Ground station space representative hierarchical method, device and storage medium

By constructing three-dimensional feature vectors and using the K-means clustering algorithm, the subjectivity and uniformity of ground station representativeness evaluation were resolved, enabling objective classification of ground stations and improving the accuracy and reliability of remote sensing product verification and observation network optimization.

CN122176403APending Publication Date: 2026-06-09SOUTHWEST JIAOTONG UNIV
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTHWEST JIAOTONG UNIV
Filing Date
2026-03-23
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies for constructing remote sensing product verification and model training suffer from problems such as strong subjectivity, difficulty in quantification, lack of multi-dimensional comprehensive evaluation, and failure to consider seasonal and interannual variations in the evaluation of the representativeness of ground observation data, resulting in significant representativeness errors and uncertainties.

Method used

A comprehensive three-dimensional feature vector evaluation method is adopted, including the degree of spatial heterogeneity, spatial representativeness error and spatial representativeness range. Ground stations are classified by K-means clustering algorithm to form a standardized station quality evaluation system.

Benefits of technology

It enables objective and systematic classification of ground stations, improves the accuracy and reliability of remote sensing product verification, provides a basis for scientific observation network optimization and deep learning training sample weighting, and has good transferability and universality.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122176403A_ABST
    Figure CN122176403A_ABST
Patent Text Reader

Abstract

The present application belongs to the technical field of quantitative remote sensing, and relates to a ground station space representative grading method, equipment and storage medium. The method comprises the following steps: obtaining ground station observation data, high spatial resolution remote sensing data and coarse resolution remote sensing product data, and performing time synchronization and spatial registration on all data; for each ground station, based on the corresponding coarse resolution pixel scale, using high spatial resolution remote sensing data to determine the complementarity indexes of spatial heterogeneity degree, spatial representative error and spatial representative range in each time sequence; combining the spatial heterogeneity degree, spatial representative error and spatial representative range of each station into a three-dimensional feature vector, performing forward conversion and standardization processing on the three-dimensional feature vector, and forming a standardized feature matrix; inputting the standardized feature matrix into a K-means clustering algorithm to complete grading. The present application significantly improves the scientificity and discrimination efficiency of station representative evaluation.
Need to check novelty before this filing date? Find Prior Art