CN112598792A - Multi-fractal quantification method and system for terrain complexity in three-dimensional scene - Google Patents
Multi-fractal quantification method and system for terrain complexity in three-dimensional scene Download PDFInfo
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Abstract
The invention provides a multi-fractal quantization method and a multi-fractal quantization system for terrain complexity in a three-dimensional scene, wherein gradient data are acquired, the gradient data comprise that scattered points are established on the three-dimensional terrain surface of a selected area, elevation values are acquired, and then gradient values of the scattered points are acquired according to the elevation value relation of the scattered points and adjacent points; performing multi-fractal calculation, namely dividing a scatter point establishing region into sub-regions with different scales by respectively and correspondingly adopting a box-dimension multi-fractal calculation method for a plurality of moment q values, calculating gradient distribution probability measure and a distribution function in each sub-region, checking whether multi-fractal characteristics of the scatter point region are obvious by judging whether the logarithm of the distribution function and the logarithm of the scale are linearly related or not, and extracting and reserving corresponding singular indexes and fractal dimensions if the multi-fractal characteristics are obvious; and drawing a multi-fractal spectrum according to the obtained multiple groups of singular indexes and fractal dimensions, extracting multi-fractal characteristic parameters through a multi-fractal spectrum image, and supporting quantitative expression of terrain complexity.
Description
Technical Field
The invention belongs to the field of three-dimensional geographic information science, and particularly relates to a multi-fractal quantization method and system for terrain complexity in a three-dimensional scene.
Background
Geographic Information Science (GIS), also known as a geographic information system, is a science for collecting, storing, operating, and visually displaying various geographic-related data information on the earth. In the past, due to the limitation of various technologies, a traditional GIS usually adopts a two-dimensional plane display mode on a visualization level, so that part of information contained in a three-dimensional space cannot be visually presented to people. With the development of computer software and hardware and the improvement of the requirement of users on visual perception, a three-dimensional geographic information system comes into play, and presents various geographic related data information in a three-dimensional stereo form, so that the three-dimensional geographic information system is very consistent with the common three-dimensional visual perception of people in daily life, and is well received by practitioners in the field and common people since the appearance of the three-dimensional geographic information system.
At present, three-dimensional geographic information systems have a plurality of related platforms which are mainly divided into two categories: one type is desktop-end 3D GIS software such as CityMaker, and the other type is a webpage-end 3D GIS open source library such as Cesium. Whether the desktop side or the webpage side is adopted, one basic and important content in the three-dimensional geographic information platform is the presentation and analysis of three-dimensional terrain. The presentation is that the original topographic data is processed and rendered by a visualization method and then is visually displayed on a carrier such as a screen; the analysis is to deeply mine and describe some implicit information of the terrain data. Analyzing three-dimensional terrain data, besides acquiring common terrain factors such as elevation, gradient and the like, the complexity of three-dimensional terrain in an area is also an important content.
The complexity of three-dimensional terrain describes the steepness, non-uniformity of the terrain over an area. For example, in a three-dimensional scene, the terrain of a plain region is relatively flat and uniform, and the terrain of a mountain region is relatively steep and disordered, so that the characteristic that the terrain is flat or steep can be obtained through visual observation, but the concept of complexity is required for the expression of quantifying the terrain. At present, in various three-dimensional scenes, a method for quantitatively expressing terrain complexity does not exist, and the invention provides a method for quantizing the terrain complexity of the three-dimensional scene by using multi-fractal in a fractal theory.
Fractal theory is often used to provide a quantitative description of those complex things, phenomena and processes in nature that are difficult to describe using traditional mathematics. Compared with the traditional mathematics, the method has the following differences in describing geographic objects:
the geographic objects described by traditional mathematics can be described by determined mathematical expressions, such as gradient, average altitude and the like. The geographic objects described by the fractal theory are often irregular, that is, the geographic objects described by the traditional mathematics are generally analyzable by a formula, and the form described by the fractal theory is generally not analyzable by the formula.
Secondly, the traditional mathematics describes geographic objects which have limited hierarchical features in the process of scale change. And the object form described by the fractal theory has infinite hierarchical levels in a mathematical view.
