CN-121996734-A - Ground observation point location method based on regional representativeness and pixel representativeness

CN121996734ACN 121996734 ACN121996734 ACN 121996734ACN-121996734-A

Abstract

The invention discloses a ground observation point location method based on regional representativeness and pixel representativeness, and relates to the technical field of remote sensing. The method comprises the steps of constructing an earth surface parameter matrix for each grid in a target area, dividing the categories of the grids, selecting the grid which meets a space distance constraint threshold and is closest to a clustering center as a representative sample point area for each grid category, acquiring multi-parameter time sequence characteristics of all high-resolution pixels in the representative sample point area for each representative sample point area, calculating representative errors of all parameters in the multi-parameter time sequence characteristics, screening candidate pixel combinations which meet a preset threshold condition, traversing random combinations in the candidate pixel combinations, selecting a plurality of high-resolution pixels with the smallest total representative errors as ground observation points of the representative sample point area, and integrating the ground observation points of all the representative sample point areas into the ground observation points of the target area. The method improves the site selection precision of the ground observation points.

Inventors

WU XIAODAN
LI YUKUN
WEN JIANGUANG
XIAO QING
YIN GAOFEI
LI XIAOJUN
LIN XINGWEN

Assignees

西南交通大学

Dates

Publication Date: 20260508
Application Date: 20260130

Claims (9)

1. The ground observation point location method based on the regional representativeness and the pixel representativeness is characterized by comprising the following steps: Dividing the target area into a plurality of grids according to longitude and latitude information of low-resolution remote sensing data in the target area; Constructing an earth surface parameter matrix for each grid, and dividing the categories of the grids according to the earth surface parameter matrix, wherein the earth surface parameter matrix comprises a normalized vegetation index, a normalized water body index, a normalized soil humidity index and a near infrared band reflectivity; Selecting a grid which meets a space distance constraint threshold and is closest to a clustering center as a representative sample point area for each grid category, wherein the space distance constraint threshold is half of a maximum variation value calculated based on a semi-variation function analysis result of a main component; For each representative sample point area, acquiring multi-parameter time sequence characteristics of all high-resolution pixels in the representative sample point area, calculating representative errors of all parameters in the multi-parameter time sequence characteristics, and screening candidate pixel combinations meeting preset threshold conditions; And traversing random combinations in the candidate pixel combinations, selecting a plurality of high-resolution pixels with the smallest total representative error as ground observation points of the representative sample point areas, and integrating the ground observation points of all the representative sample point areas into ground observation points of a target area.
2. The method of claim 1, wherein the classifying the grid according to the surface parameter matrix comprises: Performing principal component analysis and dimension reduction on the earth surface parameter matrix; And (3) analyzing the dimension-reduced data according to the principal components, and dividing the categories of the grids by adopting a K-means clustering algorithm.
3. The method according to claim 2, wherein the determining of the spatial distance constraint threshold comprises: Constructing a half-variation function based on the data after the principal component analysis and dimension reduction, wherein the half-variation function is as follows: ; Wherein, the Is a half-variance function value when the interval distance between sample points is h, For all sets of sample point pairs spaced apart by a distance h, In the picture element The characteristic value of the main component is set, Is in the pixel The characteristic value of the principal component at that point, = +H, wherein the main component is PCA dimension-reduced data; fitting the calculated semi-variation function value based on a theoretical model, and extracting a range parameter from a fitting result, wherein the theoretical model comprises a spherical model, and the range parameter is used for representing the maximum distance of the spatial variable with correlation in space; if a plurality of principal components exist in the data subjected to principal component analysis and dimension reduction, determining half of the maximum value in the variable range parameter values of all the principal components as a space distance constraint threshold; if only one principal component exists in the data subjected to principal component analysis and dimension reduction, determining half of the maximum value in the variation parameter values of the only one principal component as a space distance constraint threshold.
4. The method of claim 1, wherein the calculation formula of the representative error of the parameter is: ; Wherein RE is the representative error of the parameter, Is a time series vector of candidate sample points, To represent a time series vector of the mean value of all picture elements in the sampling point region, n is the length of the time series, The overall average value of the regional average value vector is i is a time index; the calculation formula of the total representative error is: ; Wherein, the As a result of the overall representative error, To normalize the weights of the vegetation indices, In order to normalize the weights of the water body index, To normalize the weight of the soil moisture index, As the weight of the reflectivity of the near infrared band, To normalize the representativeness error of the vegetation index, To normalize the representativeness error of the water index, To normalize the representativeness error of the soil moisture index, Is the representative error of the reflectivity of the near infrared band; The formula corresponding to the preset threshold condition is as follows: ; Wherein, the Representing pixel positions where representative errors over all parameters are less than a set threshold, The position is indicated by the position of the object, To normalize the representative error of the vegetation index, To normalize the representative error threshold of the vegetation index, To normalize the representative error of the water index, To normalize the representative error threshold of the water index, To normalize for a representative error in soil moisture index, To normalize the representative error threshold for the soil moisture index, As a representative error in the reflectivity of the near infrared band, Is a representative error threshold for near infrared band reflectivity.
5. The method of claim 1, wherein constructing a surface parameter matrix for each mesh comprises: extracting time sequence data of the high-resolution remote sensing image in each grid; obtaining band information of the grid through space aggregation average based on the time sequence data; and constructing an earth surface parameter matrix of the grid according to the wave band information.
6. The method of claim 1, wherein the method further comprises: and after the middle-low resolution remote sensing data of the target area are acquired, UTM projection is carried out on the middle-low resolution remote sensing data to convert the target area from a spherical surface to a plane.
7. The ground observation point location device based on the regional representativeness and the pixel representativeness is characterized by comprising: the dividing module is used for dividing the target area into a plurality of grids according to longitude and latitude information of the low-resolution remote sensing data in the target area; the classification module is used for constructing an earth surface parameter matrix for each grid and dividing the categories of the grids according to the earth surface parameter matrix, wherein the earth surface parameter matrix comprises a normalized vegetation index, a normalized water body index, a normalized soil humidity index and a near infrared band reflectivity; The first screening module is used for selecting a grid which meets a space distance constraint threshold and is closest to a clustering center as a representative sample point area for each grid category, wherein the space distance constraint threshold is half of a maximum variation value calculated based on a semi-variation function analysis result of a main component; The second screening module is used for acquiring multi-parameter time sequence characteristics of all high-resolution pixels in each representative sample point area, calculating representative errors of all parameters in the multi-parameter time sequence characteristics, and screening candidate pixel combinations meeting preset threshold conditions; the determining module is used for traversing random combinations in the candidate pixel combinations, selecting a plurality of high-resolution pixels with the smallest total representative error as ground observation points of the representative sample point areas, and integrating the ground observation points of all the representative sample point areas into the ground observation points of the target area.
8. A computer readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any of claims 1 to 6 when the program is executed.

