Polarized SAR (Specific Absorption Rate) image classifying method based on super-vector coding

A super vector and image technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of no significant improvement in accuracy rate, low classification accuracy, and multiple wrong points, so as to improve classification Accurate, robust, less susceptible to noise effects

Active Publication Date: 2015-07-01
XIDIAN UNIV
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Problems solved by technology

However, the shortcomings of this method are that the operation is complicated, and the accuracy rate is not significantly improved compared with that of the support vector machine. In addition, because only the scattering char

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  • Polarized SAR (Specific Absorption Rate) image classifying method based on super-vector coding
  • Polarized SAR (Specific Absorption Rate) image classifying method based on super-vector coding
  • Polarized SAR (Specific Absorption Rate) image classifying method based on super-vector coding

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Embodiment Construction

[0052] The present invention will be further described below in conjunction with the accompanying drawings.

[0053] refer to figure 1 , The specific implementation steps of the present invention are as follows.

[0054] Step 1. Input image.

[0055] Input the polarimetric synthetic aperture radar SAR image to be classified and the corresponding real object marker image.

[0056] Step 2. Perform refined Lee filtering on the polarimetric SAR image to be classified.

[0057] The refined polarimetric LEE filtering method is used to filter the polarimetric SAR image to be classified, remove the speckle noise, and obtain the filtered polarimetric SAR image. The steps are as follows:

[0058] In the first step, the sliding window of the refined polarization LEE filter is set, and the size of the sliding window is 7*7 pixels.

[0059] In the second step, the sliding window is moved from left to right and from top to bottom on the pixels of the input polarimetric SAR image, and ea...

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Abstract

The invention discloses a polarized SAR (Specific Absorption Rate) image classifying method based on super-vector coding. The method comprises the following realizing steps: (1) inputting images; (2) refining Lee filter; (3) extracting a scattering characteristic vector; (4) coding super-sparsely; (5) obtaining super-vector characteristics; (6) normalizing and expanding the sample characteristic set; (7) selecting a training sample and a test sample; (8) training the classifier to classify the images; (9) calculating the classifying precision; (10) outputting a result. According to the method provided by the invention, the image characteristics extracted are unlikely to be affected by noise points, and are small in redundancy and good in representability, thereby being applicable to effectively improving the classification precision in the course of classifying, and detecting and identifying an SAR image objective of a polarized and synthetic aperture radar.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a polarization (Synthetic Aperture Radar, SAR) image classification method based on supervector coding in the technical field of polarization synthetic aperture radar image classification. The invention adopts super vector coding to perform feature coding on the spatial neighborhood of each pixel of the polarimetric SAR image, improves the representation of the feature, and can be used for detection and target identification of polarimetric synthetic aperture radar SAR image targets. 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 m...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 焦李成屈嵘熊莎琴刘红英马文萍侯彪杨淑媛王爽马晶晶
Owner XIDIAN UNIV
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