Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Atmospheric delay estimation method based on back propagation neural network

An atmospheric delay and neural network technology, applied in the field of atmospheric delay correction based on backpropagation neural network, can solve the problem of ignoring the corresponding relationship between atmospheric delay and spatial geographic location, data that cannot be corrected for atmospheric delay, and ignoring individual differences in atmospheric errors, etc. problem, to achieve the effect of overcoming SAR image data, improving feasibility and applicability, and overcoming the influence of estimation results

Pending Publication Date: 2020-11-10
云南电网有限责任公司输电分公司
View PDF2 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that in the process of atmospheric delay interpolation, only the corresponding relationship between atmospheric delay and elevation information is considered, while the corresponding relationship between atmospheric delay and spatial geographic location is ignored, which affects the accuracy of atmospheric delay correction
There are two shortcomings of this method. First, this method needs to obtain multiple SAR images in different time periods in the same area, generally more than 20, and atmospheric delay correction cannot be performed for data with a small amount of data.
Second, this method only relies on the information of the image itself to correct the atmospheric delay, and relies on the statistical characteristics of the atmospheric delay to estimate the atmospheric delay, ignoring the individual differences of atmospheric errors in different periods. In the statistical process, its accuracy is affected by data quality and other errors. Impact

Method used

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
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Atmospheric delay estimation method based on back propagation neural network
  • Atmospheric delay estimation method based on back propagation neural network
  • Atmospheric delay estimation method based on back propagation neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] figure 1 The overall flowchart of the specific embodiment 1 is provided for the atmospheric delay estimation method based on the backpropagation neural network of the present invention. like figure 1 As shown, an atmospheric delay estimation method based on backpropagation neural network, including the following steps:

[0057] Step S1: Select pixels with continuous atmospheric delay interference from a SAR image as the area to be observed.

[0058] Step S2: Generate a training sample set. In this step, the following steps are included:

[0059] Step S21: Extracting the digital elevation of each known atmospheric delay value pixel in the area to be observed, including the azimuth coordinate value of the pixel point in the SAR image, the distance coordinate value in the SAR image, and the elevation value;

[0060] Step S22: Select an unselected known atmospheric delay value pixel point from the area to be observed;

[0061] Step S23: Using the planar distance calcul...

Embodiment 2

[0084] figure 2 A structural block diagram of a specific embodiment 2 is provided for the device for estimating atmospheric delay based on a backpropagation neural network in the present invention. like figure 2 As shown, this embodiment provides an atmospheric delay estimation device based on a backpropagation neural network, including

[0085] The data acquisition module is used to acquire pixels with continuous atmospheric delay interference from the SAR image;

[0086] The generation module of the training sample set utilizes the digital elevation and reference information of all known atmospheric delay value pixels to generate the training sample set;

[0087] The design and training module of the neural network uses the backpropagation BP algorithm to train the neural network to obtain a trained neural network;

[0088] The pixel point classification module divides all the pixel points in the area to be observed into a known atmospheric delay value point set and an ...

Embodiment 3

[0092] This embodiment provides a system for estimating atmospheric delay based on a backpropagation neural network, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the computer program, the system is implemented. steps of the method described above.

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

PUM

No PUM Login to View More

Abstract

The invention relates to the technical field of signal processing, in particular to an atmospheric delay estimation method, device and system based on a back propagation neural network and a storage medium, and the method comprises the steps: building the neural network through employing the related information of known atmospheric delay value pixel points of a to-be-observed region as training data and employing an error back propagation principle, training the neural network by using training data, inputting related information of unknown atmospheric delay value pixel points of the to-be-observed area into a trained network, and continuously solving new unknown atmospheric delay value pixel points on the basis of updating known information until atmospheric delay values of all pixel points are solved. The method can be applied to computer end software and is matched with corresponding hardware equipment. According to the method, the problem of low estimation precision caused by the fact that an atmospheric delay value model extracted in the prior art is not representative can be effectively solved, the influence of other errors of data on an estimation result in the prior art isovercome, and the atmospheric delay value estimation precision is improved.

Description

technical field [0001] The present invention relates to the technical field of signal processing, in particular to an atmospheric delay correction method, device, system and storage medium based on a backpropagation neural network in the technical field of radar signal processing. Background technique [0002] Differential Interferometry Synthetic Aperture Radar (DInSAR) is a microwave remote sensing technology with great potential. It can obtain centimeter-level or even millimeter-level surface deformation in deformation detection, and has been successfully applied, but its measurement accuracy is still limited by many. Constraints of factors: such as image registration error, complex terrain or decoherence noise in areas with more vegetation coverage, orbital error, phase unwrapping error in complex areas, atmospheric delay error, and terrain phase residuals that are not allowed to be introduced by external digital elevation models. Poor wait. Among these errors, due to t...

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

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06T7/00G06N3/04G06N3/08G01S13/90G01S7/41
CPCG06T7/0002G06N3/084G01S13/9094G01S13/9023G01S7/417G01S7/418G06T2207/10044G06T2207/20081G06T2207/20084G06T2207/30192G06N3/044G06N3/045
Inventor 张继伟杨腾杨凤符宗锐者梅林罗哲轩
Owner 云南电网有限责任公司输电分公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products