Third, the geographic objects described by traditional mathematics are usually independent in part and whole, and often have no correlation, so that the association of the part and the whole is not focused. The fractal theory highlights the research on the association, so that the object described by the fractal theory can be seen as a whole from a local part.
Fourthly, if the geographic objects described by the traditional mathematics are complex, the implicit mathematical rules are necessarily complex, and for the objects described by the fractal theory, the general expressed characteristics are complex in appearance, but the rules contained in the objects are simple in the future.
Multifractals, which are products of further development on the basis of fractals, were proposed by Mandbolol in the early 70's 20 th century when investigating the turbulence problem. The multi-fractal is a theory generated by aiming at a complex fractal object with different fractal characteristics of each part expressed in different scales, and can be generally regarded as dividing the fractal object in more detail, then conducting fractal study on each part in different scales, and finally obtaining the singularity and fractal dimension of each part, wherein the multi-fractal can be used for quantitatively expressing the multi-fractal characteristics of the object, and can also be converted into an image language, namely a multi-fractal spectrum through a mathematical method, and the multi-fractal spectrum can visually reflect the multi-fractal characteristics of the object.
However, influence factors of terrain complexity in a three-dimensional scene are complex and changeable, application conditions of some multi-fractal methods are harsh, and multi-fractal spectrum image information is difficult to apply to quantitative expression of objects, so that multi-fractal is difficult to apply to the field of quantitative analysis of the terrain complexity of the three-dimensional scene for a long time.
Disclosure of Invention
In order to enrich the quantification mode of the terrain complexity in the three-dimensional scene, the invention provides a multi-fractal quantification scheme of the terrain complexity in the three-dimensional scene, and the complexity of the terrain in the three-dimensional scene can be accurately and quantitatively described by establishing an empirical model through finally obtained multi-fractal characteristic parameters.
The technical scheme adopted by the invention is a multi-fractal quantization method of terrain complexity in a three-dimensional scene, which comprises the following steps:
and 3, drawing a multi-fractal spectrum according to the multiple groups of singular indexes and fractal dimensions obtained in the step 2, extracting multi-fractal characteristic parameters through a multi-fractal spectrum image, and supporting quantitative expression of terrain complexity.
In step 1, dense scattered points uniformly distributed in a row-column alignment manner are created on a topographic surface in a three-dimensional scene, and three-dimensional coordinates of the scattered points are acquired, wherein the three-dimensional coordinates include information representing elevation.
In the step 2, the slope value at each scatter point is calculated by considering the elevation relationship between the current scatter point and eight adjacent scatter points, the following points a to i are set to represent nine adjacent scatter points, and when the slope value at the central point e is calculated, the calculation method is as follows,
a b c
d e f
g h i
a-i in the formula are the elevation values of the corresponding points, x _ cell and y _ cell are the distances between two adjacent points of the uniform scattering point along the x direction and the y direction respectively,
being the component of the slope in the x-direction,
is the component of the slope in the y-direction;
the slope value is calculated according to the following formula,
wherein pi is a circumference ratio.
Furthermore, the implementation of step 2 is as follows,
2.1) according to a preset q value range A and a preset q distance, sequentially taking a value which is not detected as a current moment q; according to the current order moment q, a box dimension mode is adopted to divide the scatter region into sub-regions with different scales in turn, a probability measure and a distribution function for describing the gradient distribution condition in each sub-region are calculated, whether the multi-fractal characteristics of the scatter region are obvious is checked by judging whether the logarithm of the distribution function and the logarithm of the scale are linearly related,
obviously, the step 2.2) is carried out, the step 2.2) and the step 2.3) are carried out, and corresponding singular indexes and fractal dimensions are reserved;
if not obvious, directly entering step 2.4);
2.2) calculating to obtain a normalized measurement set according to the probability measurement and the partition function in each sub-region obtained in the step 2.1), and then entering the step 2.3);
2.3) calculating to obtain singular indexes and fractal dimensions of gradient multi-fractal in a group of regions based on different scales aiming at the current moment q, and then entering the step 2.4);
2.4) judging whether the iteration end condition is met, if so, entering the step 3), otherwise, returning to the step 2.1) and calculating the singular index and the fractal dimension of the gradient multi-fractal aiming at the next moment q value.
