A method to measure the complexity of nautical chart elements taking into account local differences

Abstract

The present invention provides a method for measuring the complexity of nautical chart line elements taking into account local differences, and belongs to the field of marine surveying and mapping technology. Aiming at the problem that the complexity of current line elements is difficult to measure the differences in the complexity of curves in a region, the present invention designs a method for measuring the complexity of nautical charts taking into account local differences. Through the present invention, it is possible to effectively measure the complexity of curves on the map, and it is consistent with the perception of the human eye, providing the main basis for subsequent line element simplification, workload measurement, curve segmentation, etc.

Description

Technical Field

[0001] The invention belongs to the technical field of marine surveying and mapping, and relates to a method for measuring the complexity of nautical chart elements taking into account local differences. Background Art

[0002] As an important part of nautical chart generalization, the research on automatic generalization of nautical chart line elements has always attracted people's attention. Many experts and scholars at home and abroad have proposed many algorithms for the automatic generalization of line elements on nautical charts. Most of these algorithms use fixed thresholds or the same scale to generalize the elements on the chart. However, in the descriptions of classic works, legal norms and related literature, it is emphasized that the scale of generalization should be appropriately adjusted according to the situation on the chart during the generalization process of cartography. That is, in complex areas, the scale of generalization can be appropriately reduced; in simple areas, the scale of generalization can be appropriately increased. However, since this description is relatively vague and the complexity of line elements on the chart is often difficult to define, the current scale of generalization cannot change with the complexity of local areas on the chart, which makes the research on automatic generalization fall into a bottleneck.

[0003] In order to solve the above problems, many experts and scholars have proposed a number of models and algorithms for quantitative evaluation of the complexity of line elements. Cheng Mianmian and Sun Qun established a complexity measurement model based on the vibration frequency and amplitude of the edge from three aspects: the proportion of notches, the relative increment of the perimeter of the convex shell, and the relative increment of the minimum enclosing circle area; Gao Yi and Liu Xiaoxian used fractal dimension to describe the complexity of curves based on fractal theory; Xiao Tianyuan and Liu Pengcheng transformed geographic line elements from the spatial domain to the frequency domain through Fourier transform for analysis, and analyzed and defined the complexity of line elements from the perspective of frequency; He Ailing and Liu Huimin mainly described the complexity of line elements based on geometric analysis through the geometric shape, geometric topology, and geometric distribution of line elements.

[0004] The above model methods are all aimed at single line or surface elements, and the complexity of the curve is obtained by calculating the characteristics of the curve itself (fractal dimension, frequency, vibration frequency), and good experimental results have been achieved. However, on actual nautical charts, the complexity of the curve not only needs to take into account its own basic characteristics, but also other line elements around the curve. Even for a single line element, the complexity of different parts is not the same. If the above method is directly used to evaluate the complexity of the curve, only the overall complexity of each single curve can be obtained, and the differences in the line elements on the map in various regions cannot be accurately represented, and it is difficult to achieve a refined measurement of the complexity of the line elements on the map. Summary of the invention

[0005] In order to overcome the problem that current methods for measuring the complexity of nautical chart elements are mostly based on a single line element to obtain the overall complexity without taking into account the differences of line elements in different regions, the present invention provides a method for measuring the complexity of nautical chart elements taking into account local differences.

[0006] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0007] A method for measuring the complexity of nautical chart elements taking into account local differences comprises the following steps:

[0008] a. Analyze the chart data and extract the line elements on the chart;

[0009] b. Encrypt the data points on the line elements and store them;

[0010] c. Based on the encrypted line feature data points, a constrained Delaunay triangulation network is constructed;

[0011] d. Traverse the constructed triangulated network, remove the constrained edges of each data point, and retain the unconstrained edges;

[0012] e. Determine whether there are abnormal edges among the unconstrained edges retained in step d. If there are abnormal edges, remove them. If there are no abnormal edges, proceed to step f.

[0013] f. Using the unconstrained edges retained after step e, calculate the complexity of each data point on the line element one by one and store it;

[0014] g. Use the natural neighbor interpolation method to interpolate the complexity of each data point in step f, so as to obtain the complex field of nautical chart elements and output the result.

[0015] In the step b, the encryption processing method is: first, calculate the distance between two adjacent data points on the line element; then, compare the calculated distance with a predetermined threshold value for judgment. If the distance is greater than the threshold, it needs to be encrypted, that is, insert a new data point between the two points; if the distance is less than or equal to the threshold, it does not need to be encrypted, and proceed to the next point for further judgment.

[0016] In step e, the method for removing abnormal edges is:

[0017] 1) Based on the constructed triangulated network, calculate the length of all non-constrained edges related to the data point, denoted as d 1 ,d 2 ,d 3 …d n ;

[0018] 2) Based on the length value in step 1), calculate the standard deviation σ according to formula (1):

[0019]

[0020] in, is the length of each unconstrained edge d 1 ,d 2 ,d 3 …d n The average value of , n represents the number of unconstrained edges;

[0021] 3) For all the unconstrained edge lengths in step 1), make the following judgment based on formula (2): If the length d of an unconstrained edge satisfies

[0022]

[0023] The length of the unconstrained edge is considered to be an abnormal value containing a large error value (the value of m is 1, 2 or 3), and the unconstrained edge is an abnormal edge and needs to be removed.

[0024] In step f, the complexity of a line feature data point is defined as the reciprocal of the average length of the unconstrained edges associated with the data point, and its calculation formula is as follows:

[0025]

[0026] Where, d i Represents the length of each unconstrained edge, and n represents the number of unconstrained edges.

