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
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The invention can effectively improve the classification accuracy of polarimetric SAR images, and at the same

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  • 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

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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...

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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...

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Application Information

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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
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