And in the step 2.3), the singular index and the fractal dimension of the gradient multi-fractal in the region are calculated by combining the probability measure, the normalized measure set and the scale information.
And in step 3, when the multi-fractal spectrum is drawn, in a Cartesian coordinate system, the singular index is taken as a coordinate horizontal axis, the fractal dimension is taken as a coordinate vertical axis, and a plurality of groups of the singular indexes and the fractal dimensions which correspond to each other and are obtained in step 2 are mapped in the coordinate system to draw a function image.
And extracting minimum value points, maximum value points, minimum values, maximum values, end points and end point values in the function image to form multi-fractal characteristic quantities, constructing an empirical model of the multi-fractal characteristic quantities by using a regression analysis method, and using the empirical model for quantitative expression of terrain complexity in a three-dimensional scene.
On the other hand, the invention provides a multi-fractal quantization system for terrain complexity in a three-dimensional scene, which is used for realizing the multi-fractal quantization method for terrain complexity in a three-dimensional scene.
And, including the following modules,
the first module is used for acquiring gradient data, and comprises the steps of establishing scattered points on the three-dimensional terrain surface of a selected area, acquiring elevation values of the scattered points, and calculating to obtain gradient values of the scattered points according to the elevation value relation of the scattered points and adjacent points;
the second module is used for carrying out multi-fractal calculation and comprises the steps of dividing a scatter point area into sub-areas with different scales by respectively and correspondingly adopting a box-dimension multi-fractal calculation method according to the value range of the order moment q parameter in a preset distribution function, calculating the gradient distribution probability measure and the distribution function in each sub-area, checking whether the multi-fractal characteristics of the scatter point area are obvious by judging whether the logarithm of the distribution function and the logarithm of the scale are linearly related or not, and extracting and reserving the corresponding singular index and the corresponding fractal dimension if the multi-fractal characteristics are obvious;
and the third module is used for drawing a multi-fractal spectrum according to the multiple groups of singular indexes and fractal dimensions obtained by the second module, extracting multi-fractal characteristic parameters through the multi-fractal spectrum image and supporting quantitative expression of terrain complexity.
Or, the multi-fractal quantization method comprises a processor and a memory, wherein the memory is used for storing program instructions, and the processor is used for calling the stored instructions in the memory to execute the multi-fractal quantization method for the terrain complexity in the three-dimensional scene.
According to the invention, multi-fractal is used for quantifying terrain complexity in a three-dimensional scene, and elevation information at each scattered point position in a research area can be directly obtained by inserting scattered points on the terrain surface and obtaining a three-dimensional coordinate form of the scattered points. Gradient information at each scattered point is obtained through gradient algorithm calculation considering the elevation of adjacent points, gradient multi-fractal feature parameters of a research area are obtained by using a box-dimension calculation method in multi-fractal, and finally the multi-fractal feature parameters are converted into an empirical model capable of quantitatively expressing the terrain complexity in a three-dimensional scene by means of regression analysis. Compared with the traditional method for researching the terrain complexity, the method is more suitable for quantitatively researching the terrain complexity in a three-dimensional scene, and the three-dimensional coordinates can be obtained by directly inserting scattered points on the terrain surface to be used as the original data for quantitatively researching the terrain complexity; meanwhile, the multi-fractal can reflect the autocorrelation between the local part and the whole part of the terrain of the research area, and the terrain complexity of the research area can be quantified from a plurality of layers.
The scheme of the invention is simple and convenient to implement, has strong practicability, solves the problems of low practicability and inconvenient practical application of the related technology, can improve the user experience, and has important market value.
Drawings
Fig. 1 is a flowchart illustrating how multi-fractal is applied to terrain complexity quantization in a three-dimensional scene according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating insertion of scatter points and area division on a three-dimensional scene terrain surface according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a multi-fractal feature test on a study area in an embodiment of the present invention.
Fig. 4 is a diagram illustrating a multi-fractal spectrum drawn according to a calculation result in an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is specifically described below with reference to the accompanying drawings and examples.
