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Polarimetric SAR (Synthetic Aperture Radar) image classification method based on SDIT (Secretome-Derived Isotopic Tag) and SVM (Support Vector Machine)

A classification method and image technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as filtering, image quality reduction, and introduction of coherent speckle noise, so as to avoid crosstalk, strong generalization ability, and improve classification The effect of precision

Inactive Publication Date: 2014-05-28
XIDIAN UNIV
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Problems solved by technology

However, the disadvantage of this method is that, because the polarization SAR image is not filtered, coherent speckle noise is introduced, resulting in image quality degradation

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  • Polarimetric SAR (Synthetic Aperture Radar) image classification method based on SDIT (Secretome-Derived Isotopic Tag) and SVM (Support Vector Machine)
  • Polarimetric SAR (Synthetic Aperture Radar) image classification method based on SDIT (Secretome-Derived Isotopic Tag) and SVM (Support Vector Machine)
  • Polarimetric SAR (Synthetic Aperture Radar) image classification method based on SDIT (Secretome-Derived Isotopic Tag) and SVM (Support Vector Machine)

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[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] refer to figure 1 , the steps that the present invention realizes are as follows:

[0036] Step 1, input image.

[0037]Input an optional polarimetric SAR image to be classified.

[0038] Step 2, filtering.

[0039] The refined polarimetric LEE filtering method is used to filter the polarimetric SAR image to be classified to remove the speckle noise and obtain the filtered polarimetric SAR image.

[0040] Set the sliding window of refined polarization LEE filtering, the size of the sliding window is 7×7 pixels.

[0041] The sliding window is roamed from left to right and from top to bottom on the pixels of the input polarimetric SAR image. At each roaming step, the sliding window is divided into 9 parts from left to right and top to bottom according to the pixel space position. sub-windows, the size of each sub-window is 3×3 pixels, and there is overlap bet...

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Abstract

The invention discloses a polarimetric SAR (Synthetic Aperture Radar) image classification method based on an SDIT (Secretome-Derived Isotopic Tag) and an SVM (Support Vector Machine). The method comprises the implementation steps of (1) inputting an image, (2) filtering, (3) extracting scattering and polarization textural features, (4) combining and normalizing the features, (5) training a classifier, (6) predicting classification, (7) calculating precision and (8) outputting a result. Compared with an existing method, the polarimetric SAR image classification method based on the SDIT and the SVM enables the empirical risk and the expected risk to be minimal at the same time, and has the advantages of high generalization capability and low classification complexity and also the advantages of describing the image characteristics comprehensively and meticulously and improving the classification precision, and in the meantime, the polarimetric SAR image classification method has a good denoising effect, and further is capable of enabling the outlines and edges of the polarimetric SAR images to be clear, improving the image quality, and enhancing the polarimetric SAR image classification performance.

Description

technical field [0001] The present invention belongs to the technical field of image processing, and further relates to a feature combination (Scattering Decomposition, Image Texture, SDIT) and support vector based on scattering decomposition, polarization parameters, and image texture in the technical field of polarization synthetic aperture radar image ground object classification Machine (Support Vector Machine, SVM) polarization synthetic aperture radar (Synthetic Aperture Radar, SAR) image classification method. The invention can be used to classify the ground objects of the polarimetric SAR image, and can effectively improve the classification accuracy of the polarimetric SAR image. Background technique [0002] Polarization SAR radar can obtain richer target information, and has a wide range of research and application values ​​in agriculture, forestry, military, geology, hydrology and oceans, such as the identification of ground object types, crop growth monitoring, ...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 王爽焦李成高琛琼牛东马文萍马晶晶侯彪
Owner XIDIAN UNIV
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