformed due to crustal movement, and the analysis ends without further analysis. (I) When the number of error pixels is smaller than the preset lower limit threshold (arrow b), it is considered that matching is sufficiently performed at this stage, and the optimum search target region 12 is set as it is (F). When the number of error pixels is between the upper limit threshold value and the lower limit threshold value (arrow c), fine adjustment (J) of the pixel arrangement is performed. In this case, adjustment is performed based on a pixel arrangement (approximate pixel arrangement) that approximates the pixel group 13 searched in the search target region 12 and a pixel arrangement (peripheral pixel arrangement) that is formed around the pixel arrangement. In other words, the approximate pixel arrangement and the surrounding pixel arrangement are complemented, and the pixel arrangement most closely matched with the pixel group 13 is obtained between the approximate pixel arrangement and the peripheral pixel arrangement. The search area 12 including the pixel arrangement obtained here is recalculated to obtain the optimum search area 12 (F). It should be noted that it can be appropriately designed whether or not the determination of the matching level (H in FIG. 11) is performed.

  As described above, when the optimum search target region 12 is specified, a movement vector is calculated, and based on this, a coordinate correction parameter is obtained. At this time, the movement vector can be used as the coordinate correction parameter as it is, or a value different from the movement vector, such as multiplying the movement vector by a predetermined coefficient, can be used as the coordinate correction parameter.

  Further, although one movement vector is calculated for one search area 11, it is not always necessary to obtain one coordinate correction parameter for one search area 11. An area composed of two or more search areas 11 can be referred to as a “correction area”, and one coordinate correction parameter can be obtained for this correction area. In this case, the coordinate correction parameter is obtained based on the movement vector included in two or more search areas 11 (which constitute the correction area). It may be an average value of two or more movement vectors, may be a median value, or may be obtained by another method. In any case, a correction area is constituted by one or two or more search areas 11, and one coordinate correction parameter is obtained for one correction area.

(Coordinate correction means)
The coordinate correction means 9 shown in FIG. 4 is to make a computer process using software, and corrects the coordinates (pre-correction coordinates) input by the coordinate input means 5. For example, correction is performed by adding a coordinate correction parameter to the coordinates before correction. At this time, a correction area including the pre-correction coordinates within the range is extracted, and a coordinate correction parameter corresponding to the correction area is used. The coordinates calculated in this way are given as corrected coordinates. This result can also be output by the output means 10 shown in FIG. As the output means, it is possible to use various output means such as printing on paper using a printing machine, displaying on a display, and the like.

3. Coordinate Correction Program The coordinate correction program operates the coordinate correction apparatus 4 and has a function of causing a computer to execute each means included in the coordinate correction apparatus 4. Hereinafter, it demonstrates individually. In addition, since the content of the process overlaps with the content described in the coordinate correction device 4, the description is not repeated here.

  In the terrain model reading process, the parameter calculation unit executes a process of reading the pre-change terrain model and the post-change terrain model from the terrain model storage unit. In the parameter calculation process, similarly, the parameter calculation unit calculates a movement vector as described above, and executes a process for obtaining a coordinate correction parameter based on the movement vector. In the coordinate correction process, the coordinate correction unit executes a process for obtaining coordinates after a position change due to crustal movement.

4). Coordinate Correction Method In the coordinate correction method, the content described in the coordinate correction device 4 is performed by a person. At this time, the coordinate correction apparatus 4 can also be used. Hereinafter, it demonstrates individually. In addition, since the content of the process overlaps with the content described in the coordinate correction device 4, the description is not repeated here.

  In the parameter calculation step, the contents described in the parameter calculation means are executed, and the movement vector is calculated as described above, and the coordinate correction parameter is obtained based on this. In the coordinate correction step, the contents described in the coordinate correction means are performed, and the coordinates after the position change due to crustal movement are obtained.

  The coordinate correction apparatus, the coordinate correction program, and the coordinate correction method of the present invention can quickly perform coordinate correction when there is a crustal deformation due to an earthquake or volcanic activity, for example, without waiting for a revision of an electronic reference point. Both can obtain appropriate coordinates. As a result, post-earthquake investigations and emergency measures can be implemented much more quickly than before, and the invention can be used industrially and can be expected to make a significant social contribution.