The invention provides a method for establishing sampling points on the terrain surface in a three-dimensional scene, obtaining three-dimensional scattered points containing slope distribution information in an area through slope calculation based on the elevation values of adjacent points, carrying out scale division on the scattered points to obtain scattered point sets under different scales, obtaining a multi-fractal spectrum and multi-fractal characteristic parameters through multi-fractal characteristic inspection, multi-fractal calculation, multi-fractal spectrum drawing and other processes, and then establishing an empirical model.
Referring to fig. 1, the multi-fractal quantization method for terrain complexity in a three-dimensional scene provided by the embodiment of the present invention includes the following steps:
1.1) creating scattered points on the terrain surface in the three-dimensional scene, and acquiring the three-dimensional coordinates of the positions of the scattered points.
The method comprises the following steps of creating dense scattered points which are uniformly distributed in a row-column alignment mode on a topographic surface in a three-dimensional scene, wherein the row spacing and the column spacing between two adjacent points are the same; and acquiring three-dimensional coordinates of each scattered point, wherein the coordinates comprise information representing elevation.
Step 1.1) of the embodiment, loading terrain data in a three-dimensional scene, determining a terrain area needing to study complexity, and creating scattered points which are densely and regularly arranged as shown in fig. 2 on the terrain surface of the area, wherein the scattered points are preferably arranged in a mode of line alignment. And traversing each scatter point in sequence to obtain the three-dimensional coordinates of the position of the scatter point in the scene, and if the three-dimensional coordinates are in a geographic coordinate system, obtaining the longitude, latitude and altitude information of the scatter point.
1.2) traversing each scattered point, and calculating the gradient value of each scattered point according to the elevation relation between the current point and the adjacent point.
And traversing each scattered point, and calculating the gradient value at the position of the scattered point. When traversing the scattered points at the edge, the gradient value of the scattered points is the same as the gradient value of the nearest non-edge point; when traversing the non-edge points, the slope value of each scattered point is calculated according to the altitude of the central point, the altitude of eight adjacent points closest to the central point, the distance between scattered points in each row and the distance between scattered points in each column.
For example, assuming that a-i in the following array are the positions of 9 neighboring scatters, the slope at the center point e is calculated by:
a b c
d e f
g h i
a-i in the formula are the elevation values of the corresponding points, x _ cell and y _ cell are the distances between two adjacent points of the uniform scattering point along the x direction and the y direction respectively,
being the component of the slope in the x-direction,
is the component of the slope in the y-direction. In the example, the x-axis is along the row direction, the y-axis is along the column direction, and the z-axis is along the elevation direction.
Then, calculating a gradient value (wherein pi is a circumferential rate) according to the following gradient calculation formula:
In the embodiment, this step is implemented in an iterative manner.
The box-dimension method should be used in this step when calculating the multi-fractal. According to the idea of the method, the created scatter point region is divided into sub-regions with different sizes, the concrete embodiment of dividing the different sizes is to divide the whole scatter point region into grid regions with different sizes, and then the gradient probability measurement and the distribution function of each sub-region are calculated according to a corresponding method and a formula in a box dimension. When the partition function is calculated, the order moment q in the calculation formula needs to be determined, which directly influences the proportion of different gradient probability measures on the whole partition function: when q takes a positive value and the value is larger, the distribution function can reflect the characteristics and properties of the grid units with the larger gradient probability function; when q takes a negative value and the value is smaller, the distribution function can reflect the characteristics and properties of the grid cells with the smaller gradient probability function. In actual calculation, the value range of q is often determined by combining the characteristics of terrain, the calculation is inconvenient when q is too large or too small, the value range of q which can enable the fractal characteristics of a research object to be obvious is preferably selected in advance in the specific implementation process, and then a range of a moment q is finally determined through multiple iterations, so that the multi-fractal characteristics of the research area are obviously reflected. For example, the value range a of q to be tested is a closed interval of-10 to 10, the interval is 0.1, namely, the value is-10.0 in the first iteration, the value is-9.9 in the second iteration, then-9.8 … … 9.9.9 and 10.0 are taken, and the notation MAXA is 10, and the notation MINA is-10. Finally, a smaller range B of the order moment q is obtained, wherein each value can obviously reflect the multi-fractal characteristics of the research area, for example, the multi-fractal characteristics are-5 to 5, and the multi-fractal characteristics are marked as MAXB-5 and MINB-5. Range B is a sub-interval of range a. When q is 0, the requirement is clearly expressed.
