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

Neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method

A technology of neighborhood information and classification methods, applied in the field of image processing, can solve the problems of low calculation efficiency of polarimetric SAR images, and achieve the effects of overcoming the slow convergence speed of dictionary learning, improving classification accuracy, and reducing computing time

Active Publication Date: 2016-09-07
XIDIAN UNIV
View PDF6 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The invention can effectively improve the classification accuracy of polarimetric SAR images, and at the same time can effectively improve the problem of low calculation efficiency of polarimetric SAR image classification

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
  • Neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method
  • Neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method
  • Neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] The present invention will be further described below in conjunction with the accompanying drawings.

[0041] refer to figure 1 , the steps that the present invention realizes are as follows:

[0042] Step 1, input a coherence matrix of a polarimetric SAR image to be classified.

[0043] Step 2, filtering.

[0044] Filter the coherence matrix of the polarimetric SAR image to be classified by using an exquisite Lee filter with a filtering window size of 7*7 pixels, remove speckle noise, and obtain the coherence matrix of the filtered polarimetric SAR image;

[0045] Step 3, extracting polarized neighborhood features.

[0046] Eigen decomposition is performed on each element of the coherence matrix of the filtered polarimetric SAR image to obtain the eigenvalue and corresponding eigenvector of each element.

[0047] The specific operation steps of Claude Cloude decomposition method are as follows:

[0048] In the first step, the scattering entropy of the coherence matr...

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 neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method. The objective of the invention is to solve the problems of long operation time and low computation efficiency caused by low dictionary learning convergence rate in a polarimetric synthetic aperture radar (SAR) image classification process in the prior art. The method includes the following specific steps of: (1) inputting a polarimetric SAR images; (2) performing filtering; (3) extracting polarimetric neighborhood features; (4) performing dimensionality reduction; (5) selecting a training sample and a test sample; (6) training a dictionary and a classifier; (7) testing the dictionary and the classifier; (8) performing coloring; and (9) outputting a classification result diagram. Compared with the prior art, the neighborhood information and SVGDL (support vector guide dictionary learning)-based polarimetric SAR image classification method of the invention can effectively improve the correction rate and computation efficiency of polarimetric SAR image classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarimetric synthetic aperture radar based on neighborhood information and support vector guide dictionary learning (Support VectorGuide Dictionary Learning, SVGDL) in the technical field of polarimetric synthetic aperture radar image feature classification (Synthetic ApertureRadar, SAR) image classification method. The invention can be used to classify the ground objects of the polarimetric SAR image, and can effectively improve the problem of low calculation efficiency of the polarimetric SAR image classification. Background technique [0002] Polarimetric SAR image classification is an important part of SAR image interpretation. The current classic polarimetric SAR image classification methods are: [0003] Wuhan University proposed a polarization SAR data classification method based on a hybrid classifier in its patent application "Polarization SAR Data Cla...

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): G06K9/46G06K9/62
CPCG06V10/422G06F18/24
Inventor 焦李成屈嵘李亚茹张丹马文萍马晶晶尚荣华赵进赵佳琦侯彪杨淑媛
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