Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Classification Method of Polarized SAR Image Based on Freeman Decomposition and Spectral Clustering

A classification method and spectral clustering technology, applied in the field of polarimetric SAR image classification based on Freeman decomposition and spectral clustering algorithm, can solve the problems that hinder the performance of the algorithm, the amount of calculation and storage is unbearable, and the stability of image segmentation is degraded, etc. question

Active Publication Date: 2016-01-20
XIDIAN UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the algorithm is applied to the field of image segmentation, the amount of calculation and storage is unbearable, which seriously hinders the performance of the algorithm.
And when the Gaussian function is used to construct the similarity matrix, the scale parameter has a great influence on the classification structure and it is difficult to obtain the optimal parameter, which reduces the stability of image segmentation

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
  • Classification Method of Polarized SAR Image Based on Freeman Decomposition and Spectral Clustering
  • Classification Method of Polarized SAR Image Based on Freeman Decomposition and Spectral Clustering
  • Classification Method of Polarized SAR Image Based on Freeman Decomposition and Spectral Clustering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0049] Step 1, filter the polarimetric SAR image to be classified.

[0050] Select a polarimetric SAR image to be classified, the size is R×Q, and filter the polarimetric SAR image to be classified to remove speckle noise. The filtering methods that can be used include polarization whitening filtering, Boxcar filtering, and refined polarization LEE filtering And the filtering method based on unsupervised classification, etc., the filtering method adopted in the present invention is the refined polarization LEE filtering method, and the size of the filtering window is 7×7.

[0051] Step 2: Perform Freeman decomposition on the coherence matrix T of each pixel in the filtered polarimetric SAR image to obtain the volume scattering power P of each pixel v , dihedral scattered power P d and surface scattered power P s .

[0052] (2a) Read in each pixel of the filtered image, ...

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 method for classifying polarimetric SAR (synthetic aperture radar) images on the basis of Freeman decomposition and spectral clustering and mainly solves the problem that the existing unsupervised polarimetric SAR classification method is low in classification precision. The method includes the steps of subjecting each pixel to Freeman decomposition, and extracting volume scattering power, dihedral angle scattering power and surface scattering power of each pixel; applying the scattering powers and a coordinate of the pixel as input feature vectors of Mean Shift algorithm, and segmenting images by the Mean Shift algorithm to obtain M regions; selecting representative points of the M regions as input points for spectral clustering, subjecting each region to spectral clustering to obtain results of image pre-classification; and finally subjecting a whole image obtained by pre-classification to iterative classification through a Wishart classifier so as to obtain final classification results. Experimental results show that classification of the polarimetric SAR images by the method is better, and the method is applicable to unsupervised classification of various polarimetric SAR images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to applications in the field of polarization synthetic aperture radar (SAR) image ground object classification, specifically a polarization SAR image classification method based on Freeman decomposition and spectral clustering algorithm, which can be used for polarization Object classification and target recognition in SAR images. Background technique [0002] With the development of radar technology, polarimetric SAR has become the development trend of SAR. Polarimetric SAR can obtain more abundant target information, and has a wide range of research and applications in agriculture, forestry, military, geology, hydrology and oceans. Value, such as identification of ground object types, crop growth monitoring, yield assessment, land object classification, sea ice monitoring, land subsidence monitoring, target detection and marine pollution detection, etc. The purpose of pola...

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 焦李成刘坤郭卫英李婷婷王爽马晶晶马文萍刘亚超侯小瑾张涛
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products