The specific implementation sub-flow of step 2 of the embodiment is as follows,
2.1) according to a preset q value range A and a preset q distance, sequentially taking a value which is not detected as a current moment q; according to the current order moment q, a box dimension method is adopted to divide the scatter region into sub-regions with different scales (different size ranges) in sequence, a probability measure and a distribution function for describing the gradient distribution condition in each sub-region are calculated, whether the multi-fractal characteristics of the scatter region are obvious or not is checked by checking whether the logarithm of the distribution function and the logarithm of the scale are linearly related or not,
obviously, the step 2.2) is carried out, the step 2.2) and the step 2.3) are carried out, and corresponding singular indexes and fractal dimensions are reserved;
if not, directly entering the step 2.4) to judge whether the iteration end condition is met.
In the embodiment, a box dimension method is adopted to carry out multi-scale division on the scatter region and calculate the slope probability measurement and the distribution function in each unit under each scale. The box dimension method, as the name implies, is to cover the fractal with a series of closely connected square boxes, which is equivalent to dividing the study object into a plurality of square grid cells (assuming that i rows and j columns total N) with the dimension of r. It is to be understood that N and r are not independent physical quantities, and that the larger N, the smaller r is necessarily, and conversely, the smaller N, the larger r is also necessarily. In the embodiment, r takes the values of 600m, 900m, 1200m, … …, 5700m and 6000m, and the total number is 19.
Calculating the probability measure and the distribution function of the gradient in each grid unit, wherein the calculation formula of the gradient probability measure of each small grid unit is as follows:
in the formula Pij(r) a gradient probability function representing a grid located at the ith row and the jth column; a. theij(r) represents slope statistics located within the grid at row i and column j; n represents the total number of grid cells, which numerically satisfies: n — I × J, where I and J denote the total number of rows and columns of the grid, respectively, I — 1,2, … I, and J — 1,2, … J.
In the box-dimension method, whether a research object has a multi-fractal feature is checked, and a distribution function concept is provided, wherein a calculation formula of the distribution function is as follows:
x in the formulaq(r) a partition function representing a fractal; pij(r) gradient profile of grid located at ith row and jth columnA rate function; q represents an order moment, which is a specified constant.
According to the multi-fractal theory, when the research object has the multi-fractal characteristics, the logarithm of the distribution function and the logarithm of the scale obtained by calculation should be linearly related, so that the multi-fractal characteristics of the terrain of the research area need to be examined here, and lnX shown in fig. 3 can be drawnq(r) -lnr images, wherein the labeled image similar to "q-10" represents the specific value of the order moment parameter q in the process of calculating the allocation function in the instantiated application, and if the image indicates lnXqAnd (r) is linearly related to lnr, the research region has a multi-fractal characteristic, and the corresponding result is retained. Wherein, lnXq(r) is the logarithm of the partition function, lnr is the logarithm of the scale.
2.2) according to the probability measure and the partition function in each subregion obtained in the step 2.1), a normalization measure set is obtained through calculation of a corresponding formula, and then the step 2.3) is carried out.
In this step, a normalized measurement set is calculated according to the obtained probability measurement and the partition function. The normalized measurement set is used as an intermediate variable, and is helpful for calculating singular indexes and fractal dimensions in multi-fractal more simply.
In the process of calculating the multi-fractal spectrum, a normalized measurement set mu is established according to the probability measurement and the partition function in each sub-regionij(q, r) the calculation formula is as follows:
μ in the formulaij(q, r) representing the normalized measurement set obtained by calculation; pij(r) a gradient probability function representing a grid located at the ith row and the jth column; xq(r) a partition function representing a fractal; q represents an order moment, which is a specified constant.
2.3) calculating to obtain singular indexes and fractal dimensions of gradient multi-fractal in a group of regions based on different scales aiming at the current moment q, and then entering the step 2.4) to judge whether an iteration ending condition is met;
when a scale is constantly changed, a singular index and a fractal dimension of gradient multi-fractal in a group of areas can be calculated by combining probability measurement, a normalized measurement set and scale information every time a moment is given. By giving different orders, a plurality of groups of corresponding singular indexes and fractal dimensions can be obtained.
