CN113284139B - Method for rapidly estimating ground object space pattern based on periodic variation function - Google Patents
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Abstract
The invention discloses a method for rapidly estimating a ground object space pattern based on a periodic variation function, which is characterized by comprising the following steps of: firstly, preprocessing and registering an original remote sensing image, and selecting a research area of interest; and secondly, coarsening the remote sensing image, calculating a variation function curve, and obtaining the size of the ground feature space pattern by utilizing the variation rate of adjacent points of the variation function curve. The method achieves the purpose of estimating the spatial pattern of the ground object by calculating the variation function curve and the variation rate thereof, can reflect the spatial variability of the specific object in different directions by calculating the variation function, and utilizes the periodic variation function obtained by calculating the remote sensing image compounded manually, thereby having the characteristics of periodicity, stability, intuitiveness and the like and being capable of intuitively reflecting the spatial pattern of the ground object.
Description
Technical Field
The invention belongs to the technical field of geostatistics, and particularly relates to a method for rapidly estimating a space pattern of a ground object based on a periodic variation function.
Background
Variation function theory is often used to quantitatively describe the structural and randomness of a localized variable, which is the fundamental statistic of the localized variable. Under the condition that quasi-second order stability and quasi-eigenvalue assumption are satisfied, the half-mutation function value of two zoned variables Z (x) and Z (x+h) divided at any distance h is defined as 1/2Var [ Z (x) -Z (x+h)] 2 . The above formula can be explained as follows: half variance is a mathematically expected 1/2 of the square difference of the property values between a pair of sample points (pixel points). Assuming that the DN values in the image are not randomly distributed, each land type has its unique spatial distribution structure. Therefore, on the premise that the DN value of the remote sensing image is regarded as a regional variable with randomness and knot, the change of the ground feature distribution of the remote sensing image in the regular domain and the trend thereof can be further researched through a variation function. The existing method generally needs to utilize mathematical processes such as specific index extraction, binarization, calculation area and the like, so that the working efficiency is greatly reduced, and the phenomenon of large error when the space pattern of the ground object of the remote sensing image is estimated by utilizing the slope of a variation function curve cannot be solved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for rapidly estimating the space pattern of the ground object based on the periodic variation function aiming at the defects of the prior art, which achieves the purpose of estimating the space pattern of the ground object by calculating the variation function curve and the change rate thereof, can reflect the space variability of the specific ground object in different directions by calculating the variation function, and utilizes the periodic variation function obtained by calculating the remote sensing image which is artificially compounded, has the characteristics of periodicity, stability, intuitiveness and the like, and can intuitively reflect the space pattern of the ground object.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a method for rapidly estimating a spatial pattern of a feature based on a periodic variation function, comprising the steps of:
the first step: acquiring a remote sensing image, selecting ground objects in a research area of interest to perform space pattern analysis, and obtaining a related research area through cutting;
and a second step of: preprocessing a remote sensing image, including geometric fine correction and research area cutting by using control points;
and a third step of: compounding the remote sensing image research areas, artificially splicing the remote sensing composite images with n x n, wherein n is 5 to 8;
fourth step: calculating variation function curves in different directions of ground objects in a research area by using the spliced remote sensing images;
fifth step: and obtaining the average length and the width by using the hysteresis distance when the slope change rate of the variation function curve calculated in the fourth step starts to be gentle.
The variation function curve calculation in the fourth step is composed of the following steps:
step one: setting the coordinate value of the current point in the composite image as (x 0, y 0) gray value as zi, and setting the gray value of the point (x0+h, y0+h) with interval h as zi+h;
step two: calculating the variances of the zi and the zi+h, namely the difference degree between the current point and the point with the interval h;
step three: computing the half variances Y (zi) and Y (zi+h) of zi and zi+h;
step four: and drawing a broken line by taking the gray value as an abscissa and the half variance value Y as an ordinate to obtain a variation function curve.
The invention adopts the theory of the variation function in the geostatistics, can reflect the spatial variability of specific objects in different directions by calculating the variation function, and utilizes the artificial composite remote sensing image to calculate the periodic variation function, thereby having the characteristics of periodicity, stability, intuitiveness and the like. The space pattern of the ground object can be intuitively reflected.
The invention provides a calculation method for rapidly estimating the size of a ground object space pattern, which solves the problem that a general variation function is influenced by local maximum values or minimum values when estimating the ground object space pattern of a remote sensing image, and solves the problem that errors are large when estimating the ground object space pattern of the remote sensing image by using the slope of a variation function curve; the algorithm concept is as follows: setting the coordinate value of the current point in the composite image as (x 0, y 0) gray value as zi, and setting the gray value of the point (x0+h, y0+h) with interval h as zi+h; calculating the variances of the zi and the zi+h, namely the difference degree of the current point and the interval which are points; when h is gradually increased, the difference degree gradually tends to be stable; due to the realization of image compounding, the variation of the difference degree and the stable trend can show periodicity; the periodicity of the degree of image difference may reflect the specific physical pattern.
The invention has the advantages that: the average length and width of the ground object can be quickly obtained, and mathematical processes of extracting, binarizing, calculating the area and the like by using specific indexes in the traditional method are reduced; the phenomenon that the general variation function is influenced by local maximum or minimum when estimating the ground object space pattern of the remote sensing image is solved; the problem of large error when the slope of a variation function curve is generally utilized to estimate the ground object space pattern of the remote sensing image is solved.
