Polarization SAR image classification method for polarization scattering non-stationary modeling

A technology of non-stationarity and classification method, applied in the field of image processing, can solve the problems of inability to balance accuracy and operation time, inaccurate classification of scattering mechanism, and large influence of noise in classification results, so as to achieve reliable classification results and reduce the probability of misclassification. , to ensure the effect of the classification effect

Active Publication Date: 2018-09-04
XIDIAN UNIV
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Problems solved by technology

However, due to the extreme complexity of the scene and the diversity of ground objects, coupled with the noise of the data itself and the limited image resolution, there will be many mixed pixels in the image, and the type of scattering mechanism at the mixed pixels is extremely complex. One or both of the scattering components may have close scattering powers, and there is no obvious dominant scattering mechanism, and it is imprecise to use the dominant scattering mechanism to represent the current target's scattering mechanism class
[0004] In the current commonly used image classification technology, for the mixed pixels with complex scattering mechanism, in the absence of significant main scattering mechanism, the classification result is greatly affected by noise, the misclassification rate is high, and the accuracy and computing time cannot be balanced

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  • Polarization SAR image classification method for polarization scattering non-stationary modeling

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Embodiment 1

[0030] In the classification of polarimetric SAR images, the main scattering mechanism, that is, the scattering component with the largest proportion, is introduced to better preserve the information of the main scattering mechanism in the classification. However, there are often a large number of mixed pixels in the image, and the type of scattering mechanism at the mixed pixel is extremely complex, and it is difficult to determine which scattering component plays a decisive role. Therefore, it is not accurate to define the scattering component with the largest proportion as the main scattering mechanism of. Benboudjema et al. defined that if the group potential energy function of the label field composed of the category to which the pixel belongs changes with the position of the group, the image is called non-stationary, and the real image is often non-stationary. However, it is difficult to study the non-stationary characteristics of polarimetric SAR data due to factors suc...

Embodiment 2

[0041] The polarization SAR image classification method based on the non-stationary modeling of polarization scattering mechanism is the same as embodiment 1, and the estimation auxiliary random field process described in step (2) includes the following steps to complete:

[0042] a) Divide the polarimetric SAR image into four scattering categories using the following formula: volume scattering P v , dihedral scattering P d , surface scattering P s and mixed scattering P m , this decomposition method has a clear physical meaning, and the classification criterion for the polarization scattering mechanism is easy to define:

[0043]

[0044] Among them, P i is the main scattering mechanism at the pixel point i, η is the preset threshold, take η=0.5, P s ,P d and P v Respectively indicate that the main scattering mechanism categories at pixel i are surface scattering, dihedral scattering and volume scattering, and P m Indicates that there is no obvious main scattering m...

Embodiment 3

[0049] The polarimetric SAR image classification method based on the non-stationarity modeling of the polarimetric scattering mechanism is the same as that in Embodiment 1-2. The specific rules for classifying and iterating pixels in the iterative process are:

[0050] Auxiliary random field u 1 , u 2 , u 3 The pixels in the set are all stable and have their own clear main scattering mechanism. In order to better preserve the information of the main scattering mechanism in the classification process, the iteration rule of the present invention is: during iteration, the constrained stationary pixels belong to a main scattering mechanism. The pixels of the scattering mechanism category can only be classified with the pixels belonging to the same main scattering mechanism category in the next classification process, and the unstable pixels without the main scattering mechanism category can be classified according to the specific results in the next classification process. categ...

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Abstract

The present invention discloses a polarization SAR image classification method based on non-stationary modeling of a polarization scattering mechanism, in order to solve the problems that the existingpolarization SAR image classification is affected by noise and has low accuracy for the mixed pixels with no obvious main scattering mechanism. The implementation steps are: initially classifying measured images; estimating the auxiliary random field according to the polarization scattering characteristics, and associating the polarization scattering characteristics with the non-stationarity; dividing the pixel point stationarity by using the auxiliary random field; calculating correlation functions for the stationary pixel points to obtain a unitary potential energy function, a data item, and a binary potential energy function; calculating the membership degree for non-stationary pixel points; constructing a posterior probability model of a fuzzy triple-recognition random field (FTDF) model by using the obtained functions, and performing classification by using the maximum posterior probability criterion; and if it is marked that the random field converges, outputting a result, and otherwise repeatedly constructing the classification model according the iterative rule until the termination iteration requirement is reached, and outputting a classification result. The method disclosed by the present invention has high detection precision and good anti-noise performance, and can be used for polarization SAR image classification.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to image classification, in particular to a polarization SAR image classification method based on polarization scattering non-stationary modeling, which is used for polarization SAR image classification. Background technique [0002] Using polarization target decomposition to extract the polarization scattering features of the target is the key point of the polarization SAR image classification technology. It is hoped to find a way to make full use of the polarization scattering information of the target to improve the accuracy of the polarization SAR image classification. The current polarization target decomposition is divided into three-component decomposition method, four-component decomposition method and multi-component decomposition method, such as Pauli decomposition, SDH decomposition, Freeman decomposition, Cameron decomposition, Cloude decomposition, etc., among whic...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/13G06F18/253G06F18/24
Inventor 李明宋琳张鹏宋婉莹吴艳
Owner XIDIAN UNIV
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