In the step, the singular index and the fractal dimension of the slope multi-fractal in the region are calculated by combining the probability measurement, the normalized measurement set and the scale information. And when one order moment is set, a group of corresponding singular indexes and fractal dimensions can be determined and obtained by changing the scale r. In the method, the values of the scale and the order moment are changed through multiple iterations, so that multiple groups of singular indexes and fractal dimensions which correspond to each other are obtained.
In step 2.3), by combining the information such as the probability measure, the normalized measure set, and the scale obtained by the previous calculation, the singular index α and the fractal dimension f (α) in the multi-fractal can be calculated by the following formulas:
and 2.4) judging whether an iteration ending condition is met, namely judging whether candidate q values in a preset q value range A are correspondingly checked, if so, integrating results obtained by iteration before, determining a final order moment q range B capable of obviously embodying the multi-fractal characteristics of the research area, entering a step 3), otherwise, returning to the step 2.1), sequentially taking a value which is not checked as the current order moment q, calculating the singular index and the fractal dimension of the gradient multi-fractal according to the next order moment q value, and checking.
In the embodiment, the order moment q in a given normalized measurement set is changed, the operation from step 2.1) to step 2.3) is executed when the multi-fractal characteristics are obvious, the singular index alpha and the fractal dimension f (alpha) are extracted by continuously changing the scale r in each iteration until the iteration is finished when the iteration finishing condition is met, and thus a series of singular indexes alpha and fractal dimensions f (alpha) which correspond to each other are obtained.
And 3, drawing a multi-fractal spectrum according to a plurality of groups of corresponding singular indexes and fractal dimensions reserved in the step 2, extracting multi-fractal characteristic parameters through inflection points, minimum value points, maximum value points, minimum values, maximum values and the like on the multi-fractal spectrum image, and establishing an empirical model to quantitatively express the terrain complexity on the basis. Because the empirical model can establish the numerical relationship between the multi-fractal characteristic parameters and the terrain complexity, when the terrain complexity information of a new research area needs to be acquired through the established terrain complexity multi-fractal empirical model, three-dimensional scatter points, scatter point elevations, scatter point gradients and area multi-fractal characteristic parameters are sequentially established in the area according to the three-dimensional terrain information of the research area through the method provided by the invention according to corresponding steps, and then the terrain complexity numerical value of the research area is acquired.
When the multi-fractal spectrum is drawn, the singular index is taken as a coordinate horizontal axis and the fractal dimension is taken as a coordinate vertical axis in a Cartesian coordinate system, and a plurality of groups of the singular index and the fractal dimension which correspond to each other and are obtained by calculation in the previous step are mapped in the coordinate system to draw a function image. And extracting a function minimum value point, a maximum value point, a minimum value, a maximum value, an end point and an end point value in the image to form multi-fractal characteristic quantities, constructing an empirical model of the multi-fractal characteristic quantities by using a regression analysis method, and using the model for quantitative expression of terrain complexity in a three-dimensional scene.
In the embodiment step 3, according to the paired singular index and fractal dimension obtained in the step 2, a multi-fractal spectrum as shown in fig. 4 is drawn in a cartesian coordinate system by taking the singular index α as a coordinate horizontal axis and the fractal dimension f (α) as a coordinate vertical axis, and the minimum singular index α can be extracted from the multi-fractal spectrum imageminMaximum singular index αmaxExtreme difference delta alpha of singular index and minimum singular indexNumber-corresponding fractal dimension f (alpha)min) Fractal dimension f (alpha) corresponding to maximum singular indexmax) Minimum fractal dimension f (alpha)minMaximum fractal dimension f (alpha)maxAnd the fractal feature parameters such as fractal dimension range delta f (alpha) and the like are selected from a plurality of research areas, the multi-fractal feature parameters are calculated by the method, and are summarized into an empirical model through regression analysis by contrasting with a traditional terrain complexity evaluation method, so that the terrain complexity in the three-dimensional scene can be quantitatively expressed.