[0027] Beneficial effects of the invention: The invention mainly aims at the problem that the complexity of the current line elements is difficult to measure the differences in the complexity of the curves in the region, and designs a method for measuring the complexity of nautical charts that takes into account local differences. Through the invention, the complexity of the curves on the map can be effectively measured, and it is consistent with the perception of the human eye, providing the main basis for subsequent line element simplification, workload measurement, curve segmentation, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a general flow chart of the method for measuring the complexity of nautical chart elements taking into account local differences of the present invention.

[0029] Figure 2 It is a schematic diagram of constructing a triangulated network by encrypting data points of the present invention, wherein (a) is the original curve element, (b) is the constrained triangulated network before encryption, and (c) is the constrained triangulated network after encryption.

[0030] Figure 3 It is a schematic diagram of the definition of data point complexity of the present invention.

[0031] Figure 4It is a schematic diagram of the complex field of line elements generated by the present invention, wherein (a) is the original line element, (b) is the complexity of each data point, and (c) is the complex field after interpolation. DETAILED DESCRIPTION

[0032] The specific implementation modes of the present invention will be further described below in conjunction with the accompanying drawings.

[0033] The method for measuring the complexity of nautical chart line elements taking into account local differences proposed in the present invention is implemented by using a computer to measure the complexity of line elements. Assuming that there is an existing nautical chart A, the method of the present invention is used to measure the complexity of line elements, including the following steps: Figure 1 As shown:

[0034] Step a, reading the nautical chart data on the computer, extracting the line elements on the nautical chart and storing them in the memory, for the hypothetical nautical chart A, the set of its line elements is called S(L);

[0035] Step b, sequentially extracting line elements L from the set S(L), encrypting the data points on the line elements, and storing the encrypted data;

[0036] Step c: Based on the encrypted line feature data points, a constrained Delaunay triangulation is constructed, such as Figure 2 As shown, it is a schematic diagram of constructing a triangulated network by encrypting data points in the present invention. Figure 2 (a) is the original curve element, (b) and (c) are the triangulated networks constructed without encryption and after encryption, respectively;

[0037] Step d, traverse the constructed triangulated network, remove the constrained edges of each data point, and retain the unconstrained edges, as follows Figure 3 As shown, if we randomly select any data point A, we can know from the constructed triangulated network that the edges related to point A are AB, AC, AD, AE, AF, and AG, where edges AB and AD are line segments on the original curve L2 (i.e., constrained edges that need to be removed), and the other edges are all unconstrained edges;

[0038] Step e, determining whether there are abnormal edges among the unconstrained edges retained in step d. If there are abnormal edges, the abnormal edges need to be removed. If there are no abnormal edges, proceed to step f;

[0039] Step f, using the unconstrained edges retained after step e, calculate the complexity of each data point on the line element one by one and store it, such as Figure 4 (b) shows the complexity of each point calculated in sequence;

[0040] Step g, interpolating the complexity of each data point in step f using the natural neighbor interpolation method, thereby obtaining the complex field of the nautical chart line elements, and outputting the result, such as Figure 4 (c) shows the complex field of line elements.

[0041] The present invention is described by way of embodiments, and those skilled in the art will appreciate that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the protection scope of the present invention.

Claims

1. A method for measuring the complexity of nautical chart elements taking into account local differences, characterized in that: The method comprises the following steps: a. Analyze the chart data and extract the line elements on the chart; b. Encrypt and store the data points on the line elements; c. Based on the encrypted line feature data points, a constrained Delaunay triangulation network is constructed; d. Traverse the constructed triangulated network, remove the constrained edges of each data point, and retain the unconstrained edges; e. Determine whether there are abnormal edges among the unconstrained edges retained in step d. If there are abnormal edges, remove them. If there are no abnormal edges, proceed to step f. In step e, the method for removing abnormal edges is: 1) Based on the constructed triangulated network, calculate the lengths of all non-constrained edges related to the data points, denoted as d1, d2, d3…d n ; 2) Based on the length value in step 1), calculate the standard deviation σ according to formula (1): in, The lengths of the unconstrained edges are d1, d2, d3…d n The average value of , n represents the number of unconstrained edges; 3) For all the unconstrained edge lengths in step 1), make the following judgment based on formula (2): If the length d of an unconstrained edge satisfies The length of the unconstrained edge is considered to be an outlier with a large error value, and the unconstrained edge is an outlier and needs to be removed; the value of m is 1, 2 or 3; f. Using the unconstrained edges retained after step e, calculate the complexity of each data point on the line element one by one and store it; In step f, the complexity of a line feature data point is defined as the reciprocal of the average length of the unconstrained edges associated with the data point, and its calculation formula is as follows: Where, d i represents the length of each unconstrained edge, and n represents the number of unconstrained edges; g. Use the natural neighbor interpolation method to interpolate the complexity of each data point in step f to obtain the complex field of nautical chart elements and output the result.

2. A method for measuring the complexity of nautical chart elements taking into account local differences according to claim 1, characterized in that: In step b, the encryption processing method is: first, calculate the distance between two adjacent data points on the line element; then, compare the calculated distance with a given threshold value for judgment. If the distance is greater than the threshold value, it needs to be encrypted, that is, insert a new data point between the two points; if the distance is less than or equal to the threshold value, it does not need to be encrypted, and proceed to the next point for further judgment.

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