TIN-DDM terrain complexity measurement method based on rolling ball transform

Abstract

The present invention provides a TIN-DDM terrain complexity measurement method based on rolling ball transformation, belonging to the technical fields of seabed terrain information mining and analysis and TIN-DDM multi-scale expression technology. The present invention introduces the change in sampling point depth during the rolling ball transformation process into the construction process of the TIN-DDM sampling point TCI calculation model. The present invention clarifies the correlation between the depth change of the TIN-DDM sampling point and its TCI during the rolling ball transformation process, analyzes the selection principle of the optimal rolling ball radius, constructs and optimizes the sampling point TCI calculation model, and finally achieves an effective measurement of TIN-DDM terrain complexity by interpolating the complex field with the TCI. This solves the problem that the TCI calculation models established by the current TIN-DDM terrain complexity measurement methods are unable to effectively reflect the differences in morphological change characteristics between different terrain units by comparing the TCI values of the sampling points.

Description

Technical Field

[0001] The present invention belongs to the technical field of seabed terrain information mining and analysis and TIN-DDM multi-scale expression, and particularly relates to a TIN-DDM terrain complexity measurement method based on rolling ball transformation. Background Art

[0002] Currently, among the research on TIN-DDM complexity, the most representative approach is the measurement method based on the spatial relationship of adjacent triangles. This type of method uses TIN-DDM sampling points as the research object and uses the TCI (Terrain Complexity Index, TCI) calculation model based on the spatial relationship of adjacent triangles as a quantitative means to quantitatively calculate the terrain morphological information within the first-order neighborhood of the sampling point. By comparing the TCI values of the sampling points, the overall TIN-DDM terrain complexity is measured. While the method based on the spatial relationship of adjacent triangles effectively measures the complexity of TIN-DDM terrain to a certain extent, as the quantification range of the complexity of TIN-DDM sampling points, the first-order neighborhood breaks the spatial derivative relationship of non-adjacent sampling points. As a result, in terrain complexity measurement methods based on the spatial relationship of adjacent triangles, regardless of how the TCI calculation model is constructed, it is difficult to effectively reflect the differences in morphological variation characteristics between different terrain units by comparing the TCI values of the sampling points. Especially during the TIN-DDM scaling process, the quantitative control of TIN-DDM terrain morphology through first-order neighborhood TCI (Transistor Coefficient of Integer) makes it difficult to effectively maintain the morphology of the terrain units located at the sampling points, making it difficult to effectively achieve high-fidelity reconstruction and simplification of TIN-DDM terrain under the accuracy threshold. It should be noted that the TCI value of a sampling point, as a quantitative indicator of the complexity of DDM terrain, is closely related to the degree of morphological variation of the terrain region in the TIN-DDM. On the one hand, sampling points with large TCI values are generally located in areas with relatively significant terrain morphological variation (complex areas). On the other hand, to reflect the differences in the degree of terrain morphological variation between different regions in the same TIN-DDM, it is necessary to compare and distinguish the TCI values of the sampling points in different regions. These two points are also important foundations for high-fidelity reconstruction of complex terrain and large-scale simplification of simple terrain in TIN-DDM. Summary of the Invention

[0003] To overcome the problem that traditional methods struggle to effectively reflect the differences in morphological variation characteristics between different terrain units by comparing TCI values at sampling points, the present invention provides a TIN-DDM terrain complexity measurement method based on the rolling ball transformation. Using the changes in the spatial derivative relationship of TIN-DDM sampling points during the rolling ball transformation as a link, the present invention demonstrates the correlation between depth variation at sampling points and their TCI. By analyzing the correlation with the rolling ball radius, the present invention clarifies the principle for selecting the optimal rolling ball radius. Furthermore, through the related properties of the critical rolling ball radius, the method for determining the optimal rolling ball radius during the TCI calculation process of the sampling points is clarified, thereby achieving an effective measurement of TIN-DDM terrain complexity.

[0004] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0005] A TIN-DDM terrain complexity measurement method based on rolling ball transformation includes the following steps:

[0006] Step 1) Calculate the positive critical rolling ball radius r for any sampling point (water depth point) Pi in the TIN-DDM dataset S i ' maxl , negative critical rolling ball radius r i ″ maxl Where i represents the number of sampling points, i=1...N, and N is the total number of sampling points.

[0007] Step 2) Establish an optimal critical rolling ball radius solution sequence list<>, traverse the sampling point Pi respectively, compare the positive critical rolling ball radius value of Pi with its negative critical rolling ball radius value, and store the smaller value in list<>. Finally, the largest radius value in list<> is used as the optimal critical rolling ball radius r for the TCI value of the sampling point optimum , its expression is shown in formula (1):

[0008] r optimum =MAX(list<>)................................(1)

[0009] In the formula, the MAX() function is used to find the maximum value in the sequence.

[0010] Step 3) Use r optimum For the buffer radius, the upper buffer surface dataset S' and the lower buffer surface dataset S" of the TIN-DDM dataset S are constructed respectively. i '、P i"" are the sampling points in S' and S", respectively. The expression form, number and plane coordinates of the sampling points in S, S' and S" are the same. Only the depth value z changes. The change process of z of each sampling point in S' and S" is shown in formula (2):

[0011]

[0012] Where K U (r) represents the generation of an upper buffer surface for the target surface; K D (r) indicates generating a lower buffer surface for the target surface.