The multi-fractal characteristic parameters can be used as indexes for quantitatively representing the complexity of the region, and the multi-fractal spectrum intuitively reflects the complexity of the region terrain in the three-dimensional scene in an image mode.
In specific implementation, a person skilled in the art can implement the automatic operation process by using a computer software technology, and a system device for implementing the method, such as a computer-readable storage medium storing a corresponding computer program according to the technical solution of the present invention and a computer device including a corresponding computer program for operating the computer program, should also be within the scope of the present invention.
In some possible embodiments, a multi-fractal quantization system for terrain complexity in a three-dimensional scene is provided, comprising the following modules,
a first module for inputting facial image
And reference makeup face image
Will refer to the makeup human face image
Morphing to plain face images
Obtaining a pseudo makeup-carrying face image
A second module for using color texture information of makeup to the pseudo-makeup-carrying face image through the SOM network based on the number k of preset makeup styles
Performing makeup segmentation, decomposing the makeup style into sub-makeup styles, and simultaneously dividing the whole makeup into different sub-makeup style areas to obtain an approximate result of the makeup segmentation result of the target makeup-bearing face image;
a third module for generating a face image
And reference makeup face image
Inputting the facial image into a confrontation generation network, adjusting a generation network module in the confrontation generation network through regional self-adaptive normalization, applying the same normalization mode in the same sub-style region to enhance the consistency of the remote similar makeup region, and outputting a target makeup-bearing face image by the generation network module
In some possible embodiments, a multi-fractal quantization system for terrain complexity in a three-dimensional scene is provided, which includes a processor and a memory, where the memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute a multi-fractal quantization method for terrain complexity in a three-dimensional scene as described above.
In some possible embodiments, a multi-fractal quantization system for terrain complexity in a three-dimensional scene is provided, which includes a readable storage medium, on which a computer program is stored, and when the computer program is executed, the multi-fractal quantization system for terrain complexity in a three-dimensional scene is implemented as described above.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. A multi-fractal quantization method for terrain complexity in a three-dimensional scene is characterized by comprising the following steps:
step 1, obtaining gradient data, including creating scattered points on the three-dimensional terrain surface of a selected area, obtaining elevation values of the scattered points, and calculating gradient values of the scattered points according to the elevation value relationship between the scattered points and adjacent points;
step 2, performing multi-fractal calculation, including dividing a scatter point establishing region into sub-regions of different scales according to a value range of a step q parameter in a preset distribution function by respectively and correspondingly adopting a box-dimension multi-fractal calculation method for a plurality of step q values, calculating gradient distribution probability measurement and a distribution function in each sub-region, checking whether multi-fractal characteristics of the scatter point region are obvious by judging whether a distribution function logarithm and a scale logarithm are linearly related, and extracting and retaining corresponding singular indexes and fractal dimensions if the multi-fractal characteristics are obvious;
and 3, drawing a multi-fractal spectrum according to the multiple groups of singular indexes and fractal dimensions obtained in the step 2, extracting multi-fractal characteristic parameters through a multi-fractal spectrum image, and supporting quantitative expression of terrain complexity.
2. The multi-fractal quantization method for terrain complexity in three-dimensional scene according to claim 1, characterized in that: in the step 1, dense scattered points which are uniformly distributed in a row-column alignment mode are created on a topographic surface in a three-dimensional scene, and three-dimensional coordinates of the scattered points are obtained, wherein the three-dimensional coordinates contain information representing elevation.
3. The multi-fractal method for terrain complexity in a three-dimensional scene according to claim 1, wherein in the step 2, the elevation relationship between the current scatter point and eight adjacent scatter points is considered when calculating the slope value at each scatter point, the following a-i are assumed to represent nine adjacent scatter points, and in the step of calculating the slope value at the central point e, the calculation method is as follows,
a-i in the formula are the elevation values of the corresponding points, x _ cell and y _ cell are the distances between two adjacent points of the uniform scattering point along the x direction and the y direction respectively,
being the component of the slope in the x-direction,
is the component of the slope in the y-direction;
the slope value is calculated according to the following formula,
wherein pi is a circumference ratio.