DESCRIPTION OF SYMBOLS 1 Terrain 2 Aircraft 3 Laser 4 Coordinate correction apparatus 5 Input means 6 Terrain model storage means 7 Data registration means 8 Parameter calculation means 9 Coordinate correction means 10 Output means 11 Search area 12 Search area 13 Pixel group

Claims (6)

In a coordinate correction device that corrects changes in position due to crustal movements,
Coordinate input means for inputting arbitrary coordinates;
A pre-change terrain model that represents the terrain before crustal deformation and consists of a plurality of meshes, and a terrain model storage means that stores the post-change terrain model that represents the terrain after crustal deformation and consists of a plurality of meshes;
Based on the pre-change terrain model and the post-change terrain model, parameter calculation means for obtaining a coordinate correction parameter for correcting a change in position;
Coordinate correction means for obtaining coordinates after position change due to crustal movement based on the coordinates input by the coordinate input means and the coordinate correction parameters;
The parameter calculation means includes
While calculating the terrain amount for each mesh constituting the pre-change terrain model, calculating the terrain amount for each mesh constituting the post-change terrain model,
Form pixels for image creation based on the mesh,
By giving pixel information according to the amount of topography to the pixel, create a pre-change image representing the topography before crustal deformation, and a post-change image representing the topography after crustal deformation,
By calculating the terrain movement vector by comparing the pre-change image and the post-change image,
A coordinate correction apparatus that obtains a coordinate correction parameter based on the movement vector. The parameter calculation means includes
Select one of the pre-change image and the post-change image as a search image and the other as a search image,
Set a search area that is a partial range of the search image;
Extracting a pixel group consisting of a combination of two or more pixels from the pixels constituting the search area,
By detecting a combination of pixels to be matched with the pixel group from the searched image, a movement vector of the search area is obtained,
The coordinate correction apparatus according to claim 1, wherein the movement vector is used as a coordinate correction parameter of a correction area configured based on the search area. In a coordinate correction program for causing a computer to execute a process for correcting a change in position due to crustal movement,
A pre-change terrain model that represents the terrain before the crustal deformation and consists of a plurality of meshes, and a terrain model read process that reads the post-change terrain model that represents the terrain after the crustal deformation and consists of a plurality of meshes;
Based on the pre-change terrain model and the post-change terrain model, a parameter calculation process for obtaining a coordinate correction parameter for correcting a change in position;
A function for causing the computer to execute coordinate correction processing for obtaining coordinates after position change due to crustal movement in arbitrary coordinates based on the coordinate correction parameters;
The parameter calculation process includes:
While calculating the terrain amount for each mesh constituting the pre-change terrain model, calculating the terrain amount for each mesh constituting the post-change terrain model,
Set pixel information according to the amount of topography,
By giving the pixel information to pixels configured based on the mesh, create a pre-change image that represents the terrain before crustal deformation, and a post-change image that represents the terrain after crustal movement,
By calculating the terrain movement vector by comparing the pre-change image and the post-change image,
A coordinate correction program for obtaining a coordinate correction parameter based on the movement vector. The parameter calculation process includes:
Select one of the pre-change image and the post-change image as a search image and the other as a search image,
Set a search area that is a partial range of the search image;
Extracting a pixel group consisting of a combination of two or more pixels from the pixels constituting the search area,
By detecting a combination of pixels to be collated with the pixel group from the searched image, a movement vector of the search area is calculated,
4. The coordinate correction program according to claim 3, wherein the movement vector is used as a coordinate correction parameter of a correction area configured based on the search area. In the coordinate correction method to correct the position change due to crustal movement,
A parameter that determines the coordinate correction parameter that corrects the change in position based on the pre-change terrain model that represents the terrain before crustal deformation and consists of multiple meshes and the post-change terrain model that represents the terrain after crustal deformation and consists of multiple meshes A calculation process;
A coordinate correction step for obtaining coordinates after a position change due to crustal movement in arbitrary coordinates based on the coordinate correction parameters, and
The parameter calculation step includes:
While calculating the terrain amount for each mesh constituting the pre-change terrain model, calculating the terrain amount for each mesh constituting the post-change terrain model,
Set pixel information according to the amount of topography,
By giving the pixel information to pixels configured based on the mesh, create a pre-change image that represents the terrain before crustal deformation, and a post-change image that represents the terrain after crustal movement,
By calculating the terrain movement vector by comparing the pre-change image and the post-change image,
A coordinate correction method, wherein a coordinate correction parameter is obtained based on the movement vector. The parameter calculation step includes:
Select one of the pre-change image and the post-change image as a search image and the other as a search image,
Set a search area that is a partial range of the search image;
Extracting a pixel group consisting of a combination of two or more pixels from the pixels constituting the search area,
By detecting a combination of pixels to be collated with the pixel group from the searched image, a movement vector of the search area is calculated,
The coordinate correction method according to claim 5, wherein the movement vector is used as a coordinate correction parameter of a correction area configured based on the search area.

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