[0013] Step 4) Calculate the depth change values Δz' and Δz" of each sampling point in S and the corresponding sampling points of the upper and lower buffer surfaces in sequence. The calculation process is expressed as formula (3). Finally, the maximum value of Δz' and Δz" is expressed as the TCI value of Pi. The calculation model is expressed as formula (4):

[0014]

[0015] TCI(P i )=max(Δz',Δz″)................................(4)

[0016] Where max() represents the maximum value function.

[0017] Step 5) interpolates the TCI values of each TIN-DDM sampling point Pi calculated in step 4) to intuitively reflect the changes in the complexity of the entire TIN-DDM in the form of a heat map, thereby obtaining a continuous TIN-DDM complex field and realizing the improvement of TIN-DDM complexity assessment from discrete points to surfaces.

[0018] The beneficial effects of the present invention are as follows: the present invention introduces the change in the depth of the sampling points during the rolling ball transformation process into the TCI calculation model construction process of the TIN-DDM sampling points. Based on the analysis of the rolling ball transformation principle, the correlation between the depth change of the sampling points and their TCI during the rolling ball transformation of TIN-DDM is clarified, and the selection principle of the optimal rolling ball radius is analyzed. The sampling point TCI calculation model is constructed and optimized. Finally, the effective measurement of the TIN-DDM terrain complexity is realized by interpolating the complex field with TCI, which solves the problem that the TCI calculation model established by the current TIN-DDM terrain complexity measurement method cannot well reflect the differences in morphological change characteristics between different terrain units by comparing the TCI values of the sampling points. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1It is the “depth difference” in the present invention that quantitatively describes and analyzes the TCI of the sampling point.

[0020] Figure 2 This is an analysis of the principle for selecting the optimal rolling ball radius under small radius in the present invention.

[0021] Figure 3 This is an analysis of the principle for selecting the optimal rolling ball radius under large radius in the present invention.

[0022] Figure 4(a) shows the correlation analysis between the critical rolling ball radius and the optimal rolling ball radius at concave terrain sampling points.

[0023] Figure 4(b) shows the correlation analysis between the critical rolling ball radius and the optimal rolling ball radius of the convex terrain sampling point.

[0024] Figure 5 This is the solution process for the positive and negative critical rolling ball radius of the TIN-DDM sampling point in the present invention.

[0025] FIG6( a ) is an analysis of TCI limitations of the sampling points in the present invention.

[0026] FIG6( b ) shows the TCI adjustment process of the sampling point in the present invention.

[0027] Figure 7 for Figure 1 -6 legend. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0029] The application principle of the present invention is described in detail below with reference to the accompanying drawings.

[0030] As attached Figure 1 The following is a quantitative description and analysis of the TCI of the sampling point using the "depth difference". A rolling ball physical model with a fixed radius rolls on the positive and negative terrain surfaces. The upper and lower buffer surfaces formed after rolling have a certain depth interpolation with the original terrain surface. The TCI value of the TIN-DDM sampling point can be expressed using this depth interpolation. Figure 2 、 3As shown in the figure, the rolling ball physical model with different radii forms different depth interpolation values, and the degree of representation of terrain morphology differences is also different. Under small radius conditions, the "depth difference" obtained by some TIN-DDM sampling points is 0. Under large radius conditions, the difference distinction of the "depth difference" obtained by the TIN-DDM sampling points is weakened. In order to find the optimal rolling ball physical model radius (the radius can ensure that the complexity between all sampling points is effectively compared while achieving the maximum distinction of the local terrain morphology differences expressed by the sampling points), it is necessary to solve the positive and negative critical rolling ball radii of all sampling points. As shown in Figures 4(a) and 4(b), since the critical rolling ball radius has the physical meaning of reflecting the critical state of contact between the TIN-DDM sampling point and the rolling ball, the critical rolling ball radius has the ability to define whether the "depth difference" is 0. Therefore, in order to meet the selection principle of the optimal rolling ball radius, the positive critical rolling ball radius of the concave terrain sampling point and the negative critical rolling ball radius of the convex terrain sampling point need to be sorted, and the largest radius value is screened out. This value is the optimal rolling ball radius for obtaining the TCI of the sampling point. In summary, in the actual process of obtaining the TCI value of the sampling point, the critical rolling ball radius of all TIN-DDM sampling points should be calculated first. In addition, in order to realize the improvement of the TIN-DDM complexity evaluation from discrete points to surfaces, the interpolation operation is applied to obtain its complex field. The specific implementation steps are as follows:

[0031] Step 1) Calculate the positive critical rolling ball radius r for any sampling point (water depth point) Pi in the TIN-DDM dataset S i ' maxl , negative critical rolling ball radius r i ″ maxl Where i represents the number of sampling points, i=1...N. i ' maxl 、r i ″ maxl The specific solution process is as follows Figure 5 express, Figure 5 Only the solution process of the positive critical rolling ball radius is shown. The solution process of the negative critical rolling ball radius is the same as that of the positive critical rolling ball radius.