Drawings
FIG. 1 is a workflow diagram of the present invention;
FIG. 2 is a view of the study area after pretreatment in accordance with the present invention;
FIG. 3 is a view of the present invention as a composite study area;
FIG. 4 is a graph showing the variation function of a general feature of a lake according to the present invention;
FIG. 5 is a graph of a general feature complex variation function of a lake in a complex study area according to the present invention;
FIG. 6 is a graph showing the accuracy of the variation function calculated by the composite period according to the present invention.
Detailed Description
The following is a further description of embodiments of the invention, taken in conjunction with the accompanying drawings:
a method for rapidly estimating a ground object space pattern based on a periodic variation function is characterized by comprising the following steps: the method is characterized by comprising the following steps of:
the first step: acquiring a remote sensing image, selecting ground objects in a research area of interest to perform space pattern analysis, and obtaining a related research area through cutting;
and a second step of: preprocessing a remote sensing image, including geometric fine correction and research area clipping by using control points, as shown in fig. 2;
and a third step of: compounding a remote sensing image research area, and artificially splicing the remote sensing composite image with n x n, wherein n is 5 to 8, as shown in fig. 3;
fourth step: calculating variation function curves in different directions of ground objects in a research area by using the spliced remote sensing images, as shown in fig. 5;
fifth step: the average length and width were obtained using the hysteresis distance at the beginning of the flattening with the slope change rate of the variation function curve calculated in the fourth step, as shown in table 1.
In an embodiment, the variation function curve calculation in the fourth step consists of the following steps:
step one: setting the coordinate value of the current point in the composite image as (x 0, y 0) gray value as zi, and setting the gray value of the point (x0+h, y0+h) with interval h as zi+h;
step two: calculating the variances of the zi and the zi+h, namely the difference degree between the current point and the point with the interval h;
step three: computing the half variances Y (zi) and Y (zi+h) of zi and zi+h;
step four: and drawing a broken line by taking the gray value as an abscissa and the half variance value Y as an ordinate to obtain a variation function curve.
The invention adopts the theory of the variation function in the geostatistics, can reflect the spatial variability of specific objects in different directions by calculating the variation function, and utilizes the artificial composite remote sensing image to calculate the periodic variation function, thereby having the characteristics of periodicity, stability, intuitiveness and the like. The space pattern of the ground object can be intuitively reflected.
The invention provides a calculation method for rapidly estimating the size of a ground object space pattern, which solves the problem that a general variation function is influenced by local maximum values or minimum values when estimating the ground object space pattern of a remote sensing image, and solves the problem that errors are large when estimating the ground object space pattern of the remote sensing image by using the slope of a variation function curve; the algorithm concept is as follows: setting the coordinate value of the current point in the composite image as (x 0, y 0) gray value as zi, and setting the gray value of the point (x0+h, y0+h) with interval h as zi+h; calculating the variances of the zi and the zi+h, namely the difference degree of the current point and the interval which are points; when h is gradually increased, the difference degree gradually tends to be stable; due to the realization of image compounding, the variation of the difference degree and the stable trend can show periodicity; the periodicity of the degree of image difference may reflect the specific physical pattern.
TABLE 1
| Year of year | 2013 | 2014 | 2015 | 2016 | 2017 | 2019 | 2020 |
| Average length of water index | 9673.008 | 9988.416 | 10014.29 | 10110.05 | 10097.47 | 10027.58 | 10179.94 |
| Average lake length of general variation function | 7800 | 8400 | 14400 | 12000 | 9600 | 7200 | 12000 |
| Precision of | 80.64% | 84.10% | 69.54% | 84.25% | 95.07% | 71.80% | 84.83% |
| Average lake length of composite variation function | 9000 | 9600 | 11500 | 10500 | 10500 | 9000 | 10500 |
| Precision of | 93.04% | 96.11% | 86.70% | 96.29% | 96.17% | 89.75% | 96.95% |
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (1)
1. A method for rapidly estimating a spatial pattern of a feature based on a periodic variation function, comprising the steps of:
the first step: acquiring a remote sensing image, selecting ground objects in a research area of interest to perform space pattern analysis, and obtaining a related research area through cutting;
and a second step of: preprocessing a remote sensing image, including geometric fine correction and research area cutting by using control points;
and a third step of: compounding the remote sensing image research areas, artificially splicing the remote sensing composite images with n x n, wherein n is 5 to 8;
fourth step: calculating variation function curves in different directions of ground objects in a research area by using the spliced remote sensing images;
fifth step: acquiring average length and width by using the slope change rate of the variation function curve calculated in the fourth step and the delay distance when the slope change rate becomes gentle;
the variation function curve calculation in the fourth step comprises the following steps:
step one: setting the coordinate value of the current point in the composite image as (x 0, y 0) gray value as zi, and setting the gray value of the point (x0+h, y0+h) with interval h as zi+h;
step two: calculating the variances of the zi and the zi+h, namely the difference degree between the current point and the point with the interval h;
step three: computing the half variances Y (zi) and Y (zi+h) of zi and zi+h;
step four: and drawing a broken line by taking the gray value as an abscissa and the half variance value Y as an ordinate to obtain a variation function curve.
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