Description

Ground observation point location method based on regional representativeness and pixel representativeness Technical Field The invention relates to the technical field of remote sensing, in particular to a ground observation point location method based on regional representativeness and pixel representativeness. Background In the technical field of quantitative remote sensing, along with rapid improvement of sensor precision, data processing algorithm and remote sensing platform performance, the quantitative remote sensing is widely applied to a plurality of important scenes such as agricultural monitoring, ecological assessment and resource exploration, and has the core value that the key parameters of the earth surface (such as vegetation index, leaf area index, earth surface temperature, soil moisture and vegetation coverage and the like) can be inverted through remote sensing data, and the parameters can provide accurate spatial data support for various scientific researches, engineering application and decision making, so that the quantitative remote sensing technology is a core foundation for pushing the remote sensing technology to span from qualitative description to quantitative analysis. In the practical application process of the quantitative remote sensing technology, ground site observation data are indispensable key support data, the prior art is limited by factors such as topography, accessibility, electric power and the like when the ground observation points are selected, the prior art is mostly arranged in areas with convenient conditions and uniform ground surface, and the lack of the area representativeness leads to low site selection precision of the ground observation points. Disclosure of Invention Based on the above, it is necessary to provide a ground observation point location method based on region representativeness and pixel representativeness in order to solve the above-mentioned technical problems. The method improves the site selection precision of the ground observation points. The invention adopts the following technical scheme: The invention provides a ground observation point location method based on regional representativeness and pixel representativeness, which comprises the following steps: Dividing the target area into a plurality of grids according to longitude and latitude information of low-resolution remote sensing data in the target area; Constructing an earth surface parameter matrix for each grid, and dividing the categories of the grids according to the earth surface parameter matrix, wherein the earth surface parameter matrix comprises a normalized vegetation index, a normalized water body index, a normalized soil humidity index and a near infrared band reflectivity; Selecting a grid which meets a space distance constraint threshold and is closest to a clustering center as a representative sample point area for each grid category, wherein the space distance constraint threshold is half of a maximum variation value calculated based on a half variation function analysis result of a main component; For each representative sample point area, acquiring multi-parameter time sequence characteristics of all high-resolution pixels in the representative sample point area, calculating representative errors of all parameters in the multi-parameter time sequence characteristics, and screening candidate pixel combinations meeting preset threshold conditions; And traversing random combinations in the candidate pixel combinations, selecting a plurality of high-resolution pixels with the smallest total representative error as ground observation points of the representative sample point areas, and integrating the ground observation points of all the representative sample point areas into the ground observation points of the target area. Preferably, classifying the grids according to the surface parameter matrix specifically comprises: Performing principal component analysis and dimension reduction on the earth surface parameter matrix; And (3) analyzing the dimension-reduced data according to the principal components, and dividing the categories of the grids by adopting a K-means clustering algorithm. Preferably, the determining process of the spatial distance constraint threshold specifically includes: constructing a half-variation function based on the data after the principal component analysis and dimension reduction, wherein the half-variation function is as follows: ; Wherein, the Is a half-variance function value when the interval distance between sample points is h,For all sets of sample point pairs spaced apart by a distance h,In the picture elementThe characteristic value of the main component is set,Is in the pixelThe characteristic value of the principal component at that point,=+H, wherein the main component is PCA dimension-reduced data; Fitting the calculated semi-variation function value based on a theoretical model, and extracting a range parameter from the f