[0032] Figure 5 First, the numerical correlation between the rolling ball contact point and the rolling ball radius corresponding to the buffer surface point on TIN_DDM is stored in the data chain. The data chain is from P1 to P l The upper buffer surface point on the z-axis direction of the point is the research object, j1 to j l Indicates the number of rolling ball contact points corresponding to each upper buffer surface point during the rolling ball radius change process, Indicates the rolling ball radius range boundary value of each rolling ball contact point, Indicates the rolling ball contact point corresponding to each radius range; then, in the dotted box 1 area, the rolling ball radius range (dashed box 3 area) when the sampling point P1 (dashed box 2 area) becomes the rolling ball contact point is queried and a union operation is performed, and finally the rolling ball radius range when the sampling point P1 becomes the rolling ball contact point is obtained. Due to the discreteness of each sampling point in the TIN-DEM and the data error, the rolling ball radius range corresponding to the rolling ball contact point is discontinuous, which is inconsistent with the actual situation. The boundary maximum value can be selected from the union operation result as the critical rolling ball radius when the rolling ball contact point state changes, and finally the positive critical rolling ball radius is obtained.

[0033] Step 2) Establish an optimal critical rolling ball radius solution sequence list<>, traverse the sampling point Pi respectively, compare the positive critical rolling ball radius value of Pi with its negative critical rolling ball radius value, and store the smaller value in list<>. Finally, the largest radius value in list<> is used as the optimal critical rolling ball radius r for the TCI value of the sampling point optimum , its expression is shown in formula (1):

[0034] r optimum =MAX(list<>)................................(1)

[0035] In the formula, the MAX() function is used to find the maximum value in the sequence.

[0036] Step 3) Use r optimum For the buffer radius, the upper buffer surface dataset S' and the lower buffer surface dataset S" of S are constructed respectively. The representation form, number, and plane coordinates of the sampling points in S, S', and S" are consistent, and only the depth value z changes. The change process of z of each sampling point in S' and S" is shown in formula (2):

[0037]

[0038] Where K U (r) represents the generation of an upper buffer surface for the target surface; K D (r) indicates generating a lower buffer surface for the target surface.

[0039] Step 4) Calculate the depth change values Δz' and Δz" of each sampling point in S and the corresponding sampling points of the upper and lower buffer surfaces in sequence. The calculation process is expressed as formula (3). Finally, the maximum value of Δz' and Δz" is expressed as the TCI value of Pi. The calculation model is expressed as formula (4):

[0040]

[0041] TCI(Pi )=max(Δz',Δz″)................................(4)

[0042] Where max() represents the maximum value function.

[0043] Step 5) interpolates the TCI values of each TIN-DDM sampling point calculated according to step 4) to intuitively reflect the complexity changes of the entire TIN-DDM in the form of a heat map.

[0044] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the invention to be protected. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents. Ordinary method personnel should understand that their modification of the aforementioned method schemes, or equivalent replacement of some or all of the method features therein, does not deviate the essence of the corresponding method schemes from the scope of the method schemes of the present invention.

Claims

1. A TIN-DDM terrain complexity measurement method based on rolling ball transform, characterized by: The method comprises the following steps: Step 1): Calculate the positive critical rolling ball radius r for any sampling point Pi in the TIN-DDM dataset S. i ' maxl , negative critical rolling ball radius r i ” maxl ; Where i represents the number of sampling points, i=1...N, N is the total number of sampling points; Step 2): Establish an optimal critical rolling ball radius solution sequence list<>, traverse the sampling points Pi respectively, compare the positive critical rolling ball radius value of Pi with its negative critical rolling ball radius value, and store the smaller value in list<>. Finally, the largest radius value in list<> is used as the optimal critical rolling ball radius r for the TCI value of the sampling point. optimum , its expression is shown in formula (1): r optimum =MAX(list<>)........................................(1) In the formula, the MAX() function is used to find the maximum value in the sequence; Step 3): Use r optimum For the buffer radius, construct the upper buffer surface dataset S' and the lower buffer surface dataset S" of the TIN-DDM dataset S respectively; P i '、P i " are the sampling points in S' and S", respectively. The expression form, quantity and plane coordinates of the sampling points in S, S' and S" are the same. Only the depth value z changes. The change process of z of each sampling point in S' and S" is shown in formula (2): Where K U (r) represents the generation of an upper buffer surface for the target surface; K D (r) represents the generation of a lower buffer surface for the target surface; Step 4): Calculate the depth change values Δz' and Δz" of each sampling point in S and the corresponding sampling points of the upper and lower buffer surfaces in sequence. The calculation process is expressed as formula (3). Finally, the maximum value of Δz' and Δz" is expressed as the TCI value of Pi. The calculation model is expressed as formula (4): TCI(P i )=max(Δz',Δz”).......................................(4) In the formula, max() represents the maximum value function; Step 5): Interpolate the TCI values of each TIN-DDM sampling point Pi calculated in step 4) to intuitively reflect the complexity changes of the entire TIN-DDM in the form of a heat map.

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