Displacement boundary line extraction system

Abstract

To provide a displacement boundary line extraction system capable of extracting a boundary line where a direction of horizontal displacement is different to thereby solve conventional problems.SOLUTION: A disclosed displacement boundary line extraction system is a system for extracting a "displacement boundary" on the basis of a terrain model obtained in two periods in a same target region. The system includes: pixel value setting means; cross-correlation coefficient calculation means; horizontal displacement calculation means; displacement candidate line segment setting means; correlation candidate line segment setting means; and displacement boundary line setting means. The displacement boundary line setting means is configured so as to, when a distance between a displacement candidate line segment and a correlation candidate line segment falls below a predetermined "distance threshold", set a displacement boundary line.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a technique for calculating topographical displacement, and more specifically, to a displacement boundary extraction system that can extract boundaries where the directions of horizontal displacement differ significantly within an area where horizontal displacement has occurred. [Background technology]

[0002] The topography that forms the earth's surface changes minutely from moment to moment due to crustal movement. The rate of change is usually extremely slow, but a major earthquake or other event can cause rapid topography change, and in some cases cause large-scale soil movement. When heavy rain or an earthquake causes landslides, the earth's surface also begins to change accordingly, and ultimately the landslide can move large masses of soil, causing severe damage to the surrounding area.

[0003] In the past, Japan has repeatedly suffered devastating damage from natural disasters, but by tracking and understanding changes in topography, it may be possible to prevent such disasters or reduce the damage. In addition, once a disaster occurs, by comparing the topography before and after the disaster, it is possible to identify the damaged areas, estimate the causes of the disaster, and determine the possibility of secondary disasters, which will be an extremely effective means of emergency response and recovery.

[0004] However, it is not easy to understand the changes by comparing the topography measured in two periods. For example, it is possible to create a topographical model such as a DEM (Digital Elevation Model) or DSM (Digital Surface Model) based on point cloud data obtained by airborne laser measurement, and to understand the changes by comparing this topographical model in two periods. However, in this case, although it is possible to determine the change in height at the same position (for example, mesh), it is not possible to understand the horizontal changes by simply comparing the meshes. Of course, it is possible to track distinctive structures and the like in two periods, but it is extremely difficult to compare natural topography, which does not show many local characteristics, in two periods.

[0005] Therefore, Patent Document 1 discloses a technique for imaging a terrain model based on the terrain quantity for each mesh, and for grasping horizontal changes by comparing the images at two different times.

[0006] [Patent Document 1] Patent No. 4545219 Summary of the Invention [Problem to be solved by the invention]

[0007] According to the technology disclosed in Patent Document 1, the terrain at two different times is imaged, so partial changes in the terrain become clear, and even natural terrain can be easily compared between the two times. Furthermore, by creating a terrain model, it is also possible to obtain three-dimensional (planar and vertical) displacement vectors.

[0008] Incidentally, linear cracks can occur when shear deformation occurs in the terrain. In this case, the crack becomes a boundary line, and horizontal displacements occur in opposite directions on either side of the boundary line. For example, if a crack running in the east-west direction occurs as a result of shear deformation caused by the terrain, when horizontal displacement to the east occurs in the area to the north, horizontal displacement to the west occurs in the area to the south.

[0009] The technology of Patent Document 1 is based on the premise that the topography changes in a roughly uniform manner, so in areas where the directions of horizontal displacement differ dramatically as described above, it is difficult to match the topography from two different periods. In other words, the technology of Patent Document 1 needs improvement in order to extract boundaries where the directions of horizontal displacement differ, such as cracks caused by shear deformation of the topography.

[0010] An object of the present invention is to solve the conventional problems, that is, to provide a displacement boundary extraction system capable of extracting boundaries having different horizontal displacement directions. [Means for solving the problem]

[0011] The present invention focuses on the fact that it is difficult to match terrain in areas where the direction of horizontal displacement is different, resulting in a smaller amount of displacement being calculated than in the surrounding areas and a lower degree of matching between the two periods (e.g., cross-correlation coefficient), but that this characteristic can be used in reverse to extract boundary lines, and is an invention based on an idea that had not previously been seen.

[0012] The displacement boundary extraction system of the present invention is a system that extracts a "displacement boundary (a boundary where the direction of horizontal displacement changes)" based on terrain models (a first terrain model and a second terrain model) obtained at two times in the same target area, and is equipped with a pixel value setting means, a cross-correlation coefficient calculation means, a horizontal displacement calculation means, a displacement candidate line segment setting means, a correlation candidate line segment setting means, and a displacement boundary setting means. Among these, the pixel value setting means is a means for calculating a terrain quantity for each mesh constituting the first terrain model and the second terrain model, and for assigning a pixel value according to the terrain quantity for each mesh. The cross-correlation coefficient calculation means is a means for setting a search area consisting of a plurality of meshes for the first terrain model, setting a scanning area larger than the search area for the second terrain model, and then calculating a cross-correlation coefficient based on pixel values ​​while scanning the scanning area with the search area. The horizontal displacement calculation means is a means for detecting a "corresponding area" showing the maximum cross-correlation coefficient in the scanning area, and calculating a horizontal displacement based on the coordinates of the corresponding area and the search area. The displacement candidate line segment setting means is a means for extracting a "displacement candidate mesh" having a smaller horizontal displacement than surrounding meshes, and setting a "displacement candidate line segment" based on a plurality of continuously arranged displacement candidate meshes. The correlation candidate line segment setting means is a means for extracting a "correlation candidate mesh" having a smaller cross-correlation coefficient than surrounding meshes, and setting a "correlation candidate line segment" based on a plurality of continuously arranged correlation candidate meshes. The displacement boundary setting means is a means for setting a displacement boundary segment based on a displacement candidate line segment. Note that by setting the search area while moving it, the cross-correlation coefficient calculation means calculates the cross-correlation coefficient, and the horizontal displacement calculation means calculates the horizontal displacement. The displacement candidate line segment setting means extracts a displacement candidate mesh based on the distribution of the horizontal displacement, and the correlation candidate line segment setting means extracts a correlation candidate mesh based on the distribution of the cross-correlation coefficient. Then, the displacement boundary segment setting means sets a displacement boundary segment when the distance between the displacement candidate line segment and the correlation candidate line segment falls below a predetermined "distance threshold".

