A Remote Sensing Quantitative Inversion Method for Sampling Learning Machine Adapted to Noise Conditions

A learning machine and inversion technology, applied in the field of remote sensing applications, can solve the problems of nonlinear noise and interference in the quantitative inversion of remote sensing, and achieve the effect of strong generalization ability, eliminating the influence of noise, and improving accuracy.
CN104899464BInactive Publication Date: 2017-12-29广西中马园区数字城市科技有限公司
3 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
广西中马园区数字城市科技有限公司
Publication Date
2017-12-29
Estimated Expiration
Not applicable · inactive patent

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The present invention provides a remote sensing quantitative inversion method for sampling learning machines adapted to noise conditions, and uses the characteristics of fixed small order weights in extreme learning machines to simulate the nonlinear complex mathematics between influencing factors and inversion objects in remote sensing quantitative inversion Transform it into solving a linear system Hβ=TT; adaptively select the model parameter estimation algorithm according to the dimension of the network model parameter β; use the selected model parameter estimation algorithm to realize the solution of the network model parameter β in Hβ=TT. The present invention establishes a complex mathematical relationship model between influencing factors and inversion objects in remote sensing quantitative inversion; in the process of solving model parameters, it can filter the interference of sample data noise and adaptively select model parameter estimation algorithms, thereby quickly obtaining the model The best parameter results.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The invention relates to the field of remote sensing applications, in particular to a sampling learning machine remote sensing quantitative inversion method adapted to noise conditions. Background technique

[0002] As an important means of earth system observation, remote sensing technology can provide continuous information on global land surface changes. In recent years, the application demand for quantitative inversion of water, atmospheric and ecological environment parameters based on remote sensing data has become increasingly prominent, and increasingly urgent requirements have been put forward for the accuracy of quantitative inversion. The main problem to be solved in quantitative remote sensing is how to use remote sensing data to accurately estimate surface parameters, realize the link of remote sensing data industry application models, and improve the prediction accuracy of the model. Taking the application of water quality remote sensing...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More