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

Polarization SAR Image Classification Method Based on Scattered Energy and Stack Autoencoder

A classification method and self-encoding technology, applied in the field of image processing, can solve the problems affecting the classification accuracy of polarimetric synthetic aperture radar SAR images, unreasonable feature extraction, etc., so as to improve the classification efficiency, overcome the decline of the classification accuracy, and improve the classification accuracy. Effect

Active Publication Date: 2019-10-08
XIDIAN UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although this method combines the texture characteristics of the power image, it still has the disadvantage that when using the deep neural network to extract the features of the polarimetric synthetic aperture radar SAR image, it fails to introduce the spatial neighborhood features, which leads to unreasonable feature extraction and greatly affects Classification Accuracy of Synthetic Aperture Radar SAR Images

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
  • Polarization SAR Image Classification Method Based on Scattered Energy and Stack Autoencoder
  • Polarization SAR Image Classification Method Based on Scattered Energy and Stack Autoencoder
  • Polarization SAR Image Classification Method Based on Scattered Energy and Stack Autoencoder

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] refer to figure 1 , the concrete steps that the present invention realizes are as follows:

[0038] Step 1, input the coherence matrix of the polarimetric SAR SAR image to be classified.

[0039] Step 2, compensating the polarimetric SAR SAR coherence matrix.

[0040] Take the polarization azimuth angle for the polarization synthetic aperture radar SAR coherence matrix, use the polarization azimuth angle to calculate the rotation matrix of the polarization coherence matrix, use the rotation matrix to perform rotation transformation on the coherence matrix, and obtain the compensated polarization coherence matrix, the specific operation Proceed as follows:

[0041] The first step is to calculate the polarization azimuth angle of the polarization synthetic aperture radar SAR coherence matrix according to the following formula:

[0042]

[0043] Among them, ...

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 discloses a scattering energy and stack self-code-based polarimetric SAR image classification method, and mainly aims at solving the problems that classification process is complicated,polarimetric SAR image spatial features cannot be extracted and classification precision is not high due to the influences of extraction data independence and redundancy of polarimetric synthetic aperture radar SAR image features in the prior art. The method comprises the following specific realization steps of: (1) inputting a coherence matrix of a to-be-classified polarimetric synthetic apertureradar SAR image; (2) compensating the polarimetric SAR coherence matrix; (3) obtaining scattering energy of a scanning model; (4) obtaining a sample; (5) training stack self-codes; (6) classifying the stack self-codes; and (7) outputting a classification result. The method has the advantage of being remarkable in polarimetric synthetic aperture radar SAR image classification effect.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarization synthetic aperture radar (Synthetic Aperture Radar SAR) image classification method based on scattered energy and stack self-encoding in the field of target recognition. The invention can be used for ground object classification and target recognition of polarization synthetic aperture radar SAR images. Background technique [0002] Compared with traditional synthetic aperture radar, polarimetric synthetic aperture radar (SAR) uses the scattering information of multiple channels to obtain a more comprehensive understanding of the target. The classification of polarimetric SAR SAR images is an important research content of polarimetric SAR SAR image interpretation. The classification map can be used as an intermediate result to provide auxiliary information for edge extraction, target detection, recognition, etc., and can also be directly output as th...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
Inventor 尚荣华刘永坤焦李成刘芳王荣芳马晶晶王爽侯彪
Owner XIDIAN UNIV
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

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com