[0013] The displacement boundary extraction system of the present invention may also set a displacement boundary when the length of a displacement candidate line segment exceeds a predetermined "displacement extension threshold."

[0014] The displacement boundary extraction system of the present invention may further include a transverse axis setting means, a distribution map creating means, and a peculiar section extraction means. The transverse axis setting means is a means for setting a "displacement transverse axis" which is a horizontal axis that intersects obliquely with the displacement candidate line segment, the distribution map creating means is a means for creating a "displacement distribution map" in which horizontal displacements are arranged along the displacement transverse axis, and the peculiar section extraction means is a means for extracting a "displacement peculiar section" in which the horizontal displacement falls below a predetermined "displacement section tolerance" from the displacement distribution map. In this case, the displacement boundary setting means sets a displacement boundary when the length of the displacement peculiar section is included in the predetermined "section tolerance". The section tolerance is set according to the size of the meshes that constitute the search area.

[0015] The displacement boundary extraction system of the present invention may set the displacement boundary when the lengths of the displacement peculiar section and the "correlation peculiar section" are each included in a predetermined section tolerance. In this case, the cross-axis setting means sets a "correlation cross-axis" which is a horizontal axis that intersects obliquely with the correlation candidate line segment, the distribution map creating means creates a "correlation distribution map" in which the cross-correlation coefficients are arranged along the correlation cross-axis, and the peculiar section extraction means extracts a "correlation peculiar section" whose cross-correlation coefficient falls below a predetermined "correlation section threshold" from the correlation distribution map.

[0016] The displacement boundary extraction system of the present invention may further include an area division means for dividing the target area into a first target area and a second target area using the displacement boundary set by the displacement boundary setting means as a boundary. In this case, the first and second terrain models in the first target area are compared to determine the horizontal displacement, and the first and second terrain models in the second target area are compared to determine the horizontal displacement. Effect of the Invention

[0017] The displacement boundary extraction system of the present invention has the following advantages. (1) The “displacement boundary line” can be extracted by simply using the two-time terrain models without relying on landmarks such as survey stakes, anti-aircraft markers, or distinctive structures. (2) By dividing the target area in which changes over two periods are to be grasped into two regions by a displacement boundary line and then analyzing the area using the technology described in Patent Document 1, it is possible to grasp the appropriate changes in each region. [Brief description of the drawings]

[0018] [Figure 1] FIG. 11 is a plan view showing an area where a displacement boundary line extending to the left and right occurs. [Diagram 2] 1 is a block diagram showing the main configuration of a displacement boundary extraction system according to the present invention; [Diagram 3] 1A is a schematic diagram showing a search area composed of multiple meshes, and FIG. 1B is a schematic diagram showing a scanning area composed of multiple meshes. [Figure 4] A model diagram showing the search area superimposed on the corresponding area. [Diagram 5] A model diagram showing contour lines of horizontal displacement and cross-correlation coefficient. [Figure 6] Distribution plot of cross-correlation coefficients. [Figure 7] FIG. 13 is a model diagram illustrating the distance between a displacement candidate line segment and a correlation candidate line segment. [Figure 8] (a) is a model diagram that shows a schematic of a displacement transverse axis set perpendicular to the displacement candidate line segment, and (b) is a model diagram that shows a schematic of a correlation transverse axis set at a shallow intersection angle to the correlation candidate line segment. [Figure 9] FIG. 13 is a graph diagram showing a displacement distribution map and a displacement singular section. [Figure 10] Graph diagrams illustrating correlation distribution diagrams and correlation specific intervals. [Figure 11] FIG. 2 is a plan view showing a "first target region" and a "second target region" that are set with a displacement boundary line as their boundary. [Figure 12]FIG. 2 is a flow chart showing the flow of main processes from "calculating topographical quantities" to "setting displacement candidate line segments" in the displacement boundary extraction system of the present invention. [Figure 13] FIG. 2 is a flow diagram showing the flow of main processes from "extraction of correlation candidate meshes" to "division of target region" in the displacement boundary extraction system of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] An example of an embodiment of the displacement boundary extraction system of the present invention will be described with reference to the drawings. The displacement boundary extraction system of the present invention extracts change boundaries (hereinafter referred to as "displacement boundaries") where the directions of horizontal displacement differ greatly. These displacement boundaries may be formed by phenomena such as shear deformation associated with faults. For example, in the plan view shown in Figure 1, displacement to the right occurs in roughly the upper half of the region, and displacement to the left occurs in roughly the lower half of the region, resulting in displacement boundaries extending to the left and right.

