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Method of classifying polarimetric SAR (synthetic aperture radar) images based on scattering entropy and three-component decomposed plane

A classification method and scattering entropy technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as misclassification, misclassification of moving objects, misjudgment of vegetation areas as surface scattering, etc., to achieve good detail information, The effect of reducing misclassification

Inactive Publication Date: 2017-05-31
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

Although the algorithm retains the main scattering characteristics of ground object information and the convergence stability of classification results, it still has the following shortcomings: First, the axisymmetric Freeman-Durden decomposition has rotation invariance, and the change of target orientation relative to the radar observation direction may lead to misclassification; secondly, rough surfaces are misjudged as volume scattering; thirdly, vegetation areas are misjudged as surface scattering; finally, misclassification caused by moving objects

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  • Method of classifying polarimetric SAR (synthetic aperture radar) images based on scattering entropy and three-component decomposed plane
  • Method of classifying polarimetric SAR (synthetic aperture radar) images based on scattering entropy and three-component decomposed plane
  • Method of classifying polarimetric SAR (synthetic aperture radar) images based on scattering entropy and three-component decomposed plane

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[0043] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0044] Such as figure 1 As shown, it is the original polarimetric SAR image power map of this embodiment, and a SAR image classification method based on scattering entropy and three-component decomposition plane is used to process the image. The process is as follows figure 2 As shown, the specific method is as follows.

[0045] Step 1: Input the polarimetric SAR data to be classified, and perform Lee filter processing to obtain the denoised data. In this example, for figure 1 After denoising the original image as image 3 shown.

[0046] Step 2: Calculate the coherence matrix of each pixel after denoising.

[0047] The Pauli basis is used to vectorize the scattering ...

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Abstract

The invention provides a method of classifying polarimetric SAR (synthetic aperture radar) images based on scattering entropy and three-component decomposed plane, and relates to the technical field of radar image processing. The method divides target ground objects into 9 classes initially according to scattering entropies and Freeman three-component, calculates a clustering center according to the 9 classes, and clusters the target ground objects to expected quantity according to Wishart distance; the ground objects are divided into high-entropy scattered ground objects, medium-entropy scattered ground objects and low-entropy scattered ground objects according to the scattering entropy H, and the 3 classes are then divided into 9 classes of ground objects according to surface scattering, even scattering and volume scattering, and initial classes are further divided by Wishart classifier. More accurate classification may be provided for polarimetric SAR images, no approximation occurs during processing, detailed information may be well retained, and the number of mistaken classes for urban areas is decreased greatly.

Description

technical field [0001] The invention relates to the technical field of radar image processing, in particular to a polarization SAR image classification method based on scattering entropy and three-component decomposition planes. Background technique [0002] Polarimetric synthetic aperture radar (PolSAR or polarimetric SAR for short), as an active aerospace and aviation remote sensing method, has the characteristics of all-day and all-weather work. It has a wide range of applications in exploration, precision agriculture, geological surveying and mapping, and government public decision-making. [0003] Surface object and land use classification is the most important application of polarimetric synthetic aperture radar, and supervised classification and unsupervised classification algorithms for surface object classification emerge in endlessly. Among them, the unsupervised classification can be summarized into three categories. The first category method only uses the statis...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/243
Inventor 张正鹏卜丽静王丽英陈亚欣权亚楠
Owner LIAONING TECHNICAL UNIVERSITY
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