4. The multi-fractal method of terrain complexity in three-dimensional scenes according to claim 1,2 or 3, characterized in that: the implementation of step 2 is as follows,
2.1) according to a preset q value range A and a preset q distance, sequentially taking a value which is not detected as a current moment q; according to the current order moment q, a box dimension mode is adopted to divide the scatter region into sub-regions with different scales in turn, a probability measure and a distribution function for describing the gradient distribution condition in each sub-region are calculated, whether the multi-fractal characteristics of the scatter region are obvious is checked by judging whether the logarithm of the distribution function and the logarithm of the scale are linearly related,
obviously, the step 2.2) is carried out, the step 2.2) and the step 2.3) are carried out, and corresponding singular indexes and fractal dimensions are reserved;
if not obvious, directly entering step 2.4);
2.2) calculating to obtain a normalized measurement set according to the probability measurement and the partition function in each sub-region obtained in the step 2.1), and then entering the step 2.3);
2.3) calculating to obtain singular indexes and fractal dimensions of gradient multi-fractal in a group of regions based on different scales aiming at the current moment q, and then entering the step 2.4);
2.4) judging whether the iteration end condition is met, if so, entering the step 3), otherwise, returning to the step 2.1) and calculating the singular index and the fractal dimension of the gradient multi-fractal aiming at the next moment q value.
5. The multi-fractal method of terrain complexity in a three-dimensional scene according to claim 4, wherein: and 2.3) calculating the singular index and fractal dimension of the gradient multi-fractal in the region by combining the probability measure, the normalized measure set and the scale information.
6. The multi-fractal method of terrain complexity in three-dimensional scenes according to claim 1,2 or 3, characterized in that: and 3, when the multi-fractal spectrum is drawn in the step 3, in a Cartesian coordinate system, taking the singular index as a coordinate horizontal axis and the fractal dimension as a coordinate vertical axis, and establishing mapping of a plurality of groups of the singular indexes and the fractal dimensions which correspond to each other and are obtained in the step 2 in the coordinate system to draw a function image.
7. The multi-fractal method of terrain complexity in a three-dimensional scene according to claim 6, wherein: extracting minimum value points, maximum value points, minimum values, maximum values, end points and end point values in the function image to form multi-fractal characteristic quantities, constructing an empirical model of the various fractal characteristic quantities by using a regression analysis method, and using the empirical model for quantitative expression of terrain complexity in a three-dimensional scene.
8. A multi-fractal quantization system for terrain complexity in a three-dimensional scene is characterized in that: multi-fractal quantization method for realizing the terrain complexity in a three-dimensional scene as claimed in any of claims 1-7.
9. The multi-fractal quantization system for terrain complexity in three-dimensional scenes of claim 8, wherein: comprises the following modules which are used for realizing the functions of the system,
the first module is used for acquiring gradient data, and comprises the steps of establishing scattered points on the three-dimensional terrain surface of a selected area, acquiring elevation values of the scattered points, and calculating to obtain gradient values of the scattered points according to the elevation value relation of the scattered points and adjacent points;
the second module is used for carrying out multi-fractal calculation and comprises the steps of dividing a scatter point area into sub-areas with different scales by respectively and correspondingly adopting a box-dimension multi-fractal calculation method according to the value range of the order moment q parameter in a preset distribution function, calculating the gradient distribution probability measure and the distribution function in each sub-area, checking whether the multi-fractal characteristics of the scatter point area are obvious by judging whether the logarithm of the distribution function and the logarithm of the scale are linearly related or not, and extracting and reserving the corresponding singular index and the corresponding fractal dimension if the multi-fractal characteristics are obvious;
and the third module is used for drawing a multi-fractal spectrum according to the multiple groups of singular indexes and fractal dimensions obtained by the second module, extracting multi-fractal characteristic parameters through the multi-fractal spectrum image and supporting quantitative expression of terrain complexity.
10. The multi-fractal quantization system for terrain complexity in three-dimensional scenes of claim 8, wherein: comprising a processor and a memory, the memory being used for storing program instructions, the processor being used for calling the stored instructions in the memory to execute a method of multi-fractal quantization of terrain complexity in a three-dimensional scene as claimed in any one of claims 1 to 7.
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