[0020] 2 is a block diagram showing the main components of the displacement boundary extraction system 100 of the present invention. As shown in this figure, the displacement boundary extraction system 100 of the present invention includes a pixel value setting means 101, a cross-correlation coefficient calculation means 102, a horizontal displacement calculation means 103, a displacement candidate line segment setting means 104, a correlation candidate line segment setting means 105, and a displacement boundary setting means 106, and may further include a transverse axis setting means 107, a distribution map creation means 108, a singular section extraction means 109, an area division means 110, a topographical model storage means 111, etc.

[0021] Each of the means constituting the displacement boundary extraction system 100 (particularly the pixel value setting means 101 to the area division means 110) can be manufactured as a dedicated means, or a general-purpose computer device can be used. That is, the computer device executes arithmetic processing according to a predetermined program, thereby performing processing specific to each means. This computer device includes a processor such as a CPU, memories such as a ROM and a RAM, and some further include input means such as a mouse and a keyboard, and a display, and can be configured, for example, by a personal computer (PC) or a server.

[0022] The terrain model storage means 111 can utilize a storage device of a general-purpose computer (for example, a personal computer) or can be built in a database server. When built in a database server, it can be placed in a local network (LAN: Local Area Network) or can be a cloud server that stores data via the Internet.

[0023] Hereinafter, each of the main elements constituting the displacement boundary extraction system 100 of the present invention will be described in detail.

[0024] (Terrain model storage means) The terrain model storage means 111 is a means for storing a terrain model. When the ground or the like is measured by airborne laser measurement or aerial photogrammetry, a large number of measurement point data having three-dimensional coordinates (hereinafter referred to as "three-dimensional point cloud") are obtained. Here, the "terrain model" is a model generated based on this three-dimensional point cloud, and for example, DSM (Digital Surface Model) and DEM (Digital Elevation Model) are known. Usually, a terrain model is composed of small areas (hereinafter referred to as "meshes") obtained by dividing a planar range of a measurement target into a plurality of parts. This mesh is a section formed by dividing the planar range into, for example, orthogonal grids, and each mesh has a representative point to which an elevation is assigned. Since the three-dimensional point cloud obtained by airborne laser measurement is composed of random data (data that is irregularly arranged on a plane), a geometric calculation is often performed to assign an elevation to the representative point of each mesh. Calculation methods include the TIN method, which determines height using an irregular triangulation network formed from random data, the Nearest Neighbor method, which uses the nearest laser measurement point, the Inverse Distance Weighting (IDW) method, the Kriging method, and the averaging method.

[0025] The terrain model storage means 111 stores two or more terrain models obtained at different times in the same target area. The displaced boundary extraction system 100 of the present invention reads out the two terrain models stored in the terrain model storage means 111 and executes each process. For convenience, one of the two terrain models will be referred to as the "first terrain model" and the other as the "second terrain model." Note that the model measured earlier (i.e., the older one) can be the first terrain model, or the model measured later (i.e., the newer one) can be the first terrain model.

[0026] (Pixel value setting means) The pixel value setting means 101 is a means for assigning a "pixel value" to each mesh constituting the terrain model. Here, the pixel value is a value set in a "color model" that can be handled by a computer, or a value set in grayscale. Examples of color models include RGB, which has three basic colors of red, green, and blue, CMYK, which has four basic colors of cyan, magenta, yellow, and black (key color), and NCS and Ostwald color system, which have six basic colors of yellow, red, blue, green, black, and white. The procedure for the pixel value setting means 101 to assign a pixel value to each mesh will be described below.

[0027] First, the first and second terrain models are read from the terrain model storage means 111. Next, the "terrain quantity" is calculated for each mesh constituting the first and second terrain models. Here, the terrain quantity is a kind of index that indicates the characteristics of the terrain, and is calculated based on the representative points of the meshes and the original random data. For example, the terrain quantity can be an elevation value, a Laplacian value, an aboveground opening value, an underground opening value, a slope amount, or a combination of these.

[0028] The Laplacian value is used to draw a Laplacian diagram, which shows the rate of change in slope. The Laplacian diagram is positive for concave terrain and negative for prominent terrain, and the absolute value is large where the change in terrain is large. The aboveground opening value is used to draw an opening diagram, which shows the width of the sky visible within a certain distance from a point of interest, and is characterized by emphasizing prominent mountain peaks and ridges. The underground opening value is used to draw an opening diagram, which shows the width of the underground within a certain distance when looking underground from the ground surface, and is characterized by emphasizing depressions and valleys. The slope value is used to draw a slope map, which shows the degree of slope of the terrain. The greater the slope, the greater the slope value, and conversely, the gentler the slope, the smaller the slope value.

[0029] When the terrain quantities are calculated for each mesh constituting the first terrain model and the second terrain model, an image is created based on the terrain quantities. For convenience, the image created based on the terrain quantities is referred to as a "terrain image", and the image created from the first terrain model is referred to as a "first terrain image", and the image created from the second terrain model is referred to as a "second terrain image". Specifically, the first terrain image is created by assigning pixel values ​​corresponding to the terrain quantities to each mesh constituting the first terrain model, and the second terrain image is created by assigning pixel values ​​corresponding to the terrain quantities to each mesh constituting the second terrain model. To create a terrain image, pixels are set and then pixel values ​​are assigned to the pixels, but for convenience, an example will be described here in which one mesh is one pixel. Of course, it is also possible to configure one pixel by multiple meshes.

[0030] (Means for calculating cross-correlation coefficient) The cross-correlation coefficient calculation means 102 is a means for calculating a "cross-correlation coefficient" based on pixel values ​​while scanning the "scanning area" with the "search area." The procedure by which the cross-correlation coefficient calculation means 102 calculates the cross-correlation coefficient will be described below.

[0031] First, the cross-correlation coefficient calculation means 102 sets a search area for the first terrain model (first terrain image) and sets a scanning area for the second terrain model (second terrain image). As described above, of the first terrain model and the second terrain model, the older one can be the first terrain model, or the newer one can be the first terrain model. In other words, the search area can be set in the old terrain model and the scanning area can be set in the new terrain model, or the search area can be set in the new terrain model and the scanning area can be set in the old terrain model.

[0032] As shown in FIG. 3, the search area and the scanning area are each composed of multiple meshes. However, as shown in this figure, the scanning area is larger than the search area. FIG. 3(a) is a model diagram that shows a search area composed of multiple meshes, and FIG. 3(b) is a model diagram that shows a scanning area composed of multiple meshes. The search area is set within the range of the scanning area, and is set at multiple locations while being moved (arrows in FIG. 3(a)) so as to cover the scanning area. At this time, it is advisable to set multiple search areas while moving (shifting) them by one mesh (one pixel). On the other hand, the scanning area does not move and its range is fixed. However, the scanning area can be set at one location, or at two or more locations.

[0033] When the search area and the scanning area are set, a partial image (hereinafter referred to as a "search image") related to the search area is cut out from the first topographical image. Next, the search image is matched with a partial image (hereinafter referred to as a "scanned image") related to the scanning area from the second topographical image. Specifically, as shown by the arrow in FIG. 3(b), the search image is scanned so as to cover the scanned image, and the search image and the scanned image are matched. At this time, it is preferable to scan while moving (shifting) the image by one mesh (one pixel). Then, the result of the matching is calculated as a cross-correlation coefficient. Since the search image moves within the scanning area, it is matched with the scanned image at each position where it moves, that is, the cross-correlation coefficient is calculated each time it moves. In addition, since the search image is set at multiple positions while moving as described above, the cross-correlation coefficient is calculated while manipulating the scanned image for each set search image. For example, if a query image is set at 16 locations to cover the scan area, the query image will be matched with the scan image at 16 positions, i.e., 16×16 cross-correlation coefficients will be calculated.

[0034] The cross-correlation coefficient is a value calculated based on pixel values, and is a so-called index value indicating the similarity between the search image and the scanned image. In calculating the cross-correlation coefficient, any of various conventional methods can be used as long as the similarity between the search image and the scanned image can be expressed. For example, the cross-correlation coefficient can be calculated by the cross-correlation method in PIV (Particle Image Velocimetry).

[0035] The cross-correlation coefficient is calculated as the similarity between the search image and the scanned image, and is calculated for each of the multiple search images. In other words, one type of cross-correlation coefficient is calculated for one search area, and the cross-correlation coefficient can be assigned to all meshes that make up the search area. On the other hand, since the search area is set while moving, different search areas may be formed even for the same mesh, and multiple cross-correlation coefficients are assigned to one mesh. In this case, it is advisable to set a statistical value (such as the average, median, mode, or standard deviation) based on multiple cross-correlation coefficients as the cross-correlation coefficient for the mesh.

[0036] (Horizontal displacement calculation means) The horizontal displacement calculation means 103 is a means for detecting a "corresponding area" that shows the maximum cross-correlation coefficient (hereinafter simply referred to as "maximum cross-correlation coefficient") in the scanning area, and for calculating the horizontal displacement based on the coordinates of the corresponding area and the search area. The procedure for calculating the horizontal displacement by the horizontal displacement calculation means 103 will be described below.

[0037] First, the horizontal displacement calculation means 103 selects the maximum cross-correlation coefficient for each set search area (search image), and detects the range of the scanning area (scanned image) related to the maximum cross-correlation coefficient as a "corresponding area". Therefore, the corresponding area will be an area of ​​the same size as the search area as shown in FIG. 4, but its position may be different. Note that in FIG. 4, the search area is displayed superimposed on the second topographical image (scanned image) for convenience. Also, since the maximum cross-correlation coefficient is selected for each set search area, corresponding areas are detected as many times as the set number of search areas. For example, when the search image is set at 16 locations, the maximum cross-correlation coefficient is selected at 16 positions, that is, corresponding areas are detected at 16 positions.

[0038] Once a corresponding area is detected for each search area, a "displacement vector" is calculated based on the search area and the corresponding area, and the horizontal displacement, which is the magnitude of the displacement vector, is calculated, as shown in Figure 4. Note that since the first terrain model and the second terrain model are composed of three-dimensional point clouds, the displacement vector can be calculated as a two-dimensional (on a plane) vector, or as a three-dimensional (in space) vector.

[0039] As shown in Fig. 4, one type of displacement vector is found based on one pair of search area and corresponding area, so that displacement vector (i.e., horizontal displacement) can be assigned to all meshes that make up the search area. On the other hand, since the search area is set while moving, different search areas may be configured even for the same mesh, and multiple horizontal displacements will be assigned to one mesh. In this case, it is advisable to set a statistical value (average, median, mode, standard deviation, etc.) based on the multiple horizontal displacements as the horizontal displacement of the mesh.

[0040] (Displacement candidate line segment setting means) The displacement candidate line segment setting means 104 is a means for extracting a "displacement candidate mesh" that has a smaller horizontal displacement than the surrounding meshes, and setting a "displacement candidate line segment" based on a plurality of displacement candidate meshes that are arranged consecutively. The procedure for setting the displacement candidate line segment by the displacement candidate line segment setting means 104 will be described below.

[0041] As mentioned above, the technology of Patent Document 1 (hereinafter referred to as the "3DGIV method") had the disadvantage that it was difficult to match the topography of two time periods in the area surrounding the displacement boundary line. Therefore, even though the horizontal displacement occurring was similar to that of the surrounding area, there was a tendency for a smaller horizontal displacement to be output as the analysis value than the actual one. Therefore, by using this tendency in a way that is inverted, it was decided to detect candidates for the displacement boundary line. Specifically, meshes with smaller horizontal displacement than the surrounding meshes are extracted as "displacement candidate meshes," and the line segments that are continuous with these displacement candidate meshes are set as "displacement candidate line segments."

[0042] A threshold value can be set before extracting displacement candidate meshes. For example, a statistical value (average value, median value, mode, standard deviation, etc.) of horizontal displacement for the meshes constituting the scanning area is calculated, and a threshold value is set by multiplying the statistical value by a predetermined coefficient, and meshes related to horizontal displacements below this threshold value can be extracted as displacement candidate meshes.

[0043] Alternatively, displacement candidate meshes can be extracted based on the distribution of horizontal displacement. In other words, displacement candidate meshes are extracted in the same way as ridge lines and valley lines are set in contour lines. Specifically, as shown in Figure 5, isolines of horizontal displacement are generated, and displacement candidate meshes are extracted using these isolines to set the displacement candidate line segment BLD.

[0044] (Means for setting correlation candidate lines) The correlation candidate line segment setting means 105 is a means for extracting a "correlation candidate mesh" having a smaller cross-correlation coefficient than the surrounding meshes, and setting a "correlation candidate line segment" based on a plurality of correlation candidate meshes that are arranged consecutively. The procedure by which the correlation candidate line segment setting means 105 sets the correlation candidate line segment will be described below.

[0045] As mentioned above, the 3DGIV method has the difficulty of matching the topography of two periods in the area surrounding the displacement boundary. Therefore, although the cross-correlation coefficient should be similar to that of the surrounding area, the analysis value tends to output a smaller cross-correlation coefficient than the surrounding area, as shown in Figure 6. Figure 6 shows the distribution of cross-correlation coefficients, where the darker the color, the larger the cross-correlation coefficient, and the lighter the color, the smaller the cross-correlation coefficient. Therefore, we decided to detect candidates for displacement boundary by using this tendency in a reverse way. Specifically, we extract meshes with smaller cross-correlation coefficients than the surrounding meshes as "correlation candidate meshes," and set the line segments that connect these correlation candidate meshes as "correlation candidate line segments."

[0046] In extracting correlation candidate meshes, a threshold value can be set before extraction. For example, a statistical value (average value, median value, mode value, standard deviation, etc.) of the cross-correlation coefficients related to the meshes constituting the scanning region is calculated, and a value obtained by multiplying the statistical value by a predetermined coefficient is set as a threshold value, and meshes related to the cross-correlation coefficients below this threshold value can be extracted as correlation candidate meshes.

[0047] Alternatively, correlation candidate meshes can be extracted based on the distribution of cross-correlation coefficients. In other words, correlation candidate meshes are extracted in a manner similar to setting ridge lines and valley lines in contour lines. Specifically, contours of the cross-correlation coefficients are generated as shown in Figure 5, and correlation candidate meshes are extracted using these contours to set the correlation candidate line segment BLC.

[0048] (Displacement boundary setting means) The displacement boundary setting means 106 is a means for setting a "displacement boundary" based on the displacement candidate line segment BLD, and is a means for determining a "displacement boundary" in a target area, so to speak. For example, the displacement boundary setting means 106 can set the displacement candidate line segment BLD as the displacement boundary as it is, or can set the displacement boundary by performing a smoothing process (smoothing) on ​​the displacement candidate line segment BLD.

[0049] By the way, when the processing described so far is executed, the displacement candidate line segment setting means 104 may set a plurality of displacement candidate line segments BLD, and the correlation candidate line segment setting means 105 may set a plurality of correlation candidate line segments BLC. Of course, it is possible to set a displacement boundary line based on all of these displacement candidate line segments BLD, but it is usually rare for many displacement boundary lines to occur in a target area. Therefore, in the displacement boundary extraction system 100 of the present invention, the displacement boundary line setting means 106 sets a displacement boundary line when a predetermined condition is satisfied. Below, the conditions under which the correlation candidate line segment setting means 105 sets a displacement boundary line (hereinafter simply referred to as "setting conditions") will be described.

[0050] The setting conditions include an "approach condition," a "line segment length condition," and a "singular section condition." The correlation candidate line segment setting means 105 may set the displacement boundary line only based on the approach condition, or the correlation candidate line segment setting means 105 may set the displacement boundary line by combining the approach condition, the line segment length condition, and the singular section condition. Among these, the approach condition requires that the displacement candidate line segment BLD and the correlation candidate line segment BLC are close to each other to a certain extent. In order to extract the displacement boundary line, the displacement boundary extraction system 100 of the present invention sets the displacement candidate line segment BLD based on the horizontal displacement and sets the correlation candidate line segment BLC based on the cross-correlation coefficient, but these should essentially match. However, due to measurement accuracy, analysis errors, etc., the displacement candidate line segment BLD and the correlation candidate line segment BLC do not necessarily match. Nevertheless, if they show the same displacement boundary line, the displacement candidate line segment BLD and the correlation candidate line segment BLC should be quite close to each other, that is, the distance between them should be small.

[0051] Therefore, a threshold value (hereinafter, referred to as "distance threshold value") is set in advance, and the displacement boundary setting means 106 sets the displacement boundary line under the condition (i.e., the approach condition) that the distance between the displacement candidate line segment BLD and the correlation candidate line segment BLC is less than the distance threshold value. For example, as shown in Fig. 7, a plurality of distances are obtained between the displacement candidate line segment BLD and the correlation candidate line segment BLC, and a statistical value (average value, median, mode, standard deviation, etc.) based on these distances is set as a representative value, or the longest (or shortest) distance is set as a representative value, and the representative value is compared with the distance threshold value. Alternatively, the area of ​​the region surrounded by the displacement candidate line segment BLD and the correlation candidate line segment BLC (the shaded area in Fig. 7) divided by the displacement candidate line segment BLD (or the correlation candidate line segment BLC) can be set as a representative value, and the representative value can be compared with the distance threshold value.

[0052] The line segment length condition is a condition that the line segment length of the displacement candidate line segment BLD, which is the basis of the displacement boundary line, has a certain length. Usually, a displacement boundary line in the natural world is formed with a considerable extension. Therefore, the displacement candidate line segment BLD should also be set with a considerable length, but a very short displacement candidate line segment BLD may be set due to measurement accuracy, analysis error, etc. Therefore, a threshold value (hereinafter, referred to as a "displacement extension threshold value") is set in advance, and the displacement boundary line setting means 106 sets the displacement boundary line under the condition (i.e., the line segment length condition) that the line segment length LD of the displacement candidate line segment BLD shown in FIG. 7 exceeds this displacement extension threshold value. Furthermore, it is also possible to set a condition that the line segment length LC of the correlation candidate line segment BLC shown in FIG. 7 exceeds the threshold value.

[0053] As shown in FIG. 8(a), a horizontal axis (hereinafter referred to as a "displacement transverse axis") is set obliquely to the displacement candidate line segment BLD, and a distribution map (hereinafter referred to as a "displacement distribution map") in which horizontal displacements are arranged along the displacement transverse axis as shown in FIG. 9 is considered. The closer to the displacement transverse axis, the smaller the horizontal displacement becomes. On the other hand, at a position sufficiently far from the displacement transverse axis, the horizontal displacement shows approximately the same value. In other words, the displacement distribution map shows an approximately constant horizontal displacement overall as shown in FIG. 9, but a concave shape that is depressed downward is formed in a predetermined section (hereinafter referred to as a "displacement singular section") centered on the displacement transverse axis. In the displacement boundary extraction system 100 of the present invention, the displacement transverse axis is set by the transverse axis setting means 107, the displacement distribution map is created by the distribution map creation means 108, and the displacement singular section is extracted by the singular section extraction means 109.

[0054] Similarly, when a horizontal axis (hereinafter referred to as the "correlation cross axis") is set obliquely to the correlation candidate line segment BLC as shown in FIG. 8(b), and a distribution diagram (hereinafter referred to as the "correlation distribution diagram") in which cross-correlation coefficients are arranged along the correlation cross axis as shown in FIG. 10 is considered, the cross-correlation coefficient gradually decreases as it approaches the correlation cross axis. On the other hand, at a position sufficiently far from the correlation cross axis, the cross-correlation coefficient shows approximately the same value. In other words, the correlation distribution diagram shows an approximately constant cross-correlation coefficient overall as shown in FIG. 1, but a concave shape that is depressed downward is formed in a predetermined section (hereinafter referred to as the "correlation specific section") centered on the correlation cross axis. In the displacement boundary extraction system 100 of the present invention, the correlation cross axis is set by the cross axis setting means 107, the correlation distribution diagram is created by the distribution diagram creation means 108, and the correlation specific section is extracted by the specific section extraction means 109.

[0055] The displacement transverse axis and correlation transverse axis can be set at any angle with respect to the displacement candidate line segment BLD and correlation candidate line segment BLC. For example, in Fig. 8(a), the displacement transverse axis is set perpendicular to the displacement candidate line segment BLD, and in Fig. 8(b), the correlation transverse axis is set so as to form a shallow cross angle (acute angle) with the correlation candidate line segment BLC.

[0056] The singular section condition is that the displacement singular section or correlation singular section has a predetermined section length. The inventors of the present invention have found that when the displacement candidate line segment BLD or the correlation candidate line segment BLC indicates a displacement boundary line, the section length of the displacement singular section or correlation singular section is equivalent to the size of the search area. For example, if the search area is composed of 9x9 meshes and the mesh size of 1 is 2cmx2cm, the displacement singular section or correlation singular section indicates a displacement boundary line when its section length is "9x2cm (mesh size)".

[0057] Therefore, the displacement boundary setting means 106 sets the displacement boundary line under the condition that the section length of the displacement singular section is within the range of the displacement segment tolerance (i.e., the singular section condition) after setting a tolerance for the correlation singular section (hereinafter, referred to as the correlation segment tolerance) beforehand. Furthermore, the displacement boundary line setting means 106 can set the displacement boundary line under the condition that the section length of the correlation singular section is within the range of the correlation segment tolerance after setting a tolerance for the correlation singular section beforehand (hereinafter, referred to as the correlation segment tolerance). For example, the displacement segment tolerance and the correlation segment tolerance can be set as a reference length by multiplying the mesh size (number of meshes × mesh size) by a first coefficient to set a minimum length and multiplying the reference length by a second coefficient to set a maximum length, and set the range between the minimum length and the maximum length as the tolerance. Note that the first coefficient and the second coefficient can each be 1 or more, or each can be less than 1.

[0058] (area division means) The area division means 110 is a means for dividing the target area into a "first target area" and a "second target area" using the displacement boundary set by the displacement boundary setting means 106 as the boundary, as shown in Fig. 11. As described above, the 3DGIV method has an aspect that it is difficult to match the topography of two time periods in the area around the displacement boundary. If the target area for which changes in two time periods are to be understood can be divided into the first target area and the second target area, the first target area and the second target area can be analyzed separately, that is, there is no need to analyze across the displacement boundary, and therefore the appropriate changes can be understood by the 3DGIV method.

[0059] When the area division means 110 divides the target area into the first target area and the second target area, it can be configured to divide it automatically (mechanically) based on coordinates, etc., or it can be configured so that an operator sets the first target area and the second target area using a pointing device (mouse, touch panel, pen tablet, touchpad, trackpad, trackball, etc.) or a keyboard.

[0060] (Processing flow) Hereinafter, the main processing of the displacement boundary extraction system 100 of the present invention will be described in detail with reference to Figures 12 and 13. Figure 12 is a flow diagram showing the flow of main processing from "calculation of topographical quantities" to "setting of displacement candidate line segment BLD" in the displacement boundary extraction system 100 of the present invention, and Figure 13 is a flow diagram showing the flow of main processing from "extraction of correlation candidate meshes" to "division of target area" in the displacement boundary extraction system 100 of the present invention. In Figures 12 and 13, the central column shows the actions to be performed, the left column shows what is necessary for the actions, and the right column shows what results from the actions.

[0061] When extracting a displacement boundary using the displacement boundary extraction system 100 of the present invention, as shown in Fig. 12, first, the terrain quantity is calculated for each mesh constituting the first terrain model and the second terrain model (Step 201 in Fig. 12). Next, a first terrain image and a second terrain image are created by assigning pixel values ​​corresponding to the terrain quantities to each mesh (Step 202 in Fig. 12). After creating the terrain images, a search area is set for the first terrain model (first terrain image) (Step 203 in Fig. 12), and a scanning area is set for the second terrain model (second terrain image) (Step 204 in Fig. 12).

[0062] Once the search area and scanning area are set, the search image is scanned so as to cover the entire scanned image, while the search image is matched with the scanned image (Step 205 in FIG. 12), and the cross-correlation coefficients of each are calculated (Step 206 in FIG. 12). Image matching (Step 205 in FIG. 12) and calculation of the cross-correlation coefficient (Step 206 in FIG. 12) are repeated until the entire scanned image is covered and performed for all set search areas. Then, for each search area, a corresponding area in the scanning area that shows the maximum cross-correlation coefficient is detected (Step 207 in FIG. 12), and the horizontal displacement is calculated based on the coordinates of the corresponding area and the search area (Step 208 in FIG. 12).

[0063] Once the horizontal displacement is obtained, a displacement candidate mesh is extracted (Step 209 in FIG. 12), and a displacement candidate line segment BLD is set based on the multiple displacement candidate meshes that are arranged consecutively (Step 210 in FIG. 12). In addition, a correlation candidate mesh is extracted (Step 211 in FIG. 13), and a correlation candidate line segment BLC is set based on the multiple correlation candidate meshes that are arranged consecutively (Step 212 in FIG. 13).

[0064] When the displacement candidate line segment BLD is obtained, a displacement transverse axis is set obliquely intersecting the displacement candidate line segment BLD (Step 213 in FIG. 13), a displacement distribution map is created (Step 214 in FIG. 13), and a displacement singular section is extracted (Step 215 in FIG. 13). When the correlation candidate line segment BLC is obtained, a correlation transverse axis is set obliquely intersecting the correlation candidate line segment BLC (Step 216 in FIG. 13), a correlation distribution map is created (Step 217 in FIG. 13), and a correlation singular section is extracted (Step 218 in FIG. 13).

[0065] After the processing up to this point has been performed, the approach condition is set as a set condition, or a combination of the approach condition, the line segment length condition, and the singular section condition is set as a set condition, and a displacement boundary line is set based on the displacement candidate line segment BLD, with the condition that the set condition is satisfied (Step 219 in FIG. 13). After the displacement boundary line is set, the object area is divided into a first object area and a second object area, with the displacement boundary line as a boundary (Step 220 in FIG. 13). Then, the first object area and the second object area are individually analyzed using the 3DGIV method, and the displacement including the periphery of the displacement boundary line is obtained. [Industrial Applicability]

[0066] The displacement boundary extraction system of the present invention can be used to discover displacement boundaries that have occurred in various topographical features, such as natural slopes in mountainous areas, embankment slopes, flat ground, etc. Considering that the present invention can prevent natural disasters or reduce damage caused by natural disasters by understanding the status of fault activity and landslide activity, it can be said that the present invention is not only applicable to industry, but is also an invention that is expected to make a great contribution to society. [Explanation of symbols]

[0067] 100 Displacement boundary extraction system of the present invention 101 (Displacement boundary line extraction system) pixel value setting means 102 (Displacement boundary extraction system) cross-correlation coefficient calculation means 103 (Displacement boundary extraction system) horizontal displacement calculation means 104 (Displacement boundary line extraction system) displacement candidate line segment setting means 105 (Displacement boundary extraction system) correlation candidate line segment setting means 106 (Displacement boundary extraction system) displacement boundary setting means 107 (Displacement boundary extraction system) transverse axis setting means 108 (Displacement boundary extraction system) distribution map creation method 109 (Displacement boundary extraction system) singular section extraction means 110 (Displacement boundary extraction system) area division means 111 Terrain model storage means (for displacement boundary extraction system) BLD Displacement candidate line BLC Correlation candidate line segment LD: Line length of the displacement candidate line segment LC Correlation candidate line segment length

Claims

1. A system for extracting a displacement boundary line, which is a boundary of change in a direction of horizontal displacement, based on a first terrain model and a second terrain model obtained at different times in a same target area, comprising: a pixel value setting means for calculating a terrain quantity for each of meshes constituting the first terrain model and the second terrain model, and for assigning pixel values ​​corresponding to the terrain quantities to each of the meshes; a cross-correlation coefficient calculation means for setting a search area consisting of a plurality of the meshes in the first terrain model, and setting a scanning area larger than the search area in the second terrain model, and determining a cross-correlation coefficient based on the pixel values ​​while scanning the scanning area with the search area; a horizontal displacement calculation means for detecting a corresponding area showing a maximum cross-correlation coefficient in the scanning area, and calculating the horizontal displacement based on the coordinates of the corresponding area and the search area; a displacement candidate line segment setting means for extracting displacement candidate meshes having a smaller horizontal displacement than the surrounding meshes, and setting displacement candidate line segments based on the plurality of displacement candidate meshes that are successively arranged; a correlation candidate line segment setting means for extracting correlation candidate meshes having a smaller cross-correlation coefficient than the surrounding meshes, and setting correlation candidate line segments based on a plurality of the correlation candidate meshes that are successively arranged; a displacement boundary setting means for setting the displacement boundary line based on the displacement candidate line segment, The search area is set while being moved, so that the cross-correlation coefficient calculation means calculates the cross-correlation coefficient and the horizontal displacement calculation means calculates the horizontal displacement; the displacement candidate line segment setting means extracts the displacement candidate meshes based on the distribution of the horizontal displacement; The correlation candidate line segment setting means extracts the correlation candidate meshes based on a distribution of the cross-correlation coefficients, the displacement boundary setting means sets the displacement boundary when a distance between the displacement candidate line segment and the correlation candidate line segment falls below a predetermined distance threshold value. A displacement boundary extraction system comprising:

2. the displacement boundary setting means sets the displacement boundary when a length of the displacement candidate line segment exceeds a predetermined displacement extension threshold value.

2. The displacement boundary extraction system according to claim 1.

3. a transverse axis setting means for setting a displacement transverse axis which is a horizontal axis obliquely intersecting the displacement candidate line segment; a distribution map creating means for creating a displacement distribution map in which the horizontal displacements are arranged along the displacement transverse axis; A peculiar section extraction means for extracting a displacement peculiar section in which the horizontal displacement falls below a predetermined displacement section allowable value from the displacement distribution map, the displacement boundary line setting means sets the displacement boundary line when a length of the displacement singular section is included in a predetermined section allowable value; The interval tolerance is set according to the size of the mesh that constitutes the search area.

2. The displacement boundary extraction system according to claim 1.

4. the cross-axis setting means sets a correlation cross-axis which is a horizontal axis obliquely intersecting the correlation candidate line segment; the distribution map creating means creates a correlation distribution map in which the cross-correlation coefficients are arranged along the correlation cross axis; The singular section extraction means extracts a correlation singular section in which the cross-correlation coefficient falls below a predetermined correlation section tolerance value from the correlation distribution diagram, the displacement boundary line setting means sets the displacement boundary line when the lengths of the displacement singular section and the correlation singular section are each included in the predetermined section allowable value.

4. The displacement boundary extraction system according to claim 3.

5. The apparatus further includes a region dividing means for dividing the target region into a first target region and a second target region using the displacement boundary line set by the displacement boundary line setting means as a boundary.

2. The displacement boundary extraction